This section describes some recommended performance tuning configurations to optimize the performance of WSO2 DAS. It assumes that you have set up WSO2 DAS on a server running Unix/Linux, which is recommended for a production deployment.
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Within the WSO2 platform, we use Tomcat JDBC pooling as the default pooling framework due to its production ready stability and high performance. The table below indicates some recommendations on how to configure the JDBC pool using the <PRODUCT_HOME>/repository/conf/datasources/master-datasources.xml file. For more details about recommended JDBC configurations, see The Tomcat JDBC Connection Pool.
| Property | Description | Recommendation |
|---|---|---|
| maxActive | The maximum number of active connections that can be allocated from the connection pool at the same time. The default value is | This value should match the maximum number of requests that can be expected at a time in your production environment. This is to ensure that, whenever there is a sudden increase in the number of requests to the server, all of them can be connected successfully without causing any delays. Note that this value should not exceed the maximum number of requests allowed for your database. |
| minIdle | The minimum number of connections that can remain idle in the pool, without extra ones being created. The connection pool can shrink below this number if validation queries fail. Default value is 0. | This value should be similar or near to the average number of requests that will be received by the server at the same time. With this setting, you can avoid having to open and close new connections every time a request is received by the server. |
| testOnBorrow | The indication of whether connection objects will be validated before they are borrowed from the pool. If the object validation fails, it will be dropped from the pool, and we will attempt to borrow another connection. | Setting this property to 'true' is recommended as it will avoid connection requests from failing. The |
| validationInterval | To avoid excess validation, run validation at most at this frequency (time in milliseconds). If a connection is due for validation, but has been validated previously within this interval, it will not be validated again. The default value is | This time out can be as high as the time it takes for your DBMS to declare a connection as stale. For example, MySQL will keep a connection open for as long as 8 hours, which requires the validation interval to be within that range. However, note that having a low value for validation interval will not incur a big performance penalty, specially when database requests have a high throughput. For example, a single extra validation query run every 30 seconds is usually negligible. |
| validationQuery | The SQL query used to validate connections from this pool before returning them to the caller. If specified, this query does not have to return any data, it just can't throw an SQLException. The default value is null. Example values are SELECT 1(mysql), select 1 from dual(oracle), SELECT 1(MS Sql Server). | Specify an SQL query, which will validate the availability of a connection in the pool. This query is necessary when testOnBorrow property is true. |
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When it comes to web applications, users are free to experiment and package their own pooling framework such BoneCP. |
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The following parameters which affect the performance relating to receiving events are configured in the <DAS_HOME>/repository/conf/data-bridge/data-bridge-config.xml file. These configurations are common for both thrift and binary protocols.
| Property | Description | Default Value | Recommendation |
|---|---|---|---|
workerThreads | The number of threads reserved to handle the load of events received. | 10 | This value should be increased if you want to increase the throughput by receiving a higher number of events at a given time. The number of available CPU cores should be considered when specifying this value. If the value specified exceeds the number of CPU cores, higher latency would occur as a result of context switching taking place more often. |
maxEventBufferCapacity | The maximum size allowed for the event receiving buffer in mega bytes. The event receiving buffer temporarily stores the events received before they are forwarded to an event stream. | 10 | This value should be increased when there is an increase in the receiving throughput. When increasing the value heap memory size also needs to be increased accordingly. |
eventBufferSize | The number of messages that is allowed in the receiving queue at a given time. | 2000 | This value should be increased when there is an increase in the receiving throughput. |
Publishing events
The following parameters which affect the performance relating to publishing events are configured in the <DAS_HOME>/repository/conf/data-bridge/data-agent-config.xml file. These configurations are common for both thrift and binary protocols.
| Property | Description | Default Value | Recommendation |
|---|---|---|---|
QueueSize | The size of the queue event disruptor which handles events before they are published to an application/data store. | 32768 | The value specified should always be the result of an exponent with 2 as the base. (e.g., 32768 is 215). A higher value should be specified when a higher throughput needs to be handled. However, the increase in the load handled at a given time can reduce the speed at which the events are processed. Therefore, a lower value should be specified if you want to reduce the latency. |
BatchSize | The maximum number of events in a batch sent to the queue event disruptor at a given time. | 200 | This value should be assigned proportionally to the throughput of events handled. Greater the batch size, higher will be the number of events sent to the queue event disruptor at a given time. |
CorePoolSize | The number of threads that will be reserved to handle events at the time you start the CEP server. This value will increase as throughput of events handled increases, but it will not exceed the value specified for the MaxPoolSize parameter. | 1 | The number of available CPU cores should be taken into account when specifying this value. Increasing the core pool size may improve the throughput, but latency will also be increased due to context switching. |
MaxPoolSize | The maximum number of threads that should be reserved at any given time to handle events. | 1 | The number of available CPU cores should be taken into account when specifying this value. Increasing the maximum core pool size may improve the throughput since more threads can be spawned to handle an increased number of events. However, latency will also increase since a higher number of threads would cause context switching to take place more frequently. |
For better througput you can configure the parameters as follows.
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<QueueSize>256</QueueSize> <BatchSize>200</BatchSize> <CorePoolSize>1</CorePoolSize> <MaxPoolSize>1</MaxPoolSize> |
Spark Cluster Tuning
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In a DAS production environment, it is important to allocate the resources correctly to each node, in order to achieve optimum performance.
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Parameters to be configured are as follows.
Cores
| Parameter | Default Value | Description |
|---|---|---|
spark.executor.cores | All the available cores on the worker. | The number of cores to use on each executor. Setting this parameter allows an application to run multiple executors on the same worker, provided that there are enough cores on that worker. Otherwise, only one executor per application is run on each worker. |
spark.cores.max | Int.MAX_VALUE | The maximum amount of CPU cores to request for the application from across the cluster (not from each machine). |
spark.worker.cores | 1 | The number of cores assigned for a worker. |
Memory
| Parameter | Default Value | Description |
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spark.worker.memory | 1g | Amount of memory to use per worker, in the same format as JVM memory strings (e.g., 512m, 2g). |
spark.executor.memory | 512m | Amount of memory to use per executor process, in the same format as JVM memory strings (e.g., 512m, 2g). |
The number of executors in a single worker for the carbon-application can be derived as follows:
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spark.executor.memory=12g
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| Having large amount of memory for a single JVM is not advisable, due to GC (Garbage Collection) performance. |
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