Hibernate SpringBoot
1.0.0
| Wenn Sie einen tiefen Einblick in die in diesem Repository bereitgestellten Leistungsrezepte benötigen, dann bin ich sicher, dass Ihnen mein Buch „Spring Boot Persistence Best Practices“ gefallen wird. | Wenn Sie Tipps und Illustrationen zu mehr als 100 Java-Persistenz-Leistungsproblemen benötigen, ist „Java Persistence Performance Illustrated Guide“ genau das Richtige für Sie. |
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Hibernate- und Spring Boot-Beispiele
Beschreibung: Diese Anwendung ist ein Beispiel für das Speichern von Datum, Uhrzeit und Zeitstempeln in der UTC-Zeitzone. Die zweite Einstellung, useLegacyDatetimeCode wird nur für MySQL benötigt. Andernfalls legen Sie nur hibernate.jdbc.time_zone fest.
Kernpunkte:
spring.jpa.properties.hibernate.jdbc.time_zone=UTCspring.datasource.url=jdbc:mysql://localhost:3306/screenshotdb?useLegacyDatetimeCode=falseBeschreibung: Zeigen Sie die vorbereiteten Anweisungsbindungs-/extrahierten Parameter über die Log4J 2-Logger-Einstellung an.
Kernpunkte:
pom.xml die Standardprotokollierung von Spring Boot auspom.xml eine Log4j 2-Abhängigkeit hinzulog4j2.xml <Logger name="org.hibernate.type.descriptor.sql" level="trace"/> hinzu Ausgabebeispiel: 
Beschreibung: Zeigen Sie die Abfragedetails (Abfragetyp, Bindungsparameter, Stapelgröße, Ausführungszeit usw.) über DataSource-Proxy an
Kernpunkte:
pom.xml die Abhängigkeit datasource-proxy hinzuDataSource Bean abzufangenDataSource Bean über ProxyFactory und eine Implementierung von MethodInterceptor Ausgabebeispiel: 
saveAll(Iterable<S> entities) in MySQL Beschreibung: Batch-Einfügungen über die Methode SimpleJpaRepository#saveAll(Iterable<S> entities) in MySQL
Kernpunkte:
application.properties spring.jpa.properties.hibernate.jdbc.batch_size festapplication.properties spring.jpa.properties.hibernate.generate_statistics festlegen (nur um zu überprüfen, ob die Stapelverarbeitung funktioniert)application.properties JDBC-URL mit rewriteBatchedStatements=true festlegen (Optimierung für MySQL)application.properties die JDBC-URL mit cachePrepStmts=true fest (aktivieren Sie das Caching und ist nützlich, wenn Sie sich entscheiden, auch prepStmtCacheSize , prepStmtCacheSqlLimit “ usw. festzulegen. Ohne diese Einstellung ist der Cache deaktiviert.)application.properties die JDBC-URL mit useServerPrepStmts=true fest (auf diese Weise wechseln Sie zu serverseitig vorbereiteten Anweisungen (kann zu einer erheblichen Leistungssteigerung führen))spring.jpa.properties.hibernate.order_inserts=true einrichten, um die Stapelverarbeitung durch die Reihenfolge von Einfügungen zu optimierenIDENTITY dazu führt, dass die Stapelverarbeitung von Einfügungen deaktiviert wird@Version hinzu, um zu vermeiden, dass vor der Stapelverarbeitung zusätzliche SELECT -Anweisungen ausgelöst werden (verhindern Sie außerdem verlorene Aktualisierungen bei Transaktionen mit mehreren Anforderungen). Zusätzliche SELECT Anweisungen sind die Auswirkung der Verwendung von merge() anstelle von persist() ; Hinter den Kulissen verwendet saveAll() save() , das bei nicht neuen Entitäten (Entitäten mit IDs) merge() aufruft, was Hibernate anweist, eine SELECT Anweisung auszulösen, um sicherzustellen, dass kein Datensatz in der Datei vorhanden ist Datenbank mit derselben KennungsaveAll() übergebenen Einfügungen, um den Persistenzkontext nicht zu „überfordern“. Normalerweise sollte der EntityManager von Zeit zu Zeit geleert und geleert werden, aber während der Ausführung von saveAll() ist das einfach nicht möglich. Wenn also in saveAll() eine Liste mit einer großen Datenmenge vorhanden ist, werden alle diese Daten in die Persistenz gelangen Kontext (Cache der 1. Ebene) und bleibt bis zum Spülzeitpunkt im Speicher; Die Verwendung einer relativ kleinen Datenmenge sollte in Ordnung sein (in diesem Beispiel wird jeder Stapel von 30 Entitäten in einer separaten Transaktion und einem dauerhaften Kontext ausgeführt).saveAll() gibt eine List<S> zurück, die die persistenten Entitäten enthält. Jede persistente Entität wird dieser Liste hinzugefügt. Wenn Sie diese List einfach nicht benötigen, wird sie umsonst erstelltspring.jpa.properties.hibernate.cache.use_second_level_cache=false deaktiviert ist Beschreibung: Diese Anwendung ist ein Beispiel für Batch-Einfügungen über EntityManager in MySQL. Auf diese Weise können Sie die flush() und clear() Zyklen des Persistenzkontexts (Cache der 1. Ebene) innerhalb der aktuellen Transaktion einfach steuern. Dies ist über Spring Boot, saveAll(Iterable<S> entities) nicht möglich, da diese Methode einen einzelnen Flush pro Transaktion ausführt. Ein weiterer Vorteil besteht darin, dass Sie persist() anstelle von merge() aufrufen können – dies wird im Hintergrund von den SpringBoot saveAll(Iterable<S> entities) und save(S entity) verwendet.
Wenn Sie einen Batch pro Transaktion ausführen möchten (empfohlen), sehen Sie sich dieses Beispiel an.
Kernpunkte:
application.properties spring.jpa.properties.hibernate.jdbc.batch_size festapplication.properties spring.jpa.properties.hibernate.generate_statistics festlegen (nur um zu überprüfen, ob die Stapelverarbeitung funktioniert)application.properties JDBC-URL mit rewriteBatchedStatements=true festlegen (Optimierung für MySQL)application.properties die JDBC-URL mit cachePrepStmts=true fest (aktivieren Sie das Caching und ist nützlich, wenn Sie sich entscheiden, auch prepStmtCacheSize , prepStmtCacheSqlLimit “ usw. festzulegen. Ohne diese Einstellung ist der Cache deaktiviert.)application.properties die JDBC-URL mit useServerPrepStmts=true fest (auf diese Weise wechseln Sie zu serverseitig vorbereiteten Anweisungen (kann zu einer erheblichen Leistungssteigerung führen))spring.jpa.properties.hibernate.order_inserts=true einrichten, um die Stapelverarbeitung durch die Reihenfolge von Einfügungen zu optimierenIDENTITY dazu führt, dass die Stapelverarbeitung von Einfügungen deaktiviert wirdspring.jpa.properties.hibernate.cache.use_second_level_cache=false deaktiviert ist Ausgabebeispiel: 
Beschreibung: Batch-Einfügungen über JpaContext/EntityManager in MySQL.
Kernpunkte:
application.properties spring.jpa.properties.hibernate.jdbc.batch_size festapplication.properties spring.jpa.properties.hibernate.generate_statistics festlegen (nur um zu überprüfen, ob die Stapelverarbeitung funktioniert)application.properties JDBC-URL mit rewriteBatchedStatements=true festlegen (Optimierung für MySQL)application.properties die JDBC-URL mit cachePrepStmts=true fest (aktivieren Sie das Caching und ist nützlich, wenn Sie sich entscheiden, auch prepStmtCacheSize , prepStmtCacheSqlLimit “ usw. festzulegen. Ohne diese Einstellung ist der Cache deaktiviert.)application.properties die JDBC-URL mit useServerPrepStmts=true fest (auf diese Weise wechseln Sie zu serverseitig vorbereiteten Anweisungen (kann zu einer erheblichen Leistungssteigerung führen))spring.jpa.properties.hibernate.order_inserts=true einrichten, um die Stapelverarbeitung durch die Reihenfolge von Einfügungen zu optimierenIDENTITY dazu führt, dass die Stapelverarbeitung von Einfügungen deaktiviert wirdEntityManager wird pro Entitätstyp über JpaContext#getEntityManagerByManagedType(Class<?> entity) abgerufen.spring.jpa.properties.hibernate.cache.use_second_level_cache=false deaktiviert ist Ausgabebeispiel: 
Beschreibung: Batch-Einfügungen über Hibernate-Batching auf Sitzungsebene (Hibernate 5.2 oder höher) in MySQL.
Kernpunkte:
application.properties spring.jpa.properties.hibernate.generate_statistics festlegen (nur um zu überprüfen, ob die Stapelverarbeitung funktioniert)application.properties JDBC-URL mit rewriteBatchedStatements=true festlegen (Optimierung für MySQL)application.properties die JDBC-URL mit cachePrepStmts=true fest (aktivieren Sie das Caching und ist nützlich, wenn Sie sich entscheiden, auch prepStmtCacheSize , prepStmtCacheSqlLimit “ usw. festzulegen. Ohne diese Einstellung ist der Cache deaktiviert.)application.properties die JDBC-URL mit useServerPrepStmts=true fest (auf diese Weise wechseln Sie zu serverseitig vorbereiteten Anweisungen (kann zu einer erheblichen Leistungssteigerung führen))spring.jpa.properties.hibernate.order_inserts=true einrichten, um die Stapelverarbeitung durch die Reihenfolge von Einfügungen zu optimierenIDENTITY dazu führt, dass die Stapelverarbeitung von Einfügungen deaktiviert wirdSession wird durch Entpacken über EntityManager#unwrap(Session.class) erhalten.Session#setJdbcBatchSize(Integer size) festgelegt und über Session#getJdbcBatchSize() abgerufen.spring.jpa.properties.hibernate.cache.use_second_level_cache=false deaktiviert ist Ausgabebeispiel: 
findById() , JPA EntityManager und Hibernate Session Beschreibung: Beispiele für direktes Abrufen über Spring Data, EntityManager und Hibernate Session .
Kernpunkte:
findById()EntityManager verwendet find()Session verwendet get()Hinweis: Vielleicht möchten Sie auch das Rezept „So bereichern Sie DTOs mit virtuellen Eigenschaften über Federprojektionen“ lesen.
Beschreibung: Rufen Sie über Spring Data Projections (DTO) nur die benötigten Daten aus der Datenbank ab.
Kernpunkte:
List<projection> zurückgibtLIMIT ).Hinweis: Die Verwendung von Projektionen ist nicht auf die Verwendung des Abfrageerstellungsmechanismus beschränkt, der in die Spring Data-Repository-Infrastruktur integriert ist. Wir können Prognosen auch über JPQL oder native Abfragen abrufen. In dieser Anwendung verwenden wir beispielsweise eine JPQL.
Ausgabebeispiel (die ersten beiden Zeilen auswählen; nur „Name“ und „Alter“ auswählen): 
| Wenn Sie einen tiefen Einblick in die in diesem Repository bereitgestellten Leistungsrezepte benötigen, dann bin ich sicher, dass Ihnen mein Buch „Spring Boot Persistence Best Practices“ gefallen wird. | Wenn Sie Tipps und Illustrationen zu mehr als 100 Java-Persistenz-Leistungsproblemen benötigen, ist „Java Persistence Performance Illustrated Guide“ genau das Richtige für Sie. |
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Beschreibung: Standardmäßig werden die Attribute einer Entität eifrig (alle auf einmal) geladen. Wir können sie aber auch verzögert laden. Dies ist nützlich für Spaltentypen, die große Datenmengen speichern: CLOB , BLOB , VARBINARY usw. oder Details , die bei Bedarf geladen werden sollen. In dieser Anwendung haben wir eine Entität namens Author . Seine Eigenschaften sind: id , name , genre , avatar und age . Und wir wollen den avatar lazy laden. Der avatar sollte also bei Bedarf geladen werden.
Kernpunkte:
pom.xml die Hibernate -Bytecode-Erweiterung (z. B. verwenden Sie das Maven -Bytecode-Erweiterungs-Plugin ).@Basic(fetch = FetchType.LAZY)application.properties die Option „Sitzung in Ansicht öffnen“. Überprüfen Sie auch:
- Standardwerte für Lazy Loaded-Attribute
- Attribut Lazy Loading und Jackson-Serialisierung
Beschreibung: Ein Hibernate-Proxy kann nützlich sein, wenn eine untergeordnete Entität mit einem Verweis auf ihre übergeordnete Entität ( @ManyToOne oder @OneToOne Zuordnung) beibehalten werden kann. In solchen Fällen ist das Abrufen der übergeordneten Entität aus der Datenbank (Ausführen der SELECT -Anweisung) eine Leistungseinbuße und eine sinnlose Aktion, da Hibernate den zugrunde liegenden Fremdschlüsselwert für einen nicht initialisierten Proxy festlegen kann.
Kernpunkte:
EntityManager#getReference()JpaRepository#getOne() -> wird in diesem Beispiel verwendetload()Author und Book , an einer unidirektionalen @ManyToOne -Zuordnung beteiligt sind ( Author ist die übergeordnete Seite).SELECT aus), wir erstellen ein neues Buch, wir legen den Proxy als Autor für dieses Buch fest und wir speichern das Buch (dies löst ein INSERT in der book aus)Ausgabebeispiel:
INSERT ausgelöst wird und kein SELECTBeschreibung: Bei N+1 handelt es sich um ein Problem des Lazy Fetching (Aber Eager ist nicht ausgenommen). Diese Anwendung reproduziert das N+1-Verhalten.
Kernpunkte:
Author und Book in einer verzögerten bidirektionalen @OneToMany -ZuordnungBook träge abrufen, also ohne Author (ergibt 1 Abfrage)Book ab und rufen Sie für jeden Eintrag den entsprechenden Author ab (ergibt N Abfragen).Author lazy abrufen, also ohne Book (ergibt 1 Abfrage)Author ab und rufen Sie für jeden Eintrag das entsprechende Book ab (ergibt N Abfragen). Ausgabebeispiel: 
SELECT DISTINCT über den Hibernate HINT_PASS_DISTINCT_THROUGH Hinweis Beschreibung: Ab Hibernate 5.2.2 können wir JPQL (HQL)-Abfrageeinheiten vom Typ SELECT DISTINCT über HINT_PASS_DISTINCT_THROUGH -Hinweis optimieren. Beachten Sie, dass dieser Hinweis nur für JPQL (HQL) JOIN FETCH-Abfragen nützlich ist. Ist für Skalarabfragen (z. B. List<Integer> ), DTO oder HHH-13280 nicht nützlich. In solchen Fällen muss das JPQL-Schlüsselwort DISTINCT an die zugrunde liegende SQL-Abfrage übergeben werden. Dadurch wird die Datenbank angewiesen, Duplikate aus dem Ergebnissatz zu entfernen.
Kernpunkte:
@QueryHints(value = @QueryHint(name = HINT_PASS_DISTINCT_THROUGH, value = "false")) Ausgabebeispiel: 
Hinweis: Der Hibernate Dirty Checking- Mechanismus ist dafür verantwortlich, die Entitätsänderungen zur Flush-Zeit zu identifizieren und in unserem Namen die entsprechenden UPDATE Anweisungen auszulösen.
Beschreibung: Vor Hibernate Version 5 stützt sich der Dirty Checking- Mechanismus auf die Java Reflection API, um jede Eigenschaft jeder verwalteten Entität zu überprüfen. Ab Hibernate Version 5 kann sich der Dirty-Checking -Mechanismus auf den Dirty-Tracking- Mechanismus verlassen (das ist die Fähigkeit einer Entität, ihre eigenen Attributänderungen zu verfolgen), was erfordert, dass Hibernate Bytecode Enhancement in der Anwendung vorhanden ist. Der Dirty-Tracking -Mechanismus sorgt für eine bessere Leistung, insbesondere wenn Sie über eine relativ große Anzahl von Entitäten verfügen.
Für Dirty Tracking wird während des Bytecode-Verbesserungsprozesses der Bytecode der Entitätsklasse von Hibernate instrumentiert, indem ein Tracker , $$_hibernate_tracker , hinzugefügt wird. Zum Zeitpunkt der Spülung verwendet Hibernate diesen Tracker, um die Änderungen der Entitäten zu erkennen (jeder Entitäts -Tracker meldet die Änderungen). Dies ist besser, als jede Eigenschaft jeder verwalteten Entität zu überprüfen.
Im Allgemeinen (standardmäßig) erfolgt die Instrumentierung zur Build-Zeit, sie kann jedoch auch so konfiguriert werden, dass sie zur Laufzeit oder zur Bereitstellungszeit erfolgt. Es ist vorzuziehen, dies zur Build-Zeit zu tun, um einen Overhead zur Laufzeit zu vermeiden.
Das Hinzufügen einer Bytecode-Verbesserung und die Aktivierung von Dirty Tracking können über ein über Maven oder Gradle hinzugefügtes Plugin erfolgen (Ant kann ebenfalls verwendet werden). Wir verwenden Maven und fügen es daher in pom.xml hinzu.
Kernpunkte:
pom.xml ein Ausgabebeispiel: 
Der Bytecode-Verbesserungseffekt kann hier auf Author.class gesehen werden. Beachten Sie, wie der Bytecode mit $$_hibernate_tracker instrumentiert wurde.
Optional in Entitäten und Abfragen Beschreibung: Diese Anwendung ist ein Beispiel für die korrekte Verwendung von Java 8 Optional in Entitäten und Abfragen.
Kernpunkte:
Optional zurückgeben (z. B. findById() ).Optional zurückgebenOptional in Entity-Getterndata-mysql.sql@OneToMany -Assoziation abzubilden Beschreibung: Diese Anwendung ist ein Proof of Concept dafür, wie die bidirektionale @OneToMany -Assoziation aus Leistungssicht richtig implementiert werden kann.
Kernpunkte:
mappedBy für das übergeordnete ElementorphanRemoval für das übergeordnete Element, um untergeordnete Elemente ohne Referenzen zu entfernen@NaturalId )) und/oder von der Datenbank generierte Bezeichner und überschreiben Sie (auf der untergeordneten Seite) ordnungsgemäß die Methoden equals() und hashCode() wie hiertoString() überschrieben werden muss, achten Sie darauf, nur die Basisattribute einzubeziehen, die beim Laden der Entität aus der Datenbank abgerufen werden Hinweis: Achten Sie auf Entfernungsvorgänge, insbesondere auf das Entfernen untergeordneter Entitäten. CascadeType.REMOVE und orphanRemoval=true erzeugen möglicherweise zu viele Abfragen. In solchen Szenarien ist die Verwendung von Massenvorgängen in den meisten Fällen die beste Lösung für Löschvorgänge.
Beschreibung: Diese Anwendung ist ein Beispiel dafür, wie eine Abfrage über JpaRepository , EntityManager und Session geschrieben wird.
Kernpunkte:
JpaRepository @Query oder Spring Data Query CreationEntityManager und Session die Methode createQuery()AUTO -Generator-Typ in Hibernate 5 und MySQL vermeidet Beschreibung: In MySQL und Hibernate 5 führt der Generatortyp GenerationType.AUTO zur Verwendung des TABLE -Generators. Dies führt zu einer erheblichen Leistungseinbuße. Die Umwandlung dieses Verhaltens in den IDENTITY -Generator kann mithilfe von GenerationType.IDENTITY oder dem nativen Generator erreicht werden.
Kernpunkte:
GenerationType.IDENTITY anstelle von GenerationType.AUTO Ausgabebeispiel: 
Beschreibung: Diese Anwendung ist ein Beispiel, wenn der Aufruf von save() für eine Entität redundant (nicht notwendig) ist.
Kernpunkte:
UPDATE Anweisung aus, ohne dass die Methode save() explizit aufgerufen werden musssave() ist nicht unbedingt erforderlich) nicht auf die Anzahl der ausgelösten Abfragen aus, führt jedoch zu einer Leistungseinbuße in den zugrunde liegenden Hibernate-Prozessen| Wenn Sie einen tiefen Einblick in die in diesem Repository bereitgestellten Leistungsrezepte benötigen, dann bin ich sicher, dass Ihnen mein Buch „Spring Boot Persistence Best Practices“ gefallen wird. | Wenn Sie Tipps und Illustrationen zu mehr als 100 Java-Persistenz-Leistungsproblemen benötigen, ist „Java Persistence Performance Illustrated Guide“ genau das Richtige für Sie. |
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BIG ) SERIAL beim Stapeln von Einfügungen über den Ruhezustand vermeiden sollte Beschreibung: In PostgreSQL wird durch die Verwendung GenerationType.IDENTITY die Stapelverarbeitung von Einfügungen deaktiviert. Das (BIG)SERIAL verhält sich „fast“ wie MySQL, AUTO_INCREMENT . In dieser Anwendung verwenden wir die GenerationType.SEQUENCE , die die Stapelverarbeitung von Einfügungen ermöglicht, und optimieren sie über den hi/lo -Optimierungsalgorithmus.
Kernpunkte:
GenerationType.SEQUENCE anstelle von GenerationType.IDENTITYhi/lo -Algorithmus, um einen Hi- Wert in einem Datenbank-Roundtrip abzurufen (der Hi- Wert ist nützlich, um eine bestimmte/gegebene Anzahl von Bezeichnern im Speicher zu generieren; bis Sie nicht alle Bezeichner im Speicher erschöpft haben, besteht keine Notwendigkeit um noch ein Hallo zu holen)pooled und pooled-lo Identifier-Generatoren verwenden (dies sind Optimierungen von hi/lo , die es externen Diensten ermöglichen, die Datenbank zu verwenden, ohne Fehler bei der Duplizierung von Schlüsseln zu verursachen).spring.datasource.hikari.data-source-properties.reWriteBatchedInserts=true Ausgabebeispiel: 
SINGLE_TABLE Beschreibung: Diese Anwendung ist ein Beispiel für die Verwendung der JPA-Einzeltabellen-Vererbungsstrategie ( SINGLE_TABLE ).
Kernpunkte:
@Inheritance(strategy=InheritanceType.SINGLE_TABLE) )@NotNull und MySQL-Trigger sichergestelltTINYINT deklariert wurde Ausgabebeispiel (unten ist eine einzelne Tabelle, die aus drei Entitäten erhalten wurde): 
Beschreibung: Diese Anwendung ist ein Beispiel für das Zählen und Bestätigen von SQL-Anweisungen, die „hinter den Kulissen“ ausgelöst werden. Es ist sehr nützlich, die SQL-Anweisungen zu zählen, um sicherzustellen, dass Ihr Code nicht mehr SQL-Anweisungen generiert, als Sie vielleicht denken (z. B. kann N+1 leicht erkannt werden, indem die Anzahl der erwarteten Anweisungen bestätigt wird).
Kernpunkte:
pom.xml Abhängigkeiten für die DataSource-Proxy-Bibliothek und die db-util-Bibliothek von Vlad Mihalcea hinzuProxyDataSourceBuilder mit countQuery()SQLStatementCountValidator.reset() zurückINSERT , UPDATE , DELETE und SELECT über assertInsert/Update/Delete/Select/Count(long expectedNumberOfSql) Ausgabebeispiel (wenn die Anzahl der erwarteten SQLs nicht mit der Realität übereinstimmt, wird eine Ausnahme ausgelöst): 
Beschreibung: Diese Anwendung ist ein Beispiel für die Einstellung der JPA-Rückrufe ( Pre/PostPersist , Pre/PostUpdate , Pre/PostRemove und PostLoad ).
Kernpunkte:
void zurückgeben und dürfen keine Argumente annehmen Ausgabebeispiel: 
@MapsId zum Teilen von Identifikatoren in einer @OneToOne -Beziehung Beschreibung: Anstelle des regulären unidirektionalen/bidirektionalen @OneToOne sollten Sie sich besser auf ein unidirektionales @OneToOne und @MapsId verlassen. Bei dieser Anwendung handelt es sich um einen Proof of Concept.
