This section lists performance analysis experiments conducted focusing on stream query processing and event ingestion with persistence.
Summary
The following table shows a complete summary of the content presented in this section.
| Tested Item | Data Store | Query Type | Amount of events processed | Average Throughput (events per second) | Average Latency (milliseconds) |
|---|---|---|---|---|---|
| Simple Pass-through | None | None | 30 million | 900K | 0.9 |
| Filter | None | Filter out all the events | 30 million | 900K | 1.5 |
| Window-small (1 second) | None | Sliding time window | 30 million | 100K | 48 |
| Window - Large (1 minute) | None | Sliding time window | 30 million | 100K | 130 |
| Patterns | None | Temporal event sequence patterns | 1250 million | 500K | 550 |
Event Ingestion with Persistence | Oracle Event Store | Insert | 252 million | 70K | 42 |
| Update | 75 million | 20K | 12 | ||
MS SQL Event Store | Insert | 198 million | 55K | 44.2 | |
| Update | 3.6 million | 1K | 4.6 | ||
MySQL Event Store | Insert | 12.2 million | 3.4K | 2.14 | |
| Update | 3 million | 500 | 0.5 |
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 1000K events per second during the first four tests.
- All the tests were conducted using TCP transport.
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.
| Query Type | Sample Query | Amount of Events Processed | Average Throughput (events per second) | Latency (ms) |
|---|---|---|---|---|
| Simple Passthrough |
| 30 million | 900K | 0.9 |
Filter |
define stream inputStream (iijtimestamp long,value float);from inputStream[value<=1] select iijtimestamp,value insert into tempStream; | 30 million | 900K | 1.5 |
| 30 million | 450K | 1.1 | |
| 30 million | 226K | 0.6 | |
Window |
| 30 million | 100K | 48 |
from inputStream#window.time(1 min) select iijtimestamp,value insert into tempStream; | 30 million | 100K | 130 |
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 kickoff.
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.
@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 per second) | 500,000 |
|---|---|
| Latency (ms) | 550 |
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
| Throughput (events per second) | 70,000 |
|---|---|
| Latency (ms) | 42 |
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
| Number Of Persisted Events | 10 million |
|---|---|
| Throughput (events per 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 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
| Throughput (event per second) | 55,000 |
|---|---|
| Latency (ms) | 44.2 |
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
| Number Of Persisted Events | 10 million |
|---|---|
| Throughput (events per 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 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
| Throughput (events per second) | 3400 |
|---|---|
| Latency (ms) | 2.14 |
MySQL Upper Limit
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
| Number Of Persisted Events | 100K |
|---|---|
| Throughput (events per second) | 500 |
| Latency (ms) | 0.5 |
Conclusion
The performance test results indicate that the event persistence performance of WSO2 Stream Processor is characterized by the event store database performance.