Take Migration Process To Next Level Using Dexecutor

You have Data Migration process, which updates the Application from version X to X+1, by running Migration Scripts (each script consists of sequence of instructions) sequentially, to bring the application to a desired state.

Problem

The synchronous process is causing delays leading to unproductive wait times and dissatisfaction from users. There is a need for process to decrease the scripts execution time by running tasks in parallel where ever applicable to come to desired state.

Driving Forces

The following are driving forces behind Dexecutor.

  • Supports Parallel execution, conditionally may revert to sequential execution (provided such logic is provided)
  • Ultra light (Version 1.1.1 is 44KB)
  • Ultra fast
  • Distributed Execution supported
  • Immediate/Scheduled Retry logic supported
  • Non-terminating behaviour supported
  • Conditionally skip the task execution

Solution

Incorporate Dexecutor into your script execution logic, additionally distribute the execution using Infinispan, Hazelcast or Ignite. Here is the sample application which demonstrate this functionality, fork it and have fun 🙂

Dexecutor can be used in this case easily by adding an Algorithmic logic on top of Dexecutor which builds the graph based on table names. Lets assume the following scripts:

Script 1 ==> operates on Tables t1 and t2 and takes 5 minute
Script 2 ==> operates on Tables t1 and t3 and takes 5 minute
Script 3 ==> operates on Tables t2 and t4 and takes 5 minute
Script 4 ==> operates on Tables t5 and t6 and takes 5 minute
Script 5 ==> operates on Tables t5 and t7 and takes 5 minute
Script 6 ==> operates on Tables t6 and t8 and takes 5 minute

Normally these scripts are executed sequentially as follows.

Script 1  5 minutes
  |
  V
Script 2  5 minutes
  |
  V
Script 3  5 minutes
  |
  V
Script 4  5 minutes
  |
  V
Script 5  5 minutes
  |
  V
Script 6  5 minutes

Total time 30 minutes 

In sequential case, total execution time would be 30 minutes, However if we could parallelize the script execution, make sure scripts are executed in right sequence and order, then we could save time, decreasing the total execution time to just 10 minutes.

       +----------+                       +----------+
       | Script 1 |                       | Script 4 |             ==> 5 minutes
  +----+----------+--+               +----+----------+-----+
  |                  |               |                     |
  |                  |               |                     |
+-----v----+   +-----v----+     +----v-----+        +------v---+
| Script 2 |   | Script 3 |     | Script 5 |        | Script 6 |   ==> 5 minutes
+----------+   +----------+     +----------+        +----------+

Total Time 10 minutes

Using Dexecutor, we just have to write the algorithm which facilitates building graph using the API exposed by Dexecutor, and rest would be taken care by Dexecutor.  MigrationTasksExecutor implements that algorithm, considering the SQLs in the migration scripts. Since table names in the SQL plays a crucial role in building the graph, we need an efficient, ultra light and ultra fast library to extract table names out of SQLs, and hence we would use sql-table-name-parser, use it by adding the following dependency in your POM.

<dependency>
    <groupId>com.github.mnadeem</groupId>
    <artifactId>sql-table-name-parser</artifactId>
    <version>0.0.2</version>
  </dependency>

And of course, Dexecutor should be added as dependency as well

<dependency>
   <groupId>com.github.dexecutor</groupId>
   <artifactId>dexecutor-core</artifactId>
   <version>LATEST_VERSION</version>
 </dependency>

The graph, that would be built, considering the migration script is the following.

 

dexecutor-graph

As can be seen here node base1, base3 and base 4 runs in parallel and once, one of them finishes its children are executed, for example if node base1 is finished then its children base2 and app3-1 are executed and so on.

Notice that for node app2-4 to start, app1-4 and app2-1 must finish, similarly for node app3-2 to start, app3-1 and app2-4 must finish.

Just Run this class to see how things proceed.

Conclusion

We can indeed run dependent/independent tasks in easy and reliable way with Dexecutor.

References

Multi-Node Distributed Execution Using Hazelcast and Dexecutor

We will try to execute Dexecutor in a distributed mode using Hazelcast. For the demo we would be setting up multiple Hazelast nodes on single machine.

Refer Introducing Dexecutor, to get an introduction on Dexecutor  and to understand the problem we would solve in a distribute fashion. In short:

We would be distributing the execution of dexecutor tasks on Hazelcast compute nodes in a single machine.

To do that one of the nodes would act as master and submit the tasks to Hazelcast compute nodes to be executed by other Hazelcast compute nodes using Dexecutor.

