Spring + hibernate : Query Plan Cache Memory usage

Question

我正在使用最新版本的 Spring Boot 编写应用程序。我最近遇到了堆增长的问题，即不能被垃圾收集。使用 Eclipse MAT 对堆的分析表明，在运行应用程序的一小时内，堆增长到 630MB，而 Hibernate 的 SessionFactoryImpl 使用了整个堆的 75% 以上。

我正在寻找可能的查询计划缓存周围的资源，但我发现的唯一内容是 this ，但这并没有发挥出来。属性设置如下:

spring.jpa.properties.hibernate.query.plan_cache_max_soft_references=1024
spring.jpa.properties.hibernate.query.plan_cache_max_strong_references=64

数据库查询都是由 Spring 的查询魔法生成的，使用像 in this documentation 这样的存储库接口(interface)。 .使用这种技术生成了大约 20 个不同的查询。没有使用其他 native SQL 或 HQL。示例:

@Transactional
public interface TrendingTopicRepository extends JpaRepository<TrendingTopic, Integer> {
    List<TrendingTopic> findByNameAndSource(String name, String source);
    List<TrendingTopic> findByDateBetween(Date dateStart, Date dateEnd);
    Long countByDateBetweenAndName(Date dateStart, Date dateEnd, String name);
}

或

List<SomeObject> findByNameAndUrlIn(String name, Collection<String> urls);

作为 IN 用法的示例。

问题是:为什么查询计划缓存一直在增长(它不会停止，它会以满堆结束)以及如何防止这种情况？有没有人遇到过类似的问题？

版本:

• Spring Boot 1.2.5
• hibernate 4.3.10

Answer 1

我也遇到过这个问题。它基本上归结为在您的 IN 子句中具有可变数量的值，并且 Hibernate 试图缓存这些查询计划。

关于这个主题有两篇很棒的博客文章。 The first :

Using Hibernate 4.2 and MySQL in a project with an in-clause query such as: select t from Thing t where t.id in (?)

Hibernate caches these parsed HQL queries. Specifically the Hibernate SessionFactoryImpl has QueryPlanCache with queryPlanCache and parameterMetadataCache. But this proved to be a problem when the number of parameters for the in-clause is large and varies.

These caches grow for every distinct query. So this query with 6000 parameters is not the same as 6001.

The in-clause query is expanded to the number of parameters in the collection. Metadata is included in the query plan for each parameter in the query, including a generated name like x10_, x11_ , etc.

Imagine 4000 different variations in the number of in-clause parameter counts, each of these with an average of 4000 parameters. The query metadata for each parameter quickly adds up in memory, filling up the heap, since it can't be garbage collected.

This continues until all different variations in the query parameter count is cached or the JVM runs out of heap memory and starts throwing java.lang.OutOfMemoryError: Java heap space.

Avoiding in-clauses is an option, as well as using a fixed collection size for the parameter (or at least a smaller size).

For configuring the query plan cache max size, see the property hibernate.query.plan_cache_max_size, defaulting to 2048 (easily too large for queries with many parameters).

还有 second (也从第一个引用):

Hibernate internally uses a cache that maps HQL statements (as strings) to query plans. The cache consists of a bounded map limited by default to 2048 elements (configurable). All HQL queries are loaded through this cache. In case of a miss, the entry is automatically added to the cache. This makes it very susceptible to thrashing - a scenario in which we constantly put new entries into the cache without ever reusing them and thus preventing the cache from bringing any performance gains (it even adds some cache management overhead). To make things worse, it is hard to detect this situation by chance - you have to explicitly profile the cache in order to notice that you have a problem there. I will say a few words on how this could be done later on.

So the cache thrashing results from new queries being generated at high rates. This can be caused by a multitude of issues. The two most common that I have seen are - bugs in hibernate which cause parameters to be rendered in the JPQL statement instead of being passed as parameters and the use of an "in" - clause.

Due to some obscure bugs in hibernate, there are situations when parameters are not handled correctly and are rendered into the JPQL query (as an example check out HHH-6280). If you have a query that is affected by such defects and it is executed at high rates, it will thrash your query plan cache because each JPQL query generated is almost unique (containing IDs of your entities for example).

The second issue lays in the way that hibernate processes queries with an "in" clause (e.g. give me all person entities whose company id field is one of 1, 2, 10, 18). For each distinct number of parameters in the "in"-clause, hibernate will produce a different query - e.g. select x from Person x where x.company.id in (:id0_) for 1 parameter, select x from Person x where x.company.id in (:id0_, :id1_) for 2 parameters and so on. All these queries are considered different, as far as the query plan cache is concerned, resulting again in cache thrashing. You could probably work around this issue by writing a utility class to produce only certain number of parameters - e.g. 1, 10, 100, 200, 500, 1000. If you, for example, pass 22 parameters, it will return a list of 100 elements with the 22 parameters included in it and the remaining 78 parameters set to an impossible value (e.g. -1 for IDs used for foreign keys). I agree that this is an ugly hack but could get the job done. As a result you will only have at most 6 unique queries in your cache and thus reduce thrashing.

So how do you find out that you have the issue? You could write some additional code and expose metrics with the number of entries in the cache e.g. over JMX, tune logging and analyze the logs, etc. If you do not want to (or can not) modify the application, you could just dump the heap and run this OQL query against it (e.g. using mat): SELECT l.query.toString() FROM INSTANCEOF org.hibernate.engine.query.spi.QueryPlanCache$HQLQueryPlanKey l. It will output all queries currently located in any query plan cache on your heap. It should be pretty easy to spot whether you are affected by any of the aforementioned problems.

As far as the performance impact goes, it is hard to say as it depends on too many factors. I have seen a very trivial query causing 10-20 ms of overhead spent in creating a new HQL query plan. In general, if there is a cache somewhere, there must be a good reason for that - a miss is probably expensive so your should try to avoid misses as much as possible. Last but not least, your database will have to handle large amounts of unique SQL statements too - causing it to parse them and maybe create different execution plans for every one of them.