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Spark

This guide will get you up and running with Apache Iceberg™ using Apache Spark™, including sample code to highlight some powerful features. You can learn more about Iceberg's Spark runtime by checking out the Spark section.

Docker-Compose🔗

The fastest way to get started is to use a docker-compose file that uses the tabulario/spark-iceberg image which contains a local Spark cluster with a configured Iceberg catalog. To use this, you'll need to install the Docker CLI as well as the Docker Compose CLI.

Once you have those, save the yaml below into a file named docker-compose.yml:

services:  spark-iceberg:  image: tabulario/spark-iceberg  container_name: spark-iceberg  build: spark/  networks:  iceberg_net:  depends_on:  - rest  - minio  volumes:  - ./warehouse:/home/iceberg/warehouse  - ./notebooks:/home/iceberg/notebooks/notebooks  environment:  - AWS_ACCESS_KEY_ID=admin  - AWS_SECRET_ACCESS_KEY=password  - AWS_REGION=us-east-1  ports:  - 8888:8888  - 8080:8080  - 10000:10000  - 10001:10001  rest:  image: apache/iceberg-rest-fixture  container_name: iceberg-rest  networks:  iceberg_net:  ports:  - 8181:8181  environment:  - AWS_ACCESS_KEY_ID=admin  - AWS_SECRET_ACCESS_KEY=password  - AWS_REGION=us-east-1  - CATALOG_WAREHOUSE=s3://warehouse/  - CATALOG_IO__IMPL=org.apache.iceberg.aws.s3.S3FileIO  - CATALOG_S3_ENDPOINT=http://minio:9000  minio:  image: minio/minio  container_name: minio