Kernpunkte:
@MapsId auf der untergeordneten Seite@JoinColumn um den Namen der Primärschlüsselspalte anzupassen@MapsId bei @OneToOne Zuordnungen den Primärschlüssel mit der übergeordneten Tabelle ( id Eigenschaft fungiert sowohl als Primärschlüssel als auch als Fremdschlüssel).Notiz:
@MapsId kann auch für @ManyToOne verwendet werdenSqlResultSetMapping und EntityManager ab Beschreibung: Das Abrufen von mehr Daten als nötig kann zu Leistungseinbußen führen. Durch die Verwendung von DTO können wir nur die benötigten Daten extrahieren. In dieser Anwendung verlassen wir uns auf SqlResultSetMapping und EntityManager .
Kernpunkte:
SqlResultSetMapping und EntityManagerSqlResultSetMapping und NamedNativeQuery ab Hinweis: Wenn Sie sich auf die Namenskonvention {EntityName}.{RepositoryMethodName} verlassen möchten, um in der Repository-Schnittstelle einfach Methoden mit demselben Namen wie die native benannte Abfrage zu erstellen, überspringen Sie diese Anwendung und überprüfen Sie diese.
Beschreibung: Das Abrufen von mehr Daten als nötig kann zu Leistungseinbußen führen. Durch die Verwendung von DTO können wir nur die benötigten Daten extrahieren. In dieser Anwendung verlassen wir uns auf SqlResultSetMapping , NamedNativeQuery .
Kernpunkte:
SqlResultSetMapping , NamedNativeQueryjavax.persistence.Tuple und Native SQL ab Beschreibung: Das Abrufen von mehr Daten als nötig kann zu Leistungseinbußen führen. Durch die Verwendung von DTO können wir nur die benötigten Daten extrahieren. In dieser Anwendung setzen wir auf javax.persistence.Tuple und natives SQL.
Kernpunkte:
java.persistence.Tuple in einem Spring-Repository und markieren Sie die Abfrage als nativeQuery = truejavax.persistence.Tuple und JPQL ab Beschreibung: Das Abrufen von mehr Daten als nötig kann zu Leistungseinbußen führen. Durch die Verwendung von DTO können wir nur die benötigten Daten extrahieren. In dieser Anwendung setzen wir auf javax.persistence.Tuple und JPQL.
Kernpunkte:
java.persistence.Tuple in einem Spring-RepositoryBeschreibung: Das Abrufen von mehr Daten als nötig kann zu Leistungseinbußen führen. Durch die Verwendung von DTO können wir nur die benötigten Daten extrahieren. In dieser Anwendung verlassen wir uns auf Constructor Expression und JPQL.
Kernpunkte:
SELECT new com.bookstore.dto.AuthorDto(a.name, a.age) FROM Author a Siehe auch:
So rufen Sie DTO über den Konstruktor und den Spring Data Query Builder-Mechanismus ab
| Wenn Sie einen tiefen Einblick in die in diesem Repository bereitgestellten Leistungsrezepte benötigen, dann bin ich sicher, dass Ihnen mein Buch „Spring Boot Persistence Best Practices“ gefallen wird. | Wenn Sie Tipps und Illustrationen zu mehr als 100 Java-Persistenz-Leistungsproblemen benötigen, ist „Java Persistence Performance Illustrated Guide“ genau das Richtige für Sie. |
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ResultTransformer und Native SQL ab Beschreibung: Das Abrufen von mehr Daten als nötig kann zu Leistungseinbußen führen. Durch die Verwendung von DTO können wir nur die benötigten Daten extrahieren. In dieser Anwendung setzen wir auf Hibernate, ResultTransformer und natives SQL.
Kernpunkte:
AliasToBeanConstructorResultTransformer für DTO ohne Setter, aber mit KonstruktorTransformers.aliasToBean() für DTO mit SetternEntityManager.createNativeQuery() und unwrap(org.hibernate.query.NativeQuery.class)ResultTransformer veraltet, aber bis ein Ersatz verfügbar ist (wahrscheinlich in Hibernate 6.0), kann er verwendet werden (weiterlesen)ResultTransformer und JPQL ab Beschreibung: Das Abrufen von mehr Daten als nötig kann zu Leistungseinbußen führen. Durch die Verwendung von DTO können wir nur die benötigten Daten extrahieren. In dieser Anwendung setzen wir auf Hibernate, ResultTransformer und JPQL.
Kernpunkte:
AliasToBeanConstructorResultTransformer für DTO ohne Setter, mit KonstruktorTransformers.aliasToBean() für DTO mit SetternEntityManager.createQuery() und unwrap(org.hibernate.query.Query.class)ResultTransformer veraltet, aber bis ein Ersatz verfügbar ist (in Hibernate 6.0), kann er verwendet werden (weiterlesen)Beschreibung: Das Abrufen von mehr Daten als nötig kann zu Leistungseinbußen führen. Durch die Verwendung von DTO können wir nur die benötigten Daten extrahieren. In dieser Anwendung verlassen wir uns auf Blaze-Persistence-Entitätsansichten.
Kernpunkte:
pom.xml die für Blaze-Persistence spezifischen Abhängigkeiten hinzuCriteriaBuilderFactory und EntityViewManagerEntityViewRepository erweiternfindAll() , findOne() usw@ElementCollection (ohne @OrderColumn ). Beschreibung: Diese Anwendung zeigt die möglichen Leistungseinbußen bei der Verwendung von @ElementCollection auf. In diesem Fall ohne @OrderColumn . Wie Sie im nächsten Punkt (34) sehen können, kann das Hinzufügen von @OrderColumn einige Leistungseinbußen abmildern.
Kernpunkte:
@ElementCollection hat keinen Primärschlüssel@ElementCollection wird in einer separaten Tabelle abgebildet@ElementCollection , wenn Sie viele Einfügungen/Löschungen in dieser Sammlung haben. Einfügungen/Löschungen führen dazu, dass Hibernate alle vorhandenen Tabellenzeilen löscht, die Sammlung im Speicher verarbeitet und die verbleibenden Tabellenzeilen erneut einfügt, um die Sammlung aus dem Speicher zu spiegeln Ausgabebeispiel: 
@ElementCollection mit @OrderColumn Beschreibung: Diese Anwendung zeigt die Leistungseinbußen bei der Verwendung @ElementCollection auf. In diesem Fall mit @OrderColumn . Aber wie Sie in dieser Anwendung (im Vergleich zu Punkt 33) sehen können, können durch das Hinzufügen von @OrderColumn einige Leistungseinbußen abgemildert werden, wenn Vorgänge in der Nähe des Sammlungsendes stattfinden (z. B. Hinzufügen/Entfernen am/vom Ende der Sammlung). Im Wesentlichen bleiben alle Elemente, die sich vor dem Eintrag zum Hinzufügen/Entfernen befinden, unberührt, sodass die Leistungseinbußen ignoriert werden können, wenn wir Zeilen in der Nähe des Sammlungsendes betreffen.
Kernpunkte:
@ElementCollection hat keinen Primärschlüssel@ElementCollection wird in einer separaten Tabelle abgebildet@ElementCollection mit @OrderColumn wenn Sie viele Einfügungen und Löschungen in der Nähe des Sammlungsendes haben Ausgabebeispiel: 
Hinweis: Bevor Sie diesen Artikel lesen, versuchen Sie herauszufinden, ob Hibernate5Module nicht das ist, wonach Sie suchen.
Beschreibung: Das Anti-Pattern „Open-Session in View“ ist in SpringBoot standardmäßig aktiviert. Stellen Sie sich nun eine Lazy-Assoziation (z. B. @OneToMany ) zwischen zwei Entitäten vor, Author und Book (ein Autor hat mehrere Bücher verknüpft). Als nächstes ruft ein REST-Controller-Endpunkt einen Author ohne das zugehörige Book ab. Aber die Ansicht (genauer gesagt Jackson) erzwingt auch das verzögerte Laden des zugehörigen Book . Da OSIV die bereits geöffnete Session bereitstellt, finden die Proxy-Initialisierungen erfolgreich statt. Die Lösung zur Vermeidung dieser Leistungseinbußen beginnt mit der Deaktivierung des OSIV. Initialisieren Sie außerdem explizit die nicht abgerufenen Lazy Associations. Auf diese Weise erzwingt die Ansicht kein verzögertes Laden.
Kernpunkte:
application.properties diese Einstellung hinzufügen: spring.jpa.open-in-view=falseAuthor Entität ab und initialisieren Sie das zugehörige Book explizit mit (Standard-)Werten (z. B. null ).@JsonInclude(Include.NON_EMPTY) auf dieser Entitätsebene fest, um zu vermeiden, dass null oder das, was im resultierenden JSON als leer gilt, gerendert wird HINWEIS: Wenn OSIV aktiviert ist, kann der Entwickler die nicht abgerufenen Lazy Associations weiterhin manuell initialisieren, solange er dies außerhalb einer Transaktion tut, um ein Flushen zu vermeiden. Aber warum funktioniert das? Warum löst die manuelle Initialisierung der Zuordnungen einer verwalteten Entität nicht den Flush aus, da die Session geöffnet ist? Die Antwort finden Sie in der Dokumentation von OpenSessionInViewFilter , in der Folgendes angegeben ist: Dieser Filter leert die Session standardmäßig nicht, wobei der Flush-Modus auf FlushMode.NEVER eingestellt ist. Es wird davon ausgegangen, dass es in Kombination mit Service-Layer-Transaktionen verwendet wird, die sich um das Flushing kümmern: Der aktive Transaktionsmanager ändert den Flush-Modus während einer Lese-/Schreibtransaktion vorübergehend auf FlushMode.AUTO , wobei der Flush-Modus am Ende auf FlushMode.NEVER zurückgesetzt wird jeder Transaktion. Wenn Sie beabsichtigen, diesen Filter ohne Transaktionen zu verwenden, sollten Sie erwägen, den Standard-Flush-Modus zu ändern (über die Eigenschaft „flushMode“).
Beschreibung: Diese Anwendung ist ein Proof of Concept für die Verwendung von Spring Projections (DTO) und Inner Joins, die über JPQL und natives SQL (für MySQL) geschrieben wurden.
Kernpunkte:
Author und Book in einer (faulen) bidirektionalen @OneToMany -Assoziation).resources/data-mysql.sql ).AuthorNameBookTitle.java ).
Beschreibung: Diese Anwendung ist ein Proof of Concept für die Verwendung von Spring Projections (DTO) und Left-Joins, die über JPQL und natives SQL (für MySQL) geschrieben wurden.
Kernpunkte:
Author und Book in einer (faulen) bidirektionalen @OneToMany -Assoziation).resources/data-mysql.sql ).AuthorNameBookTitle.java ).
Beschreibung: Diese Anwendung ist ein Beweis für das Konzept für die Verwendung von Spring Projections (DTO) und Right Ens, die über JPQL und native SQL (für MySQL) geschrieben wurden.
Kernpunkte:
Author und Book in einer (faulen) bidirektionalen @OneToMany Association)resources/data-mysql.sql ).AuthorNameBookTitle.java ).
Beschreibung: Diese Anwendung ist ein Beweis für das Konzept für die Verwendung von Spring Projections (DTO) und inklusive vollständige gemeinsame Verknüpfungen über JPQL und native SQL (für PostgreSQL).
Kernpunkte:
Author und Book in einer (faulen) bidirektionalen @OneToMany Association)resources/data-mysql.sql ).AuthorNameBookTitle.java ).| Wenn Sie einen tiefen Eintauchen in die in diesem Repository entlarvten Performance -Rezepte benötigen, dann bin ich sicher, dass Sie mein Buch "Spring Boot Persistenz Best Practices" lieben werden. | Wenn Sie eine Hand von Tipps und Illustrationen von mehr als 100 Java Persistenz -Leistungsproblemen benötigen, dann ist "Java Persistence Performance Illustrated Guide" genau das Richtige für Sie. |
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Beschreibung: Diese Anwendung ist ein Beweis für das Konzept für die Verwendung von Spring Projections (DTO) und exklusive linke Zusammenhänge über JPQL und native SQL (für MySQL).
Kernpunkte:
Author und Book in einer (faulen) bidirektionalen @OneToMany Association)resources/data-mysql.sql ).AuthorNameBookTitle.java ).
Beschreibung: Diese Anwendung ist ein Beweis für das Konzept für die Verwendung von Frühlingsprojektionen (DTO) und exklusives Recht, die über JPQL und native SQL (für MySQL) geschrieben wurden.
Kernpunkte:
Author und Book in einer (faulen) bidirektionalen @OneToMany Association)resources/data-mysql.sql ).AuthorNameBookTitle.java ).
Beschreibung: Diese Anwendung ist ein Beweis für das Konzept für die Verwendung von Spring Projections (DTO) und exklusive Vollverbindungen über JPQL und native SQL (für PostgreSQL).
Kernpunkte:
Author und Book in einer (faulen) bidirektionalen @OneToMany Association)resources/data-mysql.sql ).AuthorNameBookTitle.java ).Beschreibung: Diese Anwendung ist ein Beweis für das Konzept für die Verwendung von Frühlings-Post-Commit-Hooks und wie sie sich auf die Persistenzschichtleistung auswirken können.
Kernpunkte:
Beschreibung: Diese Anwendung ist ein Beweis für das Konzept für die Verwendung von Frühlingsprojektionen (DTO) und nicht verwandte Entitäten. Hibernate 5.1 hat explizite Zusammenhänge zu nicht verwandten Einheiten eingeführt, und die Syntax und das Verhalten ähneln den SQL JOIN -Anweisungen.
Kernpunkte:
Author und Book nicht verwandte Entitäten)resources/data-mysql.sql ).BookstoreDto ).@EqualsAndHashCode und @Data in Entitäten und wie man equals() und hashCode() überschreibt, vermeiden und wie man überschreibt. Beschreibung: Entitäten sollten equals() und hashCode() wie hier implementieren. Die Hauptidee ist, dass Hibernate verlangt, dass eine Entität über alle staatlichen Übergänge gleich ist ( vorübergehend , angeschlossen , abgelöst und entfernt ). Die Verwendung von Lombok @EqualsAndHashCode (oder @Data ) wird diese Anforderung nicht respektieren.
Kernpunkte:
Vermeiden Sie diese Ansätze
@EqualsAndHashCode (Entity: LombokDefaultBook , Test: LombokDefaultEqualsAndHashCodeTest )@EqualsAndHashCode nur mit Primärschlüssel (Entity: LombokIdBook , Test: LombokEqualsAndHashCodeWithIdOnlyTest )equals() und hashCode() (Entity: DefaultBook , Test: DefaultEqualsAndHashCodeTest )equals() hashCode() IdBook IdEqualsAndHashCodeTestBevorzugen Sie diese Ansätze
BusinessKeyBook BusinessKeyEqualsAndHashCodeTest@NaturalId NaturalIdBook NaturalIdEqualsAndHashCodeTestIdManBook IdManEqualsAndHashCodeTestIdGenBook , Test: IdGenEqualsAndHashCodeTest ) 
LazyInitializationException über JOIN FETCH Fetch vermeidetSiehe auch:
Beschreibung: Wenn wir eine LazyInitializationException erhalten, neigen wir normalerweise dazu, den Assoziationsrufttyp von LAZY zu EAGER zu ändern. Das ist sehr schlecht! Dies ist ein Codegeruch. Der beste Weg, um diese Ausnahme zu vermeiden, besteht darin, sich auf JOIN FETCH zu verlassen (wenn Sie vorhaben, die abgerufenen Entitäten zu ändern) oder JOIN + DTO (wenn die abgerufenen Daten nur gelesen werden). JOIN FETCH können Assoziationen zusammen mit ihren übergeordneten Objekten mithilfe eines einzelnen SELECT initialisiert werden. Dies ist besonders nützlich, um die damit verbundenen Sammlungen abzurufen.
Diese Anwendung ist ein Beispiel für JOIN FETCH zur Vermeidung LazyInitializationException .
Kernpunkte:
Author und Book in einer @OneToMany faul-bidirektionalen Vereinigung)JOIN FETCH um einen Autor mit seinen Büchern zu holenJOIN FETCH (oder JOIN ), um ein Buch mit dem Autor zu holen Ausgabebeispiel: 
Beschreibung: Dies ist ein Spring -Boot -Beispiel, das auf dem folgenden Artikel basiert. Ist eine funktionale Implementierung des Beispiels des Vlad. Es wird dringend empfohlen, diesen Artikel zu lesen.
Kernpunkte:
Beschreibung: Dies ist ein Feder -Boot -Beispiel, das den Hibernate 5.2.10 ausnutzt, um die Verbindungserfassung nach Bedarf zu verzögern. Standardmäßig wird im Ressourcen-lokalen Modus eine Datenbankverbindung unmittelbar nach dem Aufrufen einer mit @Transactional angegebenen Methode eingestellt. Wenn diese Methode vor der ersten SQL-Anweisung einige zeitaufwändige Aufgaben enthält, wird die Verbindung für nichts geöffnet. Hibernate 5.2.10 ermöglicht es uns jedoch, den Verbindungserwerb bei Bedarf zu verzögern. Dieses Beispiel stützt sich auf Hikaricp als Standardverbindungspool für Spring Start.
Kernpunkte:
spring.datasource.hikari.auto-commit=false in application.propertiesspring.jpa.properties.hibernate.connection.provider_disables_autocommit=true in application.properties Ausgabebeispiel: 
hi/lo -Algorithmus Sequenzen von Kennleisten HINWEIS: Wenn Systeme, die extern in Ihrer Anwendung sind, Zeilen in Ihre Tabellen einfügen müssen, verlassen Sie sich nicht auf hi/lo -Algorithmus, da dies in solchen Fällen Fehler verursachen kann, die sich aus der Erzeugung doppelter Kennung ergeben. Verlassen Sie sich auf pooled oder pooled-lo Algorithmen (Optimierungen von hi/lo ).
Beschreibung: Dies ist ein Feder -Boot -Beispiel für die Verwendung des hi/lo -Algorithmus zum Generieren von 1000 Bezeichnern in 10 Datenbank -Roundtrips zum Batching von 1000 Einfügen in Stapeln von 30.
Kernpunkte:
SEQUENCE (z. B. in PostgreSQL)hi/lo -Algorithmus wie in Author.java Entity Ausgabebeispiel: 
| Wenn Sie einen tiefen Eintauchen in die in diesem Repository entlarvten Performance -Rezepte benötigen, dann bin ich sicher, dass Sie mein Buch "Spring Boot Persistenz Best Practices" lieben werden. | Wenn Sie eine Hand von Tipps und Illustrationen von mehr als 100 Java Persistenz -Leistungsproblemen benötigen, dann ist "Java Persistence Performance Illustrated Guide" genau das Richtige für Sie. |
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@ManyToMany Association umzusetzen Beschreibung: Diese Anwendung ist ein Beweis für das Konzept, wie es korrekt ist, die bidirektionale @ManyToMany Association aus der Perspektive der Leistung zu implementieren.
Kernpunkte:
mappedBy SeiteSet NOT ListCascadeType.PERSIST und CascadeType.MERGE , aber vermeiden Sie CascadeType.REMOVE/ALL@ManyToMany ist standardmäßig faul; Behalte es so!@NaturalId )) und/oder Datenbankgenerierte Kennungen und überschreiben (auf beiden Seiten) die Methoden equals() und hashCode() wie hiertoString() überschrieben werden muss, achtenSet List @ManyToMany Beschreibung: Dies ist ein Feder -Boot -Beispiel für das Entfernen von Zeilen im Falle einer bidirektionalen @ManyToMany mit List , die Set . Die Schlussfolgerung ist, dass Set viel besser ist! Dies gilt auch für unidirektionale!
Kernpunkte:
Set ist viel effizienter als List Ausgabebeispiel: 
log4jdbc anBeschreibung: Zeigen Sie die Abfragetails über log4jdbc an.
Kernpunkte:
pom.xml log4jdbc -Abhängigkeit hinzufügen Ausgabebeispiel: 
Beschreibung: Zeigen Sie die vorbereiteten Anweisungsbindung/extrahierte Parameter über TRACE an.
Kernpunkte:
application.properties add: logging.level.org.hibernate.type.descriptor.sql=TRACE Ausgabebeispiel: 
java.time.YearMonth als Integer oder Date über Bibliothek von Hibernate Typen Beschreibung: Hibernate -Typen sind eine Reihe von zusätzlichen Typen, die im Hibernate -Kern nicht standardmäßig unterstützt werden. Einer dieser Typen ist java.time.YearMonth . Dies ist eine Spring -Boot -Anwendung, die den Hibernate -Typ verwendet, um in einer MySQL -Datenbank als YearMonth oder Datum in einer MySQL -Datenbank zu speichern.
Kernpunkte:
pom.xml hinzu@TypeDef um typeClass auf defaultForType zuzubereiten Ausgabebeispiel: 
HINWEIS : Verwenden von SQL -Funktionen im WHERE Teil (nicht im SELECT Teil) der Abfrage in JPA 2.1 können über function() wie hier durchgeführt werden.
Beschreibung: Der Versuch, SQL -Funktionen (Standard oder definiert) in JPQL -Abfragen zu verwenden, kann zu Ausnahmen führen, wenn der Winterschlaf sie nicht erkennt und die JPQL -Abfrage nicht analysieren kann. Zum Beispiel wird die Funktion von MySQL, concat_ws nicht von Winterschlafnate erkannt. Diese Anwendung ist eine Spring -Boot -Anwendung, die auf Hibernate 5.3 basiert, die die Funktion concat_ws über MetadataBuilderContributor registriert und den Winterschlaf darüber informiert, dass sie über sie, metadata_builder_contributor -Eigenschaft, darüber informiert werden. In diesem Beispiel wird auch @Query und EntityManager verwendet, sodass Sie zwei Anwendungsfälle sehen können.
Kernpunkte:
MetadataBuilderContributor und registrieren Sie die Funktion concat_ws MySQLapplication.properties set spring.jpa.properties.hibernate.metadata_builder_contributor , um auf die Implementierung MetadataBuilderContributor zu verweisen Ausgabebeispiel: 
Beschreibung: Diese Anwendung ist ein Beispiel der Protokollierung nur langsame Abfragen über DataSource-Proxy . Eine langsame Frage ist eine Abfrage, die eine Ausführungszeit hat, die größer ist als eine spezifische Schwelle in Millisekunden.
Kernpunkte:
pom.xml die DataSource-Proxy-Abhängigkeit hinzuDataSource abzufangenDataSource MethodInterceptor über ProxyFactoryafterQuery() Ausgabebeispiel: 
SELECT COUNT Abfrage und Rückgabe Page<dto> zurück Beschreibung: Diese Anwendung erfasst Daten als Page<dto> über die Spring -Start -Offset -Pagination. In den meisten Fällen sind die Daten, die paginiert werden sollten , schreibgeschützte Daten. Das Abrufen der Daten in Entitäten sollte nur erfolgen, wenn wir vorhaben, diese Daten zu ändern. Daher ist es nicht vorzuziehen, dass nur die Daten als Page<entity> gelesen werden, da dies möglicherweise in einer erheblichen Leistungsstrafe endet. Die zum Zählen der Gesamtzahl der Datensätze ausgelöste SELECT COUNT ausgewählt ist eine Unterabfrage der SELECT . Daher wird es eine einzige Datenbank -Roundtrip anstelle von zwei geben (normalerweise wird eine Abfrage zum Abholen der Daten und eine zum Zählen der Gesamtzahl der Datensätze benötigt).
Kernpunkte:
PagingAndSortingRepository erweitertList<dto> abrufenList<dto> und den richtigen Pageable um eine Page<dto> zu erstellenList<dto> SELECT COUNT Beschreibung: Diese Anwendung erfasst Daten als List<dto> über die Spring -Start -Offset -Pagination. In den meisten Fällen sind die Daten, die paginiert werden sollten , schreibgeschützte Daten. Das Abrufen der Daten in Entitäten sollte nur erfolgen, wenn wir vorhaben, diese Daten zu ändern. Daher ist es nicht vorzuziehen, dass die Lesen nur als List<entity> abgerufen wird, da dies möglicherweise in einer erheblichen Leistungsstrafe endet. Die zum Zählen der Gesamtzahl der Datensätze ausgelöste SELECT COUNT ausgewählt ist eine Unterabfrage der SELECT . Daher wird es eine einzige Datenbank -Roundtrip anstelle von zwei geben (normalerweise wird eine Abfrage zum Abholen der Daten und eine zum Zählen der Gesamtzahl der Datensätze benötigt).
