I'm building a SpringBoot application with spring-data-jpa. I know how to log all sqls from this question. How to print a query string with parameter values when using Hibernate
But what if I only want to log failed sqls?
There are two options:
Configure sql logging with parameter values. Disable jdbc batching and enable flushing through hibernate means.
Add debug JDBC driver such as p6spy that will more or less do exactly as described above.
First, let's analyze the problem and split the query types into SELECT and INSERT/UPDATE queries.
SELECT queries for them you have the flushing on by default. So when an error occurs you know exactly which query has failed.
INSERT/UPDATE queries, here things get tricky because your flushing will be off and you have query batching which means that first when you run the query it gets delayed. Second, it gets packed up with other unrelated queries, and third, Hibernate may re-order them. So the short answer is that this is not doable for INSERT/UPDATE if you are using hibernate alone.
A solution to your problem needs to do essentially two things:
1. It should log the queries with their parameter values. This can be done the following way:
# logs the SQL statements
log4j.logger.org.hibernate.SQL=debug
# Logs the JDBC parameters passed to a query
log4j.logger.org.hibernate.type=trace
2. The solution needs to disable the batching of queries, otherwise, you will get a bunch of SQLs but you will not know which SQL exactly is the problematic one.
hibernate.jdbc.batch_size=1
Not sure if this will be enough to entirely disable the batching, but you need to find out.
Alternatively, you can use a jdbc driver designed for DEBUG. This would be p6spy driver which has the option to flush every single SQL statement which is exactly what you want.
Here you need to set the autoflush=true configuration parameter to ensure every single sql statement is immediately flushed to the database.
https://p6spy.readthedocs.io/en/latest/configandusage.html
This gives you multiple log entries. In my case this was not welcome.
Here is my solution for it:
dependency net.ttddyy:datasource-proxy:1.6
Wrap Datasource in Spring Configuration
#Bean
public DataSource dataSource() {
return ProxyDataSourceBuilder.create(originalDatasource())
.logQueryBySlf4j(FLF4JLogLevel.DEBUG).build();
}
Write your own LogAppender - here you can filter the logevents, so you can filter i.e for inserts only or failed executions
public class SQLAppender extends AppenderBase
[...]
#Override
protected void append(ILoggingEvent eventObject) {
[...]
}
configure logback.xml
<appender name="mySQLAppender" class="com.my.SQLAppender" >
</appender>
<logger name="net.ttddyy.dsproxy.listener.logging.SLF4JQueryLoggingListener" level="DEBUG"\>
<appender-ref ref="mySQLAppender"/>
</logger>
Related
I have a quite heavy java webapp that serves thousands of requests/sec and it uses a master Postgresql db which replicates itself to one secondary (read-only) database using streaming (asynchronous) replication.
So, I separate the request from primary to secondary(read-only) using URLs to avoid read-only calls to bug primary database considering replication time is minimal.
NOTE: I use one sessionFactory with a RoutingDataSource provided by spring that looks up db to use based on a key. I am interested in multitenancy as I am using hibernate 4.3.4 that supports it.
I have two questions:
I dont think splitting on the basis of URLs is efficient as I can
only move 10% of traffic around means there are not many read-only
URLs. What approach should I consider?
May be,somehow, on the basis of URLs I achieve some level of
distribution among both nodes but what would I do with my quartz
jobs(that even have separate JVM)? What pragmatic approach should I
take?
I know I might not get a perfect answer here as this really is broad but I just want your opinion for the context.
Dudes I have in my team:
Spring4
Hibernate4
Quartz2.2
Java7 / Tomcat7
Please take interest. Thanks in advance.
Spring transaction routing
First, we will create a DataSourceType Java Enum that defines our transaction routing options:
public enum DataSourceType {
READ_WRITE,
READ_ONLY
}
To route the read-write transactions to the Primary node and read-only transactions to the Replica node, we can define a ReadWriteDataSource that connects to the Primary node and a ReadOnlyDataSource that connect to the Replica node.
The read-write and read-only transaction routing is done by the Spring AbstractRoutingDataSource abstraction, which is implemented by the TransactionRoutingDatasource, as illustrated by the following diagram:
The TransactionRoutingDataSource is very easy to implement and looks as follows:
public class TransactionRoutingDataSource
extends AbstractRoutingDataSource {
#Nullable
#Override
protected Object determineCurrentLookupKey() {
return TransactionSynchronizationManager
.isCurrentTransactionReadOnly() ?
