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Welcome to Livy

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

  • Interactive Scala, Python and R shells
  • Batch submissions in Scala, Java, Python
  • Multiple 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.

Prerequisites

To build Livy, you will need:

Debian/Ubuntu:
  • mvn (from maven package or maven3 tarball)
  • openjdk-7-jdk (or Oracle Java7 jdk)
  • Python 2.6+
  • R 3.x
Redhat/CentOS:
  • mvn (from maven package or maven3 tarball)
  • java-1.7.0-openjdk (or Oracle Java7 jdk)
  • Python 2.6+
  • R 3.x
MacOS:
  • Xcode command line tools
  • Oracle's JDK 1.7+
  • Maven (Homebrew)
  • Python 2.6+
  • R 3.x
Required python packages for building Livy:
  • cloudpickle
  • requests
  • flake8
  • flaky
  • pytest

To run Livy, you will also need a Spark installation. You can get Spark releases at https://spark.apache.org/downloads.html.

Livy requires at least Spark 1.6 and supports both Scala 2.10 and 2.11 builds of Spark, Livy will automatically pick repl dependencies through detecting the Scala version of Spark.

Livy also supports Spark 2.0+ for both interactive and batch submission, you could seamlessly switch to different versions of Spark through SPARK_HOME configuration, without needing to rebuild Livy.

Building Livy

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

git clone [email protected]:cloudera/livy.git
cd livy
mvn package

By default Livy is built against Apache Spark 1.6.2, but the version of Spark used when running Livy does not need to match the version used to build Livy. Livy internally uses reflection to mitigate the gaps between different Spark versions, also Livy package itself does not contain a Spark distribution, so it will work with any supported version of Spark (Spark 1.6+) without needing to rebuild against specific version of Spark.

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

Livy uses the Spark configuration under SPARK_HOME by default. You can override the Spark configuration by setting the SPARK_CONF_DIR environment variable before starting Livy.

It is strongly recommended to configure Spark to submit applications in YARN cluster mode. That makes sure that user sessions have their resources properly accounted for in the YARN cluster, and that the host running the Livy server doesn't become overloaded when multiple user sessions are running.

Livy Configuration

Livy uses a few configuration files under configuration the directory, which by default is the conf directory under the Livy installation. An alternative configuration directory can be provided by setting the LIVY_CONF_DIR environment variable when starting Livy.

The configuration files used by Livy are:

  • livy.conf: contains the server configuration. The Livy distribution ships with a default configuration file listing available configuration keys and their default values.
  • spark-blacklist.conf: list Spark configuration options that users are not allowed to override. These options will be restricted to either their default values, or the values set in the Spark configuration used by Livy.
  • log4j.properties: configuration for Livy logging. Defines log levels and where log messages will be written to. The default configuration will print log messages to stderr.

Upgrade from Livy 0.1

A few things changed between since Livy 0.1 that require manual intervention when upgrading.

  • Sessions that were active when the Livy 0.1 server was stopped may need to be killed manually. Use the tools from your cluster manager to achieve that (for example, the yarn command line tool).
  • The configuration file has been renamed from livy-defaults.conf to livy.conf.
  • A few configuration values do not have any effect anymore. Notably:
    • livy.server.session.factory: this config option has been replaced by the Spark configuration under SPARK_HOME. If you wish to use a different Spark configuration for Livy, you can set SPARK_CONF_DIR in Livy's environment. To define the default file system root for sessions, set HADOOP_CONF_DIR to point at the Hadoop configuration to use. The default Hadoop file system will be used.
    • livy.yarn.jar: this config has been replaced by separate configs listing specific archives for different Livy features. Refer to the default livy.conf file shipped with Livy for instructions.
    • livy.server.spark-submit: replaced by the SPARK_HOME environment variable.

Using the Programmatic API

Livy provides a programmatic Java API that allows applications to run code inside Spark without having to maintain a local Spark context. To use the API, add the Cloudera repository to your application's POM:

<repositories>
  <repository>
    <id>cloudera.repo</id>
    <url>https://repository.cloudera.com/artifactory/cloudera-repos</url>
    <name>Cloudera Repositories</name>
    <snapshots>
      <enabled>false</enabled>
    </snapshots>
  </repository>
</repositories>

And add the Livy client dependency:

<dependency>
  <groupId>com.cloudera.livy</groupId>
  <artifactId>livy-client-http</artifactId>
  <version>0.2.0</version>
</dependency>

To be able to compile code that uses Spark APIs, also add the correspondent Spark dependencies.

