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Stateful Stream Processor

This project is an end-to-end example to demonstrate stateful stream processing on very high throughput of machine data. The core component is the LocalStreamBuffer located in 05_LocalStreamBuffer, that enables deterministic and exactly-once time-series joins of streaming data at a high sample rate of around 15000 joins per second and low latency. This solution is tested against a the time-series join within Apache Flink, that is window-based. To learn more about the proposed algorithm go to the section Time-Series Join with the Local Stream Buffer.

flink-architecture

The setup and how to start the data streaming is documented below and tested on Ubuntu 18.04 LTS.


Deployment Notes

1. Simulator

Unzip the events in 01_Simulator with the name events.json. This file has 1402232 rows with different number of values each.

cd 01_Simulator
python3 simulator.py

Set the variables with in simulator.py. The defauls are

  • BROKER_HOST = "localhost"
  • BROKER_PORT = 1883
  • EVENT_FILE = "events.json"
  • TOPIC_NAME = "machine/states"
  • SAMPLE_RATE = 1000

The maximum sample rate is around 1 kS/s.

2. MQTT Broker

This step requires a running docker-compose installation. Therefore, run:

  1. Install Docker version 1.10.0+
  2. Install Docker Compose version 1.6.0+

To set up the MQTT Broker locally with the default port of 1883, run:

cd 02_MQTT_Broker
docker-compose up --build -d

To check if the service is available or to stop it, run:

cd 02_MQTT_Broker
docker-compose ps
docker-compose logs
docker-compose stop

3. Stream Connector

To install the requirements for the Stream Connector, run these lines:

# Install Kafka version 2.5 to /kafka
cd 03_Stream_Connector
bash install_kafka_libs-1v4.sh
pip install confluent-kafka==1.4.2

4. Kafka

To setup Apache Kafka, run these lines:

# Install Kafka version 2.5 to /kafka
cd 04_Kafka
bash install_kafka-2v5.sh
# Copy the systemd service files
sudo cp zookeeper.service /etc/systemd/system/zookeeper.service 
sudo cp kafka.service /etc/systemd/system/kafka.service

Update services and enable auto-start:

sudo systemctl enable kafka
sudo systemctl enable zookeeper

Then (re-) start the services.

sudo systemctl stop zookeeper
sudo systemctl stop kafka
sudo systemctl restart zookeeper
sudo systemctl status --no-pager zookeeper
sudo systemctl restart kafka
sudo systemctl status --no-pager kafka

For a more detailed investigation sudo journalctl -b -u zookeeper.service -f is useful (or use other parameters as -e)!

Now, we can create our topic with the specified configs. Note that the MQTT records are streamed to the topic machine.data. Flink consumes records from this topic, processes them and writes the aggregated records to machine.out.

/kafka/bin/kafka-topics.sh --bootstrap-server localhost:9092 --create --topic machine.data --replication-factor 1 --partitions 5 --config cleanup.policy=compact --config retention.ms=172800000
/kafka/bin/kafka-topics.sh --bootstrap-server localhost:9092 --create --topic machine.out --replication-factor 1 --partitions 3 --config cleanup.policy=compact --config retention.ms=172800000
# check if the topic was created with the correct configs:
/kafka/bin/kafka-topics.sh --bootstrap-server :9092 --describe --topic machine.data

Here are some other useful command for

/kafka/bin/kafka-topics.sh --bootstrap-server localhost:9092 --list
/kafka/bin/kafka-topics.sh --bootstrap-server :9092 --delete --topic machine.data

In order to test the installation, open two terminal windows and start the consumer and producer instance each:

/kafka/bin/kafka-console-producer.sh --broker-list 127.0.0.1:9092 --topic machine.data --property parse.key=true --property key.separator=,
/kafka/bin/kafka-console-consumer.sh --bootstrap-server 127.0.0.1:9092 --topic machine.data --from-beginning --property print.key=true --property key.separator=,

After that, you can stream messages of the form [key],[value] from the producer to the consumer, e.g. 0,helloKafka.

