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Quick start with Apache Livy (part 2): the REST APIs

The second post of this series focuses on how to run a Livy server instance and start playing with its REST APIs. The steps below are meant for a Linux environment (any distribution).

Prerequisites

The prerequisites to start a Livy server are the following:
  • The JAVA_HOME env variable set to a JDK/JRE 8 installation.
  • A running Spark cluster.

Starting the Livy server

Download the latest version (0.4.0-incubating at the time this post is written) from the official website and extract the archive content (it is a ZIP file). Then setup the SPARK_HOME env variable to the Spark location in the server (for simplicity in this post I am assuming that the cluster is in the same machine as for the Livy server, but in the next post I will go through the customization of the configuration files, including the connection to a remote Spark cluster, wherever it is). By default Livy writes its logs into the $LIVY_HOME/logs location: you need to manually create this directory. Finally you can start the server:

$LIVY_HOME/bin/livy-server

Verify that the server is running by connecting to its web UI, which uses port 8998 by default:

http://<livy_host>:8998/ui

Using the REST APIs with Python

Livy offers REST APIs to start interactive sessions and submit Spark code the same way you can do with a Spark shell or a PySpark shell. The examples in this post are in Python. Let's create an interactive session through a POST request first:

curl -X POST --data '{"kind": "pyspark"}' -H "Content-Type: application/json" localhost:8998/sessions

The kind attribute specifies which kind of language we want to use (pyspark is for Python). Other possible values for it are spark (for Scala) or sparkr (for R). If the request has been successful, the JSON response content contains the id of the open session:

{"id":0,"appId":null,"owner":null,"proxyUser":null,"state":"starting","kind":"pyspark","appInfo":{"driverLogUrl":null,"sparkUiUrl":null},"log":["stdout: ","\nstderr: "]}

You can double check through the web UI:

You can check the status of a given session any time through the REST API:

curl localhost:8998/sessions/<session_id> | python -m json.tool

Let's execute a code statement:

curl localhost:8998/sessions/0/statements -X POST -H 'Content-Type: application/json' -d '{"code":"2 + 2"}'

The code attribute contains the Python code you want to execute. The response of this POST request contains the id of the statement and the its execution status:

{"id":0,"code":"2 + 2","state":"waiting","output":null,"progress":0.0}

To check if a statement has been completed and get the result:

curl localhost:8998/sessions/0/statements/0

If a statement has been completed, the result of the execution is returned as part of the response:

{"id":0,"code":"2 + 2","state":"available","output":{"status":"ok","execution_count":0,"data":{"text/plain":"4"}},"progress":1.0}

This information is available through the web UI as well:



The same way you can submit any PySpark code:

curl localhost:8998/sessions/0/statements -X POST -H 'Content-Type: application/json' -d '{"code":"sc.parallelize([1, 2, 3, 4, 5]).count()"}'


When you're done, you can close the session:

curl localhost:8998/sessions/0 -X DELETE
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