Hibernate SpringBoot
1.0.0
| Jika Anda perlu menyelami lebih dalam resep kinerja yang dipaparkan dalam repositori ini maka saya yakin Anda akan menyukai buku saya "Praktik Terbaik Persistensi Boot Musim Semi" | Jika Anda memerlukan tip dan ilustrasi tentang 100+ masalah kinerja persistensi Java, maka "Panduan Ilustrasi Kinerja Persistensi Java" cocok untuk Anda. |
|
|
Sampel Hibernasi & Boot Musim Semi
Deskripsi: Aplikasi ini adalah contoh cara menyimpan tanggal, waktu, dan stempel waktu dalam zona waktu UTC. Pengaturan kedua, useLegacyDatetimeCode diperlukan hanya untuk MySQL. Jika tidak, setel hanya hibernate.jdbc.time_zone .
Poin-poin penting:
spring.jpa.properties.hibernate.jdbc.time_zone=UTCspring.datasource.url=jdbc:mysql://localhost:3306/screenshotdb?useLegacyDatetimeCode=falseDeskripsi: Lihat parameter pengikatan/ekstraksi pernyataan yang disiapkan melalui pengaturan logger Log4J 2.
Poin-poin penting:
pom.xml , kecualikan Logging Default Spring Bootpom.xml , Tambahkan Ketergantungan Log4j 2log4j2.xml tambahkan, <Logger name="org.hibernate.type.descriptor.sql" level="trace"/> Contoh keluaran: 
Deskripsi: Melihat detail kueri (jenis kueri, parameter pengikatan, ukuran batch, waktu eksekusi, dll) melalui DataSource-Proxy
Poin-poin penting:
pom.xml ketergantungan datasource-proxyDataSourceDataSource melalui ProxyFactory dan implementasi MethodInterceptor Contoh keluaran: 
saveAll(Iterable<S> entities) di MySQL Deskripsi: Penyisipan batch melalui metode SimpleJpaRepository#saveAll(Iterable<S> entities) di MySQL
Poin-poin penting:
application.properties atur spring.jpa.properties.hibernate.jdbc.batch_sizeapplication.properties atur spring.jpa.properties.hibernate.generate_statistics (hanya untuk memeriksa apakah batching berfungsi)application.properties atur URL JDBC dengan rewriteBatchedStatements=true (optimasi untuk MySQL)application.properties atur URL JDBC dengan cachePrepStmts=true (aktifkan caching dan berguna jika Anda memutuskan untuk mengatur prepStmtCacheSize , prepStmtCacheSqlLimit , dll juga; tanpa pengaturan ini cache dinonaktifkan)application.properties setel URL JDBC dengan useServerPrepStmts=true (dengan cara ini Anda beralih ke pernyataan yang disiapkan di sisi server (dapat menghasilkan peningkatan kinerja yang signifikan))spring.jpa.properties.hibernate.order_inserts=true untuk mengoptimalkan pengelompokan dengan memesan sisipanIDENTITY akan menyebabkan penyisipan batching dinonaktifkan@Version untuk menghindari pernyataan SELECT ekstra yang diaktifkan sebelum pengelompokan (juga mencegah hilangnya pembaruan dalam transaksi multi-permintaan). Pernyataan ekstra- SELECT adalah efek dari penggunaan merge() alih-alih persist() ; di belakang layar, saveAll() menggunakan save() , yang jika ada entitas bukan baru (entitas yang memiliki ID) akan memanggil merge() , yang menginstruksikan Hibernate untuk mengaktifkan pernyataan SELECT untuk memastikan bahwa tidak ada catatan di database memiliki pengenal yang samasaveAll() agar tidak "membebani" Konteks Persistensi; biasanya EntityManager harus dibersihkan dan dibersihkan dari waktu ke waktu, tetapi selama eksekusi saveAll() Anda tidak bisa melakukan itu, jadi jika di saveAll() ada daftar dengan jumlah data yang tinggi, semua data itu akan masuk ke dalam Persistence Konteks (Cache Tingkat 1) dan akan tetap berada di memori hingga waktu pembilasan; menggunakan jumlah data yang relatif kecil seharusnya tidak masalah (dalam contoh ini, setiap batch yang terdiri dari 30 entitas dijalankan dalam transaksi terpisah dan Konteks Persisten)saveAll() mengembalikan List<S> yang berisi entitas yang bertahan; setiap entitas yang bertahan ditambahkan ke dalam daftar ini; jika Anda tidak memerlukan List ini maka Daftar ini dibuat tanpa imbalanspring.jpa.properties.hibernate.cache.use_second_level_cache=false Deskripsi: Aplikasi ini adalah contoh sisipan batching melalui EntityManager di MySQL. Dengan cara ini Anda dapat dengan mudah mengontrol siklus flush() dan clear() dari Konteks Persistensi (Cache Tingkat 1) di dalam transaksi saat ini. Hal ini tidak dapat dilakukan melalui Spring Boot, saveAll(Iterable<S> entities) , karena metode ini mengeksekusi satu flush per transaksi. Keuntungan lainnya adalah Anda dapat memanggil persist() alih-alih merge() - ini digunakan di belakang layar oleh SpringBoot saveAll(Iterable<S> entities) dan save(S entity) .
Jika Anda ingin mengeksekusi batch per transaksi (disarankan) maka periksa contoh ini.
Poin-poin penting:
application.properties atur spring.jpa.properties.hibernate.jdbc.batch_sizeapplication.properties atur spring.jpa.properties.hibernate.generate_statistics (hanya untuk memeriksa apakah batching berfungsi)application.properties atur URL JDBC dengan rewriteBatchedStatements=true (optimasi untuk MySQL)application.properties atur URL JDBC dengan cachePrepStmts=true (aktifkan caching dan berguna jika Anda memutuskan untuk mengatur prepStmtCacheSize , prepStmtCacheSqlLimit , dll juga; tanpa pengaturan ini cache dinonaktifkan)application.properties setel URL JDBC dengan useServerPrepStmts=true (dengan cara ini Anda beralih ke pernyataan yang disiapkan di sisi server (dapat menghasilkan peningkatan kinerja yang signifikan))spring.jpa.properties.hibernate.order_inserts=true untuk mengoptimalkan pengelompokan dengan memesan sisipanIDENTITY akan menyebabkan penyisipan batching dinonaktifkanspring.jpa.properties.hibernate.cache.use_second_level_cache=false Contoh keluaran: 
Deskripsi: Penyisipan batch melalui JpaContext/EntityManager di MySQL.
Poin-poin penting:
application.properties atur spring.jpa.properties.hibernate.jdbc.batch_sizeapplication.properties atur spring.jpa.properties.hibernate.generate_statistics (hanya untuk memeriksa apakah batching berfungsi)application.properties atur URL JDBC dengan rewriteBatchedStatements=true (optimasi untuk MySQL)application.properties atur URL JDBC dengan cachePrepStmts=true (aktifkan caching dan berguna jika Anda memutuskan untuk mengatur prepStmtCacheSize , prepStmtCacheSqlLimit , dll juga; tanpa pengaturan ini cache dinonaktifkan)application.properties setel URL JDBC dengan useServerPrepStmts=true (dengan cara ini Anda beralih ke pernyataan yang disiapkan di sisi server (dapat menghasilkan peningkatan kinerja yang signifikan))spring.jpa.properties.hibernate.order_inserts=true untuk mengoptimalkan pengelompokan dengan memesan sisipanIDENTITY akan menyebabkan penyisipan batching dinonaktifkanEntityManager diperoleh per tipe entitas melalui, JpaContext#getEntityManagerByManagedType(Class<?> entity)spring.jpa.properties.hibernate.cache.use_second_level_cache=false Contoh keluaran: 
Deskripsi: Penyisipan batch melalui batching tingkat sesi Hibernate (Hibernate 5.2 atau lebih tinggi) di MySQL.
Poin-poin penting:
application.properties atur spring.jpa.properties.hibernate.generate_statistics (hanya untuk memeriksa apakah batching berfungsi)application.properties atur URL JDBC dengan rewriteBatchedStatements=true (optimasi untuk MySQL)application.properties atur URL JDBC dengan cachePrepStmts=true (aktifkan caching dan berguna jika Anda memutuskan untuk mengatur prepStmtCacheSize , prepStmtCacheSqlLimit , dll juga; tanpa pengaturan ini cache dinonaktifkan)application.properties setel URL JDBC dengan useServerPrepStmts=true (dengan cara ini Anda beralih ke pernyataan yang disiapkan di sisi server (dapat menghasilkan peningkatan kinerja yang signifikan))spring.jpa.properties.hibernate.order_inserts=true untuk mengoptimalkan pengelompokan dengan memesan sisipanIDENTITY akan menyebabkan penyisipan batching dinonaktifkanSession Hibernasi diperoleh dengan membuka bungkusnya melalui EntityManager#unwrap(Session.class)Session#setJdbcBatchSize(Integer size) dan dapatkan melalui Session#getJdbcBatchSize()spring.jpa.properties.hibernate.cache.use_second_level_cache=false Contoh keluaran: 
findById() , JPA EntityManager , dan Session Hibernasi Deskripsi: Pengambilan langsung melalui contoh Spring Data, EntityManager , dan Hibernate Session .