DataSourceType.READ_ONLY :
DataSourceType.READ_WRITE;
}
}
Basically, we inspect the Spring TransactionSynchronizationManager class that stores the current transactional context to check whether the currently running Spring transaction is read-only or not.
The determineCurrentLookupKey method returns the discriminator value that will be used to choose either the read-write or the read-only JDBC DataSource.
Spring read-write and read-only JDBC DataSource configuration
The DataSource configuration looks as follows:
#Configuration
#ComponentScan(
basePackages = "com.vladmihalcea.book.hpjp.util.spring.routing"
)
#PropertySource(
"/META-INF/jdbc-postgresql-replication.properties"
)
public class TransactionRoutingConfiguration
extends AbstractJPAConfiguration {
#Value("${jdbc.url.primary}")
private String primaryUrl;
#Value("${jdbc.url.replica}")
private String replicaUrl;
#Value("${jdbc.username}")
private String username;
#Value("${jdbc.password}")
private String password;
#Bean
public DataSource readWriteDataSource() {
PGSimpleDataSource dataSource = new PGSimpleDataSource();
dataSource.setURL(primaryUrl);
dataSource.setUser(username);
dataSource.setPassword(password);
return connectionPoolDataSource(dataSource);
}
#Bean
public DataSource readOnlyDataSource() {
PGSimpleDataSource dataSource = new PGSimpleDataSource();
dataSource.setURL(replicaUrl);
dataSource.setUser(username);
dataSource.setPassword(password);
return connectionPoolDataSource(dataSource);
}
#Bean
public TransactionRoutingDataSource actualDataSource() {
TransactionRoutingDataSource routingDataSource =
new TransactionRoutingDataSource();
Map<Object, Object> dataSourceMap = new HashMap<>();
dataSourceMap.put(
DataSourceType.READ_WRITE,
readWriteDataSource()
);
dataSourceMap.put(
DataSourceType.READ_ONLY,
readOnlyDataSource()
);
routingDataSource.setTargetDataSources(dataSourceMap);
return routingDataSource;
}
#Override
protected Properties additionalProperties() {
Properties properties = super.additionalProperties();
properties.setProperty(
"hibernate.connection.provider_disables_autocommit",
Boolean.TRUE.toString()
);
return properties;
}
#Override
protected String[] packagesToScan() {
return new String[]{
"com.vladmihalcea.book.hpjp.hibernate.transaction.forum"
};
}
#Override
protected String databaseType() {
return Database.POSTGRESQL.name().toLowerCase();
}
protected HikariConfig hikariConfig(
DataSource dataSource) {
HikariConfig hikariConfig = new HikariConfig();
int cpuCores = Runtime.getRuntime().availableProcessors();
hikariConfig.setMaximumPoolSize(cpuCores * 4);
hikariConfig.setDataSource(dataSource);
hikariConfig.setAutoCommit(false);
return hikariConfig;
}
protected HikariDataSource connectionPoolDataSource(
DataSource dataSource) {
return new HikariDataSource(hikariConfig(dataSource));
}
}
The /META-INF/jdbc-postgresql-replication.properties resource file provides the configuration for the read-write and read-only JDBC DataSource components:
hibernate.dialect=org.hibernate.dialect.PostgreSQL10Dialect
jdbc.url.primary=jdbc:postgresql://localhost:5432/high_performance_java_persistence
jdbc.url.replica=jdbc:postgresql://localhost:5432/high_performance_java_persistence_replica
jdbc.username=postgres
jdbc.password=admin
The jdbc.url.primary property defines the URL of the Primary node while the jdbc.url.replica defines the URL of the Replica node.
The readWriteDataSource Spring component defines the read-write JDBC DataSource while the readOnlyDataSource component define the read-only JDBC DataSource.
Note that both the read-write and read-only data sources use HikariCP for connection pooling.
The actualDataSource acts as a facade for the read-write and read-only data sources and is implemented using the TransactionRoutingDataSource utility.
The readWriteDataSource is registered using the DataSourceType.READ_WRITE key and the readOnlyDataSource using the DataSourceType.READ_ONLY key.
So, when executing a read-write #Transactional method, the readWriteDataSource will be used while when executing a #Transactional(readOnly = true) method, the readOnlyDataSource will be used instead.
Note that the additionalProperties method defines the hibernate.connection.provider_disables_autocommit Hibernate property, which I added to Hibernate to postpone the database acquisition for RESOURCE_LOCAL JPA transactions.
Not only that the hibernate.connection.provider_disables_autocommit allows you to make better use of database connections, but it's the only way we can make this example work since, without this configuration, the connection is acquired prior to calling the determineCurrentLookupKey method TransactionRoutingDataSource.
