Spring + Hibernate: Abfrage Plan Cache Speicherauslastung

Question

Ich programmiere eine Anwendung mit der neuesten Version von Spring Boot. Ich habe vor kurzem Probleme mit wachsenden Haufen, das kann nicht Müll gesammelt werden. Die Analyse des Heaps mit Eclipse MAT zeigte, dass der Heap innerhalb einer Stunde nach Ausführung der Anwendung auf 630 MB anwuchs und mit Hibernate SessionFactoryImpl mehr als 75% des gesamten Heapspeichers nutzte.

I war auf der Suche nach möglichen Quellen rund um den Query Plan Cache, aber das einzige, was ich fand, war this, aber das hat nicht spielen. Die Eigenschaften wurden wie folgt festgelegt:

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

Die Datenbankabfragen alle von der Magie Abfrage Frühling erzeugt werden, Repository-Schnittstellen wie in this documentation verwenden. Mit dieser Technik werden etwa 20 verschiedene Abfragen generiert. Kein anderes natives SQL oder HQL wird verwendet. Probe:

@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); 
} 

oder

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

als Beispiel für den Einsatz in.

Frage ist: Warum wächst der Abfrageplancache weiter (es hört nicht auf, es endet in einem vollen Haufen) und wie man das verhindert? Hat jemand ein ähnliches Problem festgestellt?

Versionen:

• Frühlings-Boot 1.2.5
• Hibernate 4.3.10

Answer 1

Ich habe auch dieses Problem betroffen. Es läuft im Grunde darauf hinaus, eine variable Anzahl von Werten in Ihrer IN-Klausel zu haben, und Hibernate versucht, diese Abfragepläne zwischenzuspeichern.

Es gibt zwei großartige Blog-Posts zu diesem Thema. 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).

Und second (auch von den ersten verwiesen):

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.

Answer 2

ich mit diesem queryPlanCache ein großes Problem habe, so habe ich einen Hibernate-Cache-Monitor die Abfragen in der queryPlanCache zu sehen . Ich verwende in der QA-Umgebung als Spring-Aufgabe alle 5 Minuten. Ich fand mit IN-Abfragen, die ich ändern musste, um mein Cache-Problem zu lösen. Ein Detail ist: Ich benutze Hibernate 4.2.18 und ich weiß nicht, ob es mit anderen Versionen nützlich sein wird.

import java.lang.reflect.Field; 
import java.util.ArrayList; 
import java.util.Arrays; 
import java.util.List; 
import java.util.Set; 
import javax.persistence.EntityManager; 
import javax.persistence.PersistenceContext; 
import org.hibernate.ejb.HibernateEntityManagerFactory; 
import org.hibernate.internal.SessionFactoryImpl; 
import org.hibernate.internal.util.collections.BoundedConcurrentHashMap; 
import org.slf4j.Logger; 
import org.slf4j.LoggerFactory; 
import com.dao.GenericDAO; 

public class CacheMonitor { 

private final Logger logger = LoggerFactory.getLogger(getClass()); 

@PersistenceContext(unitName = "MyPU") 
private void setEntityManager(EntityManager entityManager) { 
    HibernateEntityManagerFactory hemf = (HibernateEntityManagerFactory) entityManager.getEntityManagerFactory(); 
    sessionFactory = (SessionFactoryImpl) hemf.getSessionFactory(); 
    fillQueryMaps(); 
} 

private SessionFactoryImpl sessionFactory; 
private BoundedConcurrentHashMap queryPlanCache; 
private BoundedConcurrentHashMap parameterMetadataCache; 

/* 
* I tried to use a MAP and use compare compareToIgnoreCase. 
* But remember this is causing memory leak. Doing this 
* you will explode the memory faster that it already was. 
*/ 

public void log() { 
    if (!logger.isDebugEnabled()) { 
     return; 
    } 

    if (queryPlanCache != null) { 
     long cacheSize = queryPlanCache.size(); 
     logger.debug(String.format("QueryPlanCache size is :%s ", Long.toString(cacheSize))); 

     for (Object key : queryPlanCache.keySet()) { 
      int filterKeysSize = 0; 
      // QueryPlanCache.HQLQueryPlanKey (Inner Class) 
      Object queryValue = getValueByField(key, "query", false); 
      if (queryValue == null) { 
       // NativeSQLQuerySpecification 
       queryValue = getValueByField(key, "queryString"); 
       filterKeysSize = ((Set) getValueByField(key, "querySpaces")).size(); 
       if (queryValue != null) { 
        writeLog(queryValue, filterKeysSize, false); 
       } 
      } else { 
       filterKeysSize = ((Set) getValueByField(key, "filterKeys")).size(); 
       writeLog(queryValue, filterKeysSize, true); 
      } 
     } 
    } 

