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JPA Best Practices - Optimizing JPA Mappings

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JPA Best Practices - Optimizing JPA Mappings

JPA and CockroachDB - Part 3 of 4

Kai Niemi's photo
Kai Niemi
·Jul 24, 2022·

11 min read

Table of contents

Introduction

This article is part three of a series of data access best practices when using JPA and CockroachDB. The goal is to help reduce the impact of workload contention and to optimise performance. Although most of the principles are fairly framework and database agnostic, it’s mainly targeting the following technology stack:

  • JPA 2.x and Hibernate
  • CockroachDB v22+
  • Spring Boot
  • Spring Data JPA
  • Spring AOP with AspectJ

Example Code

The code examples are available in Github.

Chapter 3: Optimizing JPA Mappings

JPA is a set of standard JakartaEE (aka JavaEE aka J2EE) interfaces, while Hibernate is a JPA vendor implementation that also provides a non-standard interface.

Object-relational-mapping frameworks such as Hibernate offer a lot of value for rich domain/entity models, but also a lot of hidden complexity. This complexity is partly due to the impedance mismatch between the relational world and the object-oriented world but also due to the very rich feature set.

While ORMs often make sense in improving development efficiency, it’s not always visible what goes on under the hood and that can result in negative performance characteristics. Mainly in the form of inefficient SQL queries.

3.1 How to use JPA and Hibernate for best performance

Problem

You want to know how to optimize JPA/Hibernate for best performance.

Solution

Let’s begin with database reads. The key to high read performance is to avoid extremes, in terms of both under fetching and over fetching data. This can only be done through careful analysis of the data access patterns and underlying schema. One starting point is to adopt lazy-loading everywhere and then eager join fetch as an exception in order to reduce the N+1 effect.

For example, if you have Order, OrderItem and Product entities linked together to form an aggregate (like purchase order), and know you will always read the full domain aggregate, then a join fetch strategy could be appropriate. Otherwise, JPA will first fetch the order, then do a lazy-initialized query for the items and then again for products attached to the items. A join fetch could be more efficient in that case, or a sub-select fetch strategy depending on the mapping. When domain aggregates are fairly small and retrieved as a whole, then it’s a good case argument for join fetching. Otherwise just fall back to lazy fetching.

For best write performance, using batch updates and multi-value INSERTs will make a big difference. There are a few pre-conditions for batching to be enabled, and it’s disabled by default. One is you need to set specific configuration parameters and also avoid the IDENTITY primary id generation strategy (covered in separate section).

In total, some common pitfalls include:

  1. Eagerly fetching too much

    Cartesian products and excessive cross joins (avoided by lazy fetching over eager)

  2. Lazy fetching too little

    Excessive amounts of N+1 queries (avoided by join fetching where needed)

  3. Cascading N+1 selects due to entity graph dependencies

    Unpredictable behaviour / performance (use lazy fetching as a default)

  4. Lazy initialization errors

    Accessing lazy-initialized proxies outside of transaction scope (lazy initialize proxies, high test coverage is mandatory)

  5. Conversational state vs transactions

    Open-session-in-view (avoid always, use lazy fetching and initialization instead).

    Attached vs detached entities vs value objects (no need to read before update or insert, use reference loading).

  6. 2nd level cache write-through and eviction

    Scales reads efficiently but adds additional complexity, cache inconsistency and eviction challenges (anomalies and stale reads).

    Distributed second-level caching is complex, rarely pays off.

Entity Life Cycle

To understand JPA tuning it helps to first understand the entity life cycle or state model (transient, managed, detached, removed) and the API details.

Developer Training - Java Persistence API.jpg

For example, you don’t need to read dependent entities with a SELECT when adding new entities by using getById (in Spring’s JPA repository) over findById. There are low-hanging fruits like that that can make a significant difference in performance.

The entity state model is relevant when entities are passed across the transaction boundary. If a lazy-loaded attribute is accessed without a transaction context, you will get the infamous lazy loading exception.

Caching

The first-level cache is one good performance reason for using JPA over plain JDBC due to the flush mode at commit-time and de-duplications. Hibernate does not send statements over the wire until it’s time to commit and de-duplicates redundant UPDATE’s (if any) before doing so. In contrast to plain JDBC where all statements are sent across the wire.