Kernpunkte:
PagingAndSortingRepository erweitertList<dto> abrufen Wenn Sie die spring-boot-starter-jdbc oder spring-boot-starter-data-jpa "Starter" verwenden, erhalten Sie automatisch eine Abhängigkeit von Hikaricp
HINWEIS: Der beste Weg, um die Verbindungspool -Parameter zu stimmen, bestehen darin, Flexy Pool von Vlad Mihalcea zu verwenden. Über den Flexy Pool finden Sie die optimalen Einstellungen, die eine hohe Leistung Ihres Verbindungspools erhalten.
Beschreibung: Dies ist eine Kickoff -Anwendung, die Hikaricp nur über application.properties einstellt. Die jdbcUrl ist für eine MySQL -Datenbank eingerichtet. Zu den Testzwecken verwendet die Anwendung einen ExecutorService zum Simulieren gleichzeitiger Benutzer. Überprüfen Sie den Hickaricp -Bericht, in dem der Status des Verbindungspools angezeigt wird.
Kernpunkte:
application.properties verlassen Sie sich auf spring.datasource.hikari.* Um Hikaricp zu konfigurieren Ausgabebeispiel: 
| Wenn Sie einen tiefen Eintauchen in die in diesem Repository entlarvten Performance -Rezepte benötigen, dann bin ich sicher, dass Sie mein Buch "Spring Boot Persistenz Best Practices" lieben werden. | Wenn Sie eine Hand von Tipps und Illustrationen von mehr als 100 Java Persistenz -Leistungsproblemen benötigen, dann ist "Java Persistence Performance Illustrated Guide" genau das Richtige für Sie. |
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DataSourceBuilder anpassen Wenn Sie die spring-boot-starter-jdbc oder spring-boot-starter-data-jpa "Starter" verwenden, erhalten Sie automatisch eine Abhängigkeit von Hikaricp
HINWEIS: Der beste Weg, um die Verbindungspool -Parameter zu stimmen, bestehen darin, Flexy Pool von Vlad Mihalcea zu verwenden. Über den Flexy Pool finden Sie die optimalen Einstellungen, die eine hohe Leistung Ihres Verbindungspools erhalten.
Beschreibung: Dies ist eine Kickoff -Anwendung, die Hikaricp über DataSourceBuilder einstellt. Die jdbcUrl ist für eine MySQL -Datenbank eingerichtet. Zu den Testzwecken verwendet die Anwendung einen ExecutorService zum Simulieren gleichzeitiger Benutzer. Überprüfen Sie den Hickaricp -Bericht, in dem der Status des Verbindungspools angezeigt wird.
Kernpunkte:
application.properties konfigurieren Sie Hikaricp über ein benutzerdefiniertes Präfix, z. B. app.datasource.*@Bean die die DataSource zurückgibt Ausgabebeispiel:
Diese Anwendung ist in diesem Dzone -Artikel beschrieben.
DataSourceBuilder anHINWEIS: Der beste Weg, um die Verbindungspool -Parameter zu stimmen, bestehen darin, Flexy Pool von Vlad Mihalcea zu verwenden. Über den Flexy Pool finden Sie die optimalen Einstellungen, die eine hohe Leistung Ihres Verbindungspools erhalten.
Beschreibung: Dies ist eine Kickoff -Anwendung, die Bonecp über DataSourceBuilder einstellt. Die jdbcUrl ist für eine MySQL -Datenbank eingerichtet. Zu den Testzwecken verwendet die Anwendung einen ExecutorService zum Simulieren gleichzeitiger Benutzer.
Kernpunkte:
pom.xml die Bonecp -Abhängigkeit hinzufügenapplication.properties konfigurieren Sie Bonecp über ein benutzerdefiniertes Präfix, z. B. app.datasource.*@Bean die die DataSource zurückgibt Ausgabebeispiel: 
DataSourceBuilder anpassenHINWEIS: Der beste Weg, um die Verbindungspool -Parameter zu stimmen, bestehen darin, Flexy Pool von Vlad Mihalcea zu verwenden. Über den Flexy Pool finden Sie die optimalen Einstellungen, die eine hohe Leistung Ihres Verbindungspools erhalten.
Beschreibung: Dies ist eine Kickoff -Anwendung, die Viburdbcp über DataSourceBuilder einstellt. Die jdbcUrl ist für eine MySQL -Datenbank eingerichtet. Zu den Testzwecken verwendet die Anwendung einen ExecutorService zum Simulieren gleichzeitiger Benutzer.
Kernpunkte:
pom.xml die viburdbcp -Abhängigkeit hinzufügenapplication.properties konfigurieren Sie ViBurdbcp über ein benutzerdefiniertes Präfix, z. B. app.datasource.*@Bean die die DataSource zurückgibt Ausgabebeispiel: 
DataSourceBuilder anpassenHINWEIS: Der beste Weg, um die Verbindungspool -Parameter zu stimmen, bestehen darin, Flexy Pool von Vlad Mihalcea zu verwenden. Über den Flexy Pool finden Sie die optimalen Einstellungen, die eine hohe Leistung Ihres Verbindungspools erhalten.
Beschreibung: Dies ist eine Kickoff -Anwendung, die C3P0 über DataSourceBuilder einstellt. Die jdbcUrl ist für eine MySQL -Datenbank eingerichtet. Zu den Testzwecken verwendet die Anwendung einen ExecutorService zum Simulieren gleichzeitiger Benutzer.
Kernpunkte:
pom.xml die c3p0 -Abhängigkeit hinzufügenapplication.properties konfigurieren Sie C3P0 über ein benutzerdefiniertes Präfix, z. B. app.datasource.*@Bean die die DataSource zurückgibt Ausgabebeispiel: 
DataSourceBuilder anpassenHINWEIS: Der beste Weg, um die Verbindungspool -Parameter zu stimmen, bestehen darin, Flexy Pool von Vlad Mihalcea zu verwenden. Über den Flexy Pool finden Sie die optimalen Einstellungen, die eine hohe Leistung Ihres Verbindungspools erhalten.
Beschreibung: Dies ist eine Kickoff -Anwendung, die DBCP2 über DataSourceBuilder eingerichtet hat. Die jdbcUrl ist für eine MySQL -Datenbank eingerichtet. Zu den Testzwecken verwendet die Anwendung einen ExecutorService zum Simulieren gleichzeitiger Benutzer.
Kernpunkte:
pom.xml die DBCP2 -Abhängigkeit hinzufügenapplication.properties konfigurieren Sie DBCP2 über ein benutzerdefiniertes Präfix, z. B. app.datasource.*@Bean die die DataSource zurückgibtDataSourceBuilder anpassenHINWEIS: Der beste Weg, um die Verbindungspool -Parameter zu stimmen, bestehen darin, Flexy Pool von Vlad Mihalcea zu verwenden. Über den Flexy Pool finden Sie die optimalen Einstellungen, die eine hohe Leistung Ihres Verbindungspools erhalten.
Beschreibung: Dies ist eine Kickoff -Anwendung, die Tomcat über DataSourceBuilder einstellt. Die jdbcUrl ist für eine MySQL -Datenbank eingerichtet. Zu den Testzwecken verwendet die Anwendung einen ExecutorService zum Simulieren gleichzeitiger Benutzer.
Kernpunkte:
pom.xml die Tomcat -Abhängigkeit hinzufügenapplication.properties konfigurieren Sie Tomcat über ein benutzerdefiniertes Präfix, z. B. app.datasource.*@Bean die die DataSource zurückgibt Ausgabebeispiel: 
HINWEIS: Der beste Weg, um die Verbindungspool -Parameter zu stimmen, bestehen darin, Flexy Pool von Vlad Mihalcea zu verwenden. Über den Flexy Pool finden Sie die optimalen Einstellungen, die eine hohe Leistung Ihres Verbindungspools erhalten.
Beschreibung: Dies ist eine Kickoff -Anwendung, die zwei Datenquellen (zwei MySQL -Datenbanken, eine namens authorsdb und eine namens booksdb ) mit zwei Verbindungspools verwendet (jede Datenbank verwendet einen eigenen Hikaricp -Verbindungspool mit unterschiedlichen Einstellungen). Basierend auf den oben genannten Elementen ist es ziemlich einfach, zwei Verbindungspools von zwei verschiedenen Anbietern zu konfigurieren.
Kernpunkte:
application.properties konfigurieren Sie zwei Hikaricp -Verbindungspools über zwei benutzerdefinierte Präfixe, z. B. app.datasource.ds1 und app.datasource.ds2@Bean , die die erste DataSource zurückgibt, und markieren Sie sie als @Primary@Bean , die die zweite DataSource zurückgibtEntityManagerFactory und weisen Sie die Pakete auf, die für jeden von ihnen scannen sollenEntityManager in die richtigen Pakete Ausgabebeispiel: 
Hinweis : Wenn Sie möchten, dass Sie eine fließende API angeben, ohne die Setzer zu ändern, sollten Sie diesen Element in Betracht ziehen.
Beschreibung: Dies ist eine Stichprobenanwendung, die die Methoden der Entitätensetzer verändert, um eine fließende API zu stärken.
Kernpunkte:
this anstelle von void in Setzen zurück Fließendes API -Beispiel: 
HINWEIS : Wenn Sie möchten, dass Sie eine fließende API durch Ändern von Setzen angeben, sollten Sie diesen Element in Betracht ziehen.
Beschreibung: Dies ist eine Beispielanwendung, die in Entitäten zusätzliche Methoden hinzufügen (z. B. für setName , wir fügen name hinzu) Methoden, um eine fließende API zu stärken.
Kernpunkte:
this anstelle von void zurückgibt Fließendes API -Beispiel: 
| Wenn Sie einen tiefen Eintauchen in die in diesem Repository entlarvten Performance -Rezepte benötigen, dann bin ich sicher, dass Sie mein Buch "Spring Boot Persistenz Best Practices" lieben werden. | Wenn Sie eine Hand von Tipps und Illustrationen von mehr als 100 Java Persistenz -Leistungsproblemen benötigen, dann ist "Java Persistence Performance Illustrated Guide" genau das Richtige für Sie. |
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Slice<T> findAll() Höchstwahrscheinlich ist dies alles, was Sie wollen: Wie man Slice<entity> / Slice<dto> über fetchAll / fetchAllDto abholt
Einige Implementierungen von Slice<T> findAll() :
"SELECT e FROM " + entityClass.getSimpleName() + " e;"CriteriaBuilder anstelle von hart codiertem SQL basiertSort bereitzustellen. Sortieren von Ergebnissen sind daher möglichSort und Specification bereitstellen könnenSort , einen LockModeType , eine QueryHints und eine Specification bereitzustellenPageable und/oder Specification bereitzustellen, indem wir das SimpleJpaRepository aus Federdaten erweitern. Basicial ist diese Implementierung die einzige Page<T> readPage(...) Page<T> anstelle von Slice<T> zurückgibt, aber sie löst die zusätzliche SELECT COUNT nicht aus SimpleJpaRepository . Der Hauptnachteil ist, dass Sie durch die Wiedererlangung einer Page<T> nicht wissen, ob es eine nächste oder die aktuelle Seite gibt. Trotzdem gibt es auch Problemumgehungen, um dies zu haben. In dieser Implementierung können Sie keine LockModeType oder Abfragedipps festlegen. Story : Spring Boot bietet einen offsetbasierten integrierten Paging-Mechanismus, der eine Page oder Slice zurückgibt. Jede dieser APIs repräsentiert eine Datenseite und einige Metadaten. Der Hauptunterschied besteht darin, dass Page die Gesamtzahl der Datensätze enthält, während Slice nur erkennen kann, ob eine andere Seite verfügbar ist. Für Page bietet Spring Boot eine findAll() -Methode, die als Argumente ein Pageable und/oder ein Specification oder ein Example annehmen kann. Um eine Page zu erstellen, die die Gesamtzahl der Datensätze enthält, löst diese Methode neben der Abfrage, mit der die Daten der aktuellen Seite abgerufen werden können, eine SELECT COUNT extra-grundlegend aus. Dies kann eine Leistungsstrafe sein, da die Abfrage der SELECT COUNT jedes Mal, wenn wir eine Seite anfordern, ausgelöst wird. Um diese extra-Querität zu vermeiden, bietet Spring Boot eine entspanntere API, die Slice -API. Durch die Verwendung von Slice anstelle von Page wird die Notwendigkeit dieser zusätzlichen SELECT COUNT ausgewählt und die Seite (Datensätze) und einige Metadaten ohne die Gesamtzahl der Datensätze zurückgegeben. Während Slice die Gesamtzahl der Datensätze nicht kennt, kann es immer noch feststellen, ob nach dem aktuellen oder dies die letzte Seite ist. Das Problem ist, dass Slice gut funktioniert für Abfragen, die die SQL enthalten, WHERE Klausel (einschließlich derjenigen, die den in Frühlingsdaten eingebauten Abfragebuildermechanismus verwenden), aber für findAll() nicht funktioniert . Diese Methode gibt weiterhin eine Page anstelle von Slice zurück, daher wird die Abfrage SELECT COUNT für Slice<T> findAll(...); .
Beschreibung: Dies ist eine Reihe von Probenanwendungen, die verschiedene Versionen einer Slice<T> findAll(...) bereitstellen. Wir haben eine minimalistische Implementierung, die auf einer festcodierten Abfrage beruht, als: "SELECT e FROM " + entityClass.getSimpleName() + " e"; (Dieses Rezept), zu einer benutzerdefinierten Implementierung, die Sortier-, Spezifikations-, Sperrmodus- und Abfrage -Hinweise auf eine Implementierung unterstützt, die sich auf die Erweiterung SimpleJpaRepository beruht.
Kernpunkte:
abstract Klasse, die die Slice<T> findAll(...) Methoden ( SlicePagingRepositoryImplementation ) enthüllt)findAll() -Methoden, um Slice<T> (oder Page<T> , jedoch ohne die Gesamtzahl der Elemente) zurückzugeben.SliceImpl ( Slice<T> ) oder ein PageImpl ( Page<T> ) ohne die Gesamtzahl der Elemente zurückreadSlice() SimpleJpaRepository#readPage() SELECT COUNTAuthor.class ) über ein Klassen -Repository ( AuthorRepository ) an diese abstract Klasse.COUNT(*) OVER und List<dto> Beschreibung: In der Offset -Pagination wird in der Regel eine Abfrage zum Abrufen der Daten und eine zum Zählen der Gesamtzahl der Datensätze benötigt. Wir können diese Informationen jedoch in einem einzelnen Datenbankrountrip über eine in der SELECT verschachtelte SELECT COUNT -Unterabfrage abrufen. Noch SELECT COUNT ist, dass Datenbanken Anbieter COUNT(*) OVER() die Fensterfunktionen unterstützen .
Kernpunkte:
COUNT(*) OVER() abgerufen werden sollten Beispiel: 
Beschreibung: Wenn wir uns auf ein Offset -Paging verlassen, haben wir die Leistungsstrafe durch das wegwerfen von N -Rekorde weggeworfene Rekorde, bevor er den gewünschten Offset erreicht hat. Größerer N führt zu einer erheblichen Leistungsstrafe. Wenn wir ein großes N haben, ist es besser, sich auf die Keyset -Pagination zu verlassen, die eine "konstante" Zeit für große Datensätze einhält. Um zu verstehen, wie schlimm der Offset durchführen kann, überprüfen Sie diesen Artikel bitte:
Screenshot aus diesem Artikel ( Offset -Pagination): 
Möchten Sie wissen, ob es mehr Datensätze gibt?
Keyset verwendet daher keine SELECT COUNT , um die Anzahl der Gesamtdatensätze abzurufen. Bei ein wenig Verbesserung können wir jedoch leicht sagen, ob es mehr Datensätze gibt, um eine Schaltfläche vom Typ Next Page anzuzeigen. Wenn Sie so etwas benötigen, sollten Sie diese Anwendung in Betracht ziehen, deren Höhepunkt unten aufgeführt ist:
public AuthorView fetchNextPage(long id, int limit) {
List<Author> authors = authorRepository.fetchAll(id, limit + 1);
if (authors.size() == (limit + 1)) {
authors.remove(authors.size() - 1);
return new AuthorView(authors, true);
}
return new AuthorView(authors, false);
}
Oder so (stützen Sie sich auf Author.toString() Methode):
public Map<List<Author>, Boolean> fetchNextPage(long id, int limit) {
List<Author> authors = authorRepository.fetchAll(id, limit + 1);
if(authors.size() == (limit + 1)) {
authors.remove(authors.size() -1);
return Collections.singletonMap(authors, true);
}
return Collections.singletonMap(authors, false);
}
Eine Previous Page Seitenschaltfläche kann einfach basierend auf dem ersten Datensatz implementiert werden.
Kernpunkte:
id ) zu fungieren.WHERE und ORDER BY Klauseln Ihres SQLBeschreibung: Dies ist ein klassisches Beispiel für den Feder -Startpagination . Es ist jedoch nicht ratsam, diesen Ansatz in der Produktion zu verwenden, da die Leistungsstrafen weiter erläutert wurden.
Wenn wir uns auf eine Offset -Pagination verlassen, haben wir die Leistungsstrafe durch Werfen von N -Schallplatten induziert, bevor wir den gewünschten Offset erreichen. Größerer N führt zu einer erheblichen Leistungsstrafe. Eine weitere Strafe ist die Extra SELECT die zum Zählen der Gesamtzahl der Datensätze erforderlich ist. Um zu verstehen, wie schlecht die Paginierung der Offset durchführen kann, überprüfen Sie diesen Artikel bitte. Ein Screenshot aus diesem Artikel ist unten: Trotzdem ist dieses Beispiel vielleicht ein bisschen extrem. Für relativ kleine Datensätze ist die Offset -Pagination nicht so schlimm (es ist in der Leistung der Keyset -Pagination in der Lage), da der Spring Boot in der integrierten Unterstützung für die Offset -Pagination über die Page -API sehr einfach ist. Abhängig vom Fall können wir jedoch die Offset -Pagination wie in den folgenden Beispielen ein wenig optimieren:
Abrufen Sie eine Seite als Page :
COUNT(*) OVER und Page<dto> zurückgebenCOUNT(*) OVER und zurückgeben Page<entity> über zusätzliche SpalteSELECT COUNT Abfrage und Rückgabe von Zählen Sie Page<dto> zurückSELECT COUNT die Abfrage und Rückgabe Page<entity> über zusätzliche SpalteSELECT COUNT Abfrage- und Rückgabe Page<projection> , die Entitäten und die Gesamtzahl der Datensätze über Projektion ordnet Abrufen Sie eine Seite als List :
COUNT(*) OVER und zurücklistet List<dto> zurückCOUNT(*) OVER und return List<entity> über zusätzliche SpalteSELECT COUNT die Abfrage- und Rücklaufliste List<dto> aus.SELECT COUNT Abfrage und List<entity> über zusätzliche SpalteSELECT COUNT Abfrage- und List<projection> aus, die Entitäten und die Gesamtzahl der Datensätze über Projektion abbildenAber: Wenn die Offset -Pagination zu Leistungsproblemen führt und Sie sich für die Keyset -Pagination entscheiden, lesen Sie bitte hier ( Keyset -Pagination).
Schlüsselpunkte der klassischen Offset -Pagination:
PagingAndSortingRepository erweitertPage<entity> zurückgebenBeispiele für klassische Offset -Pagination:
findAll(Pageable) an, ohne zu sortieren:repository.findAll(PageRequest.of(page, size));findAll(Pageable) mit Sortierung an:repository.findAll(PageRequest.of(page, size, new Sort(Sort.Direction.ASC, "name")));Page<Author> findByName(String name, Pageable pageable);Page<Author> queryFirst10ByName(String name, Pageable pageable); Beschreibung: Nehmen wir an, wir haben eine Eins-zu-Viele-Beziehung zwischen Author und Book . Wenn wir einen Autor retten, retten wir auch seine Bücher dank Cascading All/Persist. Wir möchten eine Reihe von Autoren mit Büchern erstellen und sie in der Datenbank (z. B. einer MySQL -Datenbank) unter Verwendung der Batch -Technik speichern. Standardmäßig führt dies dazu, dass jeder Autor und die Bücher pro Autor angegeben werden (eine Charge für den Autor und eine Charge für die Bücher, eine andere Charge für den Autor und eine andere Charge für die Bücher usw.). Um Autoren und Bücher zu batchieren, müssen wir Einsätze wie in dieser Anwendung bestellen .
Key points: Beside all setting specific to batching inserts in MySQL, we need to set up in application.properties the following property: spring.jpa.properties.hibernate.order_inserts=true
Example without ordered inserts: 
Example with ordered inserts: 
Implementations:
Description: Batch updates in MySQL.
Kernpunkte:
application.properties set spring.jpa.properties.hibernate.jdbc.batch_sizeapplication.properties set JDBC URL with rewriteBatchedStatements=true (optimization for MySQL, statements get rewritten into a single string buffer and sent in a single request)application.properties set JDBC URL with cachePrepStmts=true (enable caching and is useful if you decide to set prepStmtCacheSize , prepStmtCacheSqlLimit , etc as well; without this setting the cache is disabled)application.properties set JDBC URL with useServerPrepStmts=true (this way you switch to server-side prepared statements (may lead to signnificant performance boost))spring.jpa.properties.hibernate.order_updates=true to optimize the batching by ordering updatesapplication.properties a setting for enabling batching for versioned entities during update and delete operations (entities that contains @Version for implicit optimistic locking); this setting is: spring.jpa.properties.hibernate.jdbc.batch_versioned_data=true ; starting with Hibernate 5, this setting should be true by default Output example for single entity: 
Output example for parent-child relationship: 
Description: Batch deletes that don't involve associations in MySQL.
Note: Spring deleteAllInBatch() and deleteInBatch() don't use delete batching and don't take advantage of automatic optimstic locking mechanism to prevent lost updates (eg, @Version is ignored). They rely on Query.executeUpdate() to trigger bulk operations. These operations are fast, but Hibernate doesn't know which entities are removed, therefore, the Persistence Context is not updated accordingly (it's up to you to flush (before delete) and close/clear (after delete) the Persistence Context accordingly to avoid issues created by unflushed (if any) or outdated (if any) entities). The first one ( deleteAllInBatch() ) simply triggers a delete from entity_name statement and is very useful for deleting all records. The second one ( deleteInBatch() ) triggers a delete from entity_name where id=? or id=? or id=? ... statement, therefore, is prone to cause issues if the generated DELETE statement exceedes the maximum accepted size. This issue can be controlled by deleting the data in chunks, relying on IN operator, and so on. Bulk operations are faster than batching which can be achieved via the deleteAll() , deleteAll(Iterable<? extends T> entities) or delete() method. Behind the scene, the two flavors of deleteAll() relies on delete() . The delete() / deleteAll() methods rely on EntityManager.remove() therefore the Persistence Context is synchronized accordingly. Moreover, if automatic optimstic locking mechanism (to prevent lost updates ) is enabled then it will be used.
Key points for regular delete batching:
deleteAll() , deleteAll(Iterable<? extends T> entities) or delete() methodapplication.properties set spring.jpa.properties.hibernate.jdbc.batch_sizeapplication.properties set JDBC URL with rewriteBatchedStatements=true (optimization for MySQL, statements get rewritten into a single string buffer and sent in a single request)application.properties set JDBC URL with cachePrepStmts=true (enable caching and is useful if you decide to set prepStmtCacheSize , prepStmtCacheSqlLimit , etc as well; without this setting the cache is disabled)application.properties set JDBC URL with useServerPrepStmts=true (this way you switch to server-side prepared statements (may lead to signnificant performance boost))application.properties a setting for enabling batching for versioned entities during update and delete operations (entities that contains @Version for implicit optimistic locking); this setting is: spring.jpa.properties.hibernate.jdbc.batch_versioned_data=true ; starting with Hibernate 5, this setting should be true by default Ausgabebeispiel: 
Description: Batch deletes in MySQL via orphanRemoval=true .
