docs/source/user-guide/installation.md

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# Installing DataFusion Comet

## Prerequisites

Make sure the following requirements are met and software installed on your machine.

### Supported Operating Systems

- Linux
- Apple OSX (Intel and Apple Silicon)

### Supported Spark Versions

Comet currently supports the following versions of Apache Spark:

- 3.3.x (Java 8/11/17, Scala 2.12/2.13)
- 3.4.x (Java 8/11/17, Scala 2.12/2.13)
- 3.5.x (Java 8/11/17, Scala 2.12/2.13)

Experimental support is provided for the following versions of Apache Spark and is intended for development/testing
use only and should not be used in production yet.

- 4.0.0-preview1 (Java 17/21, Scala 2.13)

Note that Comet may not fully work with proprietary forks of Apache Spark such as the Spark versions offered by
Cloud Service Providers.

## Using a Published JAR File

Comet jar files are available in [Maven Central](https://central.sonatype.com/namespace/org.apache.datafusion) for amd64 and arm64 architectures for Linux. For Apple OSX, it 
is currently necessary to build from source.

Here are the direct links for downloading the Comet 0.6.0 jar file.

- [Comet plugin for Spark 3.3 / Scala 2.12](https://repo1.maven.org/maven2/org/apache/datafusion/comet-spark-spark3.3_2.12/0.6.0/comet-spark-spark3.3_2.12-0.6.0.jar)
- [Comet plugin for Spark 3.3 / Scala 2.13](https://repo1.maven.org/maven2/org/apache/datafusion/comet-spark-spark3.3_2.13/0.6.0/comet-spark-spark3.3_2.13-0.6.0.jar)
- [Comet plugin for Spark 3.4 / Scala 2.12](https://repo1.maven.org/maven2/org/apache/datafusion/comet-spark-spark3.4_2.12/0.6.0/comet-spark-spark3.4_2.12-0.6.0.jar)
- [Comet plugin for Spark 3.4 / Scala 2.13](https://repo1.maven.org/maven2/org/apache/datafusion/comet-spark-spark3.4_2.13/0.6.0/comet-spark-spark3.4_2.13-0.6.0.jar)
- [Comet plugin for Spark 3.5 / Scala 2.12](https://repo1.maven.org/maven2/org/apache/datafusion/comet-spark-spark3.5_2.12/0.6.0/comet-spark-spark3.5_2.12-0.6.0.jar)
- [Comet plugin for Spark 3.5 / Scala 2.13](https://repo1.maven.org/maven2/org/apache/datafusion/comet-spark-spark3.5_2.13/0.6.0/comet-spark-spark3.5_2.13-0.6.0.jar)

## Building from source

Refer to the [Building from Source] guide for instructions from building Comet from source, either from official
source releases, or from the latest code in the GitHub repository.

[Building from Source]: source.md

## Deploying to Kubernetes

See the [Comet Kubernetes Guide](kubernetes.md) guide.

## Run Spark Shell with Comet enabled

Make sure `SPARK_HOME` points to the same Spark version as Comet was built for.

```console
export COMET_JAR=spark/target/comet-spark-spark3.4_2.12-0.7.0-SNAPSHOT.jar

$SPARK_HOME/bin/spark-shell \
    --jars $COMET_JAR \
    --conf spark.driver.extraClassPath=$COMET_JAR \
    --conf spark.executor.extraClassPath=$COMET_JAR \
    --conf spark.plugins=org.apache.spark.CometPlugin \
    --conf spark.shuffle.manager=org.apache.spark.sql.comet.execution.shuffle.CometShuffleManager \
    --conf spark.comet.explainFallback.enabled=true \
    --conf spark.memory.offHeap.enabled=true \
    --conf spark.memory.offHeap.size=16g \
```

### Verify Comet enabled for Spark SQL query

Create a test Parquet source

```scala
scala> (0 until 10).toDF("a").write.mode("overwrite").parquet("/tmp/test")
```

Query the data from the test source and check:

- INFO message shows the native Comet library has been initialized.
- The query plan reflects Comet operators being used for this query instead of Spark ones

```scala
scala> spark.read.parquet("/tmp/test").createOrReplaceTempView("t1")
scala> spark.sql("select * from t1 where a > 5").explain
INFO src/lib.rs: Comet native library initialized
== Physical Plan ==
        *(1) ColumnarToRow
        +- CometFilter [a#14], (isnotnull(a#14) AND (a#14 > 5))
          +- CometScan parquet [a#14] Batched: true, DataFilters: [isnotnull(a#14), (a#14 > 5)],
             Format: CometParquet, Location: InMemoryFileIndex(1 paths)[file:/tmp/test], PartitionFilters: [],
             PushedFilters: [IsNotNull(a), GreaterThan(a,5)], ReadSchema: struct<a:int>
```

With the configuration `spark.comet.explainFallback.enabled=true`, Comet will log any reasons that prevent a plan from
being executed natively.

```scala
scala> Seq(1,2,3,4).toDF("a").write.parquet("/tmp/test.parquet")
WARN CometSparkSessionExtensions$CometExecRule: Comet cannot execute some parts of this plan natively because:
  - LocalTableScan is not supported
  - WriteFiles is not supported
  - Execute InsertIntoHadoopFsRelationCommand is not supported
```

## Additional Configuration

Depending on your deployment mode you may also need to set the driver & executor class path(s) to
explicitly contain Comet otherwise Spark may use a different class-loader for the Comet components than its internal
components which will then fail at runtime. For example:

```
--driver-class-path spark/target/comet-spark-spark3.4_2.12-0.7.0-SNAPSHOT.jar
```

Some cluster managers may require additional configuration, see <https://spark.apache.org/docs/latest/cluster-overview.html>

### Memory tuning

In addition to Apache Spark memory configuration parameters, Comet introduces additional parameters to configure memory
allocation for native execution. See [Comet Memory Tuning](./tuning.md) for details.