Illustrations below:

Developer Training - Java Persistence API (1).jpg

Developer Training - Java Persistence API (2).jpg

Developer Training - Java Persistence API (3).jpg

Developer Training - Java Persistence API (4).jpg

The second-level cache can also use a distributed cache provider, but there are many caveats with that around cache invalidation. Second-level caching strategies are out of scope for this documentation.

Batch Inserts and Updates

To optimize write throughput, enable batching and multi-value inserts with the following configuration settings:

  • hibernate.order_updates=true
  • hibernate.order_inserts=true
  • hibernate.jdbc.batch_versioned_data=true
  • hibernate.jdbc.batch_size=64 // size depends on schema+workload

The reWriteBatchedInserts data source property converts multiple INSERTs to multi-value INSERTs, increasing write performance with 30-40% in most cases once you discover the optimal batch size (schema and workload dependent).

Notice that reWriteBatchedInserts parameter is case sensitive!

See also section 3.2 around ID generation and its impact on batch INSERTs.

Example code:

@Bean
public HikariDataSource hikariDataSource() {
   HikariDataSource ds = properties
           .initializeDataSourceBuilder()
           .type(HikariDataSource.class)
           .build();
   ds.setAutoCommit(false);
   ds.addDataSourceProperty("reWriteBatchedInserts", "true"); // Case sensitive
   ds.addDataSourceProperty("application_name", "My App");
   return ds;
}

Setting JPA vendor properties programmatically:

private Properties jpaVendorProperties() {
   return new Properties() {
       {
           setProperty(Environment.STATEMENT_BATCH_SIZE, "64");
           setProperty(Environment.ORDER_INSERTS, "true");
           setProperty(Environment.ORDER_UPDATES, "true");
           setProperty(Environment.BATCH_VERSIONED_DATA, "true");
           setProperty(Environment.GENERATE_STATISTICS, "true");
           setProperty(Environment.LOG_SESSION_METRICS, "false");
           setProperty(Environment.CACHE_REGION_FACTORY, NoCachingRegionFactory.class.getName());
           setProperty(Environment.USE_SECOND_LEVEL_CACHE, "false");
           setProperty(Environment.USE_MINIMAL_PUTS, "true");
           setProperty(Environment.FORMAT_SQL, "false");
setProperty(Environment.CONNECTION_PROVIDER_DISABLES_AUTOCOMMIT, "true");
           setProperty(Environment.NON_CONTEXTUAL_LOB_CREATION, "true");
       }
   };
}

OSIV

Avoid the open-session-in-view (OSIV) anti-pattern. It violates the principle of least surprise and adds an increasing cost down the road. Rather than taking the OSIV shortcut to overcome the lazy-initialization problem, adopt a suitable fetch strategy and map the entity objects to value objects using resource assemblers when passed over logical boundaries.

Generated SQL

Hibernate generates quite verbose SQL based on the entity mappings and JPA queries (JPQL). It’s important to take a closer look at the generated SQL queries and be observant of things like large table scans and join types. Index joins can for example be avoided with covering indexes and projection.

One method is to use the CockroachDB Console. Another is to enable client side logging using the DataSource proxy pattern, often more readable than Hibernates internal logging. One popular library for this purpose is TTDDYY that wraps a logging proxy around the data source(s).

private final Logger traceLogger = LoggerFactory.getLogger("SQL_TRACE");


@Bean
@Primary
public DataSource primaryDataSource() {
   DataSource ds = hikariDataSource();
   return traceLogger.isTraceEnabled()
           ? ProxyDataSourceBuilder
           .create(ds)
           .asJson()
           .logQueryBySlf4j(SLF4JLogLevel.TRACE, traceLogger.getName())
           .build()
           : ds;
}

Hibernate Dialect

Enable the CockroachDB Hibernate dialect:

  • CockroachDB201Dialect (Hibernate JavaDocs)
  • Build a Spring App with CockroachDB and JPA

Example of application.yml:

spring:
 jpa:
   properties:
     hibernate:
       dialect: org.hibernate.dialect.CockroachDB201Dialect
 datasource:
   url: jdbc:postgresql://localhost:26257/spring_boot?sslmode=disable
   driver-class-name: org.postgresql.Driver
   username: root
   password:

or programmatically:

