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Welcome to Livy, the REST Spark Server

Livy is an open source REST interface for interacting with Spark from anywhere. It supports executing snippets of code or programs in a Spark context that runs locally or in YARN.

  • Interactive Scala, Python and R shells
  • Batch submissions in Scala, Java, Python
  • Multi users can share the same server (impersonation support)
  • Can be used for submitting jobs from anywhere with REST
  • Does not require any code change to your programs

Pull requests are welcomed! But before you begin, please check out the Wiki.

Quick Start

Livy is used for powering the Spark snippets of the Hadoop Notebook of Hue 3.8, which you can see the implementation here.

See the API documentation below and some curl examples:

Prerequisites

To build/run Livy, you will need:

Debian/Ubuntu:
  • mvn (from maven package or maven3 tarball)
  • openjdk-7-jdk (or Oracle Java7 jdk)
  • spark 1.4+ from (from Apache Spark tarball)
  • Python 2.6+
  • R 3.x
Redhat/CentOS:
  • mvn (from maven package or maven3 tarball)
  • java-1.7.0-openjdk (or Oracle Java7 jdk)
  • spark 1.4+ (from Apache Spark tarball)
  • Python 2.6+
  • R 3.x
MacOS:
  • Xcode command line tools
  • Oracle's JDK 1.7+
  • Maven (Homebrew)
  • apache-spark 1.5 (Homebrew)
  • Python 2.6+
  • R 3.x

Building Livy

Livy is built using Apache Maven. To checkout and build Livy, run:

% git clone [email protected]:cloudera/livy.git
% mvn -DskipTests clean package

By default Livy is built with the Cloudera distribution of Spark (currently based off Spark 1.5.0), but it is simple to support other versions, such as Spark 1.4.1, by compiling Livy with:

% mvn -DskipTests -Dspark.version=1.4.1 clean package

Running Tests

In order to run the Livy Tests, first follow the instructions in Building Livy. Then run:

% export SPARK_HOME=/usr/lib/spark
% export HADOOP_CONF_DIR=/etc/hadoop/conf
% mvn test

Running Livy

In order to run Livy with local sessions, first export these variables:

% export SPARK_HOME=/usr/lib/spark
% export HADOOP_CONF_DIR=/etc/hadoop/conf

Then start the server with:

% ./bin/livy-server

Or with YARN sessions by running:

% env \
  LIVY_SERVER_JAVA_OPTS="-Dlivy.server.session.factory=yarn" \
  CLASSPATH=`hadoop classpath` \
  $LIVY_HOME/bin/livy-server

Livy Configuration

The properties of the server can be modified by copying livy-defaults.conf.template and renaming it conf/livy-defaults.conf. The Livy configuration directory can be placed in an alternative directory by defining LIVY_CONF_DIR.

In particular the YARN mode (default is local process for development) can be set with:

livy.server.session.factory = yarn

Spark Example

Now to see it in action by interacting with it in Python with the Requests library. By default livy runs on port 8998 (which can be changed with the livy_server_port config option). We’ll start off with a Spark session that takes Scala code:

>>> import json, pprint, requests, textwrap
>>> host = 'http://localhost:8998'
>>> data = {'kind': 'spark'}
>>> headers = {'Content-Type': 'application/json'}
>>> r = requests.post(host + '/sessions', data=json.dumps(data), headers=headers)
>>> r.json()
{u'state': u'starting', u'id': 0, u’kind’: u’spark’}

Once the session has completed starting up, it transitions to the idle state:

>>> session_url = host + r.headers['location']
>>> r = requests.get(session_url, headers=headers)
>>> r.json()
{u'state': u'idle', u'id': 0, u’kind’: u’spark’}

Now we can execute Scala by passing in a simple JSON command:

>>> statements_url = session_url + '/statements'
>>> data = {'code': '1 + 1'}
>>> r = requests.post(statements_url, data=json.dumps(data), headers=headers)
>>> r.json()
{u'output': None, u'state': u'running', u'id': 0}

If a statement takes longer than a few milliseconds to execute, Livy returns early and provides a URL that can be polled until it is complete:

