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This page lists performance analysis experiments conducted focusing on stream query processing and event injestion with persistance. The following table shows a complete summary of the content presented in this page.



Data StoreQuery TypeAmount of events processedAverage Throughput (events/second)

Average Latency

(milliseconds)

Simple Pass-throughNoneNone30 million900K0.9
FilterNoneFilter out all the events30 million900K1.5
Window-small (1 second)NoneSliding time window30 million100K48
Window - Large (1 minute)NoneSliding time window30 million100K130
PatternsNoneTemporal event sequence patterns1250 million500K550




Event Ingestion with Persistence





Oracle Event Store

Insert252 million70K42
Update75 million20K12


MS SQL Event Store

Insert198 million55K44.2
Update3.6 million1K4.6


MySQL Event Store

Insert12.2 million3.4K2.14
Update3 million5000.5

Notes

  • Event ingestion with persistence tests were conducted using the default Amazon RDS configurations.

  • All the tests for event ingestion with persistence were conducted for 1 hour.

  • Performance results were aggregated in 5000K event windows.
  • The above table had an input rate of 1000Kevents per second during the first four tests.
  • All the tests were conducted using TCP transport.

Stream Query Processing

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 as a client

  • Another node as Stream Processor Node

  • Experiments were carried out using TCP as a transport.



Scenario : Running Multiple Siddhi Queries

Query TypeSample QueryAmount of Events ProcessedAverage Throughput (events/second)Latency (ms)
Simple Passthrough

@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;

30 million900K0.9








Filter


@App:name("TCP_Benchmark")


@source(type = 'tcp', context='inputStream',@map(type= 'binary'))

define stream inputStream (iijtimestamp long,value float);

from inputStream[value<=1]
select iijtimestamp,value
insert into tempStream;

30 million900K1.5

@App:name("TCP_Benchmark")


@source(type = 'tcp', context='inputStream',@map(type= 'binary'))
define stream inputStream (iijtimestamp long,value float);

from inputStream[value<=0.5]
select iijtimestamp,value
insert into tempStream;

30 million450K1.1

@App:name("TCP_Benchmark")


@source(type = 'tcp', context='inputStream',@map(type= 'binary'))
define stream inputStream (iijtimestamp long,value float);

from inputStream[value<=0.25]
select iijtimestamp,value
insert into tempStream;

30 million226K0.6






Window

@App:name("TCP_Benchmark")

@source(type='tcp',context='inputStream',@map(type='binary'))
define stream inputStream (iijtimestamp long,value float);


from inputStream#window.time(1 sec)
select iijtimestamp,value
insert into tempStream;

30 million100K48

@App:name("TCP_Benchmark")

@source(type='tcp',context='inputStream',@map(type='binary'))
define stream inputStream (iijtimestamp long,value float);


from inputStream#window.time(1 min)
select iijtimestamp,value
insert into tempStream;
30 million100K130

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 as a client

  • Another node as Stream Processor Node

  • Experiments were carried out using TCP as a transport.


Data Set

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 goal keeper is equipped with two additional sensors, one at each hand.The sensors in the players’ shoes and hands produce data with 200Hz frequency, while the sensor in the ball produces data with 2000Hz frequency. The total data rate reaches roughly 15 position events per second. Every position event describes 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 please refer :


Used Pattern

We created patterns for goal scoring scenario. In this case the pattern matching query involves two events e1 and e2 which should occur one after the other with some preconditions being satisfied such as 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 x, y, and z values are compared correspond to the boundary points of the goal reg.


Sample Siddhi App
@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

Throughput (events / second)500,000
Latency (ms)550

Event Ingestion with Persistence

All the event ingestion with persistance tests were conducted using the following deployment cofiguration.


Oracle Event Store

Infrastructure used
  • c4.2xlarge (8 vCPU, 16GB RAM, EBS storage with 1000 Mbps max dedicated bandwidth)  Amazon EC2 instances 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 Oracle as the database node

  • Customized TCP client 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 on Oracle.

This test involved persisting process monitoring events each of approximately 180 bytes. The test injected 252 million events into Stream Processor with a publishing TPS of 70,000 events/second during 1 hour time period.


Throughput Graph


                                                                               

                     

 Latency Graph


Summary Results
Throughput (events/second)70,000
Latency (ms)42


Scenario 2: Update Query - Updating 10 million events on Oracle Data store

The test injected 10 million events into the properly indexed Oracle Database. This test involved persisting process monitoring events each of approximately 180 bytes.75 million update queries were performed with the publishing throughput as 20,000 events / second during 1 hour time period.

Throughput Graph

        

                                             

            Latency Graph                                 

Summary Results
Number Of Persisted Events10 million
Throughput (events/second)20,000
Latency (ms)12


Microsoft SQL Server Event Store

Infrastructure used
  • c4.2xlarge (8 vCPU, 16GB RAM, EBS storage with 1000 Mbps max dedicated bandwidth) Amazon EC2 instance 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 as the database node

  • Customized TCP client as the data publisher (Sample TCP client found in samples)

Scenario 1: Insert Query - Persisting 198 million  events of Process Monitoring Events on MS SQL

This test involved persisting process monitoring events each of approximately 180 bytes. The test injected 198 million events into Stream Processor with a publishing TPS of 55,000 events/second during 1 hour time period.

Throughput Graph


                                                                                             

                                                                                                                                                                                                                                                                                                  
                    Latency Graph


Summary Results
Throughput (events/second)55,000
Latency (ms)44.2

                           

Scenario 2: Update Query - Updating 10 million events on MS SQL Data store 

The test injected 10 million events into the properly indexed MS SQL Database. This test involved persisting process monitoring events each of approximately 180 bytes. 3.6 million update queries were performed with the publishing throughput as 1000 events / second during 1 hour time period.


                                                          Throughput Graph                                                                                                        

      

                                                                                                                                                                                                                                                                                                    

Summary Results
Number Of Persisted Events10 million
Throughput (events/second)1000
Latency (ms)4.6


MySQL Event Store

Infrastructure Used
  • c4.2xlarge (8 vCPU, 16GB RAM, EBS storage with 1000 Mbps max dedicated bandwidth)  Amazon EC2 instances 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 MySQL Community Edition version 5.7 as the database node

  • Customized TCP client 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 events of Process Monitoring Events on MySQL

This test involved persisting process monitoring events each of approximately 180 bytes. The test injected 12.2 million events into StreamProcessor with the publishing throughput as 3400 events/second during one hour time period.

Throughput Graph


Latency Graph

Summary Results
Throughput (events/second)3400
Latency (ms)2.14

MySQL Upper Limit

After around 12.2 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 with default settings. In order to continue receiving events without a major performance degradation data has to be purged periodically before it reaches the upper limit.


Scenario 2: Update Query - Updating 100K events on MySQL Data store

The test injected 100,000 events into the properly indexed My SQL Database. This test involved persisting process monitoring events each of approximately 180 bytes. 3 million update queries were performed with the publishing throughput as 500 events / second during 1 hour time period.

Throughput Graph


Latency Graph


Summary Results
Number Of Persisted Events100K
Throughput (events/second)500
Latency (ms)0.5

Conclusion

The performance analysis test results indicate that Stream Processor’s event persistence scenarios’ performance has been characterized by the event store database performance.









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