The remaining Spring components needed for building the JPA EntityManagerFactory are defined by the AbstractJPAConfiguration base class.
Basically, the actualDataSource is further wrapped by DataSource-Proxy and provided to the JPA EntityManagerFactory. You can check the source code on GitHub for more details.
Testing time
To check if the transaction routing works, we are going to enable the PostgreSQL query log by setting the following properties in the postgresql.conf configuration file:
log_min_duration_statement = 0
log_line_prefix = '[%d] '
The log_min_duration_statement property setting is for logging all PostgreSQL statements while the second one adds the database name to the SQL log.
So, when calling the newPost and findAllPostsByTitle methods, like this:
Post post = forumService.newPost(
"High-Performance Java Persistence",
"JDBC", "JPA", "Hibernate"
);
List<Post> posts = forumService.findAllPostsByTitle(
"High-Performance Java Persistence"
);
We can see that PostgreSQL logs the following messages:
[high_performance_java_persistence] LOG: execute <unnamed>:
BEGIN
[high_performance_java_persistence] DETAIL:
parameters: $1 = 'JDBC', $2 = 'JPA', $3 = 'Hibernate'
[high_performance_java_persistence] LOG: execute <unnamed>:
select tag0_.id as id1_4_, tag0_.name as name2_4_
from tag tag0_ where tag0_.name in ($1 , $2 , $3)
[high_performance_java_persistence] LOG: execute <unnamed>:
select nextval ('hibernate_sequence')
[high_performance_java_persistence] DETAIL:
parameters: $1 = 'High-Performance Java Persistence', $2 = '4'
[high_performance_java_persistence] LOG: execute <unnamed>:
insert into post (title, id) values ($1, $2)
[high_performance_java_persistence] DETAIL:
parameters: $1 = '4', $2 = '1'
[high_performance_java_persistence] LOG: execute <unnamed>:
insert into post_tag (post_id, tag_id) values ($1, $2)
[high_performance_java_persistence] DETAIL:
parameters: $1 = '4', $2 = '2'
[high_performance_java_persistence] LOG: execute <unnamed>:
insert into post_tag (post_id, tag_id) values ($1, $2)
[high_performance_java_persistence] DETAIL:
parameters: $1 = '4', $2 = '3'
[high_performance_java_persistence] LOG: execute <unnamed>:
insert into post_tag (post_id, tag_id) values ($1, $2)
[high_performance_java_persistence] LOG: execute S_3:
COMMIT
[high_performance_java_persistence_replica] LOG: execute <unnamed>:
BEGIN
[high_performance_java_persistence_replica] DETAIL:
parameters: $1 = 'High-Performance Java Persistence'
[high_performance_java_persistence_replica] LOG: execute <unnamed>:
select post0_.id as id1_0_, post0_.title as title2_0_
from post post0_ where post0_.title=$1
[high_performance_java_persistence_replica] LOG: execute S_1:
COMMIT
The log statements using the high_performance_java_persistence prefix were executed on the Primary node while the ones using the high_performance_java_persistence_replica on the Replica node.
GitHub Repository
This is not just theory. It's all on GitHub and works like a charm. Use this test case as a reference.
So you can you use it a starting point for your transaction routing solution, as you have a fully-functional example.
Second-level caching
Once you are using replication, you are operating in a distributed environment, so you need to use a distributed caching solution, like Infinispan.
Since we are using replication to distribute traffic to more database nodes, it's obvious that we also have multiple application nodes which have to connect to those database nodes.
Therefore, using the READ_WRITE CacheConcurrencyStrategy in such an environment is a terrible anti-pattern as each distributed node will keep its own copy of the cached entries, leading you to consistency issues even if you didn't use transaction routing.
Not to mention the cold cache issue you'd face if you employed auto-scaling for your application nodes, as they would amplify the database traffic because new nodes would start with a cold cache.
So, if you plan to use transaction routing with the second-level cache mechanism, then you can do better than this.
Use the NONSTRICT_READ_WRITE cache concurrency strategy with a second-level caching provider that can store the cached data in a distributed system of nodes that are readily available even when you create new application nodes.
Conclusion
You need to make sure you set the right size for your connection pools because that can make a huge difference. For this, I recommend using Flexy Pool.
You need to be very diligent and make sure you mark all read-only transactions accordingly. It's unusual that only 10% of your transactions are read-only. Could it be that you have such a write-most application or you are using write transactions where you only issue query statements?