>>> statement_url = host + r.headers['location']
>>> r = requests.get(statement_url, headers=headers)
>>> pprint.pprint(r.json())
[{u'id': 0,
  u'output': {u'data': {u'text/plain': u'res0: Int = 2'},
              u'execution_count': 0,
              u'status': u'ok'},
  u'state': u'available'}]

That was a pretty simple example. More interesting is using Spark to estimate PI. This is from the Spark Examples:

>>> data = {
...   'code': textwrap.dedent("""\
...      val NUM_SAMPLES = 100000;
...      val count = sc.parallelize(1 to NUM_SAMPLES).map { i =>
...        val x = Math.random();
...        val y = Math.random();
...        if (x*x + y*y < 1) 1 else 0
...      }.reduce(_ + _);
...      println(\"Pi is roughly \" + 4.0 * count / NUM_SAMPLES)
...      """)
... }
>>> r = requests.post(statements_url, data=json.dumps(data), headers=headers)
>>> pprint.pprint(r.json())
{u'id': 1,
 u'output': {u'data': {u'text/plain': u'Pi is roughly 3.14004\nNUM_SAMPLES: Int = 100000\ncount: Int = 78501'},
             u'execution_count': 1,
             u'status': u'ok'},
 u'state': u'available'}

Finally, lets close our session:

>>> session_url = 'http://localhost:8998/sessions/0'
>>> requests.delete(session_url, headers=headers)
<Response [204]>

PySpark Example

pyspark has the exact same API, just with a different initial command:

>>> data = {'kind': 'pyspark'}
>>> r = requests.post(host + '/sessions', data=json.dumps(data), headers=headers)
>>> r.json()
{u'id': 1, u'state': u'idle'}

The PI example from before then can be run as:

>>> data = {
...   'code': textwrap.dedent("""\
...     import random
...     NUM_SAMPLES = 100000
...     def sample(p):
...       x, y = random.random(), random.random()
...       return 1 if x*x + y*y < 1 else 0
...
...     count = sc.parallelize(xrange(0, NUM_SAMPLES)).map(sample) \
...               .reduce(lambda a, b: a + b)
...     print "Pi is roughly %f" % (4.0 * count / NUM_SAMPLES)
...     """)
... }
>>> r = requests.post(statements_url, data=json.dumps(data), headers=headers)
>>> pprint.pprint(r.json())
{u'id': 12,
 u'output': {u'data': {u'text/plain': u'Pi is roughly 3.136000'},
             u'execution_count': 12,
             u'status': u'ok'},
 u'state': u'running'}

SparkR Example

SparkR also has the same API:

>>> data = {'kind': 'sparkR'}
>>> r = requests.post(host + '/sessions', data=json.dumps(data), headers=headers)
>>> r.json()
{u'id': 1, u'state': u'idle'}

The PI example from before then can be run as:

>>> data = {
...   'code': textwrap.dedent("""\
...      n <- 100000
...      piFunc <- function(elem) {
...        rands <- runif(n = 2, min = -1, max = 1)
...        val <- ifelse((rands[1]^2 + rands[2]^2) < 1, 1.0, 0.0)
...        val
...      }
...      piFuncVec <- function(elems) {
...        message(length(elems))
...        rands1 <- runif(n = length(elems), min = -1, max = 1)
...        rands2 <- runif(n = length(elems), min = -1, max = 1)
...        val <- ifelse((rands1^2 + rands2^2) < 1, 1.0, 0.0)
...        sum(val)
...      }
...      rdd <- parallelize(sc, 1:n, slices)
...      count <- reduce(lapplyPartition(rdd, piFuncVec), sum)
...      cat("Pi is roughly", 4.0 * count / n, "\n")
...     """)
... }
>>> r = requests.post(statements_url, data=json.dumps(data), headers=headers)
>>> pprint.pprint(r.json())
{u'id': 12,
 u'output': {u'data': {u'text/plain': u'Pi is roughly 3.136000'},
             u'execution_count': 12,
             u'status': u'ok'},
 u'state': u'running'}

Community

REST API

GET /sessions

Returns all the active interactive sessions.