To run Spark jobs within your applications, extend com.cloudera.livy.Job and implement the functionality you need. Here's an example job that calculates an approximate value for Pi:

import java.util.*;

import org.apache.spark.api.java.*;
import org.apache.spark.api.java.function.*;

import com.cloudera.livy.*;

public class PiJob implements Job<Double>, Function<Integer, Integer>,
  Function2<Integer, Integer, Integer> {

  private final int samples;

  public PiJob(int samples) {
    this.samples = samples;
  }

  @Override
  public Double call(JobContext ctx) throws Exception {
    List<Integer> sampleList = new ArrayList<Integer>();
    for (int i = 0; i < samples; i++) {
      sampleList.add(i + 1);
    }

    return 4.0d * ctx.sc().parallelize(sampleList).map(this).reduce(this) / samples;
  }

  @Override
  public Integer call(Integer v1) {
    double x = Math.random();
    double y = Math.random();
    return (x*x + y*y < 1) ? 1 : 0;
  }

  @Override
  public Integer call(Integer v1, Integer v2) {
    return v1 + v2;
  }

}

To submit this code using Livy, create a LivyClient instance and upload your application code to the Spark context. Here's an example of code that submits the above job and prints the computed value:

LivyClient client = new LivyClientBuilder()
  .setURI(new URI(livyUrl))
  .build();

try {
  System.err.printf("Uploading %s to the Spark context...\n", piJar);
  client.uploadJar(new File(piJar)).get();

  System.err.printf("Running PiJob with %d samples...\n", samples);
  double pi = client.submit(new PiJob(samples)).get();

  System.out.println("Pi is roughly: " + pi);
} finally {
  client.stop(true);
}

To learn about all the functionality available to applications, read the javadoc documentation for the classes under the api module.

Spark Example

Here's a step-by-step example of interacting with Livy 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:

sudo pip install requests
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 statement 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, close the session:

session_url = 'http://localhost:8998/sessions/0'
requests.delete(session_url, headers=headers)

<Response [204]>

PySpark Example

PySpark has the same API, just with a different initial request:

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 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 User to impersonate when starting the session string
conf Spark configuration properties Map of key=val

Response Body

The created Session.

GET /sessions/{sessionId}

Returns the session information.

Response

The Session.

DELETE /sessions/{sessionId}

Kills the Session job.

GET /sessions/{sessionId}/logs

Gets the log lines from this session.

Request Parameters

name description type
from Offset int
size Max number of log lines to return int

Response Body

name description type
id The session id int
from Offset from start of log int
size Number of log lines int
log The log lines list of strings

GET /sessions/{sessionId}/statements

Returns all the statements in a session.

Response Body

name description type
statements statement list list

POST /sessions/{sessionId}/statements

Runs a statement in a session.

Request Body

name description type
code The code to execute string

Response Body

The statement object.

GET /batches

Returns all the active batch jobs.

Response Body

name description type
sessions batch list list

POST /batches

Request Body

name description type
file File containing the application to execute path (required)
proxyUser User to impersonate when running the job string
className Application Java/Spark main class string
args Command line arguments for the application list of strings
conf Spark configuration properties Map of key=val

Response Body

The created Batch object.

GET /batches/{batchId}

Request Parameters

name description type
from Offset int
size Max number 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}

Kills the Batch job.

GET /batches/{batchId}/log

Gets the log lines from this batch.

Request Parameters

name description type
from Offset int
size Max number of log lines to return int

Response Body

name description type
id The batch id int
from Offset from start of log int
size Number of log lines int
log The log lines list of strings

REST Objects

Session

A session represents 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

value 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

value description
spark Interactive Scala Spark session
pyspark Interactive Python Spark session
sparkr Interactive R Spark session

Statement

A statement represents 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

value description
running Statement is currently running
available Statement has a response ready
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 is a JSON value.

Batch

name description type
id The session id int
log The log lines list of strings
state The batch state string

License

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

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