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https://github.com/alexarchambault/ammonite-spark

Run spark calculations from Ammonite
https://github.com/alexarchambault/ammonite-spark

ammonite scala spark

Last synced: about 2 months ago
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Run spark calculations from Ammonite

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README 

        
# ammonite-spark



Run [spark](https://spark.apache.org/) calculations from [Ammonite](http://ammonite.io/)



[![Build Status](https://github.com/alexarchambault/ammonite-spark/actions/workflows/ci.yml/badge.svg)](https://github.com/alexarchambault/ammonite-spark/actions/workflows/ci.yml?query=branch%3Amain)



*ammonite-spark* allows to create SparkSessions from Ammonite. It passes some Ammonite internals to a `SparkSession`, so that spark calculations can be driven from Ammonite, as one would do from a [spark-shell](https://spark.apache.org/docs/2.3.1/quick-start.html#interactive-analysis-with-the-spark-shell).






## Table of content



1. [Quick start](#quick-start)

2. [`AmmoniteSparkSession` vs `SparkSession`](#ammonitesparksession-vs-sparksession)

   1. [Syncing dependencies](#syncing-dependencies)

3. [Using with standalone cluster](#using-with-standalone-cluster)

4. [Using with YARN cluster](#using-with-yarn-cluster)

5. [Troubleshooting](#troubleshooting)

6. [Compatibility](#compatibility)



## Quick start



Start Ammonite >= [`1.6.3`](https://github.com/lihaoyi/Ammonite/releases/download/1.6.3/2.11-1.6.3), with the `--class-based` option.

The `--tmp-output-directory` option, available since Ammonite `3.0.0-M0-14-c12b6a59` is also recommended, especially in "tight" network environments (Kubernetes, …). The [compatibility section](#compatibility) lists the compatible versions of Ammonite and ammonite-spark. Start Ammonite by either following [the Ammonite instructions](http://ammonite.io/#Ammonite-REPL) on its website, then do

```

$ amm --class-based --tmp-output-directory

```

or use [coursier](https://github.com/coursier/coursier),

```

$ cs launch ammonite:3.0.0-M0-60-89836cd8 --scala 2.13.12 -- --class-based --tmp-output-directory

```

or [Scala CLI](https://github.com/VirtusLab/scala-cli)

```

$ scala-cli repl --amm --ammonite-version 3.0.0-M0-60-89836cd8 --scala 2.13.12 -- --class-based --tmp-output-directory

```

Ensure you are using scala 2.12 or 2.13, the only supported Scala versions as of writing this.



At the Ammonite prompt, load the Spark 2.x or 3.x version of your choice, along with ammonite-spark,

```scala

@ import $ivy.`org.apache.spark::spark-sql:3.3.0`

@ import $ivy.`sh.almond::ammonite-spark:0.13.12`

```

(Note the two `::` before `spark-sql` or `ammonite-spark`, as these are scala dependencies.)



Then create a `SparkSession` using the builder provided by *ammonite-spark*

```scala

@ import org.apache.spark.sql._



@ val spark = {

    AmmoniteSparkSession.builder()

      .master("local[*]")

      .getOrCreate()

  }

```



Note the use of `AmmoniteSparkSession.builder()`, instead of `SparkSession.builder()` that one would use when e.g. writing a Spark job.



The builder returned by `AmmoniteSparkSession.builder()` extends the one of `SparkSession.builder()`, so that one can call `.appName("foo")`, `.config("key", "value")`, etc. on it.



See below for how to use it with [standalone clusters](#using-with-standalone-cluster), and how to use it with [YARN clusters](#using-with-yarn-cluster).



Note that *ammonite-spark* does *not* rely on a Spark distribution. The driver and executors classpaths are handled from the Ammonite session only, via ``import $ivy.`…` `` statements. See [INTERNALS](https://github.com/alexarchambault/ammonite-spark/blob/develop/INTERNALS.md) for more details.