Response Body

name description type
sessions session list list

POST /sessions

Creates a new interative Scala, Python or R shell in the cluster.

Request Body

name description type
kind The session kind (required) session kind
proxyUser The user to impersonate that will run this session (e.g. bob) string
jars Files to be placed on the java classpath list of paths
pyFiles Files to be placed on the PYTHONPATH list of paths
files Files to be placed in executor working directory list of paths
driverMemory Memory for driver (e.g. 1000M, 2G) string
driverCores Number of cores used by driver (YARN mode only) int
executorMemory Memory for executor (e.g. 1000M, 2G) string
executorCores Number of cores used by executor int
numExecutors Number of executors (YARN mode only) int
archives Archives to be uncompressed in the executor working directory (YARN mode only) list of paths
queue The YARN queue to submit too (YARN mode only) string
name Name of the application string
conf Spark configuration property Map of key=val

Response Body

The created Session.

GET /sessions/{sessionId}

Return the session information

Response

The Session.

DELETE /sessions/{sessionId}

Kill the Session job.

GET /sessions/{sessionId}/logs

Get the log lines from this session.

Request Parameters

name description type
from offset int
size amount of batches to return int

Response Body

name description type
id The session id int
from offset int
size total amount of lines int
log The log lines list of strings

GET /sessions/{sessionId}/statements

Return all the statements in a session.

Response Body

name description type
statements statement list list

POST /sessions/{sessionId}/statements

Execute a statement in a session.

Request Body

name description type
code The code to execute string

Response Body

The statement object.

GET /batches

Return all the active batch jobs.

Response Body

name description type
batches batch list list

POST /batches

Request Body

name description type
proxyUser The user to impersonate that will execute the job string
file Archive holding the file path (required)
args Command line arguments list of strings
className Application's java/spark main class string
jars Files to be placed on the java classpath list of paths
pyFiles Files to be placed on the PYTHONPATH list of paths
files Files to be placed in executor working directory list of paths
driverMemory Memory for driver (e.g. 1000M, 2G) string
driverCores Number of cores used by driver int
executorMemory Memory for executor (e.g. 1000M, 2G) string
executorCores Number of cores used by executor int
numExecutors Number of executor int
archives Archives to be uncompressed (YARN mode only) list of paths
queue The YARN queue to submit too (YARN mode only) string
name Name of the application string
conf Spark configuration property Map of key=val

Response Body

The created Batch object.

GET /batches/{batchId}

Request Parameters

name description type
from offset int
size amount of batches to return int

Response Body

name description type
id The batch id int
state The state of the batch batch state
log The output of the batch job list of strings

DELETE /batches/{batchId}

Kill the Batch job.

GET /batches/{batchId}/logs

Get the log lines from this batch.

Request Parameters

name description type
from offset int
size amount of batches to return int

Response Body

name description type
id The batch id int
from offset int
size total amount of lines int
log The log lines list of strings

REST Objects

Session

Sessions represent an interactive shell.

name description type
id The session id int
kind session kind (spark, pyspark, or sparkr) session kind (required)
log The log lines list of strings
state The session state string

Session State

name description
not_started session has not been started
starting session is starting
idle session is waiting for input
busy session is executing a statement
error session errored out
dead session has exited

Session Kind

name description
spark interactive scala/spark session
pyspark interactive python/spark session
sparkr interactive R/spark session

Statement

Statements represent the result of an execution statement.

name description type
id The statement id integer
state The execution state statement state
output The execution output statement output

Statement State

name description
running Statement is currently executing
available Statement has a ready response
error Statement failed

Statement Output

name description type
status execution status string
execution_count a monotomically increasing number integer
data statement output an object mapping a mime type to the result. If the mime type is application/json, the value will be a JSON value

Batch

name description type
id The session id int
kind session kind (spark, pyspark, or sparkr) session kind (required)
log The log lines list of strings
state The session state string

License

Apache License, Version 2.0 http://www.apache.org/licenses/LICENSE-2.0

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