Here are the steps to do that :

Step 1: Add dexecutor-hazelcast dependency

<dependency>
     <groupId>com.github.dexecutor
     <artifactId>dexecutor-hazelcast
     <version>LATEST_RELEASE

Step 2: Get an Instance of Hazelcast IExecutorService from Hazelcast

 Config cfg = new Config();
 HazelcastInstance instance = Hazelcast.newHazelcastInstance(cfg);
 IExecutorService executorService = instance.getExecutorService("test");

Step 3 : Create Dexecutor using IExecutorService

if (isMaster) {
  DefaultDependentTasksExecutor<Integer, Integer> dexecutor = newTaskExecutor(executorService);

  buildGraph(dexecutor);
  dexecutor.execute(ExecutionConfig.TERMINATING);
 }
private DefaultDependentTasksExecutor<Integer, Integer> newTaskExecutor(IExecutorService executorService) {
  DependentTasksExecutorConfig<Integer, Integer> config = new DependentTasksExecutorConfig<Integer, Integer>(
  new HazelcastExecutionEngine<Integer, Integer>(executorService), new SleepyTaskProvider());
  return new DefaultDependentTasksExecutor<Integer, Integer>(config);
 }

  private static class SleepyTaskProvider implements TaskProvider<Integer, Integer> {

  public Task<Integer, Integer> provideTask(final Integer id) {
     return new HazelcastTask(id);
  }
 }

Step 4: Execution

Open three terminals and execute the following :

Terminal #1

 mvn test-compile exec:java -Djava.net.preferIPv4Stack=true -Dexec.mainClass="com.github.dexecutor.hazelcast.Node" -Dexec.classpathScope="test" -Dexec.args="s node-A"

Terminal #2

 mvn test-compile exec:java -Djava.net.preferIPv4Stack=true -Dexec.mainClass="com.github.dexecutor.hazelcast.Node" -Dexec.classpathScope="test" -Dexec.args="s node-B"
Terminal #3
 mvn test-compile exec:java -Djava.net.preferIPv4Stack=true -Dexec.mainClass="com.github.dexecutor.hazelcast.Node"  -Dexec.classpathScope="test" -Dexec.args="m node-C"

Here is the Execution
dexecutor-hazelcast-execution

Here is the Complete Node Implementation

References

Multi Node Distributed Execution Using Ignite And Dexecutor

We will try to execute Dexecutor in a distributed mode using Apache Ignite. For the demo we would be setting up multiple Ignite nodes on single machine.

Refer Introducing Dexecutor, to get an introduction on Dexecutor  and to understand the problem we would solve in a distribute fashion. In short:

We would be distributing the execution of dexecutor tasks on Apache Ignite nodes in a single machine.

To do that one of the nodes would act as master and submit the tasks to Ignite to be executed by other Ignite compute nodes using Dexecutor.

Here are the steps to do that :

Step 1: Add dexecutor-ignite dependency

<dependency>
     <groupId>com.github.dexecutor<groupId>
     <artifactId>dexecutor-ignite<artifactId>
     <version>LATEST_RELEASE<version>
 <dependency>

Step 2: Start Ignite

IgniteConfiguration cfg = new IgniteConfiguration();
cfg.setGridName(nodeName);

Ignite ignite = Ignition.start(cfg);

Step 3 : Create Dexecutor using Ignite

if (isMaster) {
 DefaultDependentTasksExecutor<Integer, Integer> dexecutor = newTaskExecutor(ignite.compute());

 buildGraph(dexecutor);
 dexecutor.execute(ExecutionConfig.TERMINATING);
 }
private DefaultDependentTasksExecutor<Integer, Integer> newTaskExecutor(final IgniteCompute igniteCompute) {
        DependentTasksExecutorConfig<Integer, Integer> config = new DependentTasksExecutorConfig<Integer, Integer>(
                new IgniteExecutionEngine<Integer, Integer>(igniteCompute), new SleepyTaskProvider());
        return new DefaultDependentTasksExecutor<Integer, Integer>(config);
}

Step 4: Execution

Open three terminals and execute the following :

Terminal #1

  mvn test-compile exec:java -Djava.net.preferIPv4Stack=true -Dexec.mainClass="com.github.dexecutor.ignite.Node" -Dexec.classpathScope="test" -Dexec.args="s node-A"

Terminal #2

 mvn test-compile exec:java -Djava.net.preferIPv4Stack=true -Dexec.mainClass="com.github.dexecutor.ignite.Node" -Dexec.classpathScope="test" -Dexec.args="s node-B"
Terminal #3
  mvn test-compile exec:java  -Dexec.classpathScope="test" -Djava.net.preferIPv4Stack=true -Dexec.mainClass="com.github.dexecutor.ignite.Node" -Dexec.args="m node-C"

dexecutor-ignite-execution

Here is the Node Implementation.

References