Poin-poin penting:
findById()EntityManager menggunakan find()Session Hibernate menggunakan get()Catatan: Anda mungkin juga ingin membaca resep, "Cara Memperkaya DTO Dengan Properti Virtual Melalui Proyeksi Pegas"
Deskripsi: Ambil hanya data yang diperlukan dari database melalui Spring Data Projections (DTO).
Poin-poin penting:
List<projection>LIMIT )Catatan: Penggunaan proyeksi tidak terbatas pada penggunaan mekanisme pembuat kueri yang dibangun dalam infrastruktur repositori Spring Data. Kami juga dapat mengambil proyeksi melalui JPQL atau kueri asli. Misalnya pada aplikasi ini kami menggunakan JPQL.
Contoh keluaran (pilih 2 baris pertama; pilih hanya "nama" dan "usia"): 
| Jika Anda perlu menyelami lebih dalam resep kinerja yang dipaparkan dalam repositori ini maka saya yakin Anda akan menyukai buku saya "Praktik Terbaik Persistensi Boot Musim Semi" | Jika Anda memerlukan tip dan ilustrasi tentang 100+ masalah kinerja persistensi Java, maka "Panduan Ilustrasi Kinerja Persistensi Java" cocok untuk Anda. |
|
|
Deskripsi: Secara default, atribut suatu entitas dimuat dengan cepat (sekaligus). Tapi, kita juga bisa memuatnya dengan malas . Ini berguna untuk tipe kolom yang menyimpan data dalam jumlah besar: CLOB , BLOB , VARBINARY , dll atau detail yang harus dimuat sesuai permintaan. Dalam aplikasi ini, kami memiliki entitas bernama Author . Propertinya adalah: id , name , genre , avatar dan age . Dan, kami malas ingin memuat avatar . Jadi, avatar harus dimuat sesuai permintaan.
Poin-poin penting:
pom.xml , aktifkan peningkatan bytecode Hibernate (misalnya gunakan plugin peningkatan bytecode Maven )@Basic(fetch = FetchType.LAZY)application.properties , nonaktifkan Sesi Terbuka di Tampilan Periksa juga:
- Nilai Default Untuk Atribut yang Dimuat Lambat
- Atribut Pemuatan Malas dan Serialisasi Jackson
Deskripsi: Proksi Hibernasi dapat berguna ketika entitas anak dapat dipertahankan dengan referensi ke induknya (asosiasi @ManyToOne atau @OneToOne ). Dalam kasus seperti ini, mengambil entitas induk dari database (mengeksekusi pernyataan SELECT ) adalah penalti kinerja dan tindakan sia-sia, karena Hibernate dapat menetapkan nilai kunci asing yang mendasari proksi yang tidak diinisialisasi.
Poin-poin penting:
EntityManager#getReference()JpaRepository#getOne() -> digunakan dalam contoh iniload()Author dan Book , terlibat dalam asosiasi @ManyToOne searah ( Author adalah pihak induk)SELECT ), kita membuat buku baru, kita menetapkan proxy sebagai penulis untuk buku ini dan kita menyimpan buku tersebut (ini akan memicu INSERT di tabel book )Contoh keluaran:
INSERT yang dipicu, dan tidak ada SELECTDeskripsi: N+1 adalah masalah pengambilan yang lambat (tetapi, keinginan tidak terkecuali). Aplikasi ini mereproduksi perilaku N+1.
Poin-poin penting:
Author dan Book dalam asosiasi @OneToMany dua arah yang malasBook malas, jadi tanpa Author (menghasilkan 1 kueri)Book yang diambil dan untuk setiap entri ambil Author yang sesuai (hasil N kueri)Author yang malas, jadi tanpa Book (hasil dalam 1 kueri)Author yang diambil dan untuk setiap entri ambil Book yang sesuai (hasil N kueri) Contoh keluaran: 
SELECT DISTINCT Melalui Hibernate HINT_PASS_DISTINCT_THROUGH Petunjuk Deskripsi: Dimulai dengan Hibernate 5.2.2, kita dapat mengoptimalkan entitas kueri JPQL (HQL) bertipe SELECT DISTINCT melalui petunjuk HINT_PASS_DISTINCT_THROUGH . Ingatlah bahwa petunjuk ini hanya berguna untuk kueri JPQL (HQL) JOIN FETCH. Tidak berguna untuk kueri skalar (misalnya, List<Integer> ), DTO, atau HHH-13280. Dalam kasus seperti itu, kata kunci DISTINCT JPQL diperlukan untuk diteruskan ke kueri SQL yang mendasarinya. Ini akan menginstruksikan database untuk menghapus duplikat dari kumpulan hasil.
Poin-poin penting:
@QueryHints(value = @QueryHint(name = HINT_PASS_DISTINCT_THROUGH, value = "false")) Contoh keluaran: 
Catatan: Mekanisme Hibernate Dirty Checking bertanggung jawab untuk mengidentifikasi modifikasi entitas pada waktu flush dan memicu pernyataan UPDATE yang sesuai atas nama kami.
Deskripsi: Sebelum Hibernate versi 5, mekanisme Dirty Checking mengandalkan Java Reflection API untuk memeriksa setiap properti dari setiap entitas yang dikelola. Dimulai dengan Hibernate versi 5, mekanisme Dirty Checking dapat mengandalkan mekanisme Dirty Tracking (yang merupakan kemampuan suatu entitas untuk melacak perubahan atributnya sendiri) yang memerlukan Hibernate Bytecode Enhancement untuk hadir dalam aplikasi. Mekanisme Pelacakan Kotor mempertahankan kinerja yang lebih baik, terutama bila Anda memiliki jumlah entitas yang relatif besar.
Untuk Dirty Tracking , selama proses Peningkatan Bytecode , bytecode kelas entitas diinstrumentasi oleh Hibernate dengan menambahkan pelacak , $$_hibernate_tracker . Pada waktu flush, Hibernate akan menggunakan pelacak ini untuk menemukan perubahan entitas (setiap pelacak entitas akan melaporkan perubahan tersebut). Ini lebih baik daripada memeriksa setiap properti dari setiap entitas yang dikelola.
Umumnya (secara default), instrumentasi dilakukan pada waktu build, namun dapat dikonfigurasi untuk dilakukan pada waktu proses atau waktu penerapan juga. Sebaiknya dilakukan pada waktu build untuk menghindari overhead saat runtime.
Menambahkan Peningkatan Bytecode dan mengaktifkan Pelacakan Kotor dapat dilakukan melalui plugin yang ditambahkan melalui Maven atau Gradle (Ant juga dapat digunakan). Kami menggunakan Maven, oleh karena itu kami menambahkannya di pom.xml .
Poin-poin penting:
pom.xml Contoh keluaran: 
Efek Peningkatan Bytecode dapat dilihat pada Author.class di sini. Perhatikan bagaimana bytecode diinstrumentasikan dengan $$_hibernate_tracker .
Optional Dalam Entitas Dan Kueri Deskripsi: Aplikasi ini adalah contoh cara menggunakan Java 8 Optional dengan benar dalam entitas dan kueri.
Poin-poin penting:
Optional (misalnya, findById() )OptionalOptional di pengambil entitasdata-mysql.sql@OneToMany Deskripsi: Aplikasi ini adalah bukti konsep tentang cara mengimplementasikan asosiasi @OneToMany dua arah yang benar dari perspektif kinerja.
Poin-poin penting:
mappedBy pada induknyaorphanRemoval pada orang tua untuk menghapus anak-anak tanpa referensi@NaturalId )) dan/atau pengidentifikasi yang dihasilkan basis data dan ganti (di sisi anak) dengan benar metode equals() dan hashCode() seperti di sinitoString() perlu diganti, maka perhatikan untuk hanya melibatkan atribut dasar yang diambil saat entitas dimuat dari database Catatan: Perhatikan operasi penghapusan, terutama penghapusan entitas anak. CascadeType.REMOVE dan orphanRemoval=true mungkin menghasilkan terlalu banyak kueri. Dalam skenario seperti itu, mengandalkan operasi massal sering kali merupakan cara terbaik untuk melakukan penghapusan.
Deskripsi: Aplikasi ini adalah contoh cara menulis query melalui JpaRepository , EntityManager dan Session .
Poin-poin penting:
JpaRepository gunakan @Query atau Pembuatan Kueri Data Musim SemiEntityManager dan Session gunakan metode createQuery()AUTO Generator Di Hibernate 5 Dan MySQL Deskripsi: Pada MySQL & Hibernate 5, tipe generator GenerationType.AUTO akan menghasilkan penggunaan generator TABLE . Ini menambah penalti kinerja yang signifikan. Mengubah perilaku ini menjadi generator IDENTITY dapat diperoleh dengan menggunakan GenerationType.IDENTITY atau generator asli .