For batch processing, you definitely need read-write transactions, so make sure you enable JDBC batching, like this:
<property name="hibernate.order_updates" value="true"/>
<property name="hibernate.order_inserts" value="true"/>
<property name="hibernate.jdbc.batch_size" value="25"/>
For batching you can also use a separate DataSource that uses a different connection pool that connects to the Primary node.
Just make sure your total connection size of all connection pools is less than the number of connections PostgreSQL has been configured with.
Each batch job must use a dedicated transaction, so make sure you use a reasonable batch size.
More, you want to hold locks and to finish transactions as fast as possible. If the batch processor is using concurrent processing workers, make sure the associated connection pool size is equal to the number of workers, so they don't wait for others to release connections.
You are saying that your application URL's are only 10% read only so the other 90% have at least some form of database writing.
10% READ
You can think about using a CQRS design that may improve your database read performance. It can certainly read from the secondary database, and possibly be made more efficient by designing the queries and domain models specifically for the read/view layer.
You haven't said whether the 10% requests are expensive or not (e.g. running reports)
I would prefer to use a separate sessionFactory if you were to follow the CQRS design as the objects being loaded/cached will most likely be different to those being written.
90% WRITE
As far as the other 90% go, you wouldn't want to read from the secondary database (while writing to the primary) during some write logic as you will not want potentially stale data involved.
Some of these reads are likely to be looking up "static" data. If Hibernate's caching is not reducing database hits for reads, I would consider an in memory cache like Memcached or Redis for this type of data. This same cache could be used by both 10%-Read and 90%-write processes.
For reads that are not static (i.e. reading data you have recently written) Hibernate should hold data in its object cache if its' sized appropriately. Can you determine your cache hit/miss performance?
QUARTZ
If you know for sure that a scheduled job won't impact the same set of data as another job, you could run them against different databases, however if in doubt always perform batch updates to one (primary) server and replicate changes out. It is better to be logically correct, than to introduce replication issues.
DB PARTITIONING
If your 1,000 requests per second are writing a lot of data, look at partitioning your database. You may find you have ever growing tables. Partitioning is one way to address this without archiving data.
Sometimes you need little or no change to your application code.
Archiving is obviously another option
Disclaimer: Any question like this is always going to be application specific. Always try to keep your architecture as simple as possible.
Since the replication is async, the accepted solution will cause hard to debug and hard to reproduce bugs with the second level cache. This is demonstrated here .
This automated test shows this can lead to manipulate incomplete entity graphs.
The cleanest path is to have one EntityManagerFactory per DataSource.
If I correctly understand, 90% of the HTTP requests to your webapp involve at least one write and have to operate on master database. You can direct read only transactions to the copy database, but the improvement will only affect 10% of global databases operation and even those read only operations will hit a database.
The common architecture here is to use a good database cache (Infinispan or Ehcache). If you can offer a big enough cache, you can hope the a good part of the database reads only hit the cache and become memory only operations, either being part of a read only transaction or not. Cache tuning is a delicate operation, but IMHO is necessary to achieve high performance gain. Those cache even allow for distributed front ends even if the configuration is a bit harder in that case (you might have to look for Terracotta clusters if you want to use Ehcache).
Currently, database replication is mainly used to secure the data, and is used as an concurrency improvement mechanizme only if you have high parts of the Information Systems that only read data - and it is not what you are describing.
You can also run a proxySQL infront of your DB nodes (Can be a galera cluster setup), and set query read write split rules, the proxy will distribute traffic according to the defined rule. Ex: SELECT query routed to read node whereas UPDATE queries or read-write transaction goes to write node.
I think the question is general, not sure why the preferred answer steers it into Spring internals? Anyway, you may want to take a look Apache ShardingSphere, which has this feature:
Read/write Splitting
---------------------
Read/write splitting can be used to cope with business access with high stress. ShardingSphere provides flexible read/write splitting capabilities and can achieve read access load balancing based on the understanding of SQL semantics and the ability to perceive the underlying database topology.
One thing I am concerned about is the "understanding of SQL semantics" claim, because how would any library "understand" if:select myfunct(1) from dual changes data in the function, or not.
I have a requirement where I need to grab the Insert and Update Native SQL statement being fired and store it in a log table in the DB. Is it possible for me to get the query in eclipselink?
Register your own custom logger that logs to your database rather than to the file system and configure eclipselink.logging.level.sql=FINE in the persistence unit. Then you can piggy-back on the existing solution for logging SQL statements and save them to the log table. See http://wiki.eclipse.org/EclipseLink/Examples/JPA/CustomLogger and https://wiki.eclipse.org/EclipseLink/Examples/JPA/Logging for details.
