This section lists performance analysis experiments conducted focusing on stream query processing and event ingestion with persistence.
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Summary
The following table shows a complete summary of the content presented on this page.
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Average Latency
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Oracle Event Store
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MS SQL Event Store
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MySQL Event Store
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Stream query processing
Scenario: Running multiple Siddhi queries
Infrastructure used
The experiments were carried out in two c4.2xlarge (8 vCPU, 16GB RAM, EBS storage with 1000 Mbps max dedicated bandwidth) Amazon EC2 instances.
Linux kernel 4.44, java version "1.8.0_131", JVM flags : -Xmx4g -Xms2g
One node operated as a client.
Another node operated as a Stream Processor node.
Experiments were carried out using TCP as the transport.
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@App:name("TCP_Benchmark")
@source(type = 'tcp', context='inputStream',@map(type='binary'))
define stream inputStream (iijtimestamp long,value float);
from inputStream
select iijtimestamp,value
insert into tempStream;
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Filter
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define stream inputStream (iijtimestamp long,value float);...
@App:name("TCP_Benchmark")
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@App:name("TCP_Benchmark")
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Window
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Scenario: Running Siddhi Pattern query on debs-2013-grand-challenge-soccer-monitoring dataset
Infrastructure used
The experiments were carried out in two c4.2xlarge (8 vCPU, 16GB RAM, EBS storage with 1000 Mbps max dedicated bandwidth) Amazon EC2 instances.
- Linux kernel 4.44, java version "1.8.0_131", JVM flags : -Xmx4g -Xms2g
One node operated as a client.
Another node operated as a Stream Processor node.
Experiments were carried out using TCP as the transport.
Dataset
The data used in 2013 DEBS Grand Challenge is collected by the Real-Time Locating System deployed on a football field of the Nuremberg Stadium in Germany. Data originates from sensors located near the players’ shoes (1 sensor per leg) and in the ball (1 sensor). The goalkeeper is equipped with two additional sensors in each hand. The sensors in the players’ shoes and hands produce data at a frequency of 200Hz, while the sensor in the ball produces data at a frequency of 2000Hz. The total data rate reaches roughly 15 position events per second. Every position event describes the position of a given sensor in a three-dimensional coordinate system. The center of the playing field is at coordinate (0, 0, 0) for the dimensions of the playing field and the coordinates of the kick off.
For more details about the dataset, see DEBS 2013 Grand Challenge: Soccer monitoring.
Pattern used
We created patterns for goal scoring scenario. In this scenario, the pattern matching query involves two events referrred to as e1 and e2 that should occur one after the other with some preconditions being satisfied. These preconditions include the position (denoted by x, y, and z) and the acceleration of the ball (denoted by a_abs). The numerical constants (such as 29880, 22560, etc.,) in the sample pattern matching query with which the values for x, y, and z are compared correspond to the boundary points of the goal reg.
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@App:name("TCP_Benchmark")
@source(type = 'tcp', context='inputStream',@map(type='binary'))
define stream innerStream(iij_timestamp long,sid int, eventtt long, x double, y, double, z int, v_abs double, a_abs int, vx int, vy int, vz int, ax int,ay int, az int);
from e1=innerStream[(x>29880 or x<22560) and y>-33968 and y<33965 and (sid==4 or sid ==12 or sid==10 or sid==8)]
-> e2=innerStream[(x<=29898 and x>22579) and y<=-33968 and z<2440 and a_abs>=55000 and (sid==4 or sid ==12 or sid==10 or sid==8)]
select
e2.sid as sid, e2.eventtt as eventtt, e2.x as x,e2.y as y, e2.z as z,e2.v_abs as v_abs,e2.a_abs as a_abs, e2.vx as vx,e2.vy as vy, e2.vz as
vz, e2.ax as ax,e2.ay as ay, e2.az as az, e1.iij_timestamp as iij_timestamp
insert into outputStream; |
Summary Results
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Ingesting events with persistence
All the event ingestion with persistence tests were conducted using the following deployment configuration.
Oracle event store
Infrastructure used
The following infrastructure was used in both scenarios:
c4.2xlarge (8 vCPU, 16GB RAM, EBS storage with 1000 Mbps max dedicated bandwidth) Amazon EC2 instances operated as the SP node and the TCP client.
Linux kernel 4.44, java version "1.8.0_131", JVM flags : -Xmx4g -Xms2g
db.m4.2xlarge (8 vCPU, 32 GB RAM, EBS-optimized storage with 100 Mbps max dedicated bandwidth) Amazon RDS instance with Oracle operated as the database node.
Customized TCP client operated as the data publisher (TCP producer found in samples).
- Experiments were carried out using Oracle 12g.
Scenario 1 : Insert query - Persisting 252 million events of process monitoring events in Oracle
This test involved persisting process monitoring events of approximately 180 bytes each. The test injected 252 million events into WSO2 Stream Processor with a publishing TPS of 70,000 events per second during a time period of one hour.
Throughput Graph
Latency Graph
Summary Results
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Scenario 2: Update Query - Updating 10 million events in Oracle Data store
The test injected 10 million events into the properly indexed Oracle Database. The test involved persisting process monitoring events of approximately 180 bytes each. 75 million update queries were performed. The publishing throughput was 20,000 events per second during a time period of one hour.
Throughput Graph
Latency Graph
Summary Results
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Microsoft SQL server event store
Infrastructure used
c4.2xlarge (8 vCPU, 16GB RAM, EBS storage with 1000 Mbps max dedicated bandwidth) Amazon EC2 instance operated as the SP node.