    if (parameterMetadataCache != null) { 
     long cacheSize = parameterMetadataCache.size(); 
     logger.debug(String.format("ParameterMetadataCache size is :%s ", Long.toString(cacheSize))); 
     for (Object key : parameterMetadataCache.keySet()) { 
      logger.debug("Query:{}", key); 
     } 
    } 
} 

private void writeLog(Object query, Integer size, boolean b) { 
    if (query == null || query.toString().trim().isEmpty()) { 
     return; 
    } 
    StringBuilder builder = new StringBuilder(); 
    builder.append(b == true ? "JPQL " : "NATIVE "); 
    builder.append("filterKeysSize").append(":").append(size); 
    builder.append("\n").append(query).append("\n"); 
    logger.debug(builder.toString()); 
} 

private void fillQueryMaps() { 
    Field queryPlanCacheSessionField = null; 
    Field queryPlanCacheField = null; 
    Field parameterMetadataCacheField = null; 
    try { 
     queryPlanCacheSessionField = searchField(sessionFactory.getClass(), "queryPlanCache"); 
     queryPlanCacheSessionField.setAccessible(true); 
     queryPlanCacheField = searchField(queryPlanCacheSessionField.get(sessionFactory).getClass(), "queryPlanCache"); 
     queryPlanCacheField.setAccessible(true); 
     parameterMetadataCacheField = searchField(queryPlanCacheSessionField.get(sessionFactory).getClass(), "parameterMetadataCache"); 
     parameterMetadataCacheField.setAccessible(true); 
     queryPlanCache = (BoundedConcurrentHashMap) queryPlanCacheField.get(queryPlanCacheSessionField.get(sessionFactory)); 
     parameterMetadataCache = (BoundedConcurrentHashMap) parameterMetadataCacheField.get(queryPlanCacheSessionField.get(sessionFactory)); 
    } catch (Exception e) { 
     logger.error("Failed fillQueryMaps", e); 
    } finally { 
     queryPlanCacheSessionField.setAccessible(false); 
     queryPlanCacheField.setAccessible(false); 
     parameterMetadataCacheField.setAccessible(false); 
    } 
} 

private <T> T getValueByField(Object toBeSearched, String fieldName) { 
    return getValueByField(toBeSearched, fieldName, true); 
} 

@SuppressWarnings("unchecked") 
private <T> T getValueByField(Object toBeSearched, String fieldName, boolean logErro) { 
    Boolean accessible = null; 
    Field f = null; 
    try { 
     f = searchField(toBeSearched.getClass(), fieldName, logErro); 
     accessible = f.isAccessible(); 
     f.setAccessible(true); 
    return (T) f.get(toBeSearched); 
    } catch (Exception e) { 
     if (logErro) { 
      logger.error("Field: {} error trying to get for: {}", fieldName, toBeSearched.getClass().getName()); 
     } 
     return null; 
    } finally { 
     if (accessible != null) { 
      f.setAccessible(accessible); 
     } 
    } 
} 

private Field searchField(Class<?> type, String fieldName) { 
    return searchField(type, fieldName, true); 
} 

private Field searchField(Class<?> type, String fieldName, boolean log) { 

    List<Field> fields = new ArrayList<Field>(); 
    for (Class<?> c = type; c != null; c = c.getSuperclass()) { 
     fields.addAll(Arrays.asList(c.getDeclaredFields())); 
     for (Field f : c.getDeclaredFields()) { 

      if (fieldName.equals(f.getName())) { 
       return f; 
      } 
     } 
    } 
    if (log) { 
     logger.warn("Field: {} not found for type: {}", fieldName, type.getName()); 
    } 
    return null; 
} 
} 

Answer 3

hatte ich genau das gleiche Problem mit Spring-Boot 1.5.7 mit Spring Data (Hibernate) und die folgende Konfiguration hat das Problem gelöst (memory leak):

spring: 
    jpa: 
    properties: 
     hibernate: 
     query: 
      plan_cache_max_size: 64 
      plan_parameter_metadata_max_size: 32 

Answer 4

Beginnend mit Hibernate 5.2.12, können Sie eine Hibernate-Konfigurationseigenschaft ändern festlegen, wie Literale der zugrunde liegenden JDBC gebunden sein, sind vorbereitete Anweisungen unter Verwendung der folgenden:

hibernate.criteria.literal_handling_mode=BIND 

Aus der Java-Dokumentation Diese Konfiguration bietet 3 Einstellungen

1. AUTO (Standard)
2. BIND - Erhöht die Wahrscheinlichkeit des JDBC-Anweisung-Caching mithilfe von Bindungsparametern.
3. INLINE - Inline die Werte und nicht Parameter (seien Sie vorsichtig bei der SQL-Injektion).