Module path: script.
Description: Setup experiment environment, install Storm, Flink, Spark, Kafka, HDFS and Zookeeper.
Usage:
    1. Set ssh passwordless login experiment cluster;
    2. Update configureation file cluster-config.json to the role of each node in the cluster;
    3. Clone this repository to home directory of master node;
    4. Run python scripts\pull-updates.py to clone this repository to each node in the cluster;
    4. Run python scripts\install.py with different parameter to install softwares

Module path: StreamBench/generator.
Description: This module defineds data generators for workloads. Parameters could be configured in configure files under resources folder.
Usage:
    1. go to module foler;
    2. execute command mvn clean package;
    3. get packaged jar file with dependencies -- generator-1.0-SNAPSHOT-jar-with-dependencies.jar;
    4. start generator: java -cp generator*.jar fi.aalto.dmg.generator.GeneratorClass (interval), interval is a parameter to control speed of generation;

Special case: For workload KMeans, we should generate real centoirds in generator class fi.aalto.dmg.generator.KMeansPoints, then generate test data. Detail information is in the source of this class.

Module path: StreamBench/flink.
Description: This module implements APIs of core module with Flink's built-in APIs. Parameters could be configured in file config.properties.
Usage:
    1. go to module foler;
    2. modify tage program-class in file pom.xml to specify the benchmark workload;
    3. execute command mvn clean package;
    3. get packaged jar file with dependencies -- flink-1.0-SNAPSHOT-jar-with-dependencies.jar;
    4. start flink cluster and submit workload job: /usr/local/flink/bin/flink run flink-1.0-SNAPSHOT-jar-with-dependencies.jar;

The output of benchmark job is log files. As the job runs in a distributed system, we use help script to collect log files. python StreamBench/script/logs-collection.py flink flink.tar

There are two script under statistic_script folder, latency.py and throughput.py Usage:

# extract latency log from original log
python latency.py extract origin.log latency.log
# combine several log files into one file
python latency.py combine output.log file1.log file2.log ...
# analysis latency log
python latency.log analysis latency.log
# All in one process command, you could put original logs under one folder and run this command to ayalysis 
python latency.py process original-log-folder

For other platform, the usage are similar to Flink.