Event Ingestion with Persistance
HBase Event Store
This test involved setting up a 10-node HBase cluster with HDFS as the undrelying file system.
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
Scenario: Persisting 1 billion events from the Smart Home DAS Sample
This test was designed to test the data layer during sustained event publication. During testing, the TPS was around the 150K mark, and the memstore flush of the HBase cluster (which suspends all writes) and minor compaction operations brought it down in bursts. Overall, a mean of 96K TPS was achieved, but a steady rate of around 100-150K TPS is achievable, as opposed to the current no-flow-control situation.
The published data took around 950GB on the Hadoop filesystem, taking the HDFS-level replication into account.
| Events | 1000000000 |
| Time (in seconds) | 10391.768 |
| Mean TPS | 96230.01591 |
Scenario: Persisting the entire Wikipedia corpus
This test involved publishing the entirety of the Wikipedia dataset, where a single event comprises of one Wikipedia article (16.8M articles in total). Events vary greatly in size, with the mean being ~3.5KB. Here, a mean throughput of around 9K TPS was observed.
| Events | 16753779 |
| Time (s) | 1862.901 |
| Mean TPS | 8993.381291 |
MS SQL Event Store
Infrastructure used
- c4.2xlarge Amazon EC2 instances as the DAS nodes
- Linux kernel 4.44, java version "1.8.0_131", JVM flags : -Xmx4g -Xms2g
- db.m4.2xlarge Amazon RDS instance with MS SQL Enterprise Edition 2016 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
This test involved persisting process monitoring events each of approximately 180 bytes. The test injected 30 million events into DAS with an input TPS of 40,000 events/second.
MySQL Event Store
Infrastructure used
- c4.2xlarge Amazon EC2 instances as the DAS nodes
- 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
This test involved persisting process monitoring events of approximately 180 bytes. The test injected 12 million events into DAS with an input TPS of 10,000 events/second.
MySQL Upper Limit
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
Scenario: Running Spark queries on the 1 billion smart home published events
Spark queries from the Smart Home DAS sample were executed against the published data, and the analyzer node count was kept at 2 and 3 respectively for 2 separate tests. The SPARK JVMs were provided with following during the test.
- 1.36 processor cores
- 12GB of dedicated memory
The following results were observed.
- Over 1 million TPS on Spark for 2 analyzers
- About 1.3 million TPS for 3 analysers.
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.
| Query | 2 Analyzers | 3 Analyzers | ||
|---|---|---|---|---|
| Time(s) | Mean TPS | Time(s) | Mean TPS | |
INSERT OVERWRITE TABLE cityUsage SELECT metro_area, avg(power_reading) AS avg_usage,min(power_reading) AS min_usage, max(power_reading) AS max_usage FROM smartHomeData GROUP BY metro_area | 958.80 | 1042968.20 | 741.15 | 1349250.90 |
INSERT OVERWRITE TABLE ct SELECT count(*) FROM smartHomeData | 953.46 | 1048806.20 | 734.99 | 1360570.13 |
INSERT OVERWRITE TABLE peakDeviceUsageRange SELECT house_id,
(max(power_reading) - min(power_reading)) AS usage_range FROM
smartHomeData WHERE is_peak = true AND metro_area = "Seattle" GROUP BY
house_id | 975.06 | 1025581.77 | 751.27 | 1331073.47 |
INSERT OVERWRITE TABLE stateAvgUsage SELECT state, avg(power_reading) AS state_avg_usage FROM smartHomeData GROUP BY state | 991.08 | 1009003.34 | 783.54 | 1276265.545 |
Scenario: Running Spark queries on the Wikipedia corpus
| Query | 2 Analyzers | 3 Analyzers | ||
|---|---|---|---|---|
| Time(s) | Mean TPS | Time(s) | Mean TPS | |
INSERT INTO TABLE wikiAvgArticleLength SELECT AVG(length) as avg_article_length FROM wiki | 222.70 | 75234.03 | 167.27 | 100164.18 |
INSERT INTO TABLE wikiTotalArticleLength SELECT SUM(length) as total_article_chars FROM wiki | 221.74 | 75554.76 | 166.92 | 100373.80 |
INSERT INTO TABLE wikiTotalArticlePages SELECT COUNT(*) as total_pages FROM wiki | 221.80 | 75536.05 | 166.14 | 100842.18 |
INSERT INTO TABLE wikiContributorSummary SELECT contributor_username,
COUNT(*) as page_count FROM wiki GROUP BY contributor_username | 236.11 | 70958.52 | 181.42 | 92350.26 |
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.
Time taken for each type of Spark query is given below.
| Data set | Event Count | Query Type | Time Taken (seconds) |
|---|---|---|---|
| Smart Home | 10000000 | INSERT OVERWRITE TABLE cityUsage SELECT metro_area, avg(power_reading) AS avg_usage, min(power_reading) AS min_usage, max(power_reading) AS max_usage FROM smartHomeData GROUP BY metro_area | 26 sec |
| Smart Home | 10000000 | INSERT OVERWRITE TABLE peakDeviceUsageRange SELECT house_id, (max(power_reading) - min(power_reading)) AS usage_range FROM smartHomeData WHERE is_peak = true AND metro_area = "Seattle" GROUP BY house_id | 22 sec |
| Smart Home | 10000000 | INSERT OVERWRITE TABLE stateAvgUsage SELECT state, avg(power_reading) AS state_avg_usage FROM smartHomeData | 21 sec |
| Smart Home | 10000000 | INSERT OVERWRITE TABLE stateUsageDifference SELECT a2.state, (a2.state_avg_usage-a1.overall_avg) AS avg_usage_difference FROM (select avg(state_avg_usage) as overall_avg from stateAvgUsage) as a1 join stateAvgUsage as a2 |
1 sec
|
| Wikipedia | 10000000 | INSERT INTO TABLE wikiAvgArticleLength SELECT AVG(length) as avg_article_length FROM wiki | 48 min |
| Wikipedia | 10000000 | INSERT INTO TABLE wikiContributorSummary SELECT contributor_username, COUNT(*) as page_count FROM wiki GROUP BY contributor_username | 1 hour 45 min |
| Wikipedia | 10000000 | INSERT INTO TABLE wikiTotalArticleLength SELECT SUM(length) as total_article_chars FROM wiki | 44 min |
| Wikipedia | 10000000 | INSERT INTO TABLE wikiTotalArticlePages SELECT COUNT(*) as total_pages FROM wiki | 1 hour 17 min |
Retrieving Results
Scenario: Retrieving Process Monitoring Data via REST API
This test was conducted on a test setup as shown in the following figure,
Infrastructure used
- JMeter, DAS 3.1.0, MySQL: c4.xlarge (4 vCPUs, 7.5 GB, EBS-Only, 750 Mbps network)
- Linux kernel 4.44, java version "1.8.0_131", JVM flags : -Xmx4g -Xms2g, MySQL version 5.7
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/second and an average latency of 29 ms was measured at the JMeter.
| API invocations per second | 2839 |
| Average Latency (ms) | 29 |