Poin-poin penting:
GenerationType.IDENTITY alih-alih GenerationType.AUTO Contoh keluaran: 
Deskripsi: Aplikasi ini adalah contoh ketika pemanggilan save() untuk suatu entitas bersifat mubazir (tidak perlu).
Poin-poin penting:
UPDATE yang sesuai tanpa perlu memanggil metode save() secara eksplisitsave() pada saat belum tentu) tidak memengaruhi jumlah kueri yang dipicu, namun hal ini menyiratkan penalti kinerja dalam proses Hibernate yang mendasarinya| Jika Anda perlu menyelami lebih dalam resep kinerja yang dipaparkan dalam repositori ini maka saya yakin Anda akan menyukai buku saya "Praktik Terbaik Persistensi Boot Musim Semi" | Jika Anda memerlukan tip dan ilustrasi tentang 100+ masalah kinerja persistensi Java, maka "Panduan Ilustrasi Kinerja Persistensi Java" cocok untuk Anda. |
|
|
SERIAL PostgreSQL ( BIG ) Dalam Sisipan Batching Melalui Hibernate Deskripsi: Di PostgreSQL, menggunakan GenerationType.IDENTITY akan menonaktifkan penyisipan batching. (BIG)SERIAL bertindak "hampir" seperti MySQL, AUTO_INCREMENT . Dalam aplikasi ini, kami menggunakan GenerationType.SEQUENCE yang mengizinkan penyisipan batching, dan kami mengoptimalkannya melalui algoritma optimasi hi/lo .
Poin-poin penting:
GenerationType.SEQUENCE alih-alih GenerationType.IDENTITYhi/lo untuk mengambil nilai hi dalam perjalanan bolak-balik database (nilai hi berguna untuk menghasilkan sejumlah pengidentifikasi dalam memori tertentu/tertentu; sampai Anda belum kehabisan semua pengidentifikasi dalam memori, tidak perlu untuk menjemput lagi hai )pooled dan pooled-lo (ini adalah optimalisasi hi/lo yang memungkinkan layanan eksternal menggunakan database tanpa menyebabkan kesalahan kunci duplikasi)spring.datasource.hikari.data-source-properties.reWriteBatchedInserts=true Contoh keluaran: 
SINGLE_TABLE Deskripsi: Aplikasi ini adalah contoh penggunaan strategi pewarisan Tabel Tunggal JPA ( SINGLE_TABLE ).
Poin-poin penting:
@Inheritance(strategy=InheritanceType.SINGLE_TABLE) )@NotNull dan MySQLTINYINT Contoh keluaran (di bawah ini adalah tabel tunggal yang diperoleh dari 3 entitas): 
Deskripsi: Aplikasi ini adalah contoh penghitungan dan penegasan pernyataan SQL yang dipicu "di belakang layar". Sangat berguna untuk menghitung pernyataan SQL untuk memastikan bahwa kode Anda tidak menghasilkan pernyataan SQL lebih banyak dari yang Anda kira (misalnya, N+1 dapat dengan mudah dideteksi dengan menyatakan jumlah pernyataan yang diharapkan).
Poin-poin penting:
pom.xml , tambahkan dependensi untuk perpustakaan DataSource-Proxy dan perpustakaan db-util Vlad MihalceaProxyDataSourceBuilder dengan countQuery()SQLStatementCountValidator.reset()INSERT , UPDATE , DELETE dan SELECT melalui assertInsert/Update/Delete/Select/Count(long expectedNumberOfSql) Contoh keluaran (ketika jumlah SQL yang diharapkan tidak sama dengan kenyataan, pengecualian akan diberikan): 
Deskripsi: Aplikasi ini adalah contoh pengaturan callback JPA ( Pre/PostPersist , Pre/PostUpdate , Pre/PostRemove dan PostLoad ).
Poin-poin penting:
void dan tidak mengambil argumen Contoh keluaran: 
@MapsId Untuk Berbagi Pengidentifikasi Dalam Hubungan @OneToOne Deskripsi: Daripada @OneToOne searah/dua arah biasa , lebih baik mengandalkan @OneToOne dan @MapsId searah. Aplikasi ini adalah bukti konsep.
Poin-poin penting:
@MapsId di sisi anak@JoinColumn untuk menyesuaikan nama kolom kunci utama@OneToOne , @MapsId akan membagikan kunci utama dengan tabel induk (properti id bertindak sebagai kunci utama dan kunci asing)Catatan:
@MapsId juga dapat digunakan untuk @ManyToOneSqlResultSetMapping Dan EntityManager Deskripsi: Mengambil lebih banyak data daripada yang dibutuhkan rentan terhadap penalti kinerja. Menggunakan DTO memungkinkan kita mengekstrak data yang diperlukan saja. Dalam aplikasi ini kami mengandalkan SqlResultSetMapping dan EntityManager .
Poin-poin penting:
SqlResultSetMapping dan EntityManagerSqlResultSetMapping Dan NamedNativeQuery Catatan: Jika Anda ingin mengandalkan konvensi penamaan {EntityName}.{RepositoryMethodName} untuk sekadar membuat metode antarmuka repositori dengan nama yang sama dengan kueri bernama asli, lewati aplikasi ini dan periksa yang ini.
Deskripsi: Mengambil lebih banyak data daripada yang dibutuhkan rentan terhadap penalti kinerja. Menggunakan DTO memungkinkan kita mengekstrak data yang diperlukan saja. Dalam aplikasi ini kami mengandalkan SqlResultSetMapping , NamedNativeQuery .
Poin-poin penting:
SqlResultSetMapping , NamedNativeQueryjavax.persistence.Tuple Dan Native SQL Deskripsi: Mengambil lebih banyak data daripada yang dibutuhkan rentan terhadap penalti kinerja. Menggunakan DTO memungkinkan kita mengekstrak data yang diperlukan saja. Dalam aplikasi ini kami mengandalkan javax.persistence.Tuple dan SQL asli.
Poin-poin penting:
java.persistence.Tuple di repositori Spring dan tandai kueri sebagai nativeQuery = truejavax.persistence.Tuple dan JPQL Deskripsi: Mengambil lebih banyak data daripada yang dibutuhkan rentan terhadap penalti kinerja. Menggunakan DTO memungkinkan kita mengekstrak data yang diperlukan saja. Dalam aplikasi ini kami mengandalkan javax.persistence.Tuple dan JPQL.
Poin-poin penting:
java.persistence.Tuple di repositori SpringDeskripsi: Mengambil lebih banyak data daripada yang dibutuhkan rentan terhadap penalti kinerja. Menggunakan DTO memungkinkan kita mengekstrak data yang diperlukan saja. Dalam aplikasi ini kami mengandalkan Constructor Expression dan JPQL.
Poin-poin penting:
SELECT new com.bookstore.dto.AuthorDto(a.name, a.age) FROM Author a Lihat juga:
Cara Mengambil DTO Melalui Mekanisme Pembuat Kueri Data Pegas dan Konstruktor
| Jika Anda perlu mendalami resep kinerja yang dipaparkan dalam repositori ini maka saya yakin Anda akan menyukai buku saya "Praktik Terbaik Persistensi Boot Musim Semi" | Jika Anda memerlukan tip dan ilustrasi tentang 100+ masalah kinerja persistensi Java, maka "Panduan Ilustrasi Kinerja Persistensi Java" cocok untuk Anda. |
|
|
ResultTransformer Dan SQL Asli Deskripsi: Mengambil lebih banyak data daripada yang diperlukan rentan terhadap penalti kinerja. Menggunakan DTO memungkinkan kita mengekstrak data yang diperlukan saja. Dalam aplikasi ini kami mengandalkan Hibernate, ResultTransformer dan SQL asli.
Poin-poin penting:
AliasToBeanConstructorResultTransformer untuk DTO tanpa setter, tetapi dengan konstruktorTransformers.aliasToBean() untuk DTO dengan setterEntityManager.createNativeQuery() dan unwrap(org.hibernate.query.NativeQuery.class)ResultTransformer tidak digunakan lagi, tetapi hingga pengganti tersedia (mungkin di Hibernate 6.0) dapat digunakan (baca lebih lanjut)ResultTransformer dan JPQL Deskripsi: Mengambil lebih banyak data daripada yang diperlukan rentan terhadap penalti kinerja. Menggunakan DTO memungkinkan kita mengekstrak data yang diperlukan saja. Dalam aplikasi ini kami mengandalkan Hibernate, ResultTransformer dan JPQL.
Poin-poin penting:
AliasToBeanConstructorResultTransformer untuk DTO tanpa setter, dengan konstruktorTransformers.aliasToBean() untuk DTO dengan setterEntityManager.createQuery() dan unwrap(org.hibernate.query.Query.class)ResultTransformer tidak digunakan lagi, tetapi hingga pengganti tersedia (di Hibernate 6.0) dapat digunakan (baca lebih lanjut)Deskripsi: Mengambil lebih banyak data daripada yang dibutuhkan rentan terhadap penalti kinerja. Menggunakan DTO memungkinkan kita mengekstrak data yang diperlukan saja. Dalam aplikasi ini kami mengandalkan tampilan entitas Blaze-Persistence.