@Bean
public LocalContainerEntityManagerFactoryBean entityManagerFactory(@Autowired DataSource dataSource) {
   LocalContainerEntityManagerFactoryBean emf = new LocalContainerEntityManagerFactoryBean();
   emf.setPersistenceUnitName(getClass().getSimpleName());
   emf.setDataSource(dataSource);
   emf.setPackagesToScan("com.example.app");
   emf.setJpaVendorAdapter(jpaVendorAdapter());
   emf.setJpaProperties(jpaVendorProperties());
   return emf;
}


private JpaVendorAdapter jpaVendorAdapter() {
   HibernateJpaVendorAdapter vendorAdapter = new HibernateJpaVendorAdapter();
   vendorAdapter.setDatabasePlatform(CockroachDB201Dialect.class.getName());
   vendorAdapter.setDatabase(Database.POSTGRESQL);
   return vendorAdapter;
}

3.2 How to generate Identifiers

Choosing the appropriate unique ID strategy is different in CockroachDB than in other single-leader databases. To prevent range and node hotspots from forming, it’s recommended to use IDs that are not sequential in nature like SEQUENCE and the SERIAL pseudo-type.

The JPA entity mappings should reflect the strategy chosen, which is done through the @GeneratedValue annotation on an @Id column. Given the above however, the strategy options are a bit more narrow and generating surrogate IDs also has some implications around batching due to how write-behind works in Hibernate.

Problem

You want to know which ID generation strategy that works best for performance.

Solution

Unless incremental sequences (still with potential gaps) is preferred or required, use an auto-generated unique ID, preferably UUIDs:

@Id
@GeneratedValue(generator = "UUID")
@GenericGenerator(name = "UUID", strategy = "org.hibernate.id.UUIDGenerator")
private UUID id;

Avoid using the IDENTITY generator strategy because Hibernate disables batch support for entities using it due to how the write-behind cache works:

@Id
@GeneratedValue(strategy = GenerationType.IDENTITY)
@Column(name = "id")
private Long id;

If you need to use batch inserts while using generated identity columns, then JDBC is the only real option.

Discussion

JPA supports four different primary key generation strategies:

  1. Auto - The persistence provider (Hibernate) picks the appropriate strategy guided by the configured database dialect and data type of the ID.
  2. Identity - Uses an auto-incremented column. In CockroachDB, you would need to adjust the current Hibernate dialect to use this (see below).
  3. Sequence - Uses a database sequence. It’s supported by CockroachDB but it’s not what you want to use for best performance.
  4. Table - Simulation of a sequence (legacy - avoid)

In addition, Hibernate supports custom ID generator strategies via @GenericGenerator.

One example is UUIDs that you can then generate client side and have assigned to the entity:

@Id
@GeneratedValue(generator = "AUTO")
private UUID id;

is equivalent to:

@Id
@GeneratedValue(generator = "UUID")
@GenericGenerator(name = "UUID", strategy = "org.hibernate.id.UUIDGenerator")
private UUID id;

Closely related to primary IDs are non-surrogate, natural IDs. You can also leverage natural IDs like user emails, ISBN number for books, SKU code for products, etc using Hibernates native API:

@NaturalId
private String sku;

Hibernate will then use a few optimizations around caching (and enforce immutability) for these IDs.

Don’t forget to add a secondary index to any natural IDs.

Lastly, if you need to use a real type for primary ID along with the IDENTITY generation strategy, and can live without Hibernate support for batch INSERTs, then a custom dialect is needed:

public class CockroachDB221Dialect extends CockroachDB201Dialect {
   @Override
   public IdentityColumnSupport getIdentityColumnSupport() {
       return new CockroachDB221IdentityColumnSupport();
   }


   private static class CockroachDB221IdentityColumnSupport extends CockroachDB1920IdentityColumnSupport {
       @Override
       public String getIdentityInsertString() {
           return "unique_rowid()";
       }
   }
}
@Id
@GeneratedValue(strategy = GenerationType.IDENTITY)
@Column(name = "id")
private Long id;

The generated SQL will then include unique_rowid() in the parameter binding of the INSERTs (column id is primary id):

INSERT INTO account(id,a,b,c) VALUES (unique_rowid(),?,?,?)