We are using pgPool in our Java project to do load balancing in our Postgres database.
pgPool sends read only queries to slave servers and write queries to the master. That is ok.
But there are very specific cases in our application when we need the query to be executed in master server only.
pgPool provides the following mechanism:
/*NO LOAD BALANCE*/ SELECT * FROM user;
The query above will always be executed in the master server.
I searched a lot but while it is possible to set a comment in a manual query (there is a setComment() in Query class), I couldn't find a way to do that using a Hibernate repository with queries created from method names.
Example:
public interface UserRepository extends Repository<User, Long> {
List<User> findByEmailAddressAndLastname(String emailAddress, String lastname);
}
How to put the comment in this query?
Well, this not answers the original question, but I've solved the problem using a different approach.
I've found the solution by reading this section of pgPool documentation: http://www.pgpool.net/docs/latest/pgpool-en.html#condition_for_load_balance
So, annotating a Java method or class with #Transactional achieves the desired result of routing the queries to master server.
When using this annotation, pgPool will route all queries made after a WRITE query (including) to the Postgres master server.
If you don't have a WRITE query, you can achieve the result by annotating with #Transactional(isolation=Isolation.SERIALIZABLE) - if using Spring. But be aware that this isolation level is the strictest available.
I have a quite heavy java webapp that serves thousands of requests/sec and it uses a master Postgresql db which replicates itself to one secondary (read-only) database using streaming (asynchronous) replication.
So, I separate the request from primary to secondary(read-only) using URLs to avoid read-only calls to bug primary database considering replication time is minimal.
NOTE: I use one sessionFactory with a RoutingDataSource provided by spring that looks up db to use based on a key. I am interested in multitenancy as I am using hibernate 4.3.4 that supports it.
I have two questions:
I dont think splitting on the basis of URLs is efficient as I can
only move 10% of traffic around means there are not many read-only
URLs. What approach should I consider?
May be,somehow, on the basis of URLs I achieve some level of
distribution among both nodes but what would I do with my quartz
jobs(that even have separate JVM)? What pragmatic approach should I
take?
I know I might not get a perfect answer here as this really is broad but I just want your opinion for the context.
Dudes I have in my team:
Spring4
Hibernate4
Quartz2.2
Java7 / Tomcat7
Please take interest. Thanks in advance.
Spring transaction routing
First, we will create a DataSourceType Java Enum that defines our transaction routing options:
public enum DataSourceType {
READ_WRITE,
READ_ONLY
}
To route the read-write transactions to the Primary node and read-only transactions to the Replica node, we can define a ReadWriteDataSource that connects to the Primary node and a ReadOnlyDataSource that connect to the Replica node.
The read-write and read-only transaction routing is done by the Spring AbstractRoutingDataSource abstraction, which is implemented by the TransactionRoutingDatasource, as illustrated by the following diagram:
The TransactionRoutingDataSource is very easy to implement and looks as follows:
public class TransactionRoutingDataSource
extends AbstractRoutingDataSource {
#Nullable
#Override
protected Object determineCurrentLookupKey() {
return TransactionSynchronizationManager
.isCurrentTransactionReadOnly() ?
DataSourceType.READ_ONLY :
DataSourceType.READ_WRITE;
}
}
Basically, we inspect the Spring TransactionSynchronizationManager class that stores the current transactional context to check whether the currently running Spring transaction is read-only or not.
The determineCurrentLookupKey method returns the discriminator value that will be used to choose either the read-write or the read-only JDBC DataSource.