Poin-poin penting:
pom.xml dependensi khusus untuk Blaze-PersistenceCriteriaBuilderFactory dan EntityViewManagerEntityViewRepositoryfindAll() , findOne() , dll@ElementCollection Biasa (Tanpa @OrderColumn ). Deskripsi: Aplikasi ini mengungkapkan kemungkinan penalti kinerja menggunakan @ElementCollection . Dalam hal ini, tanpa @OrderColumn . Seperti yang Anda lihat di item berikutnya (34) menambahkan @OrderColumn dapat mengurangi beberapa penalti kinerja.
Poin-poin penting:
@ElementCollection tidak memiliki kunci utama@ElementCollection dipetakan dalam tabel terpisah@ElementCollection ketika Anda memiliki banyak sisipan/penghapusan pada koleksi ini; menyisipkan/menghapus akan menyebabkan Hibernate menghapus semua baris tabel yang ada, memproses koleksi di memori, dan memasukkan kembali baris tabel yang tersisa untuk mencerminkan koleksi dari memori Contoh keluaran: 
@ElementCollection Dengan @OrderColumn Deskripsi: Aplikasi ini mengungkapkan penalti kinerja menggunakan @ElementCollection . Dalam hal ini, dengan @OrderColumn . Namun, seperti yang Anda lihat di aplikasi ini (dibandingkan dengan item 33), dengan menambahkan @OrderColumn dapat mengurangi beberapa penalti kinerja ketika operasi dilakukan di dekat bagian pengumpulan (misalnya, menambah/menghapus di/dari akhir koleksi). Terutama, semua elemen yang terletak sebelum penambahan/penghapusan entri tidak disentuh, sehingga penalti kinerja dapat diabaikan jika kita memengaruhi baris yang dekat dengan ekor koleksi.
Poin-poin penting:
@ElementCollection tidak memiliki kunci utama@ElementCollection dipetakan dalam tabel terpisah@ElementCollection dengan @OrderColumn ketika Anda memiliki banyak sisipan dan penghapusan di dekat ekor koleksi Contoh keluaran: 
Catatan: Sebelum membaca item ini cobalah untuk melihat apakah Hibernate5Module bukan yang Anda cari.
Deskripsi: Sesi Terbuka di Lihat anti-pola diaktifkan secara default di SpringBoot. Sekarang, bayangkan sebuah asosiasi malas (misalnya, @OneToMany ) antara dua entitas, Author dan Book (seorang penulis telah mengaitkan lebih banyak buku). Selanjutnya, titik akhir pengontrol REST mengambil Author tanpa Book yang terkait. Namun, Tampilan (lebih tepatnya, Jackson), juga memaksa pemuatan Book terkait dengan lambat. Karena OSIV akan menyediakan Session yang sudah dibuka, inisialisasi proxy berhasil dilakukan. Solusi untuk menghindari penalti kinerja ini dimulai dengan menonaktifkan OSIV. Selanjutnya, inisialisasi asosiasi malas yang tidak diambil secara eksplisit. Dengan cara ini, Tampilan tidak akan memaksa pemuatan lambat.
Poin-poin penting:
application.properties : spring.jpa.open-in-view=falseAuthor dan menginisialisasi Book yang terkait secara eksplisit dengan nilai (default) (misalnya, null )@JsonInclude(Include.NON_EMPTY) pada tingkat entitas ini untuk menghindari rendering null atau apa yang dianggap kosong di JSON yang dihasilkan CATATAN: Jika OSIV diaktifkan, pengembang masih dapat menginisialisasi asosiasi malas yang tidak diambil secara manual selama ia melakukan ini di luar transaksi untuk menghindari pembilasan. Tapi, kenapa ini berhasil? Karena Session terbuka, mengapa inisialisasi asosiasi entitas terkelola secara manual tidak memicu flush? Jawabannya dapat ditemukan dalam dokumentasi OpenSessionInViewFilter yang menetapkan bahwa: Filter ini secara default tidak akan menghapus Hibernate Session , dengan mode flush diatur ke FlushMode.NEVER . Diasumsikan digunakan dalam kombinasi dengan transaksi lapisan layanan yang menangani pembilasan: Manajer transaksi yang aktif akan mengubah sementara mode pembilasan ke FlushMode.AUTO selama transaksi baca-tulis, dengan mode pembilasan diatur ulang ke FlushMode.NEVER di akhir dari setiap transaksi. Jika Anda ingin menggunakan filter ini tanpa transaksi, pertimbangkan untuk mengubah mode flush default (melalui properti "flushMode").
Deskripsi: Aplikasi ini adalah bukti konsep penggunaan Spring Projections (DTO) dan inner join yang ditulis melalui JPQL dan SQL asli (untuk MySQL).
Poin-poin penting:
Author dan Book dalam asosiasi @OneToMany dua arah (malas))resources/data-mysql.sql )AuthorNameBookTitle.java )
Deskripsi: Aplikasi ini adalah bukti konsep penggunaan Spring Projections (DTO) dan gabungan kiri yang ditulis melalui JPQL dan SQL asli (untuk MySQL).
Poin-poin penting:
Author , dan Book dalam asosiasi @OneToMany didirectional (malas))resources/data-mysql.sql )AuthorNameBookTitle.java )
Deskripsi: Aplikasi ini adalah bukti konsep untuk menggunakan proyeksi musim semi (DTO) dan hak bergabung yang ditulis melalui JPQL dan SQL asli (untuk MySQL).
Poin-poin penting:
Author , dan Book dalam asosiasi @OneToMany didirectional (malas))resources/data-mysql.sql )AuthorNameBookTitle.java )
Deskripsi: Aplikasi ini adalah bukti konsep untuk menggunakan Spring Proyections (DTO) dan inklusif gabungan penuh yang ditulis melalui JPQL dan SQL asli (untuk PostgreSQL).
Poin-poin penting:
Author , dan Book dalam asosiasi @OneToMany didirectional (malas))resources/data-mysql.sql )AuthorNameBookTitle.java )| Jika Anda memerlukan penyelaman mendalam ke resep kinerja yang terpapar di repositori ini maka saya yakin Anda akan menyukai buku saya "Spring Boot Persistence Best Practices" | Jika Anda membutuhkan tips dan ilustrasi 100+ masalah kinerja kegigihan Java maka "Java Performance Performance Illustrated Guide" adalah untuk Anda. |
|
|
Deskripsi: Aplikasi ini adalah bukti konsep untuk menggunakan Spring Proyections (DTO) dan eksklusif kiri bergabung yang ditulis melalui JPQL dan SQL asli (untuk MySQL).
Poin-poin penting:
Author , dan Book dalam asosiasi @OneToMany didirectional (malas))resources/data-mysql.sql )AuthorNameBookTitle.java )
Deskripsi: Aplikasi ini adalah bukti konsep untuk menggunakan proyeksi musim semi (DTO) dan hak eksklusif bergabung yang ditulis melalui JPQL dan SQL asli (untuk MySQL).
Poin-poin penting:
Author , dan Book dalam asosiasi @OneToMany didirectional (malas))resources/data-mysql.sql )AuthorNameBookTitle.java )
Deskripsi: Aplikasi ini adalah bukti konsep untuk menggunakan Spring Proyections (DTO) dan gabungan lengkap eksklusif yang ditulis melalui JPQL dan SQL asli (untuk PostgreSQL).
Poin-poin penting:
Author , dan Book dalam asosiasi @OneToMany didirectional (malas))resources/data-mysql.sql )AuthorNameBookTitle.java )Deskripsi: Aplikasi ini adalah bukti konsep untuk menggunakan kait pasca-komit musim semi dan bagaimana mereka dapat mempengaruhi kinerja lapisan kegigihan.
Poin-poin penting:
Deskripsi: Aplikasi ini adalah bukti konsep untuk menggunakan proyeksi musim semi (DTO) dan bergabung dengan entitas yang tidak terkait. Hibernate 5.1 memperkenalkan gabungan eksplisit pada entitas yang tidak terkait dan sintaks dan perilaku mirip dengan pernyataan SQL JOIN .
Poin-poin penting:
Author dan Book entitas yang tidak terkait)resources/data-mysql.sql )BookstoreDto )@EqualsAndHashCode dan @Data di Entitas dan Cara Mengganti equals() dan hashCode() Deskripsi: Entitas harus mengimplementasikan equals() dan hashCode() seperti di sini. Gagasan utamanya adalah bahwa Hibernate mensyaratkan bahwa suatu entitas sama dengan dirinya sendiri di semua transisi keadaannya ( transien , terpasang , terpisah dan dihapus ). Menggunakan lombok @EqualsAndHashCode (atau @Data ) tidak akan menghormati persyaratan ini.
