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
- mvn (from
- Redhat/CentOS:
- mvn (from
maven
package or maven3 tarball) - java-1.7.0-openjdk (or Oracle Java7 jdk)
- Python 2.6+
- R 3.x
- mvn (from
- 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
- requests-kerberos
- 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 https://github.com/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
tolivy.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 underSPARK_HOME
. If you wish to use a different Spark configuration for Livy, you can setSPARK_CONF_DIR
in Livy's environment. To define the default file system root for sessions, setHADOOP_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 defaultlivy.conf
file shipped with Livy for instructions.livy.server.spark-submit
: replaced by theSPARK_HOME
environment variable.
Using the Programmatic API
Livy provides a programmatic Java/Scala and Python API that allows applications to run code inside Spark without having to maintain a local Spark context. Here shows how to use the Java 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).
Weβll 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())
statement_url = host + r.headers['location']
r = requests.get(statement_url, 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
User group: http://groups.google.com/a/cloudera.org/group/livy-user
Dev group: http://groups.google.com/a/cloudera.org/group/livy-dev
Dev slack: https://livy-dev.slack.com.
To join: http://livy-slack-invite.azurewebsites.net. Invite token:
I'm not a bot
.Pull requests: https://github.com/cloudera/livy/pulls
REST API
GET /sessions
Returns all the active interactive sessions.
Request Parameters
name | description | type |
---|---|---|
from | The start index to fetch sessions | int |
size | Number of sessions to fetch | int |
Response Body
name | description | type |
---|---|---|
from | The start index of fetched sessions | int |
total | Number of sessions fetched | int |
sessions | Session list | list |
POST /sessions
Creates a new interactive 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 |
jars | jars to be used in this session | List of string |
pyFiles | Python files to be used in this session | List of string |
files | files to be used in this session | List of string |
driverMemory | Amount of memory to use for the driver process | string |
driverCores | Number of cores to use for the driver process | int |
executorMemory | Amount of memory to use per executor process | string |
executorCores | Number of cores to use for each executor | int |
numExecutors | Number of executors to launch for this session | int |
archives | Archives to be used in this session | List of string |
queue | The name of the YARN queue to which submitted | string |
name | The name of this session | string |
conf | Spark configuration properties | Map of key=val |
heartbeatTimeoutInSecond | Timeout in second to which session be orphaned | int |
Response Body
The created Session.
GET /sessions/{sessionId}
Returns the session information.
Response Body
The Session.
GET /sessions/{sessionId}/state
Returns the state of session
Response
name | description | type |
---|---|---|
id | Session id | int |
state | The current state of session | string |
DELETE /sessions/{sessionId}
Kills the Session job.
GET /sessions/{sessionId}/log
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 /sessions/{sessionId}/statements/{statementId}
Returns a specified statement in a session.
Response Body
The statement object.
POST /sessions/{sessionId}/statements/{statementId}/cancel
Cancel the specified statement in this session.
Response Body
name | description | type |
---|---|---|
msg | is always "cancelled" | string |
GET /batches
Returns all the active batch sessions.
Request Parameters
name | description | type |
---|---|---|
from | The start index to fetch sessions | int |
size | Number of sessions to fetch | int |
Response Body
name | description | type |
---|---|---|
from | The start index of fetched sessions | int |
total | Number of sessions fetched | int |
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 |
jars | jars to be used in this session | List of string |
pyFiles | Python files to be used in this session | List of string |
files | files to be used in this session | List of string |
driverMemory | Amount of memory to use for the driver process | string |
driverCores | Number of cores to use for the driver process | int |
executorMemory | Amount of memory to use per executor process | string |
executorCores | Number of cores to use for each executor | int |
numExecutors | Number of executors to launch for this session | int |
archives | Archives to be used in this session | List of string |
queue | The name of the YARN queue to which submitted | string |
name | The name of this session | string |
conf | Spark configuration properties | Map of key=val |
Response Body
The created Batch object.
GET /batches/{batchId}
Returns the batch session information.
Response Body
The Batch.
GET /batches/{batchId}/state
Returns the state of batch session
Response
name | description | type |
---|---|---|
id | Batch session id | int |
state | The current state of batch session | string |
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 |
appId | The application id of this session | String |
owner | Remote user who submitted this session | String |
proxyUser | User to impersonate when running | String |
kind | Session kind (spark, pyspark, or sparkr) | session kind |
log | The log lines | list of strings |
state | The session state | string |
appInfo | The detailed application info | Map of key=val |
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 |
shutting_down | Session is shutting down |
error | Session errored out |
dead | Session has exited |
success | Session is successfully stopped |
Session Kind
value | description |
---|---|
spark | Interactive Scala Spark session |
pyspark | Interactive Python 2 Spark session |
pyspark3 | Interactive Python 3 Spark session |
sparkr | Interactive R Spark session |
pyspark
To change the Python executable the session uses, Livy reads the path from environment variable
PYSPARK_PYTHON
(Same as pyspark).
Like pyspark, if Livy is running in local
mode, just set the environment variable.
If the session is running in yarn-cluster
mode, please set
spark.yarn.appMasterEnv.PYSPARK_PYTHON
in SparkConf so the environment variable is passed to
the driver.
pyspark3
To change the Python executable the session uses, Livy reads the path from environment variable
PYSPARK3_PYTHON
.
Like pyspark, if Livy is running in local
mode, just set the environment variable.
If the session is running in yarn-cluster
mode, please set
spark.yarn.appMasterEnv.PYSPARK3_PYTHON
in SparkConf so the environment variable is passed to
the driver.
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 |
---|---|
waiting | Statement is enqueued but execution hasn't started |
running | Statement is currently running |
available | Statement has a response ready |
error | Statement failed |
cancelling | Statement is being cancelling |
cancelled | Statement is cancelled |
Statement Output
name | description | type |
---|---|---|
status | Execution status | string |
execution_count | A monotonically 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 |
appId | The application id of this session | String |
appInfo | The detailed application info | Map of key=val |
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