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Concord Kinesis Consumer

The Concord Kinesis consumer allows Concord users to quickly and easily ingest messages from Kinesis queues into their Concord topologies. The project is, itself, open source and buildable, but can be used by simply fetching a prebuilt megajar and configuring a task deployment.

Downloading

The latest release can be found at the following URL: https://storage.googleapis.com/concord-release/concord_kinesis_consumer-0.1.0.jar

Running

To launch a Kinesis consumer, you'll need two things:

  • A jar of a recent build of the Concord Kinesis consumer
  • A run script / "shim"
  • A json deployment manifest

Shim

The run script is responsible for defining the coniguration options. These include the following command line switches:

  • -topic (required): The name of the topic to consume from Kinesis
  • -ostreams (required): A comma separated list of Concord streams on which we will emit every record pulled off Kinesis.
  • -awskey (required): Your AWS Access Key ID
  • -awssecret (required): Your AWS Secret Key
  • -name (required): The name of the computation (to register with Concord)
  • -queuesize (default: 16384): The maximum number of messages to buffer in memory

An example shim, runner.bash might look like this:

#!/bin/bash

JAR="/path/to/concord-kinesis.jar"
AWSKEY="..."
AWSSECRET="..."

exec java -jar $JAR -topic my-topic -ostreams foo,bar\
  -awskey $AWSKEY -awssecret $AWSSECRET -name kinesis-consumer

JSON Manifest

An example JSON manifest for the above configuration might look like this:

{
  "zookeeper_hosts": "localhost:2181",
  "zookeeper_path": "/concord",
  "executable_name": "runner.bash",
  "compress_files": ["concord_kinesis_consumer.jar", "runner.bash"],
  "computation_name": "kinesis-consumer",
  "mem": 1024,
  "cpus": 1.0,
  "instances": 1
}

Directory structure

For this example, a simple flat directory structure will suffice:

kinesis-example/
  deploy.json
  runner.bash
  concord_kinesis_consumer.jar

Launching

To launch, simply run:

$ concord deploy deploy.json

and look at the Mesos dashboard. You should see the computation running!