in our Spring application we have a table that contains a lot of "Payment" record. Now we need a query that pages the results sorted from the one with the largest total to the smallest, we are facing an error because sometimes the same record is contained in two successive pages.
We are creating a PageRequest passed to the repository. Here our implementation:
Repository:
public interface StagingPaymentEntityRepository extends JpaRepository<StagingPaymentEntity, Long> {
Page<StagingPaymentEntity> findAllByStatusAndCreatedDateLessThanEqualAndOperationTypeOrderByEffectivePaymentDesc(String status, Timestamp batchStartTimestamp, String operationType, Pageable pageable);
}
public class BatchThreadReiteroStorni extends ThreadAbstract<StagingPaymentEntity> {
PageRequest pageRequest = PageRequest.of (index, 170);
Page<StagingPaymentEntity> records = ((StagingPaymentEntityRepository) repository).findAllByStatusAndCreatedDateLessThanEqualAndOperationTypeOrderByEffectivePaymentDesc("REITERO", batchStartTimestamp, "STORNO", pageRequest) ;
}
where index is the index of the page we are requesting.
There is a way to understand why it is happening ? Thank for support
This can have multiple reasons.
Non deterministic ordering: If the ordering you are using isn't deterministic, i.e. there are rows that might com in any order that order might change between selects resulting in items getting skipped or returned multiple times. Fix: add the primary key as a last column to the ordering.
If you change the entities in a way that affects the ordering, or another process does that you might end up with items getting processed multiple times.
In this scenario I see a couple of approaches:
do value based pagination. I.e. don't select pages but select the next N rows after .
Instead of paging use a Stream this allows to use a single select but still processing the results an element at a time. You might have to flush and evict entities and I'm not 100% sure that works, but certainly worth a try.
Finally you can mark all all rows that you want to process in a separate column, then select N marked entities and unmark them once they are processed.
I'm programming an application with the latest version of Spring Boot. I recently became problems with growing heap, that can not be garbage collected. The analysis of the heap with Eclipse MAT showed that, within one hour of running the application, the heap grew to 630MB and with Hibernate's SessionFactoryImpl using more than 75% of the whole heap.
Is was looking for possible sources around the Query Plan Cache, but the only thing I found was this, but that did not play out. The properties were set like this:
spring.jpa.properties.hibernate.query.plan_cache_max_soft_references=1024
spring.jpa.properties.hibernate.query.plan_cache_max_strong_references=64
The database queries are all generated by the Spring's Query magic, using repository interfaces like in this documentation. There are about 20 different queries generated with this technique. No other native SQL or HQL are used.
Sample:
#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);
}
or
List<SomeObject> findByNameAndUrlIn(String name, Collection<String> urls);
as example for IN usage.
Question is: Why does the query plan cache keep growing (it does not stop, it ends in a full heap) and how to prevent this? Did anyone encounter a similar problem?
Versions:
Spring Boot 1.2.5
Hibernate 4.3.10
I've hit this issue as well. It basically boils down to having variable number of values in your IN clause and Hibernate trying to cache those query plans.
There are two great blog posts on this topic.
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).
And second (also referenced from the first):
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.
I have same problems with many(>10000) parameters in IN-queries. The number of my parameters is always different and I can not predict this, my QueryCachePlan growing too fast.
For database systems supporting execution plan caching, there's a better chance of hitting the cache if the number of possible IN clause parameters lowers.
Fortunately Hibernate of version 5.2.18 and higher has a solution with padding of parameters in IN-clause.
Hibernate can expand the bind parameters to power-of-two: 4, 8, 16, 32, 64.
This way, an IN clause with 5, 6, or 7 bind parameters will use the 8 IN clause, therefore reusing its execution plan.
If you want to activate this feature, you need to set this property to true hibernate.query.in_clause_parameter_padding=true.
For more information see this article, atlassian.
I had the exact same problem using Spring Boot 1.5.7 with Spring Data (Hibernate) and the following config solved the problem (memory leak):
spring:
jpa:
properties:
hibernate:
query:
plan_cache_max_size: 64
plan_parameter_metadata_max_size: 32
Starting with Hibernate 5.2.12, you can specify a hibernate configuration property to change how literals are to be bound to the underlying JDBC prepared statements by using the following:
hibernate.criteria.literal_handling_mode=BIND
From the Java documentation, this configuration property has 3 settings
AUTO (default)
BIND - Increases the likelihood of jdbc statement caching using bind parameters.
INLINE - Inlines the values rather than using parameters (be careful of SQL injection).
I had a similar issue, the issue is because you are creating the query and not using the PreparedStatement. So what happens here is for each query with different parameters it creates an execution plan and caches it.
If you use a prepared statement then you should see a major improvement in the memory being used.
TL;DR: Try to replace the IN() queries with ANY() or eliminate them
Explanation:
If a query contains IN(...) then a plan is created for each amount of values inside IN(...), since the query is different each time.
