Accelerating Apache Iceberg Parquet Scans using Comet (Experimental)

Note: Iceberg integration is a work-in-progress. It is currently necessary to build Iceberg from source rather than using available artifacts in Maven

Build Comet

Run a Maven install so that we can compile Iceberg against latest Comet:

mvn install -DskipTests

Build the release JAR to be used from Spark:

make release

Set COMET_JAR env var:

export COMET_JAR=`pwd`/spark/target/comet-spark-spark3.5_2.12-0.9.0.jar

Build Iceberg

Clone the Iceberg repository.

git clone git@github.com:apache/iceberg.git

It will be necessary to make some small changes to Iceberg:

• Update Gradle files to change Comet version to 0.9.0.
• Replace import org.apache.comet.shaded.arrow.c.CometSchemaImporter; with import org.apache.comet.CometSchemaImporter;
• Modify SparkBatchQueryScan so that it implements the SupportsComet interface
• Stop shading Parquet by commenting out the following lines in the iceberg-spark build:

//    relocate 'org.apache.parquet', 'org.apache.iceberg.shaded.org.apache.parquet'
//    relocate 'shaded.parquet', 'org.apache.iceberg.shaded.org.apache.parquet.shaded'

Perform a clean build

./gradlew clean
./gradlew build -x test -x integrationTest

Test

Set ICEBERG_JAR environment variable.

export ICEBERG_JAR=`pwd`/spark/v3.5/spark-runtime/build/libs/iceberg-spark-runtime-3.5_2.12-1.10.0-SNAPSHOT.jar

Launch Spark Shell:

$SPARK_HOME/bin/spark-shell \
    --jars $COMET_JAR,$ICEBERG_JAR \
    --conf spark.driver.extraClassPath=$COMET_JAR,$ICEBERG_JAR \
    --conf spark.executor.extraClassPath=$COMET_JAR,$ICEBERG_JAR \
    --conf spark.sql.extensions=org.apache.iceberg.spark.extensions.IcebergSparkSessionExtensions \
    --conf spark.sql.catalog.spark_catalog=org.apache.iceberg.spark.SparkCatalog \
    --conf spark.sql.catalog.spark_catalog.type=hadoop \
    --conf spark.sql.catalog.spark_catalog.warehouse=/tmp/warehouse \
    --conf spark.plugins=org.apache.spark.CometPlugin \
    --conf spark.shuffle.manager=org.apache.spark.sql.comet.execution.shuffle.CometShuffleManager \
    --conf spark.sql.iceberg.parquet.reader-type=COMET \
    --conf spark.comet.explainFallback.enabled=true \
    --conf spark.memory.offHeap.enabled=true \
    --conf spark.memory.offHeap.size=16g

Create an Iceberg table. Note that Comet will not accelerate this part.

scala> spark.sql(s"CREATE TABLE IF NOT EXISTS t1 (c0 INT, c1 STRING) USING iceberg")
scala> spark.sql(s"INSERT INTO t1 VALUES ${(0 until 10000).map(i => (i, i)).mkString(",")}")

Comet should now be able to accelerate reading the table:

scala> spark.sql(s"SELECT * from t1").show()

This should produce the following output:

scala> spark.sql(s"SELECT * from t1").show()
25/04/28 07:29:37 INFO core/src/lib.rs: Comet native library version 0.9.0 initialized
25/04/28 07:29:37 WARN CometSparkSessionExtensions$CometExecRule: Comet cannot execute some parts of this plan natively (set spark.comet.explainFallback.enabled=false to disable this logging):
 CollectLimit
+-  Project [COMET: toprettystring is not supported]
   +- CometScanWrapper

+---+---+
| c0| c1|
+---+---+
|  0|  0|
|  1|  1|
|  2|  2|
|  3|  3|
|  4|  4|
|  5|  5|
|  6|  6|
|  7|  7|
|  8|  8|
|  9|  9|
| 10| 10|
| 11| 11|
| 12| 12|
| 13| 13|
| 14| 14|
| 15| 15|
| 16| 16|
| 17| 17|
| 18| 18|
| 19| 19|
+---+---+
only showing top 20 rows