Spring read-write and read-only JDBC DataSource configuration
The DataSource configuration looks as follows:
#Configuration
#ComponentScan(
basePackages = "com.vladmihalcea.book.hpjp.util.spring.routing"
)
#PropertySource(
"/META-INF/jdbc-postgresql-replication.properties"
)
public class TransactionRoutingConfiguration
extends AbstractJPAConfiguration {
#Value("${jdbc.url.primary}")
private String primaryUrl;
#Value("${jdbc.url.replica}")
private String replicaUrl;
#Value("${jdbc.username}")
private String username;
#Value("${jdbc.password}")
private String password;
#Bean
public DataSource readWriteDataSource() {
PGSimpleDataSource dataSource = new PGSimpleDataSource();
dataSource.setURL(primaryUrl);
dataSource.setUser(username);
dataSource.setPassword(password);
return connectionPoolDataSource(dataSource);
}
#Bean
public DataSource readOnlyDataSource() {
PGSimpleDataSource dataSource = new PGSimpleDataSource();
dataSource.setURL(replicaUrl);
dataSource.setUser(username);
dataSource.setPassword(password);
return connectionPoolDataSource(dataSource);
}
#Bean
public TransactionRoutingDataSource actualDataSource() {
TransactionRoutingDataSource routingDataSource =
new TransactionRoutingDataSource();
Map<Object, Object> dataSourceMap = new HashMap<>();
dataSourceMap.put(
DataSourceType.READ_WRITE,
readWriteDataSource()
);
dataSourceMap.put(
DataSourceType.READ_ONLY,
readOnlyDataSource()
);
routingDataSource.setTargetDataSources(dataSourceMap);
return routingDataSource;
}
#Override
protected Properties additionalProperties() {
Properties properties = super.additionalProperties();
properties.setProperty(
"hibernate.connection.provider_disables_autocommit",
Boolean.TRUE.toString()
);
return properties;
}
#Override
protected String[] packagesToScan() {
return new String[]{
"com.vladmihalcea.book.hpjp.hibernate.transaction.forum"
};
}
#Override
protected String databaseType() {
return Database.POSTGRESQL.name().toLowerCase();
}
protected HikariConfig hikariConfig(
DataSource dataSource) {
HikariConfig hikariConfig = new HikariConfig();
int cpuCores = Runtime.getRuntime().availableProcessors();
hikariConfig.setMaximumPoolSize(cpuCores * 4);
hikariConfig.setDataSource(dataSource);
hikariConfig.setAutoCommit(false);
return hikariConfig;
}
protected HikariDataSource connectionPoolDataSource(
DataSource dataSource) {
return new HikariDataSource(hikariConfig(dataSource));
}
}
The /META-INF/jdbc-postgresql-replication.properties resource file provides the configuration for the read-write and read-only JDBC DataSource components:
hibernate.dialect=org.hibernate.dialect.PostgreSQL10Dialect
jdbc.url.primary=jdbc:postgresql://localhost:5432/high_performance_java_persistence
jdbc.url.replica=jdbc:postgresql://localhost:5432/high_performance_java_persistence_replica
jdbc.username=postgres
jdbc.password=admin
The jdbc.url.primary property defines the URL of the Primary node while the jdbc.url.replica defines the URL of the Replica node.
The readWriteDataSource Spring component defines the read-write JDBC DataSource while the readOnlyDataSource component define the read-only JDBC DataSource.
Note that both the read-write and read-only data sources use HikariCP for connection pooling.
The actualDataSource acts as a facade for the read-write and read-only data sources and is implemented using the TransactionRoutingDataSource utility.
The readWriteDataSource is registered using the DataSourceType.READ_WRITE key and the readOnlyDataSource using the DataSourceType.READ_ONLY key.
So, when executing a read-write #Transactional method, the readWriteDataSource will be used while when executing a #Transactional(readOnly = true) method, the readOnlyDataSource will be used instead.
Note that the additionalProperties method defines the hibernate.connection.provider_disables_autocommit Hibernate property, which I added to Hibernate to postpone the database acquisition for RESOURCE_LOCAL JPA transactions.
Not only that the hibernate.connection.provider_disables_autocommit allows you to make better use of database connections, but it's the only way we can make this example work since, without this configuration, the connection is acquired prior to calling the determineCurrentLookupKey method TransactionRoutingDataSource.
The remaining Spring components needed for building the JPA EntityManagerFactory are defined by the AbstractJPAConfiguration base class.
Basically, the actualDataSource is further wrapped by DataSource-Proxy and provided to the JPA EntityManagerFactory. You can check the source code on GitHub for more details.
Testing time
To check if the transaction routing works, we are going to enable the PostgreSQL query log by setting the following properties in the postgresql.conf configuration file:
log_min_duration_statement = 0
log_line_prefix = '[%d] '
The log_min_duration_statement property setting is for logging all PostgreSQL statements while the second one adds the database name to the SQL log.