However, this disables support for batching and multi-value INSERTs. To get that, the real option is to use JDBC for critical sections. With Spring, using JDBC against JPA/Hibernate entities is quite straightforward when using the SimpleJdbcInsert template:

private SimpleJdbcInsert jdbcInsert;


@PostConstruct
public void afterInit() {
   this.jdbcInsert = new SimpleJdbcInsert(dataSource)
           .withTableName("t_account")
           .usingColumns("balance")
           .usingGeneratedKeyColumns("id");
}


@Override
@Transactional(propagation = REQUIRES_NEW)
public void create(List<AccountEntity> accounts) {
   MapSqlParameterSource[] parameters = accounts
           .stream()
           .map(account -> new MapSqlParameterSource()
                   .addValue("balance", account.getBalance()))
           .toArray(MapSqlParameterSource[]::new);


   int[] rowsAffected = this.jdbcInsert.executeBatch(parameters);


   Arrays.stream(rowsAffected).sequential().forEach(value -> {
       if (value != SUCCESS_NO_INFO) {
           throw new IncorrectResultSizeDataAccessException(1, value);
       }
   });
}

3.3 How to optimize static entity mapping

Static entity mapping refers to the JPA and Hibernate annotations used in domain entity objects.

Problem

You want to know how to optimize JPA/Hibernate entity mapping for best performance.

Solution

  • Always use lazy fetching by default, eager fetching only after close analysis:
@OneToMany(orphanRemoval = true, mappedBy = "transaction", fetch = FetchType.LAZY)
private List<TransactionItem> items;
  • Use eager fetching in JPA queries only, not in the entity mappings:
@Query(value = "from Order o "
       + "join fetch o.customer c "
       + "where c.userName=:userName")
List<Order> findOrdersByUserName(@Param("userName") String userName);
  • Avoid inheritance mapping if possible (performance cost).

  • Only use field level annotations for readability.

  • Use persistence cascades carefully (delete’s in particular).

  • Avoid mapping ManyToMany associations using java.util.List (use a Set instead).

  • Don’t expose more state than needed (no automatic getter/setter for everything).

  • Strive for immutability with @Immutable (disables dirty checking).

  • Use defensive copies:

    return new Date(date) return Collections.unmodifiableList(..)

  • If entities are not used in remoting/detached state then don’t implement java.io.Serializable.

  • Avoid business logic in entity objects, move to service/control objects instead.

Discussion

Choosing the appropriate fetch strategy is key to avoid both the N+1 select problem with excessive lazy-loading and the cartesian product problem with excessive joins.

It’s basically a spectrum where the extremes are chatty and small SQL statements vs over-fetching at the other end. Finding the optimal balance takes some careful considerations and analysis of the SQL generated by the framework.

One challenge also is that JPA and Hibernate differ in the fetch strategy. Hibernate since 3.x defaults to using only lazy-loading on related entities while JPA keeps the “old” model where X-to-many uses lazy-fetching and X-to-one uses eager-fetching. The solution is to always set “fetch” property on association mappings to lazy, even when it’s the default.

Example of lazy fetching strategy for en element collection (items to order):

@TransactionBoundary(readOnly = true)
public List<Order> findOrdersByStatus(ShipmentStatus status, int limit) {
   Assert.isTrue(TransactionSynchronizationManager.isActualTransactionActive(), "No tx");


   List<Order> orders = orderRepository.findByShipmentStatus(status,
           Pageable.ofSize(limit)).getContent();
   // Lazy-fetch associations using a join strategy
   orders.forEach(order -> order.getOrderItems().size());
   return orders;
}


@Repository
public interface OrderRepository extends JpaRepository<Order, UUID> {
   @Query(value = "select o from Order o "
           + "where o.status=:status ",
           countQuery = "select count(o.id) from Order o "
                   + "where o.status=:status")
   Page<Order> findByShipmentStatus(@Param("status") ShipmentStatus status, Pageable page);
}




@Entity
@Table(name = "orders")
@TypeDef(name = "custom_enum", typeClass = ShipmentStatusEnumType.class)
public class Order extends AbstractEntity<UUID> {
@ElementCollection(fetch = FetchType.LAZY)
@JoinColumn(name = "order_id", nullable = false)
@JoinTable(name = "order_items", joinColumns = @JoinColumn(name = "order_id"))
@OrderColumn(name = "item_pos")
private List<OrderItem> orderItems = new ArrayList<>();
}


@Embeddable
public class OrderItem {
@ManyToOne(fetch = FetchType.LAZY) // Default fetch type is EAGER for @ManyToOne
@JoinColumn(name = "product_id", updatable = false)
@Fetch(FetchMode.JOIN)
private Product product;
}

Conclusion

In this article we looked at a few JPA and Hibernate mapping optimizations to reduce the effects of workload contention.

 
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