Linux kernel 4.44, java version “1.8.0_131", JVM flags : -Xmx4g -Xms2g
db.m4.2xlarge (8 vCPU, 32 GB RAM, EBS-optimized storage with 100 Mbps max dedicated bandwidth) Amazon RDS instance with MS SQL Enterprise Edition 2016 operated as the database node
Customized TCP client operated as the data publisher (Sample TCP client found in samples).
Scenario 1: Insert Query - Persisting 198 million process monitoring events in MS SQL
This test involved persisting process monitoring events of approximately 180 bytes each. The test injected 198 million events into WSO2 Stream Processor with a publishing throughput of 55,000 events per second during a time period of one hour.
Throughput Graph
Latency Graph
Summary Results
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Scenario 2: Update Query - Updating 10 million events in MS SQL data store
This test injected 10 million events into the properly indexed MS SQL Database. This test involved persisting process monitoring events of approximately 180 bytes each. 3.6 million update queries were performed with a publishing throughput of 1000 events per second during a time period of one hour.
Throughput Graph
Summary Results
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MySQL event store
Infrastructure Used
c4.2xlarge (8 vCPU, 16GB RAM, EBS storage with 1000 Mbps max dedicated bandwidth) Amazon EC2 instances operated as the SP node and the TCP client.
Linux kernel 4.44, java version "1.8.0_131", JVM flags : -Xmx4g -Xms2g
db.m4.2xlarge (8 vCPU, 32 GB RAM, EBS-optimized storage with 100 Mbps max dedicated bandwidth) Amazon RDS instance with MySQL Community Edition version 5.7 operated as the database node.
Customized TCP client operated as the data publisher (TCP producer found in samples).
- Experiments were carried out using 5.7.19 MySQL Community Server.
Scenario 1 : Insert Query - Persisting 12.2 million process monitoring events in MySQL
This test involved persisting process monitoring events of approximately 180 bytes each. The test injected 12.2 million events into WSO2 Stream Processor with a publishing throughput of 3400 events per second during a time period of one hour.
Throughput Graph
Latency Graph
Summary Results
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After about 12.2 million events are published, a sudden drop can be observed in the receiver performance. This number can be considered as the upper limit of MySQL event store with default settings. In order to continue receiving events without a major performance degradation, data should be purged periodically from the event store before it reaches the upper limit. |
Scenario 2: Update Query - Updating 100K events in MySQL data store
The test injected 100,000 events into the properly indexed My SQL Database. This test involved persisting process monitoring events of approximately 180 bytes each. Three million update queries were performed with a publishing throughput of 500 events per second during a time period of one hour.
Throughput Graph
Latency Graph
Summary Results
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| title | Conclusion |
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This section presents the results of the latest performance test carried out for WSO2 Stream Processor.
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These performance statistics were taken when the load average was below 3.8 in the 4 core instance. |
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Consuming events using a Kafka source
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Specifications for EC2 instances
Stream Processor: c5.xLarge
Kafka server: c5.xLarge
Kafka publisher: c5.xLarge
Siddhi application used
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@App:name("HelloKafka")
@App:description('Consume events from a Kafka Topic and publish to a different Kafka Topic')
@source(type='kafka',
topic.list='kafka_topic',
partition.no.list='0',
threading.option='single.thread',
group.id="group",
bootstrap.servers='172.31.0.135:9092',
@map(type='json'))
define stream SweetProductionStream (name string, amount double);
@sink(type='log')
define stream KafkaSourceThroughputStream(count long);
from SweetProductionStream#window.timeBatch(5 sec)
select count(*)/5 as count
insert into KafkaSourceThroughputStream; |
Results
Average Publishing TPS to Kafka : 1.1M
Average Consuming TPS from Kafka: 180K
Consuming messages from an HTTP source
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Specifications for EC2 instances
Stream Processor : c5.xLarge
JMeter: c5.xLarge
Siddhi application used
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@App:name("HttpSource")
@App:description('Consume events from http clients')
@source(type='http', worker.count='20', receiver.url='http://172.31.2.99:8081/service',
@map(type='json'))
define stream SweetProductionStream (name string, amount double);
@sink(type='log')
define stream HttpSourceThroughputStream(count long);
from SweetProductionStream#window.timeBatch(5 sec)
select count(*)/5 as count
insert into HttpSourceThroughputStream; |
Results
Average Publishing TPS to Http Source : 30K
Average Consuming TPS from Http Source: 30K
Sending HTTP requests and consuming the responses
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Specifications for EC2 instances
Stream Processor : c5.xLarge
JMeter: c5.xLarge
Web server : c5.xLarge
Siddhi application used
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@App:name("HttpRequestResponse")
@App:description('Consume events from an HTTP source, ')
@source(type='http', worker.count='20', receiver.url='http://172.31.2.99:8081/service',
@map(type='json'))
define stream SweetProductionStream (name string, amount double);
@sink(type='http-request', l, sink.id='production-request', publisher.url='http://172.17.0.1:8688//netty_echo_server', @map(type='json'))
define stream HttpRequestStream (batchNumber double, lowTotal double);
@source(type='http-response' , sink.id='production-request', http.status.code='200',
@map(type='json'))
define stream HttpResponseStream(batchNumber double, lowTotal double);
@sink(type='log')
define stream FinalThroughputStream(count long);
@sink(type='log')
define stream InputThroughputStream(count long);
from SweetProductionStream
select 1D as batchNumber, 1200D as lowTotal
insert into HttpRequestStream;
from SweetProductionStream#window.timeBatch(5 sec)
select count(*)/5 as count
insert into InputThroughputStream;
from HttpResponseStream#window.timeBatch(5 sec)
select count(*)/5 as count
insert into FinalThroughputStream; |
Results
Average Publishing TPS to HTTP Source : 29K
Average Publishing TPS from HTTP request sink: 29K
Average Consuming TPS from HTTP response source: 29K