Poin-poin penting:
Hindari pendekatan ini
@EqualsAndHashCode (entitas: LombokDefaultBook , tes: LombokDefaultEqualsAndHashCodeTest )@EqualsAndHashCode dengan kunci utama saja (entitas: LombokIdBook , tes: LombokEqualsAndHashCodeWithIdOnlyTest )equals() dan hashCode() (Entitas: DefaultBook , Tes: DefaultEqualsAndHashCodeTest )equals() dan hashCode() yang hanya berisi pengidentifikasi yang dihasilkan database (Entitas: IdBook , Test: IdEqualsAndHashCodeTest )Lebih suka pendekatan ini
BusinessKeyBook , Tes: BusinessKeyEqualsAndHashCodeTest )@NaturalId (entitas: NaturalIdBook , tes: NaturalIdEqualsAndHashCodeTest )IdManBook , Tes: IdManEqualsAndHashCodeTest )IdGenBook , Tes: IdGenEqualsAndHashCodeTest ) 
LazyInitializationException melalui JOIN FETCHLihat juga:
Deskripsi: Biasanya, ketika kita mendapatkan LazyInitializationException kita cenderung memodifikasi jenis pengambilan asosiasi dari LAZY menjadi EAGER . Itu sangat buruk! Ini adalah bau kode. Cara terbaik untuk menghindari pengecualian ini adalah dengan mengandalkan JOIN FETCH (jika Anda berencana untuk memodifikasi entitas yang diambil) atau JOIN + DTO (jika data yang diambil hanya dibaca). JOIN FETCH memungkinkan asosiasi diinisialisasi bersama dengan objek induknya menggunakan satu SELECT . Ini sangat berguna untuk mengambil koleksi terkait.
Aplikasi ini adalah contoh JOIN FETCH untuk menghindari LazyInitializationException .
Poin-poin penting:
Author , dan Book dalam asosiasi @OneToMany malas-miring)JOIN FETCH untuk mengambil seorang penulis termasuk buku -bukunyaJOIN FETCH (atau JOIN ) untuk mengambil buku termasuk penulisnya Contoh output: 
Deskripsi: Ini adalah contoh boot musim semi berdasarkan artikel berikut. Adalah implementasi fungsional dari contoh VLAD. Sangat disarankan untuk membaca artikel itu.
Poin-poin penting:
Deskripsi: Ini adalah contoh boot pegas yang mengeksploitasi kemampuan hibernate 5.2.10 untuk menunda akuisisi koneksi sesuai kebutuhan. Secara default, dalam mode sumber daya-lokal , koneksi database dibawa segera setelah memanggil metode yang dijelaskan dengan @Transactional . Jika metode ini berisi beberapa tugas yang memakan waktu sebelum pernyataan SQL pertama maka koneksi tetap terbuka untuk apa-apa. Tapi, Hibernate 5.2.10 memungkinkan kita untuk menunda akuisisi koneksi sesuai kebutuhan. Contoh ini bergantung pada hikaricp sebagai kumpulan koneksi default untuk boot musim semi.
Poin-poin penting:
spring.datasource.hikari.auto-commit=false di application.propertiesspring.jpa.properties.hibernate.connection.provider_disables_autocommit=true in application.properties Contoh output: 
hi/lo Hibernate Catatan: Jika sistem eksternal ke aplikasi Anda perlu memasukkan baris ke dalam tabel Anda maka jangan bergantung pada algoritma hi/lo karena, dalam kasus seperti itu, itu dapat menyebabkan kesalahan yang dihasilkan dari menghasilkan pengidentifikasi yang digandakan. Mengandalkan algoritma pooled atau pooled-lo (optimisasi hi/lo ).
Deskripsi: Ini adalah contoh boot musim semi menggunakan algoritma hi/lo untuk menghasilkan 1000 pengidentifikasi dalam 10 trip bundar database untuk batching 1000 sisipan dalam batch 30.
Poin-poin penting:
SEQUENCE (misalnya, di PostgreSQL)hi/lo seperti di entitas Author.java Contoh output: 
| Jika Anda memerlukan penyelaman mendalam ke resep kinerja yang terpapar di repositori ini maka saya yakin Anda akan menyukai buku saya "Spring Boot Persistence Best Practices" | Jika Anda membutuhkan tips dan ilustrasi 100+ masalah kinerja kegigihan Java maka "Java Performance Performance Illustrated Guide" adalah untuk Anda. |
|
|
@ManyToMany dua arah Deskripsi: Aplikasi ini adalah bukti konsep bagaimana benar untuk mengimplementasikan asosiasi @ManyToMany dua arah dari perspektif kinerja.
Poin-poin penting:
mappedBySet not ListCascadeType.PERSIST dan CascadeType.MERGE , tetapi hindari CascadeType.REMOVE/ALL@ManyToMany malas secara default; Simpanlah dengan cara ini!@NaturalId )) dan/atau pengidentifikasi yang dihasilkan database dan mengesampingkan (di kedua sisi) dengan benar metode equals() dan hashCode() seperti di sinitoString() perlu ditimpa, maka perhatikan hanya terlibat untuk atribut dasar yang diambil ketika entitas dimuat dari databaseSet daripada List di @ManyToMany Associations Deskripsi: Ini adalah contoh boot musim semi untuk menghapus baris jika ada daftar @ManyToMany menggunakan List , masing -masing Set . Kesimpulannya adalah Set itu jauh lebih baik! Ini juga berlaku untuk searah!
Poin-poin penting:
Set jauh lebih efisien dari List Contoh output: 
log4jdbcDeskripsi: Lihat detail kueri melalui log4jdbc.
Poin-poin penting:
pom.xml , tambahkan ketergantungan log4jdbc Sampel output: 
Deskripsi: Lihat parameter pengikatan/diekstraksi pernyataan yang disiapkan melalui TRACE .
Poin-poin penting:
application.properties Tambah: logging.level.org.hibernate.type.descriptor.sql=TRACE Sampel output: 
java.time.YearMonth sebagai Integer atau Date melalui perpustakaan tipe hibernate Deskripsi: Jenis Hibernate adalah seperangkat tipe tambahan yang tidak didukung secara default dalam inti hibernate. Salah satu jenis ini adalah java.time.YearMonth . Ini adalah aplikasi Spring Boot yang menggunakan tipe hibernate untuk disimpan YearMonth ini dalam database MySQL sebagai integer atau tanggal.
Poin-poin penting:
pom.xml@TypeDef untuk memetakan typeClass ke defaultForType Contoh output: 
CATATAN : Menggunakan fungsi SQL di bagian WHERE (bukan di bagian SELECT ) kueri di JPA 2.1 dapat dilakukan melalui function() seperti di sini.
Deskripsi: Mencoba menggunakan fungsi SQL (standar atau didefinisikan) dalam kueri JPQL dapat menghasilkan pengecualian jika hibernate tidak akan mengenalinya dan tidak dapat menguraikan kueri JPQL. Misalnya, fungsi MySQL, concat_ws tidak dikenali oleh Hibernate. Aplikasi ini adalah aplikasi Spring Boot berdasarkan Hibernate 5.3, yang mendaftarkan fungsi concat_ws melalui MetadataBuilderContributor dan menginformasikan hibernate tentang hal itu melalui, properti metadata_builder_contributor . Contoh ini menggunakan @Query dan EntityManager juga, sehingga Anda dapat melihat dua kasus penggunaan.
Poin-poin penting:
MetadataBuilderContributor dan Daftarkan Fungsi concat_ws MySQLapplication.properties , atur spring.jpa.properties.hibernate.metadata_builder_contributor untuk menunjukkan hibernate ke implementasi MetadataBuilderContributor Contoh output: 
Deskripsi: Aplikasi ini adalah sampel dari penebangan hanya kueri lambat melalui datasource-proxy . Kueri yang lambat adalah kueri yang memiliki waktu eksekusi lebih besar dari ambang batas tertentu dalam milidetik.
Poin-poin penting:
pom.xml ketergantungan datasource-proxyDataSourceDataSource Bean melalui ProxyFactory dan implementasi MethodInterceptorafterQuery() Contoh output: 
SELECT COUNT SubQuery and Return Page<dto> Deskripsi: Aplikasi ini mengambil data sebagai Page<dto> melalui pagination offset boot musim semi. Sebagian besar waktu, data yang harus paginated adalah data baca saja . Mengambil data ke dalam entitas harus dilakukan hanya jika kami berencana untuk memodifikasi data itu, oleh karena itu, mengambil hanya data baca sebagai Page<entity> tidak lebih disukai karena mungkin berakhir dengan penalti kinerja yang signifikan. SELECT COUNT yang dipicu untuk menghitung jumlah total catatan adalah subquery dari SELECT utama. Oleh karena itu, akan ada bundar basis data tunggal, bukan dua (biasanya, ada satu kueri yang diperlukan untuk mengambil data dan satu untuk menghitung jumlah total catatan).