Note: Spring deleteAllInBatch() and deleteInBatch() don't use delete batching and don't take advantage of cascading removal, orphanRemoval and automatic optimstic locking mechanism to prevent lost updates (eg, @Version is ignored). They rely on Query.executeUpdate() to trigger bulk operations. These operations are fast, but Hibernate doesn't know which entities are removed, therefore, the Persistence Context is not updated accordingly (it's up to you to flush (before delete) and close/clear (after delete) the Persistence Context accordingly to avoid issues created by unflushed (if any) or outdated (if any) entities). The first one ( deleteAllInBatch() ) simply triggers a delete from entity_name statement and is very useful for deleting all records. The second one ( deleteInBatch() ) triggers a delete from entity_name where id=? or id=? or id=? ... statement, therefore, is prone to cause issues if the generated DELETE statement exceedes the maximum accepted size. This issue can be controlled by deleting the data in chunks, relying on IN operator, and so on. Bulk operations are faster than batching which can be achieved via the deleteAll() , deleteAll(Iterable<? extends T> entities) or delete() method. Behind the scene, the two flavors of deleteAll() relies on delete() . The delete() / deleteAll() methods rely on EntityManager.remove() therefore the Persistence Context is synchronized accordingly. If automatic optimstic locking mechanism (to prevent lost updates ) is enabled then it will be used. Moreover, cascading removals and orphanRemoval works as well.
Key points for using deleteAll()/delete() :
Author entity and each author can have several Book ( one-to-many )orphanRemoval=true and CascadeType.ALLBook from the corresponding AuthororphanRemoval=true to enter into the scene; thanks to this setting, all disassociated books will be deleted; the generated DELETE statements are batched (if orphanRemoval is set to false , a bunch of updates will be executed instead of deletes)Author via the deleteAll() or delete() method (since we have dissaciated all Book , the Author deletion will take advantage of batching as well)ON DELETE CASCADE Description: Batch deletes in MySQL via ON DELETE CASCADE . Auto-generated database schema will contain the ON DELETE CASCADE directive.
Note: Spring deleteAllInBatch() and deleteInBatch() don't use delete batching and don't take advantage of cascading removal, orphanRemoval and automatic optimistic locking mechanism to prevent lost updates (eg, @Version is ignored), but both of them take advantage on ON DELETE CASCADE and are very efficient. They trigger bulk operations via Query.executeUpdate() , therefore, the Persistence Context is not synchronized accordingly (it's up to you to flush (before delete) and close/clear (after delete) the Persistence Context accordingly to avoid issues created by unflushed (if any) or outdated (if any) entities). The first one simply triggers a delete from entity_name statement, while the second one triggers a delete from entity_name where id=? or id=? or id=? ... Stellungnahme. For delete in batches rely on deleteAll() , deleteAll(Iterable<? extends T> entities) or delete() method. Behind the scene, the two flavors of deleteAll() relies on delete() . Mixing batching with database automatic actions ( ON DELETE CASCADE ) will result in a partially synchronized Persistent Context.
Kernpunkte:
Author entity and each author can have several Book ( one-to-many )orphanRemoval or set it to falseCascadeType.PERSIST and CascadeType.MERGE@OnDelete(action = OnDeleteAction.CASCADE) next to @OneToManyspring.jpa.properties.hibernate.dialect to org.hibernate.dialect.MySQL5InnoDBDialect (or, MySQL8Dialect )deleteFoo() methods that uses bulk and batching deletes as wellAusgabebeispiel:
@NaturalId In Spring Boot Style Alternative implementation: In case that you want to avoid extending SimpleJpaRepository check this implementation.
Description: This is a SpringBoot application that maps a natural business key using Hibernate @NaturalId . This implementation allows us to use @NaturalId as it was provided by Spring.
Kernpunkte:
Book ), mark the properties (business keys) that should act as natural IDs with @NaturalId ; commonly, there is a single such property, but multiple are suppored as well as here@NaturalId(mutable = false) and @Column(nullable = false, updatable = false, unique = true, ...)@NaturalId(mutable = true) and @Column(nullable = false, updatable = true, unique = true, ...)equals() and hashCode() using the natural id(s)@NoRepositoryBean interface ( NaturalRepository ) to define two methods, named findBySimpleNaturalId() and findByNaturalId()NaturalRepositoryImpl ) relying on Hibernate, Session , bySimpleNaturalId() and byNaturalId() methods@EnableJpaRepositories(repositoryBaseClass = NaturalRepositoryImpl.class) to register this implementation as the base classfindBySimpleNaturalId() or findByNaturalId()| If you need a deep dive into the performance recipes exposed in this repository then I am sure that you will love my book "Spring Boot Persistence Best Practices" | If you need a hand of tips and illustrations of 100+ Java persistence performance issues then "Java Persistence Performance Illustrated Guide" is for you. |
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Description: This is a Spring Boot application that uses P6Spy. P6Spy is a framework that enables database data to be seamlessly intercepted and logged with no code changes to the application.
Kernpunkte:
pom.xml , add the P6Spy Maven dependencyapplication.properties , set up JDBC URL as, jdbc:p6spy:mysql://localhost:3306/db_usersapplication.properties , set up driver class name as, com.p6spy.engine.spy.P6SpyDriverspy.properties (this file contains P6Spy configurations); in this application, the logs will be outputed to console, but you can easy switch to a file; more details about P6Spy configurations can be found in documentation Ausgabebeispiel: 
OptimisticLockException Exception ( @Version ) Note: Optimistic locking mechanism via @Version works for detached entities as well.
Description: This is a Spring Boot application that simulates a scenario that leads to an optimistic locking exception. When such exception occur, the application retry the corresponding transaction via db-util library developed by Vlad Mihalcea.
Kernpunkte:
pom.xml , add the db-util dependencyOptimisticConcurrencyControlAspect bean@Transactional ) that is prone to throw (or that calls a method that is prone to throw (this method can be annotated with @Transactional )) an optimistic locking exception with @Retry(times = 10, on = OptimisticLockingFailureException.class) Ausgabebeispiel: 
OptimisticLockException Exception (Hibernate Version-less Optimistic Locking Mechanism)Note: Optimistic locking mechanism via Hibernate version-less doesn't work for detached entities (don't close the Persistent Context).
Description: This is a Spring Boot application that simulates a scenario that leads to an optimistic locking exception (eg, in Spring Boot, OptimisticLockingFailureException ) via Hibernate version-less optimistic locking. When such exception occur, the application retry the corresponding transaction via db-util library developed by Vlad Mihalcea.
Kernpunkte:
pom.xml , add the db-util library dependencyOptimisticConcurrencyControlAspect beanInventory ) with @DynamicUpdate and @OptimisticLocking(type = OptimisticLockType.DIRTY)@Transactional ) that is prone to throw (or that calls a method that is prone to throw (this method can be annotated with @Transactional )) an optimistic locking exception with @Retry(times = 10, on = OptimisticLockingFailureException.class)Note: You may also like to read the recipe, "How To Create DTO Via Spring Data Projections"
Description: This is an application sample that fetches only the needed columns from the database via Spring Data Projections (DTO) and enrich the result via virtual properties.
Kernpunkte:
name and ageAuthorNameAge , use the @Value and Spring SpEL to point to a backing property from the domain model (in this case, the domain model property age is exposed via the virtual property years )AuthorNameAge , use the @Value and Spring SpEL to enrich the result with two virtual properties that don't have a match in the domain model (in this case, rank and books ) Ausgabebeispiel: 
Description: Spring Data comes with the query creation mechanism for JPA that is capable to interpret a query method name and convert it into a SQL query in the proper dialect. This is possible as long as we respect the naming conventions of this mechanism. This is an application that exploit this mechanism to write queries that limit the result size. Basically, the name of the query method instructs Spring Data how to add the LIMIT (or similar clauses depending on the RDBMS) clause to the generated SQL queries.
Kernpunkte:
AuthorRepository ) Beispiele:
- List<Author> findFirst5ByAge(int age);
- List<Author> findFirst5ByAgeGreaterThanEqual(int age);
- List<Author> findFirst5ByAgeLessThan(int age);
- List<Author> findFirst5ByAgeOrderByNameDesc(int age);
- List<Author> findFirst5ByGenreOrderByAgeAsc(String genre);
- List<Author> findFirst5ByAgeGreaterThanEqualOrderByNameAsc(int age);
- List<Author> findFirst5ByGenreAndAgeLessThanOrderByNameDesc(String genre, int age);
- List<AuthorDto> findFirst5ByOrderByAgeAsc();
- Page<Author> queryFirst10ByName(String name, Pageable p);
- Slice<Author> findFirst10ByName(String name, Pageable p);
The list of supported keywords is listed below: 
schema-*.sql In MySQL Note: As a rule, in real applications avoid generating schema via hibernate.ddl-auto or set it to validate . Use schema-*.sql file or better Flyway or Liquibase migration tools.
Description: This application is an example of using schema-*.sql to generate a schema(database) in MySQL.
Kernpunkte:
application.properties , set the JDBC URL (eg, spring.datasource.url=jdbc:mysql://localhost:3306/bookstoredb?createDatabaseIfNotExist=true )application.properties , disable DDL auto (just don't add explicitly the hibernate.ddl-auto setting)application.properties , instruct Spring Boot to initialize the schema from schema-mysql.sql fileschema-*.sql And Match Entities To Them Via @Table In MySQL Note: As a rule, in real applications avoid generating schema via hibernate.ddl-auto or set it to validate . Use schema-*.sql file or better Flyway or Liquibase .
Description: This application is an example of using schema-*.sql to generate two databases in MySQL. The databases are matched at entity mapping via @Table .
Kernpunkte:
application.properties , set the JDBC URL without the database, eg, spring.datasource.url=jdbc:mysql://localhost:3306application.properties , disable DDL auto (just don't specify hibernate.ddl-auto )aaplication.properties , instruct Spring Boot to initialize the schema from schema-mysql.sql fileAuthor entity, specify that the corresponding table ( author ) is in the database authorsdb via @Table(schema="authorsdb")Book entity, specify that the corresponding table ( book ) is in the database booksdb via @Table(schema="booksdb")Ausgabebeispiel:
Author results in the following SQL: insert into authorsdb.author (age, genre, name) values (?, ?, ?)Book results the following SQL: insert into booksdb.book (isbn, title) values (?, ?)Note: For web-applications, pagination should be the way to go, not streaming. But, if you choose streaming then keep in mind the golden rule: keep th result set as small as posible. Also, keep in mind that the Execution Plan might not be as efficient as when using SQL-level pagination.
Description: This application is an example of streaming the result set via Spring Data and MySQL. This example can be adopted for databases that fetches the entire result set in a single roundtrip causing performance penalties.
Kernpunkte:
@Transactional(readOnly=true) )Integer.MIN_VALUE (recommended in MySQL))Statement fetch-size to Integer.MIN_VALUE , or add useCursorFetch=true to the JDBC URL and set Statement fetch-size to a positive integer (eg, 30)createDatabaseIfNotExist Note: For production, don't rely on hibernate.ddl-auto (or counterparts) to export schema DDL to the database. Simply remove (disable) hibernate.ddl-auto or set it to validate . Rely on Flyway or Liquibase.
Description: This application is an example of migrating a MySQL database via Flyway when the database exists (it is created before migration via MySQL specific parameter, createDatabaseIfNotExist=true ).
Kernpunkte:
pom.xml , add the Flyway dependencyspring.jpa.hibernate.ddl-autoapplication.properties , set the JDBC URL as follows: jdbc:mysql://localhost:3306/bookstoredb?createDatabaseIfNotExist=trueclasspath:db/migrationV1.1__Description.sql , V1.2__Description.sql , ...spring.flyway.schemas Note: For production, don't rely on hibernate.ddl-auto (or counterparts) to export schema DDL to the database. Simply remove (disable) hibernate.ddl-auto or set it to validate . Rely on Flyway or Liquibase.
Description: This application is an example of migrating a MySQL database when the database is created by Flyway via spring.flyway.schemas . In this case, the entities should be annotated with @Table(schema = "bookstoredb") or @Table(catalog = "bookstoredb") . Here, the database name is bookstoredb .
Kernpunkte:
pom.xml , add the Flyway dependencyspring.jpa.hibernate.ddl-autoapplication.properties , set the JDBC URL as follows: jdbc:mysql://localhost:3306/application.properties , add spring.flyway.schemas=bookstoredb , where bookstoredb is the database that should be created by Flyway (feel free to add your own database name)@Table(schema/catalog = "bookstoredb")classpath:db/migrationV1.1__Description.sql , V1.2__Description.sql , ... Output of migration history example: 
| If you need a deep dive into the performance recipes exposed in this repository then I am sure that you will love my book "Spring Boot Persistence Best Practices" | If you need a hand of tips and illustrations of 100+ Java persistence performance issues then "Java Persistence Performance Illustrated Guide" is for you. |
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Note: For production don't rely on hibernate.ddl-auto to create your schema. Remove (disable) hibernate.ddl-auto or set it to validate . Rely on Flyway or Liquibase.
Description: This application is an example of auto-creating and migrating schemas for MySQL and PostgreSQL. In addition, each data source uses its own HikariCP connection pool. In case of MySQL, where schema = database , we auto-create the schema ( authorsdb ) based on createDatabaseIfNotExist=true . In case of PostgreSQL, where a database can have multiple schemas, we use the default postgres database and auto-create in it the schema, booksdb . For this we rely on Flyway, which is capable to create a missing schema.
Kernpunkte:
pom.xml , add the Flyway dependencyspring.jpa.hibernate.ddl-auto or set it to validateapplication.properties , configure the JDBC URL for MySQL as, jdbc:mysql://localhost:3306/authorsdb?createDatabaseIfNotExist=true and for PostgreSQL as, jdbc:postgresql://localhost:5432/postgres?currentSchema=booksdbapplication.properties , set spring.flyway.enabled=false to disable default behaviorDataSource for MySQL and one for PostgreSQLFlywayDataSource for MySQL and one for PostgreSQLEntityManagerFactory for MySQL and one for PostgreSQLdbmigrationmysqldbmigrationpostgresql Note: For production, don't rely on hibernate.ddl-auto (or counterparts) to export schema DDL to the database. Simply remove (disable) hibernate.ddl-auto or set it to validate . Rely on Flyway or Liquibase.
Description: This application is an example of auto-creating and migrating two schemas in PostgreSQL using Flyway. In addition, each data source uses its own HikariCP connection pool. In case of PostgreSQL, where a database can have multiple schemas, we use the default postgres database and auto-create two schemas, authors and books . For this we rely on Flyway, which is capable to create the missing schemas.
Kernpunkte:
pom.xml , add the Flyway dependencyspring.jpa.hibernate.ddl-auto or set it to validateapplication.properties , configure the JDBC URL for books as jdbc:postgresql://localhost:5432/postgres?currentSchema=books and for authors as jdbc:postgresql://localhost:5432/postgres?currentSchema=authorsapplication.properties , set spring.flyway.enabled=false to disable default behaviorDataSource , one for books and one for authorsFlywayDataSource , one for books and one for authorsEntityManagerFactory , one for books and one for authorsbooks , place the migration SQLs files in dbmigrationbooksauthors , place the migration SQLs files in dbmigrationauthorsJOIN FETCH an @ElementCollection Description: This application is an example applying JOIN FETCH to fetch an @ElementCollection .
Kernpunkte:
@ElementCollection is loaded lazy, keep it lazyJOIN FETCH in the repository@Subselect ) in a Spring Boot Application Note: Consider using @Subselect only if using DTO, DTO and extra queries, or map a database view to an entity is not a solution.
Description: This application is an example of mapping an entity to a query via Hibernate, @Subselect . Mainly, we have two entities in a bidirectional one-to-many association. An Author has wrote several Book . The idea is to write a read-only query to fetch from Author only some fields (eg, DTO), but to have the posibility to call getBooks() and fetch the Book in a lazy manner as well. As you know, a classic DTO cannot be used, since such DTO is not managed and we cannot navigate the associations (don't support any managed associations to other entities). Via Hibernate @Subselect we can map a read-only and immutable entity to a query. This time, we can lazy navigate the associations.
Kernpunkte:
Author (including association to Book )@Immutable since no write operations are allowed@Synchronize@Subselect to write the needed query, map an entity to an SQL queryDescription: This application is an example of using Hibernate soft deletes in a Spring Boot application.
Kernpunkte:
abstract class BaseEntity with a field named deletedAuthor and Book entities) that should take advantage of soft deletes should extend BaseEntity@Where annotation like this: @Where(clause = "deleted = false")@SQLDelete annotation to trigger UPDATE SQLs in place of DELETE SQLs, as follows: @SQLDelete(sql = "UPDATE author SET deleted = true WHERE id = ?") Ausgabebeispiel: 
DataSourceBuilder If you use the spring-boot-starter-jdbc or spring-boot-starter-data-jpa "starters", you automatically get a dependency to HikariCP
Note: The best way to tune the connection pool parameters consist in using Flexy Pool by Vlad Mihalcea. Via Flexy Pool you can find the optim settings that sustain high-performance of your connection pool.
Description: This is a kickoff application that set up HikariCP via DataSourceBuilder . The jdbcUrl is set up for a MySQL database. For testing purposes, the application uses an ExecutorService for simulating concurrent users. Check the HickariCP report revealing the connection pool status.
Kernpunkte:
@Bean that returns the DataSource programmaticallyDescription: Auditing is useful for maintaining history records. This can later help us in tracking user activities.
Kernpunkte:
abstract base entity (eg, BaseEntity ) and annotate it with @MappedSuperclass and @EntityListeners({AuditingEntityListener.class})@CreatedDate protected LocalDateTime created;@LastModifiedDate protected LocalDateTime lastModified;@CreatedBy protected U createdBy;@LastModifiedBy protected U lastModifiedBy;@EnableJpaAuditing(auditorAwareRef = "auditorAware")AuditorAware (this is needed for persisting the user that performed the modification; use Spring Security to return the currently logged-in user)@Beanspring.jpa.hibernate.ddl-auto=create )Description: Auditing is useful for maintaining history records. This can later help us in tracking user activities.
Kernpunkte:
@Audited@AuditTable to rename the table used for auditingValidityAuditStrategy for fast database reads, but slower writes (slower than the default DefaultAuditStrategy )Description: By default, the attributes of an entity are loaded eager (all at once). This application is an alternative to How To Use Hibernate Attribute Lazy Loading from here. This application uses a base class to isolate the attributes that should be loaded eagerly and subentities (entities that extends the base class) for isolating the attributes that should be loaded on demand.
Kernpunkte:
BaseAuthor , and annotate it with @MappedSuperclassAuthorShallow subentity of BaseAuthor and don't add any attribute in it (this will inherit the attributes from the superclass)AuthorDeep subentity of BaseAuthor and add to it the attributes that should be loaded on demand (eg, avatar )@Table(name = "author")AuthorShallowRepository and AuthorDeepRepositoryRun the following requests (via BookstoreController):
localhost:8080/authors/shallowlocalhost:8080/authors/deepCheck as well:
Description: Fetching more data than needed is prone to performance penalities. Using DTO allows us to extract only the needed data. In this application we rely on constructor and Spring Data Query Builder Mechanism.
Kernpunkte:
Siehe auch:
Dto Via Constructor Expression and JPQL
| If you need a deep dive into the performance recipes exposed in this repository then I am sure that you will love my book "Spring Boot Persistence Best Practices" | If you need a hand of tips and illustrations of 100+ Java persistence performance issues then "Java Persistence Performance Illustrated Guide" is for you. |
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JOIN Description: Using JOIN is very useful for fetching DTOs (data that is never modified, not in the current or subsequent requests). For example, consider two entities, Author and Book in a lazy-bidirectional @OneToMany association. And, we want to fetch a subset of columns from the parent table ( author ) and a subset of columns from the child table ( book ). This job is a perfect fit for JOIN which can pick up columns from different tables and build a raw result set. This way we fetch only the needed data. Moreover, we may want to serve the result set in pages (eg, via LIMIT ). This application contains several approaches for accomplishing this task with offset pagination.
Kernpunkte:
Page (with SELECT COUNT and COUNT(*) OVER() window function)Slice and ListDENSE_RANK() for avoiding the truncation of the result set (an author can be fetched with only a subset of his books)LEFT JOIN FETCHSiehe auch:
Description: Let's assume that we have two entities engaged in a one-to-many (or many-to-many) lazy bidirectional (or unidirectional) relationship (eg, Author has more Book ). And, we want to trigger a single SELECT that fetches all Author and the corresponding Book . This is a job for JOIN FETCH which is converted behind the scene into a INNER JOIN . Being an INNER JOIN , the SQL will return only Author that have Book . If we want to return all Author , including those that doesn't have Book , then we can rely on LEFT JOIN FETCH . Similar, we can fetch all Book , including those with no registered Author . This can be done via LEFT JOIN FETCH or LEFT JOIN .
Kernpunkte:
Author and Book in a one-to-many lazy bidirectional relationship)LEFT JOIN FETCH to fetch all authors and books (fetch authors even if they don't have registered books)LEFT JOIN FETCH to fetch all books and authors (fetch books even if they don't have registered authors)JOIN VS. JOIN FETCHSiehe auch:
Description: This is an application meant to reveal the differences between JOIN and JOIN FETCH . The important thing to keep in mind is that, in case of LAZY fetching, JOIN will not be capable to initialize the associated collections along with their parent objects using a single SQL SELECT . On the other hand, JOIN FETCH is capable to accomplish this kind of task. But, don't underestimate JOIN , because JOIN is the proper choice when we need to combine/join the columns of two (or more) tables in the same query, but we don't need to initialize the associated collections on the returned entity (eg, very useful for fetching DTO).
Kernpunkte:
Author and Book in a one-to-many lazy-bidirectional relationship)JOIN and JOIN FETCH to fetch an author including his booksJOIN to fetch a book (1)JOIN to fetch a book including its author (2)JOIN FETCH to fetch a book including its authorNotice that:
JOIN , fetching Book of Author requires additional SELECT statements being prone to N+1 performance penaltyJOIN (1), fetching Author of Book requires additional SELECT statements being prone to N+1 performance penaltyJOIN (2), fetching Author of Book works exactly as JOIN FETCH (requires a single SELECT )JOIN FETCH , fetching each Author of a Book requires a single SELECT Description: If, for some reason, you need an entity in your Spring projection (DTO), then this application shows you how to do it via an example. In this case, there are two entities, Author and Book , involved in a lazy bidirectional one-to-many association (it can be other association as well, or even no materialized association). And, we want to fetch in a Spring projection the authors as entities, Author , and the title of the books.
Kernpunkte:
Author and Book in a one-to-many lazy bidirectional relationship)public Author getAuthor() and public String getTitle() Description: If, for some reason, you need an entity in your Spring projection (DTO), then this application shows you how to do it via an example. In this case, there are two entities, Author and Book , that have no materialized association between them, but, they share the genre attribute. We use this attribute to join authors with books via JPQL. And, we want to fetch in a Spring projection the authors as entities, Author , and the title of the books.
Kernpunkte:
Author and Book )public Author getAuthor() and public String getTitle() Description: Let's assume that we have two entities, Author and Book . There is no materialized association between them, but, both entities shares an attribute named, genre . We want to use this attribute to join the tables corresponding to Author and Book , and fetch the result in a DTO. The result should contain the Author entity and only the title attribute from Book . Well, when you are in a scenario as here, it is strongly advisable to avoid fetching the DTO via constructor expression . This approach cannot fetch the data in a single SELECT , and is prone to N+1. Way better than this consists of using Spring projections, JPA Tuple or even Hibernate ResultTransformer . These approaches will fetch the data in a single SELECT . This application is a DON'T DO THIS example. Check the number of queries needed for fetching the data. In place, do it as here: Entity Inside Spring Projection (no association).
@ElementCollection Description: This application is an example of fetching a DTO that includes attributes from an @ElementCollection .
Kernpunkte:
@ElementCollection is loaded lazy, keep it lazyJOIN in the repositorySet Of Associated Entities In @ManyToMany Association Via @OrderBy Description: In case of @ManyToMany association, we always should rely on Set (not on List ) for mapping the collection of associated entities (entities of the other parent-side). Warum? Well, please see Prefer Set Instead of List in @ManyToMany Relationships. But, is well-known that HashSet doesn't have a predefined entry order of elements. If this is an issue then this application relies on @OrderBy which adds an ORDER BY clause in the SQL statement. The database will handle the ordering. Further, Hibernate will preserve the order via a LinkedHashSet .
This application uses two entities, Author and Book , involved in a lazy bidirectional many-to-many relationship. First, we fetch a Book by title. Further, we call getAuthors() to fetch the authors of this book. The fetched authors are ordered descending by name. The ordering is done by the database as a result of adding @OrderBy("name DESC") , and is preserved by Hibernate.
