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https://github.com/databricks/simr

Spark In MapReduce (SIMR) - launching Spark applications on existing Hadoop MapReduce infrastructure
https://github.com/databricks/simr

Last synced: 10 months ago
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Spark In MapReduce (SIMR) - launching Spark applications on existing Hadoop MapReduce infrastructure

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README 

          
# Spark In MapReduce (SIMR) Documentation



## Quick Guide



Download the `simr` runtime script, as well as the `simr-.jar` and  `spark-assembly-.jar` that match

the version of Hadoop your cluster is running. If it is not provided, you will have to build it

yourself. [See below](#advanced-configuration).



* SIMR runtime script

  + [Download] ()

* SIMR and Spark Jars are provided for the following Hadoop versions:

  + 1.0.4 (HDP 1.0 - 1.2) [SIMR Hadoop 1.0.4] () / [Spark Hadoop 1.0.4] ()

  + 1.2.x (HDP 1.3) [SIMR Hadoop 1.2.0] () / [Spark Hadoop 1.2.0] ()

  + 0.20 (CDH3) [SIMR CDH3] () / [Spark CDH3] ()

  + 2.0.0 (CDH4) [SIMR CDH4] () / [Spark CDH4] ()



Place `simr`, `simr-.jar`, and `spark-assembly-.jar` in a directory

and call `simr` to get usage information. Try running the shell! If you get stuck, continue reading.

```shell

./simr --shell

```



## Requirements



* Java v1.6 is required

* SIMR will ship Scala 2.9.3 and Spark 0.8.1 to the Hadoop cluster and execute your program with them.

* Spark jars are provided for Hadoop 1.0.4 (HDP 1.0 - 1.2), 1.2.x (HDP 1.3), 0.20 (CDH3), 2.0.0 (CDH4)



## Guide



Ensure the `hadoop` executable is in the PATH. If it is not, set $HADOOP to point to the binary, or

the hadoop/bin directory. Set `$SIMRJAR` and `$SPARKJAR` to specifiy which SIMR and Spark jars to

use, otherwise jars will be selected from the current directory.



To run a Spark application, package it up as a JAR file and execute:

```shell

./simr jar_file main_class parameters [--outdir=] [--slots=N] [--unique]

```



* `jar_file` is a JAR file containing all your programs, e.g. `spark-examples.jar`

* `main_class` is the name of the class with a `main` method, e.g. `org.apache.spark.examples.SparkPi`

* `parameters` is a list of parameters that will be passed to your `main_class`.

 + _Important_: the special parameter `%spark_url%` will be replaced with the Spark driver URL.

* `outdir` is an optional parameter which sets the path (absolute or relative) in HDFS where your

  job's output will be stored, e.g. `/user/alig/myjob11`.

  + If this parameter is not set, a directory will be created using the current time stamp in the

    form of `yyyy-MM-dd_kk_mm_ss`, e.g.  `2013-12-01_11_12_13`

* `slots` is an optional parameter that specifies the number of Map slots SIMR should utilize.  By

  default, SIMR sets the value to the number of nodes in the cluster.

  + This value must be at least 2, otherwise no executors will be present and the task will never

    complete.

* `unique` is an optional parameter which ensures that each node in the cluster will run at most 1

  SIMR executor.



Your output will be placed in the `outdir` in HDFS, this includes output from stdout/stderr for the driver and all executors.



**Important**: to ensure that your Spark jobs terminate without

  errors, you must end your Spark programs by calling `stop()` on

  `SparkContext`. In the case of the Spark examples, this usually

  means adding `spark.stop()` at the end of `main()`.



## Example



Assuming `spark-examples.jar` exists and contains the Spark examples, the following will execute the example that computes pi in 100 partitions in parallel:

```shell

./simr spark-examples.jar org.apache.spark.examples.SparkPi %spark_url% 100

```



Alternatively, you can launch a Spark-shell like this:

```shell

./simr --shell

```



## Configuration



The `$HADOOP` environment variable should point at the `hadoop` binary or its directory. To specify

the SIMR or Spark jar the runtime script should use, set the `$SIMRJAR` and `$SPARKJAR` environment

variables respectively. If these variables are not set, the runtime script will default to a SIMR

and Spark jar in the current directory.



By default SIMR figures out the number of task trackers in the cluster

and launches a job that is the same size as the cluster. This can be

adjusted by supplying the command line parameter ``--slots=``

to ``simr`` or setting the Hadoop configuration parameter

`simr.cluster.slots`.



## Network Configuration



SIMR expects its different components to communicate over the network, which

requires opening ports for communication. SIMR does not have a set of static

ports, as this would prevent multiple SIMR jobs from executing simultaneously.

Instead the ports are in the [Ephemeral Range](http://en.wikipedia.org/wiki/Ephemeral_port).

For SIMR to function properly ports in the ephemeral range should be opened.



## Advanced Configuration



The following sections are targeted at users who aim to run SIMR on versions of Hadoop for which

jars have not been provided. It is necessary to build both the appropriate version of

`simr-.jar` and `spark-assembly-.jar` and place them in the same

directory as the `simr` runtime script.



## Building Spark



In order to build SIMR, we must first compile a version of Spark that targets the version of Hadoop

that SIMR will be run on.



1. Download Spark v0.8.1 or greater.



2. Unpack and enter the Spark directory.



3. Modify `project/SparkBuild.scala`

  + Change the value of `DEFAULT_HADOOP_VERSION` to match the version of Hadoop you are targeting, e.g.

  `val DEFAULT_HADOOP_VERSION = "1.2.0"`



4. Run `sbt/sbt assembly` which creates a giant jumbo jar containing all of Spark in

   `assembly/target/scala*/spark-assembly--SNAPSHOT-.jar`.



5. Copy `assembly/target/scala*/spark-assembly--SNAPSHOT-.jar` to the

   same directory as the runtime script `simr` and follow the instructions below to build

   `simr-.jar`.



## Building SIMR



1. Checkout the SIMR repository from https://github.com/databricks/simr.git



2. Copy the Spark jumbo jar into the SIMR `lib/` directory.

  + **Important**: Ensure the Spark jumbo jar is named `spark-assembly.jar` when placed in the `lib/` directory,

    otherwise it will be included in the SIMR jumbo jar.



3. Run `sbt/sbt assembly` in the root of the SIMR directory. This will build the SIMR jumbo jar

   which will be output as `target/scala*/simr.jar`.



4. Copy `target/scala*/simr.jar` to the same directory as the runtime script `simr` and follow the

   instructions above to execute SIMR.



## How it works (advanced)



SIMR launches a Hadoop MapReduce job that only contains mappers. It

ensures that a jumbo jar (simr.jar), containing Scala and Spark, gets

uploaded to the machines of the mappers. It also ensures that the job

jar you specified gets shipped to those nodes.



Once the mappers are all running with the right dependencies in place,

SIMR uses HDFS to do leader election to elect one of the mappers as

the Spark driver. SIMR then executes your job driver, which uses a new

SIMR scheduler backend that generates and accepts driver URLs of the

form `simr://path`.  SIMR thereafter communicates the new driver URL

to all the mappers, which then start Spark executors. The executors

connect back to the driver, which executes your program.



All output to stdout and stderr is redirected to the specified HDFS

directory. Once your job is done, the SIMR backend scheduler has

additional functionality to shut down all the executors (hence the new

required call to `stop()`).