Describe alternatives of JPA/Hibernate Whats ORM, pros/cons of JPA, Alternatives : Slick (Scala), jOOQ, Exposed (Kotlin), Requery (Java, Kotlin, Android)

1. Alternatives of JPA
CatalogTribe in Shanghai
debop@coupang.com 2018.01.16

2. Agenda
• What is ORM
• Pros / Cons of JPA
• Current status in Coupang
• Alternatives of JPA
• Slick
• jOOQ
• Exposed
• Requery

3. ORM (Object Relational Mapping)
• OOP’s object graph vs Relational Database
• Focus OOP, not Relational Database
• No matter of RDBMS vendor - Same code
• Hibernate coverage is 95% over traditional SQL statements
• ORM not suitable for data centric application in performance
• Why should you use an ORM?

4. Pros of JPA
• Focus to Java Object graph & OOP
• No need to know relations and constraints of entities
• No need to know specific DB features by various vendor
• No need to know SQL, just use Java API
• Supplement by HQL or JPQL or QueryDSL
• All support for Stateful, Stateless (Default is Stateful)

5. Cons of JPA
• If you knew SQL already, JPA is wired
• Hard to learning (exponential)
• Low performance by stateful and fetch by id
• No suitable for Bulk or Set operations
• Massive Insert, Statistical Summary (Cube …)
• Non-Threadsafe Session - Low throughput
• Need to learn specific JPAVendor (Hibernate, Eclipse Link)
• HQL, @DynamicInsert, @LazyCollection
• 2nd Cache (recommend JCache (JSR-305))

6. Current Status in Coupang
• No Deep Dive
• Missing override (hashCode, equals, toString)
• No using @NatualId
• Bad Policy / Bad Design
• Every entity has Own identifier (Some case no need)
• Poor performance -> Mislead “JPA is bad”
• Apply not suitable case
• Bulk operations, Statistical operations
• No use supplement features
• StatelessSession, 2nd Cache …

7. Features in Alternatives of JPA
• Design Principle
• OOP based, Support Multi DBVendor
• No need stateful for Reference object
• Support association, inheritance, converter in JPA
• Performance
• Speed up like Plain SQL
• Stateless
• Support Asynchronous or Reactive
• Support Bulk or Batch operations

8. Slick
ORM for Scala

9. Slick
• Database access library
• Not ORM -> Functional Relational Mapping
• Bring relational model to Scala OOP
• Natural fit ( no impedance mismatch)
• Stateless
• Matures (Slick version 3) (Non-blocking DBIO)
• Essential Slick Book

10. Slick - schema definition
Entity
Schema
Definition

11. Slick - Query
// Get Users with age > 20
Query statement
Async
Operations

12. Slick - DML

13. Slick -Transactions
Define Repository
Async
Execution with
Transaction

14. Slick - Plain SQL
ResultSet to
Tuple
ResultSet to
Entity

15. jOOQ
Java Object Oriented Query

16. jOOQ
• Reflect Database Schema to generate Entity Class
• Typesfe SQL (akaTypesafe MyBatis)
• Database First (Not ORM)
• Stateless
• Need DBMS Owner Authority
• jOOQ vs Hibernate :When to choose which

17. jOOQ - Generate Codes

18. jOOQ - typesafe SQL
SELECT * FROM BOOK
WHERE BOOK.PUBLISHED_IN = 2011
ORDER BY BOOK.TITLE
create.selectFrom(BOOK)
.where(BOOK.PUBLISHED_IN.eq(2011))
.orderBy(BOOK.TITLE)
select().from(t).where(t.a.eq(select(t2.x).from(t2));
// Type-check here: ---------------> ^^^^
select().from(t).where(t.a.eq(any(select(t2.x).from(t2)));
// Type-check here: -------------------> ^^^^
select().from(t).where(t.a.in(select(t2.x).from(t2));
// Type-check here: ---------------> ^^^^
select(t1.a).from(t1).unionAll(select(t2.a).from(t2));
// Type-check here: ----------------> ^^^^
select(t1.a, t1.b).from(t1).union(select(t2.a, t2.b).from(t2));
// Type-check here: -------------------> ^^^^^^^^^^
Predicates
Set operations

19. requery
ORM for Java & Kotlin & Android

20. requery
• No reflection (apt code generation) - Fast instancing
• Fast startup & performance
• Schema generation
• Blocking / Non-blocking API (Reactive with RxJava)
• Support partial object / refresh / upsert
• Custom type converter like JPA
• Compile time entity validation
• Support almost JPA annotations

21. @Entity
abstract class AbstractPerson {
@Key @Generated int id;
@Index("name_index") // table specification
String name;
@OneToMany // relationships 1:1, 1:many, many to many
Set<Phone> phoneNumbers;
@Converter(EmailToStringConverter.class)
Email email;
@PostLoad // lifecycle callbacks
void afterLoad() { updatePeopleList(); }
// getter, setters, equals & hashCode automatically generated into Person.java
}
requery - define entity
Identifier
Entity class
Converter
Listeners

