Flink interpreter for Apache Zeppelin

Overview

Apache Flink is an open source platform for distributed stream and batch data processing. Flink’s core is a streaming dataflow engine that provides data distribution, communication, and fault tolerance for distributed computations over data streams. Flink also builds batch processing on top of the streaming engine, overlaying native iteration support, managed memory, and program optimization.

In Zeppelin 0.9, we refactor the Flink interpreter in Zeppelin to support the latest version of Flink. Only Flink 1.10+ is supported, old versions of flink won't work. Apache Flink is supported in Zeppelin with the Flink interpreter group which consists of the five interpreters listed below.

Prerequisites

• Download Flink 1.10 for scala 2.11 (Only scala-2.11 is supported, scala-2.12 is not supported yet in Zeppelin)

Configuration

The Flink interpreter can be configured with properties provided by Zeppelin (as following table). You can also add and set other flink properties which are not listed in the table. For a list of additional properties, refer to Flink Available Properties.

StreamExecutionEnvironment, ExecutionEnvironment, StreamTableEnvironment, BatchTableEnvironment

Zeppelin will create 6 variables as flink scala (%flink) entry point:

• senv (StreamExecutionEnvironment),
• benv (ExecutionEnvironment)
• stenv (StreamTableEnvironment for blink planner)
• btenv (BatchTableEnvironment for blink planner)
• stenv_2 (StreamTableEnvironment for flink planner)
• btenv_2 (BatchTableEnvironment for flink planner)

And will create 6 variables as pyflink (%flink.pyflink or %flink.ipyflink) entry point:

• s_env (StreamExecutionEnvironment),
• b_env (ExecutionEnvironment)
• st_env (StreamTableEnvironment for blink planner)
• bt_env (BatchTableEnvironment for blink planner)
• st_env_2 (StreamTableEnvironment for flink planner)
• bt_env_2 (BatchTableEnvironment for flink planner)

Blink/Flink Planner

There are 2 planners supported by Flink's table api: flink & blink.

• If you want to use DataSet api, and convert it to flink table then please use flink planner (btenv_2 and stenv_2).
• In other cases, we would always recommend you to use blink planner. This is also what flink batch/streaming sql interpreter use (%flink.bsql & %flink.ssql)

Check this page for the difference between flink planner and blink planner.

Execution mode (Local/Remote/Yarn/Yarn Application)

Flink in Zeppelin supports 4 execution modes (flink.execution.mode):

• Local
• Remote
• Yarn
• Yarn Application

Run Flink in Local Mode

Running Flink in Local mode will start a MiniCluster in local JVM. By default, the local MiniCluster will use port 8081, so make sure this port is available in your machine, otherwise you can configure rest.port to specify another port. You can also specify local.number-taskmanager and flink.tm.slot to customize the number of TM and number of slots per TM, because by default it is only 4 TM with 1 Slots which may not be enough for some cases.

Run Flink in Remote Mode

Running Flink in remote mode will connect to an existing flink cluster which could be standalone cluster or yarn session cluster. Besides specifying flink.execution.mode to be remote. You also need to specify flink.execution.remote.host and flink.execution.remote.port to point to flink job manager.

Run Flink in Yarn Mode

In order to run flink in Yarn mode, you need to make the following settings:

• Set flink.execution.mode to yarn
• Set HADOOP_CONF_DIR in flink's interpreter setting or zeppelin-env.sh.
• Make sure hadoop command is on your PATH. Because internally flink will call command hadoop classpath and load all the hadoop related jars in the flink interpreter process

Run Flink in Yarn Application Mode

In the above yarn mode, there will be a separated flink interpreter process. This may run out of resources when there're many interpreter processes. So it is recommended to use yarn application mode if you are using flink 1.11 or afterwards (yarn application mode is only supported after flink 1.11). In this mode flink interpreter runs in the JobManager which is in yarn container. In order to run flink in yarn application mode, you need to make the following settings:

• Set flink.execution.mode to yarn-application
• Set HADOOP_CONF_DIR in flink's interpreter setting or zeppelin-env.sh.
• Make sure hadoop command is on your PATH. Because internally flink will call command hadoop classpath and load all the hadoop related jars in the flink interpreter process

How to use Hive

In order to use Hive in Flink, you have to make the following setting.