So, when calling the newPost and findAllPostsByTitle methods, like this:
Post post = forumService.newPost(
"High-Performance Java Persistence",
"JDBC", "JPA", "Hibernate"
);
List<Post> posts = forumService.findAllPostsByTitle(
"High-Performance Java Persistence"
);
We can see that PostgreSQL logs the following messages:
[high_performance_java_persistence] LOG: execute <unnamed>:
BEGIN
[high_performance_java_persistence] DETAIL:
parameters: $1 = 'JDBC', $2 = 'JPA', $3 = 'Hibernate'
[high_performance_java_persistence] LOG: execute <unnamed>:
select tag0_.id as id1_4_, tag0_.name as name2_4_
from tag tag0_ where tag0_.name in ($1 , $2 , $3)
[high_performance_java_persistence] LOG: execute <unnamed>:
select nextval ('hibernate_sequence')
[high_performance_java_persistence] DETAIL:
parameters: $1 = 'High-Performance Java Persistence', $2 = '4'
[high_performance_java_persistence] LOG: execute <unnamed>:
insert into post (title, id) values ($1, $2)
[high_performance_java_persistence] DETAIL:
parameters: $1 = '4', $2 = '1'
[high_performance_java_persistence] LOG: execute <unnamed>:
insert into post_tag (post_id, tag_id) values ($1, $2)
[high_performance_java_persistence] DETAIL:
parameters: $1 = '4', $2 = '2'
[high_performance_java_persistence] LOG: execute <unnamed>:
insert into post_tag (post_id, tag_id) values ($1, $2)
[high_performance_java_persistence] DETAIL:
parameters: $1 = '4', $2 = '3'
[high_performance_java_persistence] LOG: execute <unnamed>:
insert into post_tag (post_id, tag_id) values ($1, $2)
[high_performance_java_persistence] LOG: execute S_3:
COMMIT
[high_performance_java_persistence_replica] LOG: execute <unnamed>:
BEGIN
[high_performance_java_persistence_replica] DETAIL:
parameters: $1 = 'High-Performance Java Persistence'
[high_performance_java_persistence_replica] LOG: execute <unnamed>:
select post0_.id as id1_0_, post0_.title as title2_0_
from post post0_ where post0_.title=$1
[high_performance_java_persistence_replica] LOG: execute S_1:
COMMIT
The log statements using the high_performance_java_persistence prefix were executed on the Primary node while the ones using the high_performance_java_persistence_replica on the Replica node.
GitHub Repository
This is not just theory. It's all on GitHub and works like a charm. Use this test case as a reference.
So you can you use it a starting point for your transaction routing solution, as you have a fully-functional example.
Second-level caching
Once you are using replication, you are operating in a distributed environment, so you need to use a distributed caching solution, like Infinispan.
Since we are using replication to distribute traffic to more database nodes, it's obvious that we also have multiple application nodes which have to connect to those database nodes.
Therefore, using the READ_WRITE CacheConcurrencyStrategy in such an environment is a terrible anti-pattern as each distributed node will keep its own copy of the cached entries, leading you to consistency issues even if you didn't use transaction routing.
Not to mention the cold cache issue you'd face if you employed auto-scaling for your application nodes, as they would amplify the database traffic because new nodes would start with a cold cache.
So, if you plan to use transaction routing with the second-level cache mechanism, then you can do better than this.
Use the NONSTRICT_READ_WRITE cache concurrency strategy with a second-level caching provider that can store the cached data in a distributed system of nodes that are readily available even when you create new application nodes.
Conclusion
You need to make sure you set the right size for your connection pools because that can make a huge difference. For this, I recommend using Flexy Pool.
You need to be very diligent and make sure you mark all read-only transactions accordingly. It's unusual that only 10% of your transactions are read-only. Could it be that you have such a write-most application or you are using write transactions where you only issue query statements?
For batch processing, you definitely need read-write transactions, so make sure you enable JDBC batching, like this:
<property name="hibernate.order_updates" value="true"/>
<property name="hibernate.order_inserts" value="true"/>
<property name="hibernate.jdbc.batch_size" value="25"/>
For batching you can also use a separate DataSource that uses a different connection pool that connects to the Primary node.
Just make sure your total connection size of all connection pools is less than the number of connections PostgreSQL has been configured with.
Each batch job must use a dedicated transaction, so make sure you use a reasonable batch size.
More, you want to hold locks and to finish transactions as fast as possible. If the batch processor is using concurrent processing workers, make sure the associated connection pool size is equal to the number of workers, so they don't wait for others to release connections.
You are saying that your application URL's are only 10% read only so the other 90% have at least some form of database writing.
10% READ
You can think about using a CQRS design that may improve your database read performance. It can certainly read from the secondary database, and possibly be made more efficient by designing the queries and domain models specifically for the read/view layer.
You haven't said whether the 10% requests are expensive or not (e.g. running reports)
I would prefer to use a separate sessionFactory if you were to follow the CQRS design as the objects being loaded/cached will most likely be different to those being written.