You can then run Spark calculations, like

```scala

@ def sc = spark.sparkContext // 'def' recommended over 'val', to workaround SparkContext Java serialization issues



@ val rdd = sc.parallelize(1 to 100, 10)



@ val n = rdd.map(_ + 1).sum()

```



## Using with standalone cluster



Simply set the master to `spark://…` when building the session, e.g.

```scala

@ val spark = {

    AmmoniteSparkSession.builder()

      .master("spark://localhost:7077")

      .config("spark.executor.instances", "4")

      .config("spark.executor.memory", "2g")

      .getOrCreate()

  }

```



Ensure the version of Spark used to start the master and executors matches the one loaded in the Ammonite session (via e.g. ``import $ivy.`org.apache.spark::spark-sql:X.Y.Z` ``), and that the machine running Ammonite can access / is accessible from all nodes of the standalone cluster.



## Using with YARN cluster



Set the master to `"yarn"` when building the session, e.g.

```scala

@ val spark = {

    AmmoniteSparkSession.builder()

      .master("yarn")

      .config("spark.executor.instances", "4")

      .config("spark.executor.memory", "2g")

      .getOrCreate()

  }

```



Ensure the configuration directory of the cluster is set in `HADOOP_CONF_DIR` or `YARN_CONF_DIR` in the environment, or is available at `/etc/hadoop/conf`. This directory should contain files like `core-site.xml`, `hdfs-site.xml`, … Ensure also that the machine you run Ammonite on can indeed act as the driver (it should have access to and be accessible from the YARN nodes, etc.).



Before raising issues, ensure you are aware of all that needs to be set up to get a working spark-shell from a Spark distribution, and that all of them are passed in one way or another to the SparkSession created from Ammonite.



## Troubleshooting



### Getting `org.apache.spark.sql.AnalysisException` when calling `.toDS`



Add `org.apache.spark.sql.catalyst.encoders.OuterScopes.addOuterScope(this)` on the same lines as those where you define case classes involved, like

```scala

@ import spark.implicits._

import spark.implicits._



@ org.apache.spark.sql.catalyst.encoders.OuterScopes.addOuterScope(this); case class Foo(id: String, value: Int)

defined class Foo



@  val ds = List(Foo("Alice", 42), Foo("Bob", 43)).toDS

ds: Dataset[Foo] = [id: string, value: int]

```



(This should likely be added automatically in the future.)



## Compatibility



ammonite-spark relies on the API of Ammonite, which undergoes

non backward compatible changes from time to time. The following table lists

which versions of Ammonite ammonite-spark is built against - so is compatible

with for sure.



| ammonite-spark   | Ammonite | almond |

|------------------|----------|--------|

| `0.1.2`, `0.1.3` | `1.3.2`  |        |

| `0.2.0`          | `1.5.0`  | `0.2.0` |

| `0.3.0`          | `1.6.3`  | `0.3.0` |

| `0.4.0`          | `1.6.5`  | `0.4.0` |

| `0.4.1`          | `1.6.6`  | `0.5.0` |

| `0.4.2`          | `1.6.7`  | `0.5.0` |

| `0.5.0`          | `1.6.9-8-2a27ffe`  | `0.6.0` |

| `0.6.0`, `0.6.1` | `1.6.9-15-6720d42`  | `0.7.0`, `0.8.0` |

| `0.7.0`          | `1.7.1`  | `0.8.1` |

| `0.7.1`          | `1.7.3-3-b95f921`  |         |

| `0.7.2`          | `1.7.4`  | `0.8.2`, `0.8.3` |

| `0.8.0`          | `1.8.1`  |         |

| `0.9.0`          | `2.0.4`  |         |

| `0.10.0`          | `2.1.4`  | `0.10.0` |

| `0.10.1`          | `2.1.4`  | `0.10.1` |

| `0.11.0`          | `2.3.8-36-1cce53f3`  | `0.11.0` |

| `0.12.0`          | `2.3.8-122-9be39deb`  | skipped |

| `0.13.0`          | `2.5.4-8-30448e49` | `0.13.0` |

| `0.13.1`          | `2.5.4-13-1ebd00a6` | `0.13.1` |

| `0.13.2`          | `2.5.4-14-dc4c47bc` | `0.13.2` |

| ...               | ...                 | ...      |

[ `0.13.9`          | `3.0.0-M0-17-e7a04255` | `0.13.11` |