Poin-poin penting:
PagingAndSortingRepositoryList<dto>List<dto> dan Pageable yang tepat untuk membuat Page<dto>SELECT COUNT Subquery dan List<dto> Deskripsi: Aplikasi ini mengambil data sebagai List<dto> melalui Pagination Offset Spring Boot. Sebagian besar waktu, data yang harus paginated adalah data baca saja . Mengambil data ke dalam entitas harus dilakukan hanya jika kami berencana untuk memodifikasi data itu, oleh karena itu, mengambil hanya data baca sebagai List<entity> tidak lebih disukai karena mungkin berakhir dengan penalti kinerja yang signifikan. SELECT COUNT yang dipicu untuk menghitung jumlah total catatan adalah subquery dari SELECT utama. Oleh karena itu, akan ada bundar basis data tunggal, bukan dua (biasanya, ada satu kueri yang diperlukan untuk mengambil data dan satu untuk menghitung jumlah total catatan).
Poin-poin penting:
PagingAndSortingRepositoryList<dto> Jika Anda menggunakan "starter" spring-boot-starter-jdbc atau starters spring-boot-starter-data-jpa , Anda secara otomatis mendapatkan ketergantungan ke hikaricp
Catatan: Cara terbaik untuk menyetel parameter kumpulan koneksi terdiri dari menggunakan kumpulan flexy oleh vlad mihalcea. Via Flexy Pool Anda dapat menemukan pengaturan optimal yang menopang kinerja tinggi dari kumpulan koneksi Anda.
Deskripsi: Ini adalah aplikasi kickoff yang mengatur hikaricp melalui application.properties saja. jdbcUrl diatur untuk database MySQL. Untuk tujuan pengujian, aplikasi menggunakan ExecutorService untuk mensimulasikan pengguna bersamaan. Periksa laporan HickariCP yang mengungkapkan status kumpulan koneksi.
Poin-poin penting:
application.properties , Rely on spring.datasource.hikari.* Untuk mengonfigurasi hikaricp Sampel output: 
| Jika Anda memerlukan penyelaman mendalam ke resep kinerja yang terpapar di repositori ini maka saya yakin Anda akan menyukai buku saya "Spring Boot Persistence Best Practices" | Jika Anda membutuhkan tips dan ilustrasi 100+ masalah kinerja kegigihan Java maka "Java Performance Performance Illustrated Guide" adalah untuk Anda. |
|
|
DataSourceBuilder Jika Anda menggunakan "starter" spring-boot-starter-jdbc atau starters spring-boot-starter-data-jpa , Anda secara otomatis mendapatkan ketergantungan ke hikaricp
Catatan: Cara terbaik untuk menyetel parameter kumpulan koneksi terdiri dari menggunakan kumpulan flexy oleh vlad mihalcea. Via Flexy Pool Anda dapat menemukan pengaturan optimal yang menopang kinerja tinggi dari kumpulan koneksi Anda.
Deskripsi: Ini adalah aplikasi kickoff yang mengatur HikariCp melalui DataSourceBuilder . jdbcUrl diatur untuk database MySQL. Untuk tujuan pengujian, aplikasi menggunakan ExecutorService untuk mensimulasikan pengguna bersamaan. Periksa laporan HickariCP yang mengungkapkan status kumpulan koneksi.
Poin-poin penting:
application.properties , konfigurasikan hikariCp melalui awalan khusus, misalnya, app.datasource.*@Bean yang mengembalikan DataSource Sampel output:
Aplikasi ini dirinci dalam artikel Dzone ini.
DataSourceBuilderCatatan: Cara terbaik untuk menyetel parameter kumpulan koneksi terdiri dari menggunakan kumpulan flexy oleh vlad mihalcea. Via Flexy Pool Anda dapat menemukan pengaturan optimal yang menopang kinerja tinggi dari kumpulan koneksi Anda.
Deskripsi: Ini adalah aplikasi kickoff yang mengatur BonECP melalui DataSourceBuilder . jdbcUrl diatur untuk database MySQL. Untuk tujuan pengujian, aplikasi menggunakan ExecutorService untuk mensimulasikan pengguna bersamaan.
Poin-poin penting:
pom.xml tambahkan ketergantungan bonecpapplication.properties , konfigurasikan bonecp melalui awalan khusus, misalnya, app.datasource.*@Bean yang mengembalikan DataSource Sampel output: 
DataSourceBuilderCatatan: Cara terbaik untuk menyetel parameter kumpulan koneksi terdiri dari menggunakan kumpulan flexy oleh vlad mihalcea. Via Flexy Pool Anda dapat menemukan pengaturan optimal yang menopang kinerja tinggi dari kumpulan koneksi Anda.
Deskripsi: Ini adalah aplikasi kickoff yang mengatur VibUrdBCP melalui DataSourceBuilder . jdbcUrl diatur untuk database MySQL. Untuk tujuan pengujian, aplikasi menggunakan ExecutorService untuk mensimulasikan pengguna bersamaan.
Poin-poin penting:
pom.xml tambahkan ketergantungan viburdbcpapplication.properties , konfigurasikan viburdbcp melalui awalan khusus, misalnya, app.datasource.*@Bean yang mengembalikan DataSource Sampel output: 
DataSourceBuilderCatatan: Cara terbaik untuk menyetel parameter kumpulan koneksi terdiri dari menggunakan kumpulan flexy oleh vlad mihalcea. Via Flexy Pool Anda dapat menemukan pengaturan optimal yang menopang kinerja tinggi dari kumpulan koneksi Anda.
Deskripsi: Ini adalah aplikasi kickoff yang mengatur C3P0 melalui DataSourceBuilder . jdbcUrl diatur untuk database MySQL. Untuk tujuan pengujian, aplikasi menggunakan ExecutorService untuk mensimulasikan pengguna bersamaan.
Poin-poin penting:
pom.xml tambahkan ketergantungan c3p0application.properties , konfigurasikan C3P0 melalui awalan khusus, misalnya, app.datasource.*@Bean yang mengembalikan DataSource Sampel output: 
DataSourceBuilderCatatan: Cara terbaik untuk menyetel parameter kumpulan koneksi terdiri dari menggunakan kumpulan flexy oleh vlad mihalcea. Via Flexy Pool Anda dapat menemukan pengaturan optimal yang menopang kinerja tinggi dari kumpulan koneksi Anda.
Deskripsi: Ini adalah aplikasi kickoff yang mengatur DBCP2 melalui DataSourceBuilder . jdbcUrl diatur untuk database MySQL. Untuk tujuan pengujian, aplikasi menggunakan ExecutorService untuk mensimulasikan pengguna bersamaan.
Poin-poin penting:
pom.xml tambahkan ketergantungan dbcp2application.properties , konfigurasikan dbcp2 melalui awalan khusus, misalnya, app.datasource.*@Bean yang mengembalikan DataSourceDataSourceBuilderCatatan: Cara terbaik untuk menyetel parameter kumpulan koneksi terdiri dari menggunakan kumpulan flexy oleh vlad mihalcea. Via Flexy Pool Anda dapat menemukan pengaturan optimal yang menopang kinerja tinggi dari kumpulan koneksi Anda.
Deskripsi: Ini adalah aplikasi kickoff yang mengatur Tomcat melalui DataSourceBuilder . jdbcUrl diatur untuk database MySQL. Untuk tujuan pengujian, aplikasi menggunakan ExecutorService untuk mensimulasikan pengguna bersamaan.
Poin-poin penting:
pom.xml tambahkan ketergantungan tomcatapplication.properties , konfigurasikan tomcat melalui awalan khusus, misalnya, app.datasource.*@Bean yang mengembalikan DataSource Sampel output: 
Catatan: Cara terbaik untuk menyetel parameter kumpulan koneksi terdiri dari menggunakan kumpulan flexy oleh vlad mihalcea. Via Flexy Pool Anda dapat menemukan pengaturan optimal yang menopang kinerja tinggi dari kumpulan koneksi Anda.
Deskripsi: Ini adalah aplikasi kickoff yang menggunakan dua sumber data (dua database MySQL, satu authorsdb bernama dan satu booksdb bernamaDB) dengan dua kumpulan koneksi (masing -masing database menggunakan kumpulan koneksi HikariCp sendiri dengan pengaturan yang berbeda). Berdasarkan item di atas cukup mudah untuk mengkonfigurasi dua kumpulan koneksi dari dua penyedia yang berbeda juga.
Poin-poin penting:
application.properties , konfigurasikan dua kumpulan koneksi hikaricp melalui dua awalan khusus, misalnya, app.datasource.ds1 dan app.datasource.ds2@Bean yang mengembalikan DataSource pertama dan tandai sebagai @Primary@Bean lain yang mengembalikan DataSource keduaEntityManagerFactory dan tunjukkan paket untuk memindai masing -masingEntityManager di paket yang tepat Sampel output: 
Catatan : Jika Anda ingin Anda memberikan API yang fasih tanpa mengubah setter maka pertimbangkan item ini.
Deskripsi: Ini adalah aplikasi sampel yang mengubah metode entitas setter untuk memberdayakan API yang lancar.
Poin-poin penting:
this alih -alih void di setter Contoh API yang fasih: 
Catatan : Jika Anda ingin Anda memberikan API yang fasih dengan mengubah setter, maka pertimbangkan item ini.