5. Stream Processing

Flink in Java

Download Flink and extract to /flink:

wget https://mirror.klaus-uwe.me/apache/flink/flink-1.10.1/flink-1.10.1-bin-scala_2.12.tgz
tar -xvf flink-1.10.1-bin-scala_2.12.tgz
sudo cp -a flink-1.10.1 /flink
rm -r flink-1.10.1
cd /flink

To start the GUI of Flink on localhost:8081, and try a WordCount example, run:

/flink/bin/start-cluster.sh
/flink/bin/flink run /flink/examples/streaming/WordCount.jar --input $(pwd)/01_Simulator/events.json --output $(pwd)/01_Simulator/eventsWordCount.out

The directory 05_Flink_Java is an Intellij project, in that the class src/main/java/com/github/statful/StreamJoiner.java successfully forwards data from machine.data to machine.out and merges two types of metrics. Therefore, create a new maven/java project with Intelliji IDEA and make sure to install all maven dependencies from pom.xml.

You can also create a jar file from the java project with a maven clean and install. The resulting file target/StreamJoiner.jar can be uploaded to the local Flink Cluster and deployed. This doesn't work at the moment.

In both cases, you can check if the forwarding works by watching new arriving data via: (make sure that there is data in the topic machine.data)

/kafka/bin/kafka-console-consumer.sh --bootstrap-server 127.0.0.1:9092 --topic machine.out --property print.key=true --property key.separator=,

Flink in Python

(not working)

Load the required Jar files into /flink/lib, which can be found on maven.

flink-connector-kafka_2.12-1.10.1.jar       
flink-json-1.10.1.jar                  
flink-connector-kafka-base_2.12-1.10.1.jar  
flink-sql-connector-kafka_2.11-1.10.0.jar  
kafka-clients-2.5.0.jar             
log4j-1.2.17.jar

Then, it should be possible to test the word count test:

/flink/bin/start-cluster.sh
/flink/bin/flink run -py 06_Flink_Python/WordCount.py

This writes an output file into tmp/results.

To install Pyflink, simply run:

pip install apache-flink

More has not been tested yet.

Time-Series Join with the Local Stream Buffer

The LocalStreamBuffer is an algorithm for the deterministic join of two streams of time-series. It is optimised for high throughput and ensures both minimal latency and to join each candidate. The pre-assumption is that the records within each stream comes in order, as it is guaranteed by Streaming Platforms as Apache Kafka if each quantity has a dedicated message key. A global order across streams is not required. More information will be published in a work-in-progress paper with the title "Deterministic Time-Series Joins for Asynchronous High-Throughput Data Streams".

In a nutshell, a time-series join matches each record within one time-series with its previous and subsequent complement from the other time-series. This property should pertain independent of the record's ingestion time (the records should be ingested in order within a stream but not across them, as it is guaranteed by e.g. Apache kafka). In the following figure, for two different ingestion orders, the three join cases are depicted:

localstreambuffer_join-cases

A demo-implementation including a short test script is available in 05_LocalStreamBuffer/local_stream_buffer.py. To run it, start:

cd 05_LocalStreamBuffer
python local_stream_buffer.py
#> New ingest into 's' with timestamp: 1600000000.3101475
#> New ingest into 's' with timestamp: 1600000001.2089858
#> New ingest into 'r' with timestamp: 1600000000.8444219
#>  join case JR2:	 {'r.quantity': 'r', 'r.phenomenonTime': 1600000000.8444219, 'r.result': 0.7579544029403025, 's.quantity': 's', 's.phenomenonTime': 1600000000.3101475, 's.result': 0.7298317482601286}.
#>  join case JS2:	 {'r.quantity': 'r', 'r.phenomenonTime': 1600000000.8444219, 'r.result': 0.7579544029403025, 's.quantity': 's', 's.phenomenonTime': 1600000001.2089858, 's.result': 0.6839839319154413}.
#> ...
#> Join time-series with |r| = 10, |s| = 10.
#> joined 20 tuples in 0.001998424530029297 s.