22. Result<Person> query = data
.select(Person.class)
.where(Person.NAME.lower().like("b%"))
.and(Person.AGE.gt(20))
.orderBy(Person.AGE.desc())
.limit(5)
.get();
Observable<Person> observable = data
.select(Person.class)
.orderBy(Person.AGE.desc())
.get()
.observable();
requery - query
Query by Fluent API
Reactive Programming
Cold Observable
Non blocking

23. @Entity(model = "tree")
interface TreeNode {
@get:Key
@get:Generated
val id: Long
@get:Column
var name: String
@get:ManyToOne(cascade = [DELETE])
var parent: TreeNode?
@get:OneToMany(mappedBy = "parent", cascade = [SAVE, DELETE])
val children: MutableSet<TreeNode>
}
requery - self refence by Kotlin
Identifier
Entity class
1:N, N:1
Cascade

24. requery - Blob/Clob usage
class ByteArrayBlobConverter : Converter<ByteArray, Blob> {
override fun getPersistedSize(): Int? = null
override fun getPersistedType(): Class<Blob> = Blob::class.java
override fun getMappedType(): Class<ByteArray> = ByteArray::class.java
override fun convertToMapped(type: Class<out ByteArray>?, value: Blob?): ByteArray? {
return value?.binaryStream?.readBytes()
}
override fun convertToPersisted(value: ByteArray?): Blob? {
return value?.let { SerialBlob(it) }
}
}

25. requery - Blob property
@Entity(model = "kt")
interface BigModel {
@get:Key
@get:Generated
@get:Column(name = "model_id")
val id: Int
@get:Column(name = "model_name")
var name: String?
@get:Convert(value = ByteArrayBlobConverter::class)
@get:Column(name = "model_picture")
var picture: ByteArray?
}
Blob column

26. Exposed
Kotlin SQL Framework

27. Exposed - Kotlin SQL Framework
• Lightweight SQL Library
• Provide two layers of data access
• Typesafe SQL wrapping DSL
• Lightweight Data Access Object
• See : First steps with Kotlin/Exposed
• Cons
• Not support parameterized SQL

28. Exposed - SQL DSL
object Users : Table() {
val id = varchar("id", 10).primaryKey() // Column<String>
val name = varchar("name", length = 50) // Column<String>
val cityId = (integer("city_id") references Cities.id).nullable() // Column<Int?>
}
object Cities : Table() {
val id = integer("id").autoIncrement().primaryKey() // Column<Int>
val name = varchar("name", 50) // Column<String>
}

29. Exposed - SQL DSL
val munichId = Cities.insert {
it[name] = "Munich"
} get Cities.id
Cities.insert { it[name] = "Prague" }
Users.insert {
it[id] = "andrey"
it[name] = "Andrey"
it[cityId] = saintPetersburgId
}
Users.insert {
it[id] = "sergey"
it[name] = "Sergey"
it[cityId] = munichId
}

30. Exposed - SQL DSL - Join
(Users innerJoin Cities)
.slice(Users.name, Cities.name)
.select {
(Users.id.eq("andrey") or Users.name.eq("Sergey")) and
Users.id.eq("sergey") and Users.cityId.eq(Cities.id)
}
.forEach {
println("${it[Users.name]} lives in ${it[Cities.name]}")
}

31. Exposed - SQL DSL - Join 2
((Cities innerJoin Users)
.slice(Cities.name, Users.id.count())
.selectAll()
.groupBy(Cities.name))
.forEach {
val cityName = it[Cities.name]
val userCount = it[Users.id.count()]
if (userCount > 0) {
println("$userCount user(s) live(s) in $cityName")
} else {
println("Nobody lives in $cityName")
}
}

32. Exposed - DAO
object Users : IntIdTable() {
val name = varchar("name", 50).index()
val city = reference("city", Cities)
val age = integer("age")
}
object Cities: IntIdTable() {
val name = varchar("name", 50)
}
Schema Definition
class User(id: EntityID<Int>) : IntEntity(id){
companion object : IntEntityClass<User>(Users)
var name by Users.name
var city by City referencedOn Users.city
var age by Users.age
}
class City(id: EntityID<Int>) : IntEntity(id) {
companion object : IntEntityClass<City>(Cities)
var name by Cities.name
val users by User referrersOn Users.city
}
Entity Definition

33. Exposed - DAO Usage
val munich = City.new {
name = "Munich"
}
User.new {
name = "a"
city = munich
age = 5
}
User.new {
name = "b"
city = munich
age = 27
}
munich.users.joinToString { it.name }
User.find { Users.age.between(18, 60) }
OneTo Many
All user’s name in Munich

34. Conclusion
• Already legacy database exists ? Use only Java
• jOOQ or requery
• Scala only ? -> Slick
• Kotlin only ? -> requery, Exposed
• No matter language? -> requery
• Need Reactive programming? ->
• requery with kotlinx-requery
• kotlinx-rxjava2-jdbc ( we will open March )

35. Thank you!