Deskripsi: Ini adalah aplikasi sampel yang menambah metode tambahan entitas (misalnya, untuk setName , kami menambahkan name ) metode untuk memberdayakan API yang lancar.
Poin-poin penting:
this bukannya void Contoh API yang fasih: 
| Jika Anda memerlukan penyelaman mendalam ke resep kinerja yang terpapar di repositori ini maka saya yakin Anda akan menyukai buku saya "Spring Boot Persistence Best Practices" | Jika Anda membutuhkan tips dan ilustrasi 100+ masalah kinerja kegigihan Java maka "Java Performance Performance Illustrated Guide" adalah untuk Anda. |
|
|
Slice<T> findAll() Kemungkinan besar hanya ini yang Anda inginkan: Cara Mengambil Slice<entity> / Slice<dto> via fetchAll / fetchAllDto
Beberapa implementasi Slice<T> findAll() :
"SELECT e FROM " + entityClass.getSimpleName() + " e;"CriteriaBuilder lebih baik alih-alih SQL yang dikodekan kerasSort , sehingga hasil penyortiran dimungkinkanSort dan Specification data pegasSort , LockModeType , QueryHints dan Specification data pegasPageable dan/atau Specification dengan memperluas SimpleJpaRepository dari data pegas. Bascially, implementasi ini adalah satu -satunya yang mengembalikan Page<T> alih -alih Slice<T> , tetapi tidak memicu SELECT COUNT tambahan karena dihilangkan dengan mengesampingkan Page<T> readPage(...) dari SimpleJpaRepository . Kelemahan utama adalah bahwa dengan memperbaiki Page<T> Anda tidak tahu apakah ada halaman berikutnya atau yang saat ini adalah yang terakhir. Namun demikian, ada solusi untuk memiliki ini juga. Dalam implementasi ini Anda tidak dapat mengatur LockModeType atau petunjuk kueri. Cerita : Spring Boot menyediakan mekanisme paging bawaan berbasis offset yang mengembalikan Page atau Slice . Masing -masing API ini mewakili halaman data dan beberapa metadata. Perbedaan utama adalah bahwa Page berisi jumlah total catatan, sementara Slice hanya dapat mengetahui apakah ada halaman lain yang tersedia. Untuk Page , Spring Boot menyediakan metode findAll() yang mampu mengambil sebagai argumen yang Pageable dan/atau Specification atau Example . Untuk membuat Page yang berisi jumlah total catatan, metode ini memicu SELECT COUNT di sebelah kueri yang digunakan untuk mengambil data halaman saat ini. Ini bisa menjadi penalti kinerja karena kueri SELECT COUNT dipicu setiap kali kami meminta halaman. Untuk menghindari query ekstra ini, Spring Boot menyediakan API yang lebih santai, Slice API. Menggunakan Slice alih -alih Page menghapus kebutuhan kueri SELECT COUNT tambahan ini dan mengembalikan halaman (catatan) dan beberapa metadata tanpa jumlah total catatan. Jadi, sementara Slice tidak tahu jumlah total catatan, masih dapat mengetahui apakah ada halaman lain yang tersedia setelah yang saat ini atau ini adalah halaman terakhir. Masalahnya adalah bahwa Slice berfungsi dengan baik untuk kueri yang berisi SQL, WHERE klausa (termasuk yang menggunakan mekanisme pembangun kueri yang dibangun ke dalam data musim semi), tetapi tidak berfungsi untuk findAll() . Metode ini masih akan mengembalikan Page alih -alih Slice oleh karena itu kueri SELECT COUNT dipicu untuk Slice<T> findAll(...); .
Deskripsi: Ini adalah serangkaian aplikasi sampel yang menyediakan versi berbeda dari metode Slice<T> findAll(...) . Kami memiliki dari implementasi minimalis yang bergantung pada kueri hardcoded sebagai: "SELECT e FROM " + entityClass.getSimpleName() + " e"; (Resep ini), untuk implementasi khusus yang mendukung penyortiran, spesifikasi, mode kunci, dan petunjuk permintaan untuk implementasi yang bergantung pada memperluas SimpleJpaRepository .
Poin-poin penting:
abstract yang mengekspos metode Slice<T> findAll(...) ( SlicePagingRepositoryImplementation )findAll() untuk mengembalikan Slice<T> (atau Page<T> , tetapi tanpa jumlah total elemen)SliceImpl ( Slice<T> ) atau PageImpl ( Page<T> ) tanpa jumlah total elemenreadSlice() baru atau mengganti halaman SimpleJpaRepository#readPage() untuk menghindari SELECT COUNTAuthor.class ) ke kelas abstract ini melalui repositori kelas ( AuthorRepository )COUNT(*) OVER dan Return List<dto> Deskripsi: Biasanya, dalam pagination offset, ada satu kueri yang diperlukan untuk mengambil data dan satu untuk menghitung jumlah total catatan. Tapi, kita dapat mengambil informasi ini dalam satu basis data rountrip melalui subquery SELECT COUNT terpilih di Main SELECT . Bahkan lebih baik, untuk vendor database yang mendukung fungsi jendela ada solusi yang mengandalkan COUNT(*) OVER() SELECT COUNT dalam aplikasi ini yang menggunakan fungsi jendela ini dalam kueri asli terhadap mysql 8. .
Poin-poin penting:
COUNT(*) OVER() Contoh: 
Deskripsi: Ketika kami mengandalkan paging offset , kami memiliki penalti kinerja yang disebabkan oleh membuang catatan sebelum mencapai offset yang diinginkan. N yang lebih besar mengarah pada penalti kinerja yang signifikan. Ketika kita memiliki N besar lebih baik mengandalkan pagination keyset yang mempertahankan waktu "konstan" untuk kumpulan data besar. Untuk memahami seberapa buruk Offset dapat melakukan, silakan periksa artikel ini:
Tangkapan layar dari artikel itu ( offset pagination): 
Perlu tahu apakah ada lebih banyak catatan?
Pada dasarnya, Keyset tidak menggunakan SELECT COUNT untuk mengambil jumlah total catatan. Tetapi, dengan sedikit tweak, kita dapat dengan mudah mengatakan jika ada lebih banyak catatan, oleh karena itu untuk menampilkan tombol tipe Next Page . Terutama, jika Anda membutuhkan hal seperti itu maka pertimbangkan aplikasi ini yang klimaksnya tercantum di bawah ini:
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);
}
Atau, seperti ini (bergantung pada metode Author.toString() ):
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);
}
Tombol Previous Page dapat diimplementasikan dengan mudah berdasarkan catatan pertama.
Poin-poin penting:
id )WHERE dan ORDER BY klausul SQL AndaDeskripsi: Ini adalah contoh Pagination Offset Spring Boot Klasik. Namun, tidak disarankan untuk menggunakan pendekatan ini dalam produksi karena hukuman kinerjanya dijelaskan lebih lanjut.
Ketika kami mengandalkan pagination offset , kami memiliki penalti kinerja yang disebabkan oleh membuang catatan sebelum mencapai offset yang diinginkan. N yang lebih besar mengarah pada penalti kinerja yang signifikan. Hukuman lain adalah ekstra- SELECT yang diperlukan untuk menghitung jumlah total catatan. Untuk memahami seberapa buruk pagination offset dapat dilakukan, silakan periksa artikel ini. Tangkapan layar dari artikel itu di bawah ini: Namun demikian, mungkin contoh ini sedikit ekstrem. Untuk kumpulan data yang relatif kecil, pagination offset tidak terlalu buruk (dekat dengan kinerja pagination keyset ), dan, karena Spring Boot menyediakan dukungan bawaan untuk pagination offset melalui Page API, sangat mudah untuk menggunakannya. Namun, tergantung pada kasusnya, kita dapat mengoptimalkan sedikit pagination offset seperti pada contoh -contoh berikut:
Ambil halaman sebagai Page :
COUNT(*) OVER dan return Page<dto>COUNT(*) OVER dan return Page<entity> melalui kolom tambahanSELECT COUNT Subquery and Return Page<dto>SELECT COUNT Subquery dan Return Page<entity> Via Extra ColumnSELECT COUNT Subquery and Return Page<projection> bahwa memetakan entitas dan jumlah total catatan melalui proyeksi Ambil halaman sebagai List :
COUNT(*) OVER dan return List<dto>COUNT(*) OVER dan return List<entity> melalui kolom tambahanSELECT COUNT Subquery dan List<dto>SELECT COUNT Subquery dan List<entity> melalui kolom tambahanSELECT COUNT Subquery dan List<projection> yang memetakan entitas dan jumlah total catatan melalui proyeksiTapi: Jika Pagination Offset menyebabkan masalah kinerja Anda dan Anda memutuskan untuk menggunakan pagination keyset maka silakan periksa di sini (pagination keyset ).