Various functionality tests are implemented in tester.py, and tests including Kafka are in test_kafka_eos_joiner.py (which needs the events.json file).

Using the LocalStreamBuffer approach, one can join up to 100000 records per second from a list and around 15000 records per seconds from Kafka on localhost with exactly-once processing. That is three times more than the 5000 in Flink Java. As the Confluent Kafka Python client is limited to around 20000 published records per second and it also has to consume from Kafka, the time-series join itself makes up only a minor amount of time. In addition to the increased performance, the LocalStreamBuffer method ensures that all join candidates are joined deterministically, exactly-once and with minimal latency.

The joiner script that is the actual pendent of the solution in Apache Flink is ts_join_with_kafka_eos.py. This solution joins the time-series with 15000 joins per second (vs. 5000 in Flink), with a exactly-once processing and with minimal latency as it is not window-based. Therefore, the proposed algorithm is better for this use-case compared to the standard solution in Apache Flink.

Further steps to even improve the LocalStreamBuffer algorithm:

  • use batched production with linger times.
  • implement time triggered joins in cases the partner's succeeder has a too high latency.
  • handle more timeout policies, e.g., join even older or discard.

6. DB Connector

The Database Connector consumes Messages from Kafka, both from the topic machine.data and machine.out, and forwards them to InfluxDB via RestAPI.

This step requires a reachable InfluxDB endpoint on localhost:8086. To start the application, run:

cd 06_DB_Connector
python db_connector.py

In the connector, the package influxdb-python was used.

7. InfluxDB and Grafana

Setup

Update the configurations in the environment file 08_InfluxDB/.env. Change the password immediately and never commit this file if the service is available from other nodes!

To start InfluxDB and also Grafana, run`

cd 07_InfluxDB_Grafana
docker-compose up -d

To validate, if InfluxDB is running correctly, curl the service using:

curl -sl -I http://localhost:8086/ping
# Expected result, note the status code 204
HTTP/1.1 204 No Content
Content-Type: application/json
Request-Id: 2f7091fb-9daa-11ea-8002-0242ac110002
X-Influxdb-Build: OSS
X-Influxdb-Version: 1.8.0
X-Request-Id: 2f7091fb-9daa-11ea-8002-0242ac110002
Date: Sun, 24 May 2020 10:34:45 GMT

Grafana is started with InfluxDB and is reachable on localhost:3000.

To investigate the services or to stop them, run:

docker-compose ps
docker-compose logs [influxDB|Grafana]
docker-compose stop

First steps in InfluxDB

InfluxDB provides a RestAPI that can be executed via curl

curl -XPOST 'http://localhost:8086/query' --data-urlencode 'q=CREATE DATABASE "mydb"'
curl -XPOST 'http://localhost:8086/query?db=mydb' --data-urlencode 'q=SELECT * INTO "newmeas" FROM "mymeas"'
curl -G 'http://localhost:8086/query?db=mydb&pretty=true' --data-urlencode 'q=SELECT * FROM "mymeas"'

More API interface examples can be found here.

It is important, that the attributes time and all tags are the primary key and must be unique.

First steps in Grafana

Grafana is started with InfluxDB and is reachable on localhost:3000.

To retrieve data from the InfluxDB, add a new data source by clicking on Configuration -> Data Sources -> Add data source. Then fill the fields as shown in this screenshot:

source

The password is set in the environment file 08_InfluxDB/.env. As you can see in the green box, the test was successful!

Afterwards, a dashboard can be created that retrieves data from InfluxDB. Therefore, click on + -> Dashboard and then build a dashboard from scratch. However, it is also possible to import a dashboard that was previously exported.