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This test involved setting up a 10-node HBase cluster with HDFS as the undrelying file system.
Versions
- WSO2 DAS 3.1.0.
- Apache Hadoop 2.7.2.
- Apache HBase 1.2.1.
- Oracle Java Development Kit (JDK) v1.7 update 51 (1.7.0_51-b13).
Infrastructure used
- 3 DAS nodes (variable roles: publisher, receiver, analyzer and indexer): c4.2xlarge
- 1 HBase master and Hadoop Namenode: c3.2xlarge
- 9 HBase Regionservers and Hadoop Datanodes: c3.2xlarge
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| Events | 16753779 |
| Time (s) | 1862.901 |
| Mean TPS | 8993.381291 |
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Microsoft SQL Server Event Store
Infrastructure used
- c4.2xlarge Amazon EC2 instances as the DAS nodesdb.m4.2xlarge Amazon RDS instance as the database nodeCustomized Thrift client as the data publisher (Thrift producer found in samples)
- Linux kernel 4.44, java version "1.8.0_131", JVM flags : -Xmx4g -Xms2g
- db.m4.2xlarge Amazon RDS instance with MS SQL Server Enterprise Edition 20162016 as the database node
- Customized Thrift client as the data publisher (Thrift producer found in samples)
Scenario: Persisting 30 million events of Process Monitoring Events on MS SQL
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- c4.2xlarge Amazon EC2 instances as the DAS nodesdb.m4.2xlarge Amazon RDS instance as the database nodeCustomized Thrift client as the data publisher (Thrift producer found in samples)
- Linux kernel 4.44, java version "1.8.0_131", JVM flags : -Xmx4g -Xms2g
- db.m4.2xlarge Amazon RDS instance with MySQL Community Edition version 5.7 as the database node
- Customized Thrift client as the data publisher (Thrift producer found in samples)
Scenario: Persisting 12 million events of Process Monitoring Events on MySQL
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| Info | ||
|---|---|---|
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After around 12 million events are published, a sudden drop can be observed in receiver performance that can be considered as the upper limit of MySQL event store. In order to continue receiving events without a major performance degradation data has to be purged periodically before it reaches the upper limit. See https://docs.wso2.com/display/DAS310/Purging+Data for more information on configuring data purging. In the event that data purging is not possible an HBase event store should be used. |
Batch Analytics
The following topics describe the analyzer performance of WSO2 DAS.
Scenario: Running Spark queries on the 1 billion smart home published events
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- Over 1 million TPS on Spark for 2 analyzers
- About 1.3 million TPS for 3 analysersanalyzers.
The DAS GET operations (on HBase) make use of the HBase data locality aspect. This has the potential to perform the GET operations fast compared to random access.
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Scenario: Running Spark queries on 1 million smart home and Wikipedia events on MySQL Event Store
The following topics describe the analyzer performance of WSO2 DAS. Spark analyzing performance (time to complete execution) was measured using a 2 node DAS analyzer cluster with MySQL database.
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Scenario: Retrieving Process Monitoring Data via REST API
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Infrastructure used
- JMeter, DAS 3.1.0, MySQL: c4.xlarge (4 vCPUs, 7.5 GB, EBS-Only, 750 Mbps network)c4.xlarge Amazon EC2 instances as the DAS node
- Linux kernel 4.44, java version "1.8.0_131", JVM flags : -Xmx4g -Xms2g
- c4.xlarge Amazon EC2 instances MySQL Community Edition version 5.7 as the database node
This test was conducted on a test setup as shown in the following figure,
Using JMeter the DAS’s search REST API was invoked. Eighty JMeter users were used and they sent requests in a tight loop. The request was sent to query a single record from an event table via the DAS search API. In this experiment, the MySQL server’s event table had data which had been loaded in previous experiments. The experiment was run for 45 minutes. Average throughput value of 2,695 events 2839 events/second and an average latency of 29 ms was measured at the JMeter.
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