Kernpunkte:
@OrderByHashSet , but doesn't provide consistency across all transition states (eg, transient state)LinkedHashSet instead of HashSet Note: Alternatively, we can use @OrderColumn . This gets materialized in an additional column in the junction table. This is needed for maintaining a permanent ordering of the related data.
Description: This is a sample application that shows how versioned ( @Version ) optimistic locking and detached entity works. Running the application will result in an optimistic locking specific exception (eg, the Spring Boot specific, OptimisticLockingFailureException ).
Kernpunkte:
findById(1L) ; commit transaction and close the Persistence ContextfindById(1L) and update it; commit the transaction and close the Persistence Contextsave() and pass to it the detached entity; trying to merge ( EntityManager.merge() ) the entity will end up in an optimistic locking exception since the version of the detached and just loaded entity don't matchOptimisticLockException Shaped Via @Version Note: Optimistic locking via @Version works for detached entities as well.
Description: This is a Spring Boot application that simulates a scenario that leads to an optimistic locking exception. So, running the application should end up with a Spring specific ObjectOptimisticLockingFailureException exception.
Kernpunkte:
@Transactional method used for updating data| If you need a deep dive into the performance recipes exposed in this repository then I am sure that you will love my book "Spring Boot Persistence Best Practices" | If you need a hand of tips and illustrations of 100+ Java persistence performance issues then "Java Persistence Performance Illustrated Guide" is for you. |
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TransactionTemplate After OptimisticLockException Exception ( @Version ) Note: Optimistic locking via @Version works for detached entities as well.
Description: This is a Spring Boot application that simulates a scenario that leads to an optimistic locking exception. When such exception occurs, the application retry the corresponding transaction via db-util library developed by Vlad Mihalcea.
Kernpunkte:
pom.xml , add the db-util dependencyOptimisticConcurrencyControlAspect beanTransactionTemplateOptimisticLockException In Version-less Optimistic LockingNote: Version-less optimistic locking doesn't work for detached entities (do not close the Persistence Context).
Description: This is a Spring Boot application that simulates a scenario that leads to an optimistic locking exception. So, running the application should end up with a Spring specific ObjectOptimisticLockingFailureException exception.
Kernpunkte:
@Transactional method used for updating dataTransactionTemplate After OptimisticLockException Shaped Via Hibernate Version-less Optimistic Locking MechanismNote: Version-less optimistic locking doesn't work for detached entities (do not close the Persistence Context).
Description: This is a Spring Boot application that simulates a scenario that leads to an optimistic locking exception. When such exception occur, the application retry the corresponding transaction via db-util library developed by Vlad Mihalcea.
Kernpunkte:
pom.xml , add the db-util dependencyOptimisticConcurrencyControlAspect beanTransactionTemplateDescription: This is a sample application that shows how to take advantage of versioned optimistic locking and detached entities in HTTP long conversations. The climax consists of storing the detached entities across multiple HTTP requests. Commonly, this can be accomplished via HTTP session.
Kernpunkte:
@Version@SessionAttributes for storing the detached entities Sample output (check the message caused by optimistic locking exception): 
@Where Note: Rely on this approach only if you simply cannot use JOIN FETCH WHERE or @NamedEntityGraph .
Description: This application is a sample of using Hibernate @Where for filtering associations.
Kernpunkte:
@Where(clause = "condition to be met") in entity (check the Author entity)Description: Batch inserts (in MySQL) in Spring Boot style.
Kernpunkte:
application.properties set spring.jpa.properties.hibernate.jdbc.batch_sizeapplication.properties set spring.jpa.properties.hibernate.generate_statistics (just to check that batching is working)application.properties set JDBC URL with rewriteBatchedStatements=true (optimization for MySQL)application.properties set JDBC URL with cachePrepStmts=true (enable caching and is useful if you decide to set prepStmtCacheSize , prepStmtCacheSqlLimit , etc as well; without this setting the cache is disabled)application.properties set JDBC URL with useServerPrepStmts=true (this way you switch to server-side prepared statements (may lead to signnificant performance boost))spring.jpa.properties.hibernate.order_inserts=true to optimize the batching by ordering insertsIDENTITY will cause insert batching to be disabledspring.jpa.properties.hibernate.cache.use_second_level_cache=false Ausgabebeispiel: 
COUNT(*) OVER And Return Page<entity> Via Extra Column Description: Typically, in offset pagination, there is one query needed for fetching the data and one for counting the total number of records. But, we can fetch this information in a single database rountrip via a SELECT COUNT subquery nested in the main SELECT . Even better, for databases vendors that support Window Functions there is a solution relying on COUNT(*) OVER() as in this application that uses this window function in a native query against MySQL 8. So, prefer this one instead of SELECT COUNT subquery.This application fetches data as Page<entity> via Spring Boot offset pagination, but, if the fetched data is read-only , then rely on Page<dto> as here.
Kernpunkte:
PagingAndSortingRepository@Column(insertable = false, updatable = false)List<entity>List<entity> and Pageable to create a Page<entity>SELECT COUNT Subquery And Return List<entity> Via Extra Column Description: This application fetches data as List<entity> via Spring Boot offset pagination. The SELECT COUNT triggered for counting the total number of records is a subquery of the main SELECT . Therefore, there will be a single database roundtrip instead of two (typically, one query is needed for fetching the data and one for counting the total number of records).
Kernpunkte:
PagingAndSortingRepositoryentity , add an extra column for representing the total number of records and annotate it as @Column(insertable = false, updatable = false)SELECT COUNT subquery) into a List<entity>SELECT COUNT Subquery And Return List<projection> That Maps Entities And The Total Number Of Records Via Projection Description: This application fetches data as List<projection> via Spring Boot offset pagination. The projection maps the entity and the total number of records. This information is fetched in a single database rountrip because the SELECT COUNT triggered for counting the total number of records is a subquery of the main SELECT . Therefore, there will be a single database roundtrip instead of two (typically, there is one query needed for fetching the data and one for counting the total number of records). Use this approch only if the fetched data is not read-only . Otherwise, prefer List<dto> as here.
Kernpunkte:
PagingAndSortingRepositorySELECT COUNT subquery) into a List<projection>COUNT(*) OVER And Return List<entity> Via Extra Column Description: Typically, in offset pagination, there is one query needed for fetching the data and one for counting the total number of records. But, we can fetch this information in a single database rountrip via a SELECT COUNT subquery nested in the main SELECT . Even better, for databases vendors that support Window Functions there is a solution relying on COUNT(*) OVER() as in this application that uses this window function in a native query against MySQL 8. So, prefer this one instead of SELECT COUNT subquery.This application fetches data as List<entity> via Spring Boot offset pagination, but, if the fetched data is read-only , then rely on List<dto> as here.
Kernpunkte:
PagingAndSortingRepositoryentity , add an extra column for representing the total number of records and annotate it as @Column(insertable = false, updatable = false)COUNT(*) OVER subquery) into a List<entity>| If you need a deep dive into the performance recipes exposed in this repository then I am sure that you will love my book "Spring Boot Persistence Best Practices" | If you need a hand of tips and illustrations of 100+ Java persistence performance issues then "Java Persistence Performance Illustrated Guide" is for you. |
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SELECT COUNT Subquery And Return Page<entity> Via Extra Column Description: This application fetches data as Page<entity> via Spring Boot offset pagination. Use this only if the fetched data will be modified. Otherwise, fetch Page<dto> as here. The SELECT COUNT triggered for counting the total number of records is a subquery of the main SELECT . Therefore, there will be a single database roundtrip instead of two (typically, there is one query needed for fetching the data and one for counting the total number of records).
Kernpunkte:
PagingAndSortingRepository@Column(insertable = false, updatable = false)List<entity>List<entity> and Pageable to create a Page<entity>SELECT COUNT Subquery And Return Page<projection> That Maps Entities And The Total Number Of Records Via Projection Description: This application fetches data as Page<projection> via Spring Boot offset pagination. The projection maps the entity and the total number of records. This information is fetched in a single database rountrip because the SELECT COUNT triggered for counting the total number of records is a subquery of the main SELECT .
Kernpunkte:
PagingAndSortingRepositoryList<projection>List<projection> and Pageable to create a Page<projection>COUNT(*) OVER And Return Page<dto> Description: Typically, in offset pagination, there is one query needed for fetching the data and one for counting the total number of records. But, we can fetch this information in a single database rountrip via a SELECT COUNT subquery nested in the main SELECT . Even better, for databases vendors that support Window Functions there is a solution relying on COUNT(*) OVER() as in this application that uses this window function in a native query against MySQL 8. So, prefer this one instead of SELECT COUNT subquery . This application return a Page<dto> .
Kernpunkte:
PagingAndSortingRepositoryList<dto>List<dto> and Pageable to create a Page<dto> Beispiel:
Slice<entity> / Slice<dto> Via fetchAll / fetchAllDto Story : Spring Boot provides an offset based built-in paging mechanism that returns a Page or Slice . Each of these APIs represents a page of data and some metadata. The main difference is that Page contains the total number of records, while Slice can only tell if there is another page available. For Page , Spring Boot provides a findAll() method capable to take as arguments a Pageable and/or a Specification or Example . In order to create a Page that contains the total number of records, this method triggers an SELECT COUNT extra-query next to the query used to fetch the data of the current page . This can be a performance penalty since the SELECT COUNT query is triggered every time we request a page. In order to avoid this extra-query, Spring Boot provides a more relaxed API, the Slice API. Using Slice instead of Page removes the need of this extra SELECT COUNT query and returns the page (records) and some metadata without the total number of records. So, while Slice doesn't know the total number of records, it still can tell if there is another page available after the current one or this is the last page. The problem is that Slice work fine for queries containing the SQL, WHERE clause (including those that uses the query builder mechanism built into Spring Data), but it doesn't work for findAll() . This method will still return a Page instead of Slice therefore the SELECT COUNT query is triggered for Slice<T> findAll(...); .
Workaround: The trick is to simply define a method named fetchAll() that uses JPQL and Pageable to return Slice<entity> , and a method named fetchAllDto() that uses JPQL and Pageable as well to return Slice<dto> . So, avoid naming the method findAll() .
Anwendungsbeispiel:
public Slice<Author> fetchNextSlice(int page, int size) {
return authorRepository.fetchAll(PageRequest.of(page, size, new Sort(Sort.Direction.ASC, "age")));
}
public Slice<AuthorDto> fetchNextSliceDto(int page, int size) {
return authorRepository.fetchAllDto(PageRequest.of(page, size, new Sort(Sort.Direction.ASC, "age")));
}
Description: This application is a proof of concept for using Spring Projections(DTO) and inclusive full joins written in native SQL (for MySQL).
Kernpunkte:
Author and Book in a lazy bidirectional @OneToMany relationship)resources/data-mysql.sql )AuthorNameBookTitle.java )EhCache ) Description: This application is a sample of declaring an immutable entity. Moreover, the immutable entity will be stored in Second Level Cache via EhCache implementation.
Key points of declaring an immutable entity:
@Immutable (org.hibernate.annotations.Immutable)hibernate.cache.use_reference_entries configuration to trueDataSourceBuilder If you use the spring-boot-starter-jdbc or spring-boot-starter-data-jpa "starters", you automatically get a dependency to HikariCP
Note: The best way to tune the connection pool parameters consist in using Flexy Pool by Vlad Mihalcea. Via Flexy Pool you can find the optim settings that sustain high-performance of your connection pool.
Description: This is a kickoff application that set up HikariCP via DataSourceBuilder . The jdbcUrl is set up for a MySQL database. For testing purposes, the application uses an ExecutorService for simulating concurrent users. Check the HickariCP report revealing the connection pool status.
Kernpunkte:
@Bean that returns the DataSource programmatically Ausgabebeispiel:
@NaturalIdCache For Skipping The Entity Identifier Retrieval Description: This is a SpringBoot - MySQL application that maps a natural business key using Hibernate @NaturalId . This implementation allows us to use @NaturalId as it was provided by Spring. Moreover, this application uses Second Level Cache ( EhCache ) and @NaturalIdCache for skipping the entity identifier retrieval from the database.
Kernpunkte:
EhCache )@NaturalIdCache for caching natural ids@Cache(usage = CacheConcurrencyStrategy.READ_WRITE, region = "Book") for caching entites as well Output sample (for MySQL with IDENTITY generator, @NaturalIdCache and @Cache ): 
@PostLoad Description: This application is an example of calculating a non-persistent property of an entity based on the persistent entity attributes. In this case, we will use JPA, @PostLoad .
Kernpunkte:
@Transient@PostLoad that calculates this non-persistent property based on the persistent entity attributes@Generated Description: This application is an example of calculating an entity persistent property at INSERT and/or UPDATE time via Hibernate, @Generated .
Kernpunkte:
Calculate at INSERT time:
@Generated(value = GenerationTime.INSERT)@Column(insertable = false) Calculate at INSERT and UPDATE time:
@Generated(value = GenerationTime.ALWAYS)@Column(insertable = false, updatable = false)Further, apply:
Methode 1:
columnDefinition element of @Column to specify as an SQL query expression the formula for calculating the persistent propertyMethode 2:
CREATE TABLE Note: In production, you should not rely on columnDefinition . You should disable hibernate.ddl-auto (by omitting it) or set it to validate , and add the SQL query expression in CREATE TABLE (in this application, check the discount column in CREATE TABLE , file schema-sql.sql ). Nevertheless, not even schema-sql.sql is ok in production. The best way is to rely on Flyway or Liquibase.
| If you need a deep dive into the performance recipes exposed in this repository then I am sure that you will love my book "Spring Boot Persistence Best Practices" | If you need a hand of tips and illustrations of 100+ Java persistence performance issues then "Java Persistence Performance Illustrated Guide" is for you. |
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@Formula Description: This application is an example of calculating a non-persistent property of an entity based on the persistent entity attributes. In this case, we will use Hibernate, @Formula .
Kernpunkte:
@Transient@Formula@Formula add the SQL query expression that calculates this non-persistent property based on the persistent entity attributescreated , createdBy , lastModified And lastModifiedBy In Entities Via HibernateNote: The same thing can be obtained via Spring Data JPA auditing as here.
Description: This application is an example of adding in an entity the fields, created , createdBy , lastModified and lastModifiedBy via Hibernate support. These fields will be automatically generated/populated.
Kernpunkte:
abstract class (eg, BaseEntity ) annotated with @MappedSuperclassabstract class, define a field named created and annotate it with the built-in @CreationTimestamp annotationabstract class, define a field named lastModified and annotate it with the built-in @UpdateTimestamp annotationabstract class, define a field named createdBy and annotate it with the @CreatedBy annotationabstract class, define a field named lastModifiedBy and annotate it with the @ModifiedBy annotation@CreatedBy annotation via AnnotationValueGeneration@ModifiedBy annotation via AnnotationValueGenerationcreated , createdBy , lastModified and lastModifiedBy will extend the BaseEntityschema-mysql.sql )Description: Auditing is useful for maintaining history records. This can later help us in tracking user activities.
Kernpunkte:
@Audited@AuditTable to rename the table used for auditingValidityAuditStrategy for fast database reads, but slower writes (slower than the default DefaultAuditStrategy )spring.jpa.hibernate.ddl-auto or set it to validate for avoiding schema generated from JPA annotationsschema-mysql.sql and provide the SQL statements needed by Hibernate Enversspring.jpa.properties.org.hibernate.envers.default_catalog for MySQL or spring.jpa.properties.org.hibernate.envers.default_schema for the restDataSource Note: For production, don't rely on hibernate.ddl-auto (or counterparts) to export schema DDL to the database. Simply remove (disable) hibernate.ddl-auto or set it to validate . Rely on Flyway or Liquibase.
Description: This application is a kickoff for setting Flyway and MySQL DataSource programmatically.
Kernpunkte:
pom.xml , add the Flyway dependencyspring.jpa.hibernate.ddl-auto or set it to validateDataSource and Flyway programmaticallypostgres And Schema public Note: For production, don't rely on hibernate.ddl-auto (or counterparts) to export schema DDL to the database. Simply remove (disable) hibernate.ddl-auto or set it to validate . Rely on Flyway or Liquibase.
Description: This application is an example of migrating a PostgreSQL database via Flyway for the default database postgres and schema public .
Kernpunkte:
pom.xml , add the Flyway dependencyspring.jpa.hibernate.ddl-auto or set it to validateapplication.properties , set the JDBC URL as follows: jdbc:postgresql://localhost:5432/postgresclasspath:db/migrationV1.1__Description.sql , V1.2__Description.sql , ...postgres And Schema Created Via spring.flyway.schemas Note: For production, don't rely on hibernate.ddl-auto (or counterparts) to export schema DDL to the database. Simply remove (disable) hibernate.ddl-auto or set it to validate . Rely on Flyway or Liquibase.
Description: This application is an example of migrating a schema ( bookstore ) created by Flyway via spring.flyway.schemas in the default postgres database. In this case, the entities should be annotated with @Table(schema = "bookstore") .
Kernpunkte:
pom.xml , add the Flyway dependencyspring.jpa.hibernate.ddl-auto or set it to validateapplication.properties , set the JDBC URL as follows: jdbc:postgresql://localhost:5432/postgresapplication.properties , add spring.flyway.schemas=bookstore , where bookstore is the schema that should be created by Flyway in the postgres database (feel free to add your own database name)@Table(schema = "bookstore")classpath:db/migrationV1.1__Description.sql , V1.2__Description.sql , ...DataSource Note: For production, don't rely on hibernate.ddl-auto (or counterparts) to export schema DDL to the database. Simply remove (disable) hibernate.ddl-auto or set it to validate . Rely on Flyway or Liquibase.
Description: This application is a kickoff for setting Flyway and PostgreSQL DataSource programmatically.
Kernpunkte:
pom.xml , add the Flyway dependencyspring.jpa.hibernate.ddl-auto or set it to validateDataSource and Flyway programmatically Note: For production, don't rely on hibernate.ddl-auto (or counterparts) to export schema DDL to the database. Simply remove (disable) hibernate.ddl-auto or set it to validate . Rely on Flyway or Liquibase.
Description: This application is an example of auto-creating and migrating two databases in MySQL using Flyway. In addition, each data source uses its own HikariCP connection pool. In case of MySQL, where a database is the same thing with schema, we create two databases, authorsdb and booksdb .
Kernpunkte:
pom.xml , add the Flyway dependencyspring.jpa.hibernate.ddl-auto or set it to validateapplication.properties , configure the JDBC URL for booksdb as jdbc:mysql://localhost:3306/booksdb?createDatabaseIfNotExist=true and for authorsdb as jdbc:mysql://localhost:3306/authorsdb?createDatabaseIfNotExist=trueapplication.properties , set spring.flyway.enabled=false to disable default behaviorDataSource , one for booksdb and one for authorsdbFlywayDataSource , one for booksdb and one for authorsdbEntityManagerFactory , one for booksdb and one for authorsdbbooksdb , place the migration SQLs files in dbmigrationbooksdbauthorsdb , place the migration SQLs files in dbmigrationauthorsdbhi/lo Algorithm And External Systems Issue Description: This is a Spring Boot sample that exemplifies how the hi/lo algorithm may cause issues when the database is used by external systems as well. Such systems can safely generate non-duplicated identifiers (eg, for inserting new records) only if they know about the hi/lo presence and its internal work. So, better rely on pooled or pooled-lo algorithm which doesn't cause such issues.
Kernpunkte:
SEQUENCE generator type (eg, in PostgreSQL)hi/lo algorithm as in Author.java entityhi/loNEXTVAL('hilo_sequence') and is not aware of hi/lo presence and/or behavior) Output sample: Running this application should result in the following error:
ERROR: duplicate key value violates unique constraint "author_pkey"
Detail: Key (id)=(2) already exists.
pooled Algorithm Note: Rely on pooled-lo or pooled especially if, beside your application, external systems needs to insert rows in your tables. Don't rely on hi/lo since, in such cases, it may cause errors resulted from generating duplicated identifiers.
Description: This is a Spring Boot example of using the pooled algorithm. The pooled is an optimization of hi/lo . This algorithm fetched from the database the current sequence value as the top boundary identifier (the current sequence value is computed as the previous sequence value + increment_size ). This way, the application will use in-memory identifiers generated between the previous top boundary exclusive (aka, lowest boundary) and the current top boundary inclusive.
Kernpunkte:
SEQUENCE generator type (eg, in PostgreSQL)pooled algorithm as in Author.java entitypooledNEXTVAL('hilo_sequence') and is not aware of pooled presence and/or behavior) Conclusion: In contrast to the classical hi/lo algorithm, the Hibernate pooled algorithm doesn't cause issues to external systems that wants to interact with our tables. In other words, external systems can concurrently insert rows in the tables relying on pooled algorithm. Nevertheless, old versions of Hibernate can raise exceptions caused by INSERT statements triggered by external systems that uses the lowest boundary as identifier. This is a good reason to update to Hibernate latest versions (eg, Hibernate 5.x), which have fixed this issue.
| If you need a deep dive into the performance recipes exposed in this repository then I am sure that you will love my book "Spring Boot Persistence Best Practices" | If you need a hand of tips and illustrations of 100+ Java persistence performance issues then "Java Persistence Performance Illustrated Guide" is for you. |
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pooled-lo Algorithm Note: Rely on pooled-lo or pooled especially if, beside your application, external systems needs to insert rows in your tables. Don't rely on hi/lo since, in such cases, it may cause errors resulted from generating duplicated identifiers.
Description: This is a Spring Boot example of using the pooled-lo algorithm. The pooled-lo is an optimization of hi/lo similar with pooled . Only that, the strategy of this algorithm fetches from the database the current sequence value and use it as the in-memory lowest boundary identifier. The number of in-memory generated identifiers is equal to increment_size .
Kernpunkte:
SEQUENCE generator type (eg, in PostgreSQL)pooled-lo algorithm as in Author.java entitypooled-loNEXTVAL('hilo_sequence') and is not aware of pooled-lo presence and/or behavior)@BatchSize Description: This application uses Hibernate specific @BatchSize at class/entity-level and collection-level. Consider Author and Book entities invovled in a bidirectional-lazy @OneToMany association.
First use case fetches all Author entities via a SELECT query. Further, calling the getBooks() method of the first Author entity will trigger another SELECT query that initializes the collections of the first three Author entities returned by the previous SELECT query. This is the effect of @BatchSize at Author 's collection-level.
Second use case fetches all Book entities via a SELECT query. Further, calling the getAuthor() method of the first Book entity will trigger another SELECT query that initializes the authors of the first three Book entities returned by the previous SELECT query. This is the effect of @BatchSize at Author class-level.
Note: Fetching associated collections in the same query with their parent can be done via JOIN FETCH or entity graphs as well. Fetching children with their parents in the same query can be done via JOIN FETCH , entity graphs and JOIN as well.
Kernpunkte:
Author and Book are in a lazy relationship (eg, @OneToMany bidirectional relationship)Author entity is annotated with @BatchSize(size = 3)Author 's collection is annotated with @BatchSize(size = 3)@NamedEntityGraph ) In Spring Boot Note: In a nutshell, entity graphs (aka, fetch plans ) is a feature introduced in JPA 2.1 that help us to improve the performance of loading entities. Mainly, we specify the entity's related associations and basic fields that should be loaded in a single SELECT statement. We can define multiple entity graphs for the same entity and chain any number of entities and even use sub-graphs to create complex fetch plans . To override the current FetchType semantics there are properties that can be set:
Fetch Graph (default), javax.persistence.fetchgraph
The attributes present in attributeNodes are treated as FetchType.EAGER . The remaining attributes are treated as FetchType.LAZY regardless of the default/explicit FetchType .
Load Graph , javax.persistence.loadgraph
The attributes present in attributeNodes are treated as FetchType.EAGER . The remaining attributes are treated according to their specified or default FetchType .
Nevertheless, the JPA specs doesn't apply in Hibernate for the basic ( @Basic ) attributes. . Weitere Details hier.
Description: This is a sample application of using entity graphs in Spring Boot.