90% WRITE
As far as the other 90% go, you wouldn't want to read from the secondary database (while writing to the primary) during some write logic as you will not want potentially stale data involved.
Some of these reads are likely to be looking up "static" data. If Hibernate's caching is not reducing database hits for reads, I would consider an in memory cache like Memcached or Redis for this type of data. This same cache could be used by both 10%-Read and 90%-write processes.
For reads that are not static (i.e. reading data you have recently written) Hibernate should hold data in its object cache if its' sized appropriately. Can you determine your cache hit/miss performance?
QUARTZ
If you know for sure that a scheduled job won't impact the same set of data as another job, you could run them against different databases, however if in doubt always perform batch updates to one (primary) server and replicate changes out. It is better to be logically correct, than to introduce replication issues.
DB PARTITIONING
If your 1,000 requests per second are writing a lot of data, look at partitioning your database. You may find you have ever growing tables. Partitioning is one way to address this without archiving data.
Sometimes you need little or no change to your application code.
Archiving is obviously another option
Disclaimer: Any question like this is always going to be application specific. Always try to keep your architecture as simple as possible.
Since the replication is async, the accepted solution will cause hard to debug and hard to reproduce bugs with the second level cache. This is demonstrated here .
This automated test shows this can lead to manipulate incomplete entity graphs.
The cleanest path is to have one EntityManagerFactory per DataSource.
If I correctly understand, 90% of the HTTP requests to your webapp involve at least one write and have to operate on master database. You can direct read only transactions to the copy database, but the improvement will only affect 10% of global databases operation and even those read only operations will hit a database.
The common architecture here is to use a good database cache (Infinispan or Ehcache). If you can offer a big enough cache, you can hope the a good part of the database reads only hit the cache and become memory only operations, either being part of a read only transaction or not. Cache tuning is a delicate operation, but IMHO is necessary to achieve high performance gain. Those cache even allow for distributed front ends even if the configuration is a bit harder in that case (you might have to look for Terracotta clusters if you want to use Ehcache).
Currently, database replication is mainly used to secure the data, and is used as an concurrency improvement mechanizme only if you have high parts of the Information Systems that only read data - and it is not what you are describing.
You can also run a proxySQL infront of your DB nodes (Can be a galera cluster setup), and set query read write split rules, the proxy will distribute traffic according to the defined rule. Ex: SELECT query routed to read node whereas UPDATE queries or read-write transaction goes to write node.
I think the question is general, not sure why the preferred answer steers it into Spring internals? Anyway, you may want to take a look Apache ShardingSphere, which has this feature:
Read/write Splitting
---------------------
Read/write splitting can be used to cope with business access with high stress. ShardingSphere provides flexible read/write splitting capabilities and can achieve read access load balancing based on the understanding of SQL semantics and the ability to perceive the underlying database topology.
One thing I am concerned about is the "understanding of SQL semantics" claim, because how would any library "understand" if:select myfunct(1) from dual changes data in the function, or not.
I'm looking for way to get the SQL update script when Hibernate automatically updates tables.
I'm using hibernate.hbm2ddl.auto=update in development environment only, and I need SQL script that updates tables for production.
I want these SQL scripts in txt format for revision and potential edit.
How can this be done?
Thanks for any advice.
There are some suggestions and general discussion here.
In a nutshell, you can turn on logging (to standard output):
hibernate.show_sql=true
Alternatively, if you use log4j, you can add this to your log4j.properties file:
log4j.logger.org.hibernate.SQL=DEBUG
Both of these approaches are going to output Hibernate's prepared statements with parameters (so the parameter values themselves are not inline). To get around this, you could use an interceptor like P6Spy. Details on that can be found here.
org.hibernate.cfg.Configuration class has method:
public java.lang.String[] generateSchemaUpdateScript( Dialect, DatabaseMetadata)
what generates the reqiured update script.
I've just implemented this in grails:
configuration = new DefaultGrailsDomainConfiguration(
grailsApplication: grailsApplication,
properties: props)
//this extends hibernate config
Connection c = SessionFactoryUtils.getDataSource(sessionFactory).getConnection(props.'hibernate.connection.username', props.'hibernate.connection.password')
<br/>md = new DatabaseMetadata(c, DialectFactory.buildDialect(props.'hibernate.dialect'))
configuration.generateSchemaUpdateScript(DialectFactory.buildDialect(props.'hibernate.dialect'), md)
)
check SchemaExport script in grails, for further information, it uses hibernate to generate schema.
(I had to implent is as a service because we have external domain model)