• Set zeppelin.flink.enableHive to be true
• Set zeppelin.flink.hive.version to be the hive version you are using.
• Set HIVE_CONF_DIR to be the location where hive-site.xml is located. Make sure hive metastore is started and you have configured hive.metastore.uris in hive-site.xml
• Copy the following dependencies to the lib folder of flink installation. 
  • flink-connector-hive_2.11–1.10.0.jar
  • flink-hadoop-compatibility_2.11–1.10.0.jar
  • hive-exec-2.x.jar (for hive 1.x, you need to copy hive-exec-1.x.jar, hive-metastore-1.x.jar, libfb303–0.9.2.jar and libthrift-0.9.2.jar)

Flink Batch SQL

%flink.bsql is used for flink's batch sql. You can type help to get all the available commands. It supports all the flink sql, including DML/DDL/DQL.

• Use insert into statement for batch ETL
• Use select statement for batch data analytics

Flink Streaming SQL

%flink.ssql is used for flink's streaming sql. You just type help to get all the available commands. It supports all the flink sql, including DML/DDL/DQL.

• Use insert into statement for streaming ETL
• Use select statement for streaming data analytics

Streaming Data Visualization

Zeppelin supports 3 types of streaming data analytics: * Single * Update * Append

type=single

Single mode is for the case when the result of sql statement is always one row, such as the following example. The output format is HTML, and you can specify paragraph local property template for the final output content template. And you can use {i} as placeholder for the ith column of result.

type=update

Update mode is suitable for the case when the output is more than one rows, and always will be updated continuously. Here’s one example where we use group by.

type=append

Append mode is suitable for the scenario where output data is always appended. E.g. the following example which use tumble window.

Flink UDF

You can use Flink scala UDF or Python UDF in sql. UDF for batch and streaming sql is the same. Here're 2 examples.

• Scala UDF

%flink

class ScalaUpper extends ScalarFunction {
  def eval(str: String) = str.toUpperCase
}
btenv.registerFunction("scala_upper", new ScalaUpper())

• Python UDF

%flink.pyflink

class PythonUpper(ScalarFunction):
  def eval(self, s):
    return s.upper()

bt_env.register_function("python_upper", udf(PythonUpper(), DataTypes.STRING(), DataTypes.STRING()))

Zeppelin only supports scala and python for flink interpreter, if you want to write a java udf or the udf is pretty complicated which make it not suitable to write in Zeppelin, then you can write the udf in IDE and build an udf jar. In Zeppelin you just need to specify flink.udf.jars to this jar, and flink interpreter will detect all the udfs in this jar and register all the udfs to TableEnvironment, the udf name is the class name.

PyFlink(%flink.pyflink)

In order to use PyFlink in Zeppelin, you just need to do the following configuration. * Install apache-flink (e.g. pip install apache-flink) * Set zeppelin.pyflink.python to the python executable where apache-flink is installed in case you have multiple python installed. * Copy flink-python_2.11–1.10.0.jar from flink opt folder to flink lib folder

And PyFlink will create 6 variables for you:

• s_env (StreamExecutionEnvironment),
• b_env (ExecutionEnvironment)
• st_env (StreamTableEnvironment for blink planner)
• bt_env (BatchTableEnvironment for blink planner)
• st_env_2 (StreamTableEnvironment for flink planner)
• bt_env_2 (BatchTableEnvironment for flink planner)

IPython Support(%flink.ipyflink)

By default, zeppelin would use IPython in %flink.pyflink when IPython is available, Otherwise it would fall back to the original python implementation. For the IPython features, you can refer docPython Interpreter

ZeppelinContext

Zeppelin automatically injects ZeppelinContext as variable z in your Scala/Python environment. ZeppelinContext provides some additional functions and utilities. See Zeppelin-Context for more details. You can use z to display both flink DataSet and batch/stream table.

• Display DataSet

  

• Display Batch Table

  

• Display Stream Table

  

Paragraph local properties

In the section of Streaming Data Visualization, we demonstrate the different visualization type via paragraph local properties: type. In this section, we will list and explain all the supported local properties in flink interpreter.

Tutorial Notes

Zeppelin is shipped with several Flink tutorial notes which may be helpful for you. You can check for more features in the tutorial notes.