Kernpunkte:
Author and Book , involved in a lazy bidirectional @OneToMany associationAuthor entity use the @NamedEntityGraph to define the entity graph (eg, load in a single SELECT the authors and the associatated books)AuthorRepositry rely on Spring @EntityGraph annotation to indicate the entity graph defined at the previous step Note: In a nutshell, entity graphs (aka, fetch plans ) is a feature introduced in JPA 2.1 that help us to improve the performance of loading entities. Mainly, we specify the entity's related associations and basic fields that should be loaded in a single SELECT statement. We can define multiple entity graphs for the same entity and chain any number of entities and even use sub-graphs to create complex fetch plans . To override the current FetchType semantics there are properties that can be set:
Fetch Graph (default), javax.persistence.fetchgraph
The attributes present in attributeNodes are treated as FetchType.EAGER . The remaining attributes are treated as FetchType.LAZY regardless of the default/explicit FetchType .
Load Graph , javax.persistence.loadgraph
The attributes present in attributeNodes are treated as FetchType.EAGER . The remaining attributes are treated according to their specified or default FetchType .
Nevertheless, the JPA specs doesn't apply in Hibernate for the basic ( @Basic ) attributes. . Weitere Details hier.
Description: This is a sample application of using entity sub-graphs in Spring Boot. There is one example based on @NamedSubgraph and one based on the dot notation (.) in an ad-hoc entity graph .
Kernpunkte:
Author , Book and Publisher ( Author and Book are involved in a lazy bidirectional @OneToMany relationship, Book and Publisher are also involved in a lazy bidirectional @OneToMany relationship; between Author and Publisher there is no relationship) Using @NamedSubgraph
Author entity define an entity graph via @NamedEntityGraph ; load the authors and the associatated books and use @NamedSubgraph to define a sub-graph for loading the publishers associated with these booksAuthorRepository rely on Spring @EntityGraph annotation to indicate the entity graph defined at the previous stepUsing the dot notation (.)
PublisherRepository define an ad-hoc entity graph that fetches all publishers with associated books, and further, the authors associated with these books (eg, @EntityGraph(attributePaths = {"books.author"}) . Note: In a nutshell, entity graphs (aka, fetch plans ) is a feature introduced in JPA 2.1 that help us to improve the performance of loading entities. Mainly, we specify the entity's related associations and basic fields that should be loaded in a single SELECT statement. We can define multiple entity graphs for the same entity and chain any number of entities and even use sub-graphs to create complex fetch plans . To override the current FetchType semantics there are properties that can be set:
Fetch Graph (default), javax.persistence.fetchgraph
The attributes present in attributeNodes are treated as FetchType.EAGER . The remaining attributes are treated as FetchType.LAZY regardless of the default/explicit FetchType .
Load Graph , javax.persistence.loadgraph
The attributes present in attributeNodes are treated as FetchType.EAGER . The remaining attributes are treated according to their specified or default FetchType .
Nevertheless, the JPA specs doesn't apply in Hibernate for the basic ( @Basic ) attributes. . Weitere Details hier.
Description: This is a sample application of defining ad-hoc entity graphs in Spring Boot.
Kernpunkte:
Author and Book , involved in a lazy bidirectional @OneToMany relationshipSELECT the authors and the associatated booksAuthorRepository rely on Spring @EntityGraph(attributePaths = {"books"}) annotation to indicate the ad-hoc entity graph@Basic Attributes In Hibernate And Spring Boot Note: In a nutshell, entity graphs (aka, fetch plans ) is a feature introduced in JPA 2.1 that help us to improve the performance of loading entities. Mainly, we specify the entity's related associations and basic fields that should be loaded in a single SELECT statement. We can define multiple entity graphs for the same entity and chain any number of entities and even use sub-graphs to create complex fetch plans . To override the current FetchType semantics there are properties that can be set:
Fetch Graph (default), javax.persistence.fetchgraph
The attributes present in attributeNodes are treated as FetchType.EAGER . The remaining attributes are treated as FetchType.LAZY regardless of the default/explicit FetchType .
Load Graph , javax.persistence.loadgraph
The attributes present in attributeNodes are treated as FetchType.EAGER . The remaining attributes are treated according to their specified or default FetchType .
Nevertheless, the JPA specs doesn't apply in Hibernate for the basic ( @Basic ) attributes. In other words, by default, attributes are annotated with @Basic which rely on the default fetch policy. The default fetch policy is FetchType.EAGER . These attributes are also loaded in case of fetch graph even if they are not explicitly specified via @NamedAttributeNode . Annotating the basic attributes that should not be fetched with @Basic(fetch = FetchType.LAZY) it is not enough. Both, fetch graph and load graph will ignore these settings as long as we don't add bytecode enhancement as well.
The main drawback consists of the fact the these basic attributes are fetched LAZY by all other queries (eg, findById() ) not only by the queries using the entity graph, and most probably, you will not want this behavior.
Description: This is a sample application of using entity graphs with @Basic attributes in Spring Boot.
Kernpunkte:
Author and Book , involved in a lazy bidirectional @OneToMany associationAuthor entity use the @NamedEntityGraph to define the entity graph (eg, load the authors names (only the name basic attribute; ignore the rest) and the associatated books)@Basic(fetch = FetchType.LAZY)AuthorRepository rely on Spring @EntityGraph annotation to indicate the entity graph defined at the previous stepSoftDeleteRepository In Spring Boot ApplicationNote: Spring Data built-in support for soft deletes is discussed in DATAJPA-307.
Description: This application is an example of implementing soft deletes in Spring Data style via a repository named, SoftDeleteRepository .
Kernpunkte:
abstract class, BaseEntity , annotated with @MappedSuperclassBaseEntity define a flag-field named deleted (default this field to false or in other words, not deleted)BaseEntity classs@NoRepositoryBean named SoftDeleteRepository and extend JpaRepositorySoftDeleteRepository Ausgabebeispiel:
SKIP_LOCKED In MySQL 8 Description: This application is an example of how to implement concurrent table based queue via SKIP_LOCKED in MySQL 8. SKIP_LOCKED can skip over locks achieved by other concurrent transactions, therefore is a great choice for implementing job queues. In this application, we run two concurrent transactions. The first transaction will lock the records with ids 1, 2 and 3. The second transaction will skip the records with ids 1, 2 and 3 and will lock the records with ids 4, 5 and 6.
Key points:
Book entity)BookRepository setup @Lock(LockModeType.PESSIMISTIC_WRITE)BookRepository use @QueryHint to setup javax.persistence.lock.timeout to SKIP_LOCKEDorg.hibernate.dialect.MySQL8Dialect dialectSKIP_LOCKEDSKIP_LOCKED In PostgreSQL Description: This application is an example of how to implement concurrent table based queue via SKIP_LOCKED in PostgreSQL. SKIP_LOCKED can skip over locks achieved by other concurrent transactions, therefore is a great choice for implementing job queues. In this application, we run two concurrent transactions. The first transaction will lock the records with ids 1, 2 and 3. The second transaction will skip the records with ids 1, 2 and 3 and will lock the records with ids 4, 5 and 6.
Kernpunkte:
Book entity)BookRepository setup @Lock(LockModeType.PESSIMISTIC_WRITE)BookRepository use @QueryHint to setup javax.persistence.lock.timeout to SKIP_LOCKEDorg.hibernate.dialect.PostgreSQL95Dialect dialectSKIP_LOCKEDJOINED Description: This application is a sample of JPA Join Table inheritance strategy ( JOINED )
Key points:
@Inheritance(strategy=InheritanceType.JOINED)@PrimaryKeyJoinColumn| If you need a deep dive into the performance recipes exposed in this repository then I am sure that you will love my book "Spring Boot Persistence Best Practices" | If you need a hand of tips and illustrations of 100+ Java persistence performance issues then "Java Persistence Performance Illustrated Guide" is for you. |
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TABLE_PER_CLASS Description: This application is a sample of JPA Table-per-class inheritance strategy ( TABLE_PER_CLASS )
Key points:
IDENTITY generator@Inheritance(strategy=InheritanceType.TABLE_PER_CLASS)@MappedSuperclass Description: This application is a sample of using the JPA @MappedSuperclass .
Key points:
abstract , and is annotated with @MappedSuperclass@MappedSuperclass is the proper alternative to the JPA table-per-class inheritance strategyNote: Hibernate5Module is an add-on module for Jackson JSON processor which handles Hibernate datatypes; and specifically aspects of lazy-loading .
Description: By default, in Spring Boot, the Open Session in View anti-pattern is enabled. Now, imagine a lazy relationship (eg, @OneToMany ) between two entities, Author and Book (an author has associated more books). Next, a REST controller endpoint fetches an Author without the associated Book . But, the View (more precisely, Jackson), forces the lazy loading of the associated Book as well. Since OSIV will supply the already opened Session , the Proxy initializations take place successfully.
Of course, the correct decision is to disable OSIV by setting it to false , but this will not stop Jackson to try to force the lazy initialization of the associated Book entities. Running the code again will result in an exception of type: Could not write JSON: failed to lazily initialize a collection of role: com.bookstore.entity.Author.books, could not initialize proxy - no Session; nested exception is com.fasterxml.jackson.databind.JsonMappingException: failed to lazily initialize a collection of role: com.bookstore.entity.Author.books, could not initialize proxy - no Session .
Well, among the Hibernate5Module features we have support for dealing with this aspect of lazy loading and eliminate this exception. Even if OSIV will continue to be enabled (not recommended), Jackson will not use the Session opened via OSIV.
Key points:
pom.xml@Bean that returns an instance of Hibernate5ModuleAuthor bean with @JsonInclude(Include.NON_EMPTY) to exclude null or what is considered empty from the returned JSON Note: The presence of Hibernate5Module instructs Jackson to initialize the lazy associations with default values (eg, a lazy associated collection will be initialized with null ). Hibernate5Module doesn't work for lazy loaded attributes. For such case consider this item.
profileSQL=true In MySQL Description: View the prepared statement binding parameters via profileSQL=true in MySQL.
Key points:
application.properties append logger=Slf4JLogger&profileSQL=true to the JDBC URL (eg, jdbc:mysql://localhost:3306/bookstoredb?createDatabaseIfNotExist=true&logger=Slf4JLogger&profileSQL=true ) Ausgabebeispiel: 
Description: This application is an example of shuffling small results sets. DO NOT USE this technique for large results sets, since is extremely expensive.
Key points:
SELECT query and append to it ORDER BY RAND()RAND() (eg, in PostgreSQL is random() ) Description: Commonly, deleting a parent and the associated children via CascadeType.REMOVE and/or orphanRemoval=true involved several SQL statements (eg, each child is deleted in a dedicated DELETE statement). When the number of entities is significant, this is far from being efficient, therefore other approaches should be employed.
Consider Author and Book in a bidirectional-lazy @OneToMany association. This application exposes the best way to delete the parent(s) and the associated children in four scenarios listed below. These approaches relies on bulk deletions, therefore they are not useful if you want the deletions to take advantage of automatic optimistic locking mechanisms (eg, via @Version ):
Best way to delete author(s) and the associated books via bulk deletions when:
Author is in Persistent Context, no BookAuthor are in the Persistent Context, no BookAuthor and the associated Book are in Persistent ContextAuthor or Book is in Persistent Context Note: The most efficient way to delete all entities via a bulk deletion can be done via the built-in deleteAllInBatch() .
Description: Bulk operations (updates and deletes) are faster than batching, can benefit from indexing, but they have three main drawbacks:
@Version is ignored), therefore the lost updates are not prevented (it is advisable to signal these updates by explicitly incrementing version (if any is present))CascadeType.REMOVE ) and orphanRemoval This application provides examples of bulk updates for Author and Book entities (between Author and Book there is a bidirectional lazy @OneToMany association). Both, Author and Book , has a version field.
@OneToMany And Prefer Bidirectional @OneToMany Relationship Description: As a rule of thumb, unidirectional @OneToMany association is less efficient than the bidirectional @OneToMany or the unidirectional @ManyToOne associations. This application is a sample that exposes the DML statements generated for reads, writes and removal operations when the unidirectional @OneToMany mapping is used.
Key points:
@OneToMany is less efficient than bidirectional @OneToMany association@OrderColumn come with some optimizations for removal operations but is still less efficient than bidirectional @OneToMany association@JoinColumn eliminates the junction table but is still less efficient than bidirectional @OneToMany associationSet instead of List or bidirectional @OneToMany with @JoinColumn relationship (eg, @ManyToOne @JoinColumn(name = "author_id", updatable = false, insertable = false) ) still performs worse than bidirectional @OneToMany associationWHERE / HAVING Clause Description: This application is an example of using subqueries in JPQL WHERE clause (you can easily use it in HAVING clause as well).
Key points:
Keep in mind that subqueries and joins queries may or may not be semantically equivalent (joins may returns duplicates that can be removed via DISTINCT ).
Even if the Execution Plan is specific to the database, historically speaking joins are faster than subqueries among different databases, but this is not a rule (eg, the amount of data may significantly influence the results). Of course, do not conclude that subqueries are just a replacement for joins that doesn't deserve attention. Tuning subqueries can increases their performance as well, but this is an SQL wide topic. So, benchmark! Benchmark! Benchmark!
As a rule of thumb, prefer subqueries only if you cannot use joins, or if you can prove that they are faster than the alternative joins.
WHERE Part Of JPQL Query And JPA 2.1 Note: Using SQL functions in SELECT part (not in WHERE part) of the query can be done as here.
Description: Starting with JPA 2.1, a JPQL query can call SQL functions in the WHERE part via function() . This application is an example of calling the MySQL, concat_ws function, but user defined (custom) functions can be used as well.
Key points:
function()| If you need a deep dive into the performance recipes exposed in this repository then I am sure that you will love my book "Spring Boot Persistence Best Practices" | If you need a hand of tips and illustrations of 100+ Java persistence performance issues then "Java Persistence Performance Illustrated Guide" is for you. |
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Description: This application is an example of calling a MySQL stored procedure that returns a value (eg, an Integer ).
Key points:
@NamedStoredProcedureQuery to shape the stored procedure in the entity@Procedure in repository Description: This application is an example of calling a MySQL stored procedure that returns a result set. The application fetches entities (eg, List<Author> ) and DTO (eg, List<AuthorDto> ).
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EntiyManager since Spring Data @Procedure will not workDescription: This application is an example of calling a MySQL stored procedure that returns a result set (entity or DTO) via a native query.
Kernpunkte:
@Query(value = "{CALL FETCH_AUTHOR_BY_GENRE (:p_genre)}", nativeQuery = true)JdbcTemplate Note: Most probably you'll like to process the result set via BeanPropertyRowMapper as here. This is less verbose than the approach used here. Nevertheless, this approach is useful to understand how the result set looks like.
Description: This application is an example of calling a MySQL stored procedure that returns a result set via JdbcTemplate .
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JdbcTemplate and SimpleJdbcCallDescription: This application is an example of retrieving the database auto-generated primary keys.
Key points:
getId()JdbcTemplateSimpleJdbcInsert Description: A Hibernate proxy can be useful when a child entity can be persisted with a reference to its parent ( @ManyToOne or @OneToOne association). In such cases, fetching the parent entity from the database (execute the SELECT statement) is a performance penalty and a pointless action. Hibernate can set the underlying foreign key value for an uninitialized proxy. This topic is discussed here.
A proxy can be unproxied via Hibernate.unproxy() . This method is available starting with Hibernate 5.2.10.
Key points:
JpaRepository#getOne()Hibernate.unproxy()Boolean To Yes/No Via AttributeConverter Description: This application is an example of converting a Boolean to Yes / No strings via AttributeConverter . This kind of conversions are needed when we deal with legacy databases that connot be changed. In this case, the legacy database stores the booleans as Yes / No .
Key points:
AttributeConverter@OManyToOne Note: The @ManyToOne association maps exactly to the one-to-many table relationship. The underlying foreign key is under child-side control in unidirectional or bidirectional relationship.
Description: This application shows that using only @ManyToOne is quite efficient. On the other hand, using only @OneToMany is far away from being efficient. Always, prefer bidirectional @OneToMany or unidirectional @ManyToOne . Consider two entities, Author and Book in a unidirectional @ManyToOne relationship.
Key points:
JOIN FETCH And Pageable Pagination Description: Trying to combine JOIN FETCH / LEFT JOIN FETCH and Pageable results in an exception of type org.hibernate.QueryException: query specified join fetching, but the owner of the fetched association was not present in the select list . This application is a sample of how to avoid this exception.
Key points:
countQuery Note: Fixing the above exception will lead to an warning of type HHH000104, firstResult / maxResults specified with collection fetch; applying in memory! . If this warning is a performance issue, and most probably it is, then follow by reading here.
Description: HHH000104 is a Hibernate warning that tell us that pagination of a result set is tacking place in memory. For example, consider the Author and Book entities in a lazy-bidirectional @OneToMany association and the following query:
@Transactional
@Query(value = "SELECT a FROM Author a LEFT JOIN FETCH a.books WHERE a.genre = ?1",
countQuery = "SELECT COUNT(a) FROM Author a WHERE a.genre = ?1")
Page<Author> fetchWithBooksByGenre(String genre, Pageable pageable);
Calling fetchWithBooksByGenre() works fine only that the following warning is signaled: HHH000104: firstResult / maxResults specified with collection fetch; applying in memory! Obviously, having pagination in memory cannot be good from performance perspective. This application implement a solution for moving pagination at database-level.
Key points:
Page of entities in read-write or read-only modeSlice or List of entities in read-write or read-only mode| If you need a deep dive into the performance recipes exposed in this repository then I am sure that you will love my book "Spring Boot Persistence Best Practices" | If you need a hand of tips and illustrations of 100+ Java persistence performance issues then "Java Persistence Performance Illustrated Guide" is for you. |
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@Transactional(readOnly=true) Actually Do Description: This application is meant to reveal what is the difference between @Transactional(readOnly = false) and @Transactional(readOnly = true) . In a nuthsell, readOnly = false (default) fetches entites in read-write mode (managed). Before Spring 5.1, readOnly = true just set FlushType.MANUAL/NEVER , therefore the automatic dirty checking mechanism will not take action since there is no flush. In other words, Hibernate keep in the Persistent Context the fetched entities and the hydrated (loaded) state. By comparing the entity state with the hydrated state, the dirty checking mechanism can decide to trigger UPDATE statements in our behalf. But, the dirty checking mechanism take place at flush time, therefore, without a flush, the hydrated state is kept in Persistent Context for nothing, representing a performance penalty. Starting with Spring 5.1, the read-only mode is propagated to Hibernate, therefore the hydrated state is discarded immediately after loading the entities. Even if the read-only mode discards the hydrated state the entities are still loaded in the Persistent Context, therefore, for read-only data, relying on DTO (Spring projection) is better.
Key points:
readOnly = false load data in read-write mode (managed)readOnly = true discard the hydrated state (starting with Spring 5.1)Description: This application is an example of getting the current database transaction id in MySQL. Only read-write database transactions gets an id in MySQL. Every database has a specific query for getting the transaction id. Here it is a list of these queries.
Kernpunkte:
SELECT tx.trx_id FROM information_schema.innodb_trx tx WHERE tx.trx_mysql_thread_id = connection_id() Description: This application is a sample of inspecting the Persistent Context content via org.hibernate.engine.spi.PersistenceContext .
Key points:
SharedSessionContractImplementorPersistenceContext API Description: This application is an example of using the Hibernate SPI, org.hibernate.integrator.spi.Integrator for extracting tables metadata.
Key points:
org.hibernate.integrator.spi.Integrator and override integrate() method to return metadata.getDatabase()Integrator via LocalContainerEntityManagerFactoryBean@ManyToOne Relationship To A SQL Query Via The Hibernate @JoinFormula Description: This application is an example of mapping the JPA @ManyToOne relationship to a SQL query via the Hibernate @JoinFormula annotation. We start with two entities, Author and Book , involved in a unidirectional @ManyToOne relationship. Each book has a price. While we fetch a book by id (let's call it book A ), we want to fetch another book B of the same author whose price is the next smaller price in comparison with book A price.
Key points:
B is done via @JoinFormulaDescription: This application is an example of fetching a read-only MySQL database view in a JPA immutable entity.
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data-mysql.sql fileGenreAndTitleView.javaDescription: This application is an example of updating, inserting and deleting data in a MySQL database view. Every update/insert/delete will automatically update the contents of the underlying table(s).
Key points:
data-mysql.sql fileWITH CHECK OPTION Description: This application is an example of preventing inserts/updates of a MySQL view that are not visible through this view via WITH CHECK OPTION . In other words, whenever you insert or update a row of the base tables through a view, MySQL ensures that the this operation is conformed with the definition of the view.
Key points:
WITH CHECK OPTION to the viewjava.sql.SQLException: CHECK OPTION failed 'bookstoredb.author_anthology_view Description: This application is an example of assigning a database temporary sequence of values to rows via the window function, ROW_NUMBER() . This window function is available in almost all databases, and starting with version 8.x is available in MySQL as well.
Key points:
ROW_NUMBER() (you will use it internally, in the query, usually in the WHERE clause and CTEs), but, this time, let's write a Spring projection (DTO) that contains a getter for the column generated by ROW_NUMBER as wellROW_NUMBER() window function Ausgabebeispiel: 
Description: This application is an example of finding top N rows of every group.
Key points:
ROW_NUMBER() window function Ausgabebeispiel: 
| If you need a deep dive into the performance recipes exposed in this repository then I am sure that you will love my book "Spring Boot Persistence Best Practices" | If you need a hand of tips and illustrations of 100+ Java persistence performance issues then "Java Persistence Performance Illustrated Guide" is for you. |
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ROW_NUMBER() Window Function Description: This application is an example of using ROW_NUMBER() (and COUNT(*) OVER() for counting all elements) window function to implement pagination.
Key points:
ROW_NUMBER()Page or Slice , we return it as List , therefore Pageable is not used@Transactional annotation is being ignored Description: This application is an example of fixing the case when @Transactional annotation is ignored. Most of the time, this annotation is ignored in the following scenarios:
@Transactional was added to a private , protected or package-protected method@Transactional was added to a method defined in the same class where it is invokedKey points:
@Transactional methods therepublic@Transactional methods from other services Description: This is a Spring Boot example of using the hi/lo algorithm and a custom implementation of SequenceStyleGenerator for generating custom sequence IDs (eg, A-0000000001 , A-0000000002 , ...).
Kernpunkte:
SequenceStyleGenerator and override the configure() and generate() methodsClob And Blob To byte[] And String Description: This application is an example of mapping Clob and Blob as byte[] and String .
Key points:
LOB Locators Clob And Blob Description: This application is an example of mapping to JDBC's LOB locators Clob and Blob .
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SINGLE_TABLE Inheritance Hierarchy Description: This application is a sample of fetching a certain subclass from a SINGLE_TABLE inheritance hierarchy. This is useful when the dedicated repository of the subclass doesn't automatically add in the WHERE clause a dtype based condition for fetching only the needed subclass.
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WHERE clause a TYPE check@NaturalId Description: This is a SpringBoot application that defines a @ManyToOne relationship that doesn't reference a primary key column. It references a Hibernate @NaturalId column.
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@JoinColumn(referencedColumnName = "natural_id_column")Specification Description: This application is an example of implementing an advanced search via Specification API. Mainly, you can give the search filters to a generic Specification and fetch the result set. Pagination is supported as well. You can chain expressions via logical AND and OR to create compound filters. Nevertheless, there is room for extensions to add brackets support (eg, (x AND y) OR (x AND z) ), more operations, conditions parser and so on and forth.
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SpecificationSpecification Query Fetch Joins Description: This application contains two examples of how to define JOIN in Specification to emulate JPQL join-fetch operations.
Key points:
SELECT statements and the pagination is done in memory (very bad!)SELECT statements but the pagination is done in the databaseJOIN is defined in a Specification implementationNote: You may also like to read the recipe, "How To Enrich DTO With Virtual Properties Via Spring Projections"
Description: Fetch only the needed data from the database via Spring Data Projections (DTO). The projection interface is defined as a static interface (can be non- static as well) in the repository interface.
Key points:
List<projection>LIMIT ) - here, we can use query builder mechanism built into Spring Data repository infrastructureNote: Using projections is not limited to use query builder mechanism built into Spring Data repository infrastructure. We can fetch projections via JPQL or native queries as well. For example, in this application we use a JPQL.
Output example (select first 2 rows; select only "name" and "age"):
| If you need a deep dive into the performance recipes exposed in this repository then I am sure that you will love my book "Spring Boot Persistence Best Practices" | If you need a hand of tips and illustrations of 100+ Java persistence performance issues then "Java Persistence Performance Illustrated Guide" is for you. |
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Description: Consider an entity named Review . This entity defines three @ManyToOne relationships to Book , Article and Magazine . A review can be associated with either a book, a magazine or an article. To validate this constraint, we can rely on Bean Validation as in this application.
Key points:
null@JustOneOfMany ) added at class-level to the Review entityTRIGGER ) Description: This application uses EnumType.ORDINAL and EnumType.STRING for mapping Java enum type to database. As a rule of thumb, strive to keep the data types as small as possible (eg, for EnumType.ORDINAL use TINYINT/SMALLINT , while for EnumType.STRING use VARCHAR(max_needed_bytes) ). Relying on EnumType.ORDINAL should be more efficient but is less expressive than EnumType.STRING .
Key points:
EnumType.ORDINAL set @Column(columnDefinition = "TINYINT") )enum To Database Via AttributeConverter Description: This application maps a Java enum via AttributeConverter . In other words, it maps the enum values HORROR , ANTHOLOGY and HISTORY to the integers 1 , 2 and 3 and viceversa. This allows us to set the column type as TINYINT/SMALLINT which is less space-consuming than VARCHAR(9) needed in this case.
Key points:
AttributeConverter@Converter the corresponding entity fieldenum To PostgreSQL enum Type Description: This application maps a Java enum type to PostgreSQL enum type.
Kernpunkte:
EnumTypeEnumType via package-info.java@Typeenum To PostgreSQL enum Type Via Hibernate Types Library Description: This application maps a Java enum type to PostgreSQL enum type via Hibernate Types library.
Key points:
pom.xml@TypeDef to specify the needed type class@Type Description: Hibernate Types is a library of extra types not supported by Hibernate Core by default. This is a Spring Boot application that uses this library to persist JSON data (JSON Java Object ) in a MySQL json column and for querying JSON data from the MySQL json column to JSON Java Object . Updates are supported as well.
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pom.xml@TypeDef to map typeClass to JsonStringType Description: Hibernate Types is a library of extra types not supported by Hibernate Core by default. This is a Spring Boot application that uses this library to persist JSON data (JSON Java Object ) in a PostgreSQL json column and for querying JSON data from the PostgreSQL json column to JSON Java Object . Updates are supported as well.
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pom.xml@TypeDef to map typeClass to JsonBinaryTypeOPTIMISTIC_FORCE_INCREMENT Description: This application is a sample of how OPTIMISTIC_FORCE_INCREMENT works in MySQL. This is useful when you want to increment the version of the locked entity even if this entity was not modified. Via OPTIMISTIC_FORCE_INCREMENT the version is updated (incremented) at the end of the currently running transaction.
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Chapter (which uses @Version )Modification entityModification (child-side) and Chapter (parent-side) there is a lazy unidirectional @ManyToOne associationINSERT statement against the modification table, therefore the chapter table will not be modified by editorsChapter entity version is needed to ensure that modifications are applied sequentially (the author and editor are notified if a modificaton was added since the chapter copy was loaded)version is forcibly increased at each modification (this is materialized in an UPDATE triggered against the chapter table at the end of the currently running transaction)OPTIMISTIC_FORCE_INCREMENT in the corresponding repositoryObjectOptimisticLockingFailureExceptionPESSIMISTIC_FORCE_INCREMENT Description: This application is a sample of how PESSIMISTIC_FORCE_INCREMENT works in MySQL. This is useful when you want to increment the version of the locked entity even if this entity was not modified. Via PESSIMISTIC_FORCE_INCREMENT the version is updated (incremented) immediately (the entity version update is guaranteed to succeed immediately after acquiring the row-level lock). The incrementation takes place before the entity is returned to the data access layer.
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Chapter (which uses @Version )Modification entityModification (child-side) and Chapter (parent-side) there is a lazy unidirectional @ManyToOne associationINSERT statement against the modification table, therefore the chapter table will not be modified by editorsChapter entity version is needed to ensure that modifications are applied sequentially (each editor is notified if a modificaton was added since his chapter copy was loaded and he must re-load the chapter)version is forcibly increased at each modification (this is materialized in an UPDATE triggered against the chapter table immediately after aquiring the row-level lock)PESSIMISTIC_FORCE_INCREMENT in the corresponding repositoryOptimisticLockException and one that will lead to QueryTimeoutException Note: Pay attention to the MySQL dialect: MySQL5Dialect (MyISAM) doesn't support row-level locking, MySQL5InnoDBDialect (InnoDB) acquires row-level lock via FOR UPDATE (timeout can be set), MySQL8Dialect (InnoDB) acquires row-level lock via FOR UPDATE NOWAIT .
PESSIMISTIC_READ And PESSIMISTIC_WRITE Works In MySQL Description: This application is an example of using PESSIMISTIC_READ and PESSIMISTIC_WRITE in MySQL. In a nutshell, each database system defines its own syntax for acquiring shared and exclusive locks and not all databases support both types of locks. Depending on Dialect , the syntax can vary for the same database as well (Hibernate relies on Dialect for chosing the proper syntax). In MySQL, MySQL5Dialect doesn't support locking, while InnoDB engine ( MySQL5InnoDBDialect and MySQL8Dialect ) supports shared and exclusive locks as expected.
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@Lock(LockModeType.PESSIMISTIC_READ) and @Lock(LockModeType.PESSIMISTIC_WRITE) on query-levelTransactionTemplate to trigger two concurrent transactions that read and write the same row| If you need a deep dive into the performance recipes exposed in this repository then I am sure that you will love my book "Spring Boot Persistence Best Practices" | If you need a hand of tips and illustrations of 100+ Java persistence performance issues then "Java Persistence Performance Illustrated Guide" is for you. |
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PESSIMISTIC_WRITE Works With UPDATE / INSERT And DELETE Operations Description: This application is an example of triggering UPDATE , INSERT and DELETE operations in the context of PESSIMISTIC_WRITE locking against MySQL. While UPDATE and DELETE are blocked until the exclusive lock is released, INSERT depends on the transaction isolation level. Typically, even with exclusive locks, inserts are possible (eg, in PostgreSQL). In MySQL, for the default isolation level, REPEATABLE READ , inserts are prevented against a range of locked entries, but, if we switch to READ_COMMITTED , then MySQL acts as PostgreSQL as well.
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SELECT with PESSIMISTIC_WRITE to acquire an exclusive lockUPDATE , INSERT or DELETE on the rows locked by Transaction AUPDATE , DELETE and INSERT + REPEATABLE_READ , Transaction B is blocked until it timeouts or Transaction A releases the exclusive lockINSERT + READ_COMMITTED , Transaction B can insert in the range of rows locked by Transaction A even if Transaction A is holding an exclusive lock on this range Note: Do not test transaction timeout via Thread.sleep() ! This is not working! Rely on two transactions and exclusive locks or even better rely on SQL sleep functions (eg, MySQL, SELECT SLEEP(n) seconds, PostgreSQL, SELECT PG_SLEEP(n) seconds). Most RDBMS supports a sleep function flavor.
Description: This application contains several approaches for setting a timeout period for a transaction or query. The timeout is signaled by a specific timeout exception (eg, .QueryTimeoutException ). After timeout, the transaction is rolled back. You can see this in the database (visually or query) and on log via a message of type: Initiating transaction rollback; Rolling back JPA transaction on EntityManager [SessionImpl(... <open>)] .
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spring.transaction.default-timeout in seconds (see, application.properties )@Transactional(timeout = n) in secondsjavax.persistence.query.timeout hint in millisecondsorg.hibernate.timeout hint in seconds Note: If you are using TransactionTemplate then the timeout can be set via TransactionTemplate.setTimeout(n) in seconds.
@Embeddable Description: This application is a proof of concept of how to define a composite key via @Embeddable and @EmbeddedId . This application uses two entities, Author and Book involved in a lazy bidirectional @OneToMany association. The identifier of Author is composed by name and age via AuthorId class. The identifier of Book is just a regular auto-generated numeric value.
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AuthorId ) is publicSerializableequals() and hashCode()@IdClass Description: This application is a proof of concept of how to define a composite key via @IdClass . This application uses two entities, Author and Book involved in a lazy bidirectional @OneToMany association. The identifier of Author is composed by name and age via AuthorId class. The identifier of Book is just a typical auto-generated numeric value.
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AuthorId ) is publicSerializableequals() and hashCode() Note : The @IdClass can be useful when we cannot modify the compsite key class. Otherwise, rely on @Embeddable .
@Embeddable Composite Primary Key Description: This application is a proof of concept of how to define a relationship in an @Embeddable composite key. The composite key is AuthorId and it belongs to the Author class.
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AuthorId ) is publicSerializableequals() and hashCode() Description: This is a SpringBoot application that loads multiple entities by id via a @Query based on the IN operator and via the Hibernate 5 MultiIdentifierLoadAccess interface.
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IN operator in a @Query simply add the query in the proper repositoryMultiIdentifierLoadAccess in Spring Data style provide the proper implementationMultiIdentifierLoadAccess implementation allows us to load entities by multiple ids in batches and by inspecting or not the current Persistent Context (by default, the Persistent Context is not inspected to see if the entities are already loaded or not) Description: This application is a sample of fetching all attributes of an entity ( Author ) as a Spring projection (DTO). Commonly, a DTO contains a subset of attributes, but, sometimes we need to fetch the whole entity as a DTO. In such cases, we have to pay attention to the chosen approach. Choosing wisely can spare us from performance penalties.
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List<Object[]> or List<AuthorDto> via a JPQL of type SELECT a FROM Author a WILL fetch the result set as entities in Persistent Context as well - avoid this approachList<Object[]> or List<AuthorDto> via a JPQL of type SELECT a.id AS id, a.name AS name, ... FROM Author a will NOT fetch the result set in Persistent Context - this is efficientList<Object[]> or List<AuthorDto> via a native SQL of type SELECT id, name, age, ... FROM author will NOT fetch the result set in Persistent Context - but, this approach is pretty slowList<Object[]> via Spring Data query builder mechanism WILL fetch the result set in Persistent Context - avoid this approachList<AuthorDto> via Spring Data query builder mechanism will NOT fetch the result set in Persistent ContextfindAll() method) should be considered after JPQL with explicit list of columns to be fetched and query builder mechanism@ManyToOne Or @OneToOne Associations Description: This application fetches a Spring projection including the @ManyToOne association via different approaches. It can be easily adapted for @OneToOne association as well.
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Description: This application inspect the Persistent Context content during fetching Spring projections that includes collections of associations. In this case, we focus on a @OneToMany association. Mainly, we want to fetch only some attributes from the parent-side and some attributes from the child-side.
Description: This application is a sample of reusing an interface-based Spring projection. This is useful to avoid defining multiple interface-based Spring projections in order to cover a range of queries that fetches different subsets of fields.
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@JsonInclude(JsonInclude.Include.NON_DEFAULT) annotation to avoid serialization of default fields (eg, fields that are not available in the current projection and are null - these fields haven't been fetched in the current query)null fields)| If you need a deep dive into the performance recipes exposed in this repository then I am sure that you will love my book "Spring Boot Persistence Best Practices" | If you need a hand of tips and illustrations of 100+ Java persistence performance issues then "Java Persistence Performance Illustrated Guide" is for you. |
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Description: This application is a sample of using dynamic Spring projections.
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<T> List<T> findByGenre(String genre, Class<T> type); ) Description: This application is a sample of batching inserts via EntityManager in MySQL. This way you can easily control the flush() and clear() cycles of the Persistence Context (1st Level Cache) inside the current transaction. This is not possible via Spring Boot, saveAll(Iterable<S> entities) , since this method executes a single flush per transaction. Another advantage is that you can call persist() instead of merge() - this is used behind the scene by the SpringBoot saveAll(Iterable<S> entities) and save(S entity) .
Moreover, this example commits the database transaction after each batch excecution. This way we avoid long-running transactions and, in case of a failure, we rollback only the failed batch and don't lose the previous batches. For each batch, the Persistent Context is flushed and cleared, therefore we maintain a thin Persistent Context. This way the code is not prone to memory errors and performance penalties caused by slow flushes.
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application.properties set spring.jpa.properties.hibernate.jdbc.batch_sizeapplication.properties set spring.jpa.properties.hibernate.generate_statistics (just to check that batching is working)application.properties set JDBC URL with rewriteBatchedStatements=true (optimization for MySQL)application.properties set JDBC URL with cachePrepStmts=true (enable caching and is useful if you decide to set prepStmtCacheSize , prepStmtCacheSqlLimit , etc as well; without this setting the cache is disabled)application.properties set JDBC URL with useServerPrepStmts=true (this way you switch to server-side prepared statements (may lead to signnificant performance boost))spring.jpa.properties.hibernate.order_inserts=true to optimize the batching by ordering insertsIDENTITY will cause insert batching to be disabledspring.jpa.properties.hibernate.cache.use_second_level_cache=false Ausgabebeispiel:
Description: This is a Spring Boot application that reads a relatively big JSON file (200000+ lines) and inserts its content in MySQL via batching using ForkJoinPool , JdbcTemplate and HikariCP.
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json typeListrewriteBatchedStatements=true -> this setting will force sending the batched statements in a single request;cachePrepStmts=true -> enable caching and is useful if you decide to set prepStmtCacheSize , prepStmtCacheSqlLimit , etc as well; without this setting the cache is disableduseServerPrepStmts=true -> this way you switch to server-side prepared statements (may lead to signnificant performance boost); moreover, you avoid the PreparedStatement to be emulated at the JDBC Driver level;...?cachePrepStmts=true&useServerPrepStmts=true&rewriteBatchedStatements=true&createDatabaseIfNotExist=trueStopWatch to measure the time needed to transfer the file into the databasecitylots.zip in the current location; this is the big JSON file collected from Internet;DatasourceProxyBeanPostProcessor.java component by uncomment the line, // @Component ; This is needed because this application relies on DataSource-Proxy (for details, see the following item)CompletableFuture Description: This application is a sample of using CompletableFuture for batching inserts. This CompletableFuture uses an Executor that has the number of threads equal with the number of your computer cores. Usage is in Spring style.
Description: Let's suppose that we have a one-to-many relationship between Author and Book entities. When we save an author, we save his books as well thanks to cascading all/persist. We want to create a bunch of authors with books and save them in the database (eg, a MySQL database) using the batch technique. By default, this will result in batching each author and the books per author (one batch for the author and one batch for the books, another batch for the author and another batch for the books, and so on). In order to batch authors and books, we need to order inserts as in this application.
Moreover, this example commits the database transaction after each batch excecution. This way we avoid long-running transactions and, in case of a failure, we rollback only the failed batch and don't lose the previous batches. For each batch, the Persistent Context is flushed and cleared, therefore we maintain a thin Persistent Context. This way the code is not prone to memory errors and performance penalties caused by slow flushes.
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application.properties the following property: spring.jpa.properties.hibernate.order_inserts=true Example without ordered inserts:
Example with ordered inserts:
Description: Batch inserts (in MySQL) in Spring Boot style. This example commits the database transaction after each batch excecution. This way we avoid long-running transactions and, in case of a failure, we rollback only the failed batch and don't lose the previous batches.
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application.properties set spring.jpa.properties.hibernate.jdbc.batch_sizeapplication.properties set spring.jpa.properties.hibernate.generate_statistics (just to check that batching is working)application.properties set JDBC URL with rewriteBatchedStatements=true (optimization for MySQL)application.properties set JDBC URL with cachePrepStmts=true (enable caching and is useful if you decide to set prepStmtCacheSize , prepStmtCacheSqlLimit , etc as well; without this setting the cache is disabled)application.properties set JDBC URL with useServerPrepStmts=true (this way you switch to server-side prepared statements (may lead to signnificant performance boost))spring.jpa.properties.hibernate.order_inserts=true to optimize the batching by ordering insertsIDENTITY will cause insert batching to be disabledspring.jpa.properties.hibernate.cache.use_second_level_cache=false Ausgabebeispiel:
IN Clause Parameter Padding Description: This application is an example of using Hibernate IN cluase parameter padding. This way we can reduce the number of Execution Plans. Mainly, Hibernate is padding parameters as follows:
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application.properties set spring.jpa.properties.hibernate.query.in_clause_parameter_padding=trueDescription: Fetch only the needed data from the database via Spring Data Projections (DTO). In this case, via class-based projections.
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equals() and hashCode() only for the columns that should be fetched from the databaseList<projection>LIMIT )Note: Using projections is not limited to use query builder mechanism built into Spring Data repository infrastructure. We can fetch projections via JPQL or native queries as well. For example, in this application we use a JPQL.
Output example (select first 2 rows; select only "name" and "age"):
Description: Batch inserts via Hibernate session-level batching (Hibernate 5.2 or higher) in MySQL. This example commits the database transaction after each batch excecution. This way we avoid long-running transactions and, in case of a failure, we rollback only the failed batch and don't lose the previous batches. For each batch, the Persistent Context is flushed and cleared, therefore we maintain a thin Persistent Context. This way the code is not prone to memory errors and performance penalties caused by slow flushes.
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application.properties set spring.jpa.properties.hibernate.generate_statistics (just to check that batching is working)application.properties set JDBC URL with rewriteBatchedStatements=true (optimization for MySQL)application.properties set JDBC URL with cachePrepStmts=true (enable caching and is useful if you decide to set prepStmtCacheSize , prepStmtCacheSqlLimit , etc as well; without this setting the cache is disabled)application.properties set JDBC URL with useServerPrepStmts=true (this way you switch to server-side prepared statements (may lead to signnificant performance boost))spring.jpa.properties.hibernate.order_inserts=true to optimize the batching by ordering insertsIDENTITY will cause insert batching to be disabledSession is obtained by un-wrapping it via EntityManager#unwrap(Session.class)Session#setJdbcBatchSize(Integer size) and get via Session#getJdbcBatchSize()spring.jpa.properties.hibernate.cache.use_second_level_cache=false Ausgabebeispiel:
Description: This application highlights the difference betweeen loading entities in read-write vs. read-only mode. If you plan to modify the entities in a future Persistent Context then fetch them as read-only in the current Persistent Context.
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Note: If you never plan to modify the fetched result set then use DTO (eg, Spring projection), not read-only entities.
| If you need a deep dive into the performance recipes exposed in this repository then I am sure that you will love my book "Spring Boot Persistence Best Practices" | If you need a hand of tips and illustrations of 100+ Java persistence performance issues then "Java Persistence Performance Illustrated Guide" is for you. |
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Note: Domain events should be used with extra-caution! The best practices for using them are revealed in my book, Spring Boot Persistence Best Practices.
Description: Starting with Spring Data Ingalls release publishing domain events by aggregate roots becomes easier. Entities managed by repositories are aggregate roots. In a Domain-Driven Design application, these aggregate roots usually publish domain events. Spring Data provides an annotation @DomainEvents you can use on a method of your aggregate root to make that publication as easy as possible. A method annotated with @DomainEvents is automatically invoked by Spring Data whenever an entity is saved using the right repository. Moreover, Spring Data provides the @AfterDomainEventsPublication annotation to indicate the method that should be automatically called for clearing events after publication. Spring Data Commons comes with a convenient template base class ( AbstractAggregateRoot ) to help to register domain events and is using the publication mechanism implied by @DomainEvents and @AfterDomainEventsPublication . The events are registered by calling the AbstractAggregateRoot.registerEvent() method. The registered domain events are published if we call one of the save methods (eg, save() ) of the Spring Data repository and cleared after publication.
This is a sample application that relies on AbstractAggregateRoot and its registerEvent() method. We have two entities, Book and BookReview involved in a lazy-bidirectional @OneToMany association. A new book review is saved in CHECK status and a CheckReviewEvent is published. This event handler is responsible to check the review grammar, content, etc and switch the review status from CHECK to ACCEPT or REJECT and propagate the new status to the database. So, this event is registered before saving the book review in CHECK status and is published automatically after we call the BookReviewRepository.save() method. After publication, the event is cleared.
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AbstractAggregateRoot and provide a method for registering eventsCheckReviewEvent ), but more can be registeredCheckReviewEventHandler in an asynchronous manner via @AsyncDescription: This application is an example of testing the Hibernate Query Plan Cache (QPC). Hibernate QPC is enabled by default and, for entity queries (JPQL and Criteria API), the QPC has a size of 2048, while for native queries it has a size of 128. Pay attention to alter these values to accommodate all queries executed by your Anwendung. If the number of exectued queries is higher than the QPC size (especially for entity queries) then you will start to experiment performance penalties caused by entity compilation time added for each query execution.
In this application, you can adjust the QPC size in application.properties . Mainly, there are 2 JPQL queries and a QPC of size 2. Switching from size 2 to size 1 will cause the compilation of one JPQL query at each execution. Measuring the times for 5000 executions using a QPC of size 2, respectively 1 reveals the importance of QPC in terms of time.
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hibernate.query.plan_cache_max_sizehibernate.query.plan_parameter_metadata_max_sizeDescription: This is a SpringBoot application that enables Hibernate Second Level Cache and EhCache provider. It contains an example of caching entities and an example of caching a query result.
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EhCache )@CacheHINT_CACHEABLE Description: This is a SpringBoot application representing a kickoff application for Spring Boot caching and EhCache .
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EhCacheSqlResultSetMapping And NamedNativeQuery Note: If you want to rely on the {EntityName}.{RepositoryMethodName} naming convention for simply creating in the repository interface methods with the same name as of native named query then skip this application and check this one.
Description: This is a sample application of using SqlResultSetMapping , NamedNativeQuery and EntityResult for fetching single entity and multiple entities as List<Object[]> .
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SqlResultSetMapping , NamedNativeQuery and EntityResult Description: This is a SpringBoot application that loads multiple entities by id via a @Query based on the IN operator and via Specification .
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IN operator in a @Query simply add the query in the proper repositorySpecification rely on javax.persistence.criteria.Root.in()ResultTransformer Description: Fetching more read-only data than needed is prone to performance penalties. Using DTO allows us to extract only the needed data. Sometimes, we need to fetch a DTO made of a subset of properties (columns) from a parent-child association. For such cases, we can use SQL JOIN that can pick up the desired columns from the involved tables. But, JOIN returns an List<Object[]> and most probably you will need to represent it as a List<ParentDto> , where a ParentDto instance has a List<ChildDto> . For such cases, we can rely on a custom Hibernate ResultTransformer . This application is a sample of writing a custom ResultTransformer .
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ResultTransformer interface Description: Is a common scenario to have a big List and to need to chunk it in multiple smaller List of given size. For example, if we want to employee a concurrent batch implementation we need to give to each thread a sublist of items. Chunking a list can be done via Google Guava, Lists.partition(List list, int size) method or Apache Commons Collections, ListUtils.partition(List list, int size) method. But, it can be implemented in plain Java as well. This application exposes 6 ways to do it. The trade-off is between the speed of implementation and speed of execution. For example, while the implementation relying on grouping collector is not performing very well, it is quite simple and fast to write it.
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Chunk.java class which relies on the built-in List.subList() method Time-performance trend graphic for chunking 500, 1_000_000, 10_000_000 and 20_000_000 items in lists of 5 items: 
Description: Consider the Book and Chapter entities. A book has a maximum accepted number of pages ( book_pages ) and the author should not exceed this number. When a chapter is ready for review, the author is submitting it. At this point, the publisher should check that the currently total number of pages doesn't exceed the allowed book_pages :
This kind of checks or constraints are easy to implement via database triggers. This application relies on a MySQL trigger to empower our complex contraint ( check_book_pages ).
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AFTER INSERT OR AFTER UPDATE ) Description: This application is an example of using Spring Data Query By Example (QBE) to check if a transient entity exists in the database. Consider the Book entity and a Spring controller that exposes an endpoint as: public String checkBook(@Validated @ModelAttribute Book book, ...) . Beside writting an explicit JPQL, we can rely on Spring Data Query Builder mechanism or, even better, on Query By Example (QBE) API. In this context, QBE API is quite useful if the entity has a significant number of attributes and:
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BookRepository extends QueryByExampleExecutor<S extends T> boolean exists(Example<S> exmpl) with the proper probe (an entity instance populated with the desired fields values)ExampleMatcher which defines the details on how to match particular fields Note: Do not conclude that Query By Example (QBE) defines only the exists() method. Check out all methods here.
| If you need a deep dive into the performance recipes exposed in this repository then I am sure that you will love my book "Spring Boot Persistence Best Practices" | If you need a hand of tips and illustrations of 100+ Java persistence performance issues then "Java Persistence Performance Illustrated Guide" is for you. |
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@Transactional Description: This application is meant to highlight that the best place to use @Transactional for user defined query-methods is in repository interface, and afterwards, depending on situation, on service-methods level.