Poin -poin penting dari pagination offset klasik:
PagingAndSortingRepositoryPage<entity>Contoh pagination offset klasik:
findAll(Pageable) tanpa menyortir:repository.findAll(PageRequest.of(page, size));findAll(Pageable) dengan penyortiran: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); Deskripsi: Misalkan kita memiliki hubungan satu-ke-banyak antara Author dan entitas Book . Ketika kami menyimpan seorang penulis, kami menyimpan buku -bukunya juga berkat Cascading All/S semua. 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.
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.
Poin-poin penting:
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 pembaruanapplication.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 Output example: 
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=? ... penyataan. 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.
Poin-poin penting:
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 wellOutput example:
@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.
Poin-poin penting:
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. |
|
|
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.
Poin-poin penting:
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 Output sample: 
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.
Poin-poin penting:
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) Output sample: 
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.
Poin-poin penting:
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.
Poin-poin penting:
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 ) Output example: 
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.
Poin-poin penting:
AuthorRepository ) Contoh:
- 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.
Poin-poin penting:
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 .
Poin-poin penting:
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")Output example:
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.
Poin-poin penting:
@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 ).
Poin-poin penting:
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 .
Poin-poin penting:
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. |
|
|
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.
Poin-poin penting:
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.
Poin-poin penting:
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 .
Poin-poin penting:
@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.
Poin-poin penting:
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.
Poin-poin penting:
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 = ?") Output example: 
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.
Poin-poin penting:
@Bean that returns the DataSource programmaticallyDescription: Auditing is useful for maintaining history records. This can later help us in tracking user activities.
Poin-poin penting:
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.
Poin-poin penting:
@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.
Poin-poin penting:
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.
Poin-poin penting:
See also:
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. |
|
|
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.
Poin-poin penting:
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 FETCHSee also:
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 .
Poin-poin penting:
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 FETCHSee also:
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).
Poin-poin penting:
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.
Poin-poin penting:
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.
Poin-poin penting:
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 .
Poin-poin penting:
@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). Mengapa? 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.
Poin-poin penting:
@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 ).
Poin-poin penting:
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.
Poin-poin penting:
@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. |
|
|
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.
Poin-poin penting:
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.
Poin-poin penting:
@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.
Poin-poin penting:
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.
Poin-poin penting:
@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.
Poin-poin penting:
@Where(clause = "condition to be met") in entity (check the Author entity)Description: Batch inserts (in MySQL) in Spring Boot style.
Poin-poin penting:
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 Output example: 
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.
Poin-poin penting:
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).
Poin-poin penting:
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.
Poin-poin penting:
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.
Poin-poin penting:
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. |
|
|
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).
Poin-poin penting:
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 .
Poin-poin penting:
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> .
Poin-poin penting:
PagingAndSortingRepositoryList<dto>List<dto> and Pageable to create a Page<dto> Contoh:
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() .
Usage example:
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).
Poin-poin penting:
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.
Poin-poin penting:
@Bean that returns the DataSource programmatically Output sample:
@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.
Poin-poin penting:
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 .
Poin-poin penting:
@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 .
Poin-poin penting:
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:
Method 1:
columnDefinition element of @Column to specify as an SQL query expression the formula for calculating the persistent propertyMethod 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. |
|
|
@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 .
Poin-poin penting:
@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.
Poin-poin penting:
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.
Poin-poin penting:
@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.
Poin-poin penting:
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 .
Poin-poin penting:
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") .
Poin-poin penting:
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.
Poin-poin penting:
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 .
Poin-poin penting:
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.
Poin-poin penting:
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.
Poin-poin penting:
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. |
|
|
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 .
Poin-poin penting:
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.
Poin-poin penting:
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. . Lebih detailnya di sini.
Description: This is a sample application of using entity graphs in Spring Boot.
Poin-poin penting:
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. . Lebih detailnya di sini.
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 .
Poin-poin penting:
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. . Lebih detailnya di sini.
Description: This is a sample application of defining ad-hoc entity graphs in Spring Boot.
Poin-poin penting:
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.
Poin-poin penting:
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 .
Poin-poin penting:
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 Output example:
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.
Poin-poin penting:
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.
Poin-poin penting:
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 )
Poin-poin penting:
@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. |
|
|
TABLE_PER_CLASS Description: This application is a sample of JPA Table-per-class inheritance strategy ( TABLE_PER_CLASS )
Poin-poin penting:
IDENTITY generator@Inheritance(strategy=InheritanceType.TABLE_PER_CLASS)@MappedSuperclass Description: This application is a sample of using the JPA @MappedSuperclass .
Poin-poin penting:
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.
Poin-poin penting:
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.
Poin-poin penting:
application.properties append logger=Slf4JLogger&profileSQL=true to the JDBC URL (eg, jdbc:mysql://localhost:3306/bookstoredb?createDatabaseIfNotExist=true&logger=Slf4JLogger&profileSQL=true ) Output sample: 
Description: This application is an example of shuffling small results sets. DO NOT USE this technique for large results sets, since is extremely expensive.
Poin-poin penting:
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.
Poin-poin penting:
@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).
Poin-poin penting:
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. |
|
|
Description: This application is an example of calling a MySQL stored procedure that returns a value (eg, an Integer ).
Poin-poin penting:
@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> ).
Key points:
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.
Key points:
@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 .
Key points:
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. |
|
|
@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.
Key points:
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.
Poin-poin penting:
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.
Poin-poin penting:
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 Output sample: 
Description: This application is an example of finding top N rows of every group.
Key points:
ROW_NUMBER() window function Output sample: 
| 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. |
|
|
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 , ...).
Key points:
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 .
Key points:
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.
Key points:
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.
Key points:
@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.
Key points:
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. |
|
|
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.
Key points:
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.
Key points:
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.
Key points:
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.
Key points:
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.
Poin-poin penting:
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.
Key points:
@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. |
|
|
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.
Key points:
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>)] .
Key points:
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.
Key points:
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.
Key points:
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.
Key points:
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.
Key points:
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.
Key points:
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.
Key points:
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.
Key points:
@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. |
|
|
Description: This application is a sample of using dynamic Spring projections.
Key points:
<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.
Key points:
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 Output example:
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.
Key points:
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.
Key points:
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.
Key points:
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 Output example:
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:
Key points:
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.
Key points:
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.
Key points:
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 Output example:
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.
Key points:
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. |
|
|
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.
Key points:
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 aplikasi. 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.
Key points:
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.
Key points:
EhCache )@CacheHINT_CACHEABLE Description: This is a SpringBoot application representing a kickoff application for Spring Boot caching and EhCache .
Key points:
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[]> .
Key points:
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 .
Key points:
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 .
Key points:
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.
Key points:
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 ).
Key points:
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:
Key points:
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. |
|
|
@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.
Key points:
JOINED Inheritance Strategy And Visitor Design Pattern Description: This application is an example of using JPA JOINED inheritance strategy and Visitor pattern.
Key points:
JOINED Inheritance Strategy And Strategy Design Pattern Description: This application is an example of using JPA JOINED inheritance strategy and Strategy pattern.
Key points:
Description: This folder holds several applications that shows how each Spring transaction propagation works.
Key points:
GenerationType.AUTO And UUID Identifiers Description: This application is an example of using the JPA GenerationType.AUTO for assigning automatically UUID identifiers.
Key points:
BINARY(16) columnDescription: This application is an example of manually assigning UUID identifiers.
Key points:
BINARY(16) columnuuid2 For Generating UUID Identifiers Description: This application is an example of using the Hibernate RFC 4122 compliant UUID generator, uuid2 .
Key points:
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.
Key points:
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.
Key points:
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 .
Key points:
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. |
|
|
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.
Key points:
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.
Key points:
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.
Key points:
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.
Key points:
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.
Key points:
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.
Key points:
@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.
Poin-poin penting:
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.
Key points:
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.
Key points:
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.
Key points:
@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. |
|
|
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.
Key points:
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.
Key points:
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.
Key points:
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.
Key points:
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.
Key points:
<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).
Key points:
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 .
Key points:
JdbcTemplate , SimpleJdbcCall and BeanPropertyRowMapper@EntityListeners Description: This application is a sample of using the JPA @MappedSuperclass and @EntityListeners with JPA callbacks.
Key points:
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.
Key points:
@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.
Key points:
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 Output sample: 
| 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. |
|
|
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.
Key points:
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 Output sample: 
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.
Poin-poin penting:
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 Output sample: 
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.
Key points:
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.
Key points:
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();
Key points:
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) .
Key points:
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).
Key points:
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.
Key points:
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 .
Key points:
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.
Key points:
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.
Key points:
application.properties add hibernate.session.events.log.LOG_QUERIES_SLOWER_THAN_MS Output example: 
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.
Key points:
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.
Key points:
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.
Key points:
AuthorDto and BookDtoJdbcTemplate and ResultSetExtractorDescription: This application is a sample of using dynamic Spring projections via DTO classes.
Poin-poin penting:
<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. |
|
|
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.
Key points:
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).
Key points:
@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.
Key points:
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.
Key points:
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.
Key points:
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
Key points:
Contact )Author ) via @EmbeddedAuthorDto )