So if you have IN('a','b','c') and IN ('a','b','c','d','e') - those are two different query strings/plans to cache. This answer tells more about it.
In case of ANY(...) a single (array) parameter can be passed, so the query string will remain the same and the prepared statement plan will be cached once (example given below).
Cause:
This line might cause the issue:
List<SomeObject> findByNameAndUrlIn(String name, Collection<String> urls);
as under the hood it generates different IN() queries for every amount of values in "urls" collection.
Warning:
You may have IN() query without writing it and even without knowing about it.
ORM's such as Hibernate may generate them in the background - sometimes in unexpected places and sometimes in a non-optimal ways.
So consider enabling query logs to see the actual queries you have.
Fix:
Here is a (pseudo)code that may fix issue:
query = "SELECT * FROM trending_topic t WHERE t.name=? AND t.url=?"
PreparedStatement preparedStatement = connection.prepareStatement(queryTemplate);
currentPreparedStatement.setString(1, name); // safely replace first query parameter with name
currentPreparedStatement.setArray(2, connection.createArrayOf("text", urls.toArray())); // replace 2nd parameter with array of texts, like "=ANY(ARRAY['aaa','bbb'])"
But:
Don't take any solution as a ready-to-use answer. Make sure to test the final performance on actual/big data before going to production - no matter which answer you choose.
Why? Because IN and ANY both have pros and cons, and they can bring serious performance issues if used improperly (see examples in references below). Also make sure to use parameter binding to avoid security issues as well.
References:
100x faster Postgres performance by changing 1 line - performance of Any(ARRAY[]) vs ANY(VALUES())
Index not used with =any() but used with in - different performance of IN and ANY
Understanding SQL Server query plan cache
Hope this helps. Make sure to leave a feedback whether it worked or not - in order to help people like you. Thanks!
I had a big issue with this queryPlanCache, so I did a Hibernate cache monitor to see the queries in the queryPlanCache.
I am using in QA environment as a Spring task each 5 minutes.
I found which IN queries I had to change to solve my cache problem.
A detail is: I am using Hibernate 4.2.18 and I don't know if will be useful with other versions.
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;
}
}
We also had a QueryPlanCache with growing heap usage. We had IN-queries which we rewrote, and additionally we have queries which use custom types. Turned out that the Hibernate class CustomType didn't properly implement equals and hashCode thereby creating a new key for every query instance. This is now solved in Hibernate 5.3.
See https://hibernate.atlassian.net/browse/HHH-12463.
You still need to properly implement equals/hashCode in your userTypes to make it work properly.
We had faced this issue with query plan cache growing too fast and old gen heap was also growing along with it as gc was unable to collect it.The culprit was JPA query taking some more than 200000 ids in the IN clause. To optimise the query we used joins instead of fetching ids from one table and passing those in other table select query..
Using an hbase table as my input, of which the keys I have pre-processed in order to consist of a number concatenated with the respective row ID, I want to rest assured that all rows with the same number heading their key, will be processed from the same mapper at a M/R job. I am aware that this could be achieved through extension of TableInputFormat, and I have seen one or two posts concerning extension of this class, but I am searching for the most efficient way to do this in particular.
If anyone has any ideas, please let me know.
You can use a PrefixFilter in your scan.
http://hbase.apache.org/apidocs/org/apache/hadoop/hbase/filter/PrefixFilter.html
And parallelize the launch of your different mappers using Future
final Future<Boolean> newJobFuture = executor.submit(new Callable<Boolean>() {
#Override
public Boolean call() throws Exception {
Job mapReduceJob = MyJobBuilder.createJob(args, thePrefix,
...);
return mapReduceJob.waitForCompletion(true);
}
});
But I believe this is more an approach of a reducer you are looking for.
I am working on a HBase map reduce job and need to understand if the columns in a single column family are returned sorted by their names (key). If so, I wouldnt need to do it in the shuffle sort stage.
Thanks
I have a very similar data model as you. Upon insertion however, I set my own values for the timestamps on the Put object. However, I did so in a way that took a "seed" of the current time and appended a incrementing counter for each event I persisted in the batch.
When I pulled the results out from the Scan, I wrote a comparator:
public class KVTimestampComparator implements Comparator<KeyValue> {
#Override
public int compare(KeyValue kv1, KeyValue kv2) {
Long kv1Timestamp = kv1.getTimestamp();
Long kv2Timestamp = kv2.getTimestamp();
return kv1Timestamp.compareTo(kv2Timestamp);
}
}
Then sorted the raw row:
List<KeyValue> row = Arrays.asList(result.raw());
Collections.sort(row, new KVTimestampComparator());
Got this idea from person who answered this : Sorted results from hbase scanner
no, columns are not sorted
They are stored internally as key-value pairs in a long byte array. But, you should clarify your question about what you actually need this for.