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JOINED Inheritance Strategy And Visitor Design Pattern Description: This application is an example of using JPA JOINED inheritance strategy and Visitor pattern.
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JOINED Inheritance Strategy And Strategy Design Pattern Description: This application is an example of using JPA JOINED inheritance strategy and Strategy pattern.
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Description: This folder holds several applications that shows how each Spring transaction propagation works.
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GenerationType.AUTO And UUID Identifiers Description: This application is an example of using the JPA GenerationType.AUTO for assigning automatically UUID identifiers.
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BINARY(16) columnDescription: This application is an example of manually assigning UUID identifiers.
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BINARY(16) columnuuid2 For Generating UUID Identifiers Description: This application is an example of using the Hibernate RFC 4122 compliant UUID generator, uuid2 .
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BINARY(16) columnDescription: This Spring Boot application is a sample that reveals how Hibernate session-level repeatable reads works. Persistence Context guarantees session-level repeatable reads. Check out how it works.
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TransactionTemplateNote: For a detailed explanation of this application consider my book, Spring Boot Persistence Best Practices
hibernate.enable_lazy_load_no_trans Description: This application is an example of using Hibernate-specific hibernate.enable_lazy_load_no_trans . Check out the application log to see how transactions and database connections are used.
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hibernate.enable_lazy_load_no_trans Description: This application is an example of cloning entities. The best way to achieve this goal relies on copy-constructors. This way we can control what we copy. Here we use a bidirectional-lazy @ManyToMany association between Author and Book .
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Author (only the genre ) and associate the corresponding booksAuthor (only the genre ) and clone the books as well| If you need a deep dive into the performance recipes exposed in this repository then I am sure that you will love my book "Spring Boot Persistence Best Practices" | If you need a hand of tips and illustrations of 100+ Java persistence performance issues then "Java Persistence Performance Illustrated Guide" is for you. |
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UPDATE Statement Only The Modified Columns Via Hibernate @DynamicUpdate Description: This application is an example of using the Hibernate-specific, @DynamicUpdate . By default, even if we modify only a subset of columns, the triggered UPDATE statements will include all columns. By simply annotating the corresponding entity at class-level with @DynamicUpdate the generated UPDATE statement will include only the modified columns.
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UPDATE for different subsets of columns via JDBC statements caching (each triggered UPDATE string will be cached and reused accordingly)Description: This application is an example of logging execution time for a repository query-method.
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RepositoryProfiler ) Description: This application is an example of using the TransactionSynchronizationAdapter for overriding beforeCommit() , beforeCompletion() , afterCommit() and afterCompletion() callbacks globally (application-level) and at method-level.
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TransactionProfiler )TransactionSynchronizationManager.registerSynchronization()SqlResultSetMapping And NamedNativeQuery Using {EntityName}.{RepositoryMethodName} Naming Convention Description: Fetching more data than needed is prone to performance penalities. Using DTO allows us to extract only the needed data. In this application we rely on SqlResultSetMapping , NamedNativeQuery and the {EntityName}.{RepositoryMethodName} naming convention. This convention allows us to create in the repository interface methods with the same name as of native named query.
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SqlResultSetMapping , NamedNativeQuerySqlResultSetMapping And NamedNativeQuery Using {EntityName}.{RepositoryMethodName} Naming Convention Description: This is a sample application of using SqlResultSetMapping , NamedNativeQuery and EntityResult for fetching single entity and multiple entities as List<Object[]> . In this application we rely on the {EntityName}.{RepositoryMethodName} naming convention. This convention allows us to create in the repository interface methods with the same name as of native named query.
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SqlResultSetMapping , NamedNativeQuery and EntityResult@NamedQuery And Spring Projection (DTO) Description: This application is an example of combining JPA named queries @NamedQuery and Spring projections (DTO). For queries names we use the {EntityName}.{RepositoryMethodName} naming convention. This convention allows us to create in the repository interface methods with the same name as of named query.
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@NamedNativeQuery And Spring Projection (DTO) Description: This application is an example of combining JPA named native queries @NamedNativeQuery and Spring projections (DTO). For queries names we use the {EntityName}.{RepositoryMethodName} naming convention. This convention allows us to create in the repository interface methods with the same name as of named native query.
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Description: JPA named (native) queries are commonly written via @NamedQuery and @NamedNativeQuery annotations in entity classes. Spring Data allows us to write our named (native) queries in a typical *.properties file inside the META-INF folder of your classpath. This way, we avoid modifying our entities. This application shows you how to do it.
Warning: Cannot use native queries with dynamic sorting ( Sort ). Nevertheless, using Sort in named queries works fine. Moreover, using Sort in Pageable works fine for both, named queries and named native queries. At least this is how it behave in Spring Boot 2.2.2. From this point of view, this approach is better than using @NamedQuery / @NamedNativeQuery or orm.xml file.
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META-INF/jpa-named-queries.properties{EntityName}.{RepositoryMethodName} naming convention for a quick and slim implementationorm.xml File Description: JPA named (native) queries are commonly written via @NamedQuery and @NamedNativeQuery annotations in entity classes. Spring Data allows us to write our named (native) queries in a typical orm.xml file inside the META-INF folder of your classpath. This way, we avoid modifying our entities. This application shows you how to do it.
Warning: Pay attention that, via this approach, we cannot use named (native) queries with dynamic sorting ( Sort ). Using Sort in Pageable is ignored, therefore you need to explicitly add ORDER BY in the queries. At least this is how it behave in Spring Boot 2.2.2. A better approach relies on using a properties file for listing the named (native) queries. In this case, dynamic Sort works for named queries, but not for named native queries. Using Sort in Pageable works as expected in named (native) queries.
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META-INF/orm.xml{EntityName}.{RepositoryMethodName} naming convention for a quick and slim implementation Description: JPA named (native) queries are commonly written via @NamedQuery and @NamedNativeQuery annotations in entity classes. This application shows you how to do it.
Warning: Pay attention that, via this approach, we cannot use named (native) queries with dynamic sorting ( Sort ). Using Sort in Pageable is ignored, therefore you need to explicitly add ORDER BY in the queries. At least this is how it behave in Spring Boot 2.2.2. A better approach relies on using a properties file for listing the named (native) queries. In this case, dynamic Sort works for named queries, but not for named native queries. Using Sort in Pageable works as expected in named (native) queries. And, you don't need to modify/pollute entitites with the above annotations.
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@NamedQuery and @NamedNativeQuery annotations in entity classes{EntityName}.{RepositoryMethodName} naming convention for a quick and slim implementationSort and Pageable| If you need a deep dive into the performance recipes exposed in this repository then I am sure that you will love my book "Spring Boot Persistence Best Practices" | If you need a hand of tips and illustrations of 100+ Java persistence performance issues then "Java Persistence Performance Illustrated Guide" is for you. |
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Description: This application is an example of combining JPA named queries listed in a properties file and Spring projections (DTO). For queries names we use the {EntityName}.{RepositoryMethodName} naming convention. This convention allows us to create in the repository interface methods with the same name as of named query.
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jpa-named-queries.properties ) in a folder named META-INF the application classpath Description: This application is an example of combining JPA named native queries listed in a properties file and Spring projections (DTO). For queries names we use the {EntityName}.{RepositoryMethodName} naming convention. This convention allows us to create in the repository interface methods with the same name as of named native query.
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jpa-named-queries.properties ) in a folder named META-INF the application classpathorm.xml File And Spring Projection (DTO) Description: This application is an example of combining JPA named queries listed in orm.xml file and Spring projections (DTO). For queries names we use the {EntityName}.{RepositoryMethodName} naming convention. This convention allows us to create in the repository interface methods with the same name as of named query.
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orm.xml file in a folder named META-INF the application classpathorm.xml File And Spring Projection (DTO) Description: This application is an example of combining JPA named native queries listed in orm.xml file and Spring projections (DTO). For queries names we use the {EntityName}.{RepositoryMethodName} naming convention. This convention allows us to create in the repository interface methods with the same name as of named native query.
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orm.xml file in a folder named META-INF the application classpathorm.xml Description: Fetching more data than needed is prone to performance penalities. Using DTO allows us to extract only the needed data. In this application we rely on named native queries and result set mapping via orm.xml and the {EntityName}.{RepositoryMethodName} naming convention. This convention allows us to create in the repository interface methods with the same name as of native named query.
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<named-native-query/> and <sql-result-set-mapping/> to map the native query to AuthorDto class
Description: This application is a proof of concept for using Spring Projections(DTO) and cross joins written via JPQL and native SQL (for MySQL).
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Book and Formatresources/data-mysql.sql )BookTitleAndFormatType.java )JdbcTemplate And BeanPropertyRowMapper Description: This application is an example of calling a MySQL stored procedure that returns a result set via JdbcTemplate and BeanPropertyRowMapper .
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JdbcTemplate , SimpleJdbcCall and BeanPropertyRowMapper@EntityListeners Description: This application is a sample of using the JPA @MappedSuperclass and @EntityListeners with JPA callbacks.
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Book , is not an entity, it can be abstract , and is annotated with @MappedSuperclass and @EntityListeners(BookListener.class)BookListener defines JPA callbacks (eg, @PrePersist )Book is persisted, loaded, updated, etc the corresponding JPA callbacks are called@Fetch(FetchMode.JOIN) May Causes N+1 Issues Advice: Always evaluate JOIN FETCH and entities graphs before deciding to use FetchMode.JOIN . The FetchMode.JOIN fetch mode always triggers an EAGER load so the children are loaded when the parents are. Beside this drawback, FetchMode.JOIN may return duplicate results. You'll have to remove the duplicates yourself (eg storing the result in a Set ). But, if you decide to go with FetchMode.JOIN at least pay attention to avoid N+1 issues discussed below.
Note: Let's assume three entities, Author , Book and Publisher . Between Author and Book there is a bidirectional-lazy @OneToMany association. Between Author and Publisher there is a unidirectional-lazy @ManyToOne . Between Book and Publisher there is no association.
Now, we want to fetch a book by id ( BookRepository#findById() ), including its author, and the author's publisher. In such cases, using Hibernate fetch mode, @Fetch(FetchMode.JOIN) works as expected. Using JOIN FETCH or entity graph is also working as expected.
Next, we want to fetch all books ( BookRepository#findAll() ), including their authors, and the authors publishers. In such cases, using Hibernate fetch mode, @Fetch(FetchMode.JOIN) will cause N+1 issues. It will not trigger the expected JOIN . In this case, using JOIN FETCH or entity graph should be used.
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@Fetch(FetchMode.JOIN) doesn't work for query-methods@Fetch(FetchMode.JOIN) works in cases that fetches the entity by id (primary key) like using EntityManager#find() , Spring Data, findById() , findOne() .RANK() Description: This application is an example of assigning a database temporary ranking of values to rows via the window function, RANK() . This window function is available in almost all databases, and starting with version 8.x is available in MySQL as well.
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RANK() (you will use it internally, in the query, usually in the WHERE clause and CTEs), but, this time, let's write a Spring projection (DTO) that contains a getter for the column generated by RANK() as wellRANK() window function Ausgabebeispiel: 
| If you need a deep dive into the performance recipes exposed in this repository then I am sure that you will love my book "Spring Boot Persistence Best Practices" | If you need a hand of tips and illustrations of 100+ Java persistence performance issues then "Java Persistence Performance Illustrated Guide" is for you. |
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DENSE_RANK() Description: This application is an example of assigning a database temporary ranking of values to rows via the window function, DENSE_RANK() . In comparison with the RANK() window function, DENSE_RANK() avoid gaps within partition. This window function is available in almost all databases, and starting with version 8.x is available in MySQL as well.
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DENSE_RANK() (you will use it internally, in the query, usually in the WHERE clause and CTEs), but, this time, let's write a Spring projection (DTO) that contains a getter for the column generated by DENSE_RANK() as wellDENSE_RANK() window function Ausgabebeispiel: 
NTILE(N) Description: This application is an example of distributing the number of rows in the specified (N) number of groups via the window function, NTILE(N) . This window function is available in almost all databases, and starting with version 8.x is available in MySQL as well.
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NTILE() (you will use it internally, in the query, usually in the WHERE clause and CTEs), but, this time, let's write a Spring projection (DTO) that contains a getter for the column generated by NTILE() as wellNTILE() window function Ausgabebeispiel: 
Description: Spring Data comes with the Query Builder mechanism for JPA that is capable to interpret a query method name (known as a derived query) and convert it into a SQL query in the proper dialect. This is possible as long as we respect the naming conventions of this mechanism. Beside the well-known query of type find... , Spring Data supports derived count queries and derived delete queries.
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count... (eg, long countByGenre(String genre) ) - Spring Data will generate a SELECT COUNT(...) FROM ... querydelete... or remove... and returns long (eg, long deleteByGenre(String genre) ) - Spring Data will trigger first a SELECT to fetch entities in the Persistence Context, and, afterwards, it triggers a DELETE for each entity that must be deleteddelete... or remove... and returns List<entity> (eg, List<Author> removeByGenre(String genre) ) - Spring Data will trigger first a SELECT to fetch entities in the Persistence Context, and, afterwards, it triggers a DELETE for each entity that must be deletedDescription: Property expressions can refer to a direct property of the managed entity. However, you can also define constraints by traversing nested properties. This application is a sample of traversing nested properties for fetching entities and DTOs.
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Author has several Book and each book has several Review (between Author and Book there is a bidirectional-lazy @oneToMany association, and, between Book and Review there is also a bidirectional-lazy @OneToMany association)Review and we want to know the Author of the Book that has received this ReviewAuthorRepository the following query that will be processed by the Spring Data Query Builder mechanism: Author findByBooksReviews(Review review);SELECT with two LEFT JOINbooks.reviews . The algorithm starts by interpreting the entire part ( BooksReviews ) as the property and checks the domain class for a property with that name (uncapitalized). If the algorithm succeeds, it uses that property. If not, the algorithm splits up the source at the camel case parts from the right side into a head and a tail and tries to find the corresponding property — in our example, Books and Reviews . If the algorithm finds a property with that head, it takes the tail and continues building the tree down from there, splitting the tail up in the way just described. If the first split does not match, the algorithm moves the split point to the left and continues.Author class has an booksReview property as well. The algorithm would match in the first split round already, choose the wrong property, and fail (as the type of booksReview probably has no code property). To resolve this ambiguity you can use _ inside your method name to manually define traversal points. So our method name would be as follows: Author findByBooks_Reviews(Review review);Note: Fetching read-only data should be done via DTO, not managed entities. But, there is no tragedy to fetch read-only entities in a context as follows:
@Transactional(readOnly = true)Under these circumstances, let's tackle a common case that I saw quite a lot. There is even an SO answer about it (don't do this):
Description: Let's assume that Author and Book are involved in a bidirectional-lazy @OneToMany association. Imagine an user that loads a certain Author (without the associated Book ). The user may be interested or not in the Book , therefore, we don't load them with the Author . If the user is interested in the Book then he will click a button of type, View books . Now, we have to return the List<Book> associated to this Author .
So, at first request (query), we fetch an Author . The Author is detached. At second request (query), we want to load the Book associated to this Author . But, we don't want to load the Author again (for example, we don't care about lost updates of Author ), we just want to load the associated Book in a single SELECT . A common (not recommended) approach is to load the Author again (eg, via findById(author.getId()) ) and call the author.getBooks() . But, this end up in two SELECT statements. One SELECT for loading the Author , and another SELECT after we force the collection initialization. We force collection initialization because it will not be initialize if we simply return it. In order to trigger the collection initialization the developer call books.size() or he rely on Hibernate.initialize(books); .
But, we can avoid such solution by relying on an explicit JPQL or Query Builder property expressions. This way, there will be a single SELECT and no need to call size() or Hibernate.initialize();
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This item is detailed in my book, Spring Boot Persistence Best Practices.
Description: Behind the built-in Spring Data save() there is a call of EntityManager#persist() or EntityManager#merge() . It is important to know this aspect in several cases. Among this cases, we have the entity update case (simple update or update batching).
Consider Author and Book involved in a bidirectional-lazy @OneToMany association. And, we load an Author , detach it, update it in the detached state, and save it to the database via save() method. Calling save() will come with the following two issues resulting from calling merge() behind the scene:
SELECT (merge) and one UPDATESELECT will contain a LEFT OUTER JOIN to fetch the associated Book as well (we don't need the books!) How about triggering only the UPDATE instead of this? The solution relies on calling Session#update() . Calling Session.update() requires to unwrap the Session via entityManager.unwrap(Session.class) .
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Session.update() will trigger only the UPDATE (there is no SELECT )Session.update() works with versioned optimistic locking mechanism as well (so, lost updates are prevented)Streamable Description: This application is a sample of fetching Streamable<entity> and Streamable<dto> . But, more important, this application contains three examples of how to not use Streamable . It is very tempting and comfortable to fetch a Streamable result set and chop it via filter() , map() , flatMap() , and so on until we obtain only the needed data instead of writing a query (eg, JPQL) that fetches exactly the needed result set from the database. Mainly, we just throw away some of the fetched data to keep only the needed data. But, is not advisable to follow such practices because fetching more data than needed can cause significant performance penalties.
Moreover, pay attention to combining two or more Streamable via the and() method. The returned result may be different from what you are expecting to see. Each Streamable produces a separate SQL statement and the final result set is a concatenation of the intermediate results sets (prone to duplicate values).
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map() )filter() )Streamable via and() ; each Streamable produces a separate SQL statement and the final result set is a concatenation of the intermediate results sets (prone to duplicate values)Streamable Wrapper TypesDescription: A common practice consists of exposing dedicated wrappers types for collections resulted after mapping a query result set. This way, on a single query execution, the API can return multiple results. After we call a query-method that return a collection, we can pass it to a wrapper class by manually instantiation of that wrapper-class. But, we can avoid the manually instantiation if the code respects the following key points.
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Streamablestatic factory method named of(…) or valueOf(…) taking Streamable as argumentDescription: JPA 2.1 come with schema generation features. This feature can setup the database or export the generated commands to a file. The parameters that we should set are:
spring.jpa.properties.javax.persistence.schema-generation.database.action : Instructs the persistence provider how to setup the database. Possible values include: none , create , drop-and-create , drop
javax.persistence.schema-generation.scripts.action : Instruct the persistence provider which scripts to create. Possible values include: none , create , drop-and-create , drop .
javax.persistence.schema-generation.scripts.create-target : Indicate the target location of the create script generated by the persistence provider. This can be as a file URL or a java.IO.Writer .
javax.persistence.schema-generation.scripts.drop-target : Indicate the target location of the drop script generated by the persistence provider. This can be as a file URL or a java.IO.Writer .
Moreover, we can instruct the persistence provider to load data from a file into the database via: spring.jpa.properties.javax.persistence.sql-load-script-source . The value of this property represents the file location and it can be a file URL or a java.IO.Writer .
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application.properties Description: Sometimes, we need to write in repositories certain query-methods that return a Map instead of a List or a Set . For example, when we need a Map<Id, Entity> or we use GROUP BY and we need a Map<Group, Count> . This application shows you how to do it via default methods directly in repository.
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default methods and Collectors.toMap() Description: Consider one of the JPA inheritance strategies (eg, JOINED ). Handling entities inheritance With Spring Data repositories can be done as follows:
Description: This application is a sample of logging only slow queries via Hibernate 5.4.5, hibernate.session.events.log.LOG_QUERIES_SLOWER_THAN_MS property. A slow query is a query that has an execution time bigger than a specificed threshold in milliseconds.
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application.properties add hibernate.session.events.log.LOG_QUERIES_SLOWER_THAN_MS Ausgabebeispiel: 
Description: Fetching more data than needed is prone to performance penalities. Using DTO allows us to extract only the needed data. In this application we rely on JDK14 Records feature and Spring Data Query Builder Mechanism.
From Openjdk JEP359:
Records provide a compact syntax for declaring classes which are transparent holders for shallowly immutable data.
Key points: Define the AuthorDto as:
public record AuthorDto(String name, int age) implements Serializable {}
Description: Fetching more data than needed is prone to performance penalities. Using DTO allows us to extract only the needed data. In this application we rely on JDK 14 Records, Constructor Expression and JPQL.
From Openjdk JEP359:
Records provide a compact syntax for declaring classes which are transparent holders for shallowly immutable data.
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Define the AuthorDto as:
public record AuthorDto(String name, int age) implements Serializable {}
ResultTransformer Description: Fetching more read-only data than needed is prone to performance penalties. Using DTO allows us to extract only the needed data. Sometimes, we need to fetch a DTO made of a subset of properties (columns) from a parent-child association. For such cases, we can use SQL JOIN that can pick up the desired columns from the involved tables. But, JOIN returns an List<Object[]> and most probably you will need to represent it as a List<ParentDto> , where a ParentDto instance has a List<ChildDto> . For such cases, we can rely on a custom Hibernate ResultTransformer . This application is a sample of writing a custom ResultTransformer .
As DTO, we rely on JDK 14 Records. From Openjdk JEP359:
Records provide a compact syntax for declaring classes which are transparent holders for shallowly immutable data.
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AuthorDto and BookDtoResultTransformer interfaceJdbcTemplate And ResultSetExtractor Description: Fetching more data than needed is prone to performance penalities. Using DTO allows us to extract only the needed data. In this application we rely on JDK14 Records feature, JdbcTemplate and ResultSetExtractor .
From Openjdk JEP359:
Records provide a compact syntax for declaring classes which are transparent holders for shallowly immutable data.
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AuthorDto and BookDtoJdbcTemplate and ResultSetExtractorDescription: This application is a sample of using dynamic Spring projections via DTO classes.
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<T> List<T> findByGenre(String genre, Class<T> type); )| If you need a deep dive into the performance recipes exposed in this repository then I am sure that you will love my book "Spring Boot Persistence Best Practices" | If you need a hand of tips and illustrations of 100+ Java persistence performance issues then "Java Persistence Performance Illustrated Guide" is for you. |
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CompletableFuture And Return List<S> Description: This application is a sample of using CompletableFuture for batching inserts. This CompletableFuture uses an Executor that has the number of threads equal with the number of your computer cores. Usage is in Spring style. It returns List<S> :
CompletableFuture And Return List<S> (1)CompletableFuture And Return List<S> (2) Description: This application is an example of causing a database deadlock in MySQL. This application produces an exception of type: com.mysql.cj.jdbc.exceptions.MySQLTransactionRollbackException: Deadlock found when trying to get lock; try restarting transaction . However, the database will retry until transaction (A) succeeds.
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SELECT with PESSIMISTIC_WRITE to acquire an exclusive lock to table authorauthor genre with success and sleeps for 10sSELECT with PESSIMISTIC_WRITE to acquire an exclusive lock to table bookbook title with success and sleeps for 10s Description: This application is a proof of concept of how to define a composite key having an explicit part ( name ) and a generated part ( authorId via SEQUENCE generator).
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@IdClass Description: Sometimes we need to intercept the generated SQL that originates from Spring Data, EntityManager , Criteria API, JdbcTemplate and so on. This can be done as in this sample application. After interception, you can log, modify or even return a brand new SQL that will be executed in the end.
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StatementInspector SPIapplication.properties via spring.jpa.properties.hibernate.session_factory.statement_inspector281. Force inline params in Criteria API
NOTE Use this with high precaution since you open the gate for SQL injections.
Description: Sometimes we need to force inline params in Criteria API. By default, numeric parameters are inlined, but string parameters are not.
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application.properties the setting spring.jpa.properties.hibernate.criteria.literal_handling_mode as inlineDescription: Arthur Gavlyukovskiy provide a suite of Spring Boot starters for quickly integrate P6Spy, Datasource Proxy, and FlexyPool. In this example, we add Datasource Proxy, but please consider this for more details.
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pom.xml , add the datasource-proxy-spring-boot-starter starterapplication.properties enable DEBUG level for loggingDescription: This application is an example of using Java records as embeddable. This is available starting with Hibernate 6.0, but it was refined to be more accessible and easy to use in Hibernate 6.2
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Contact )Author ) via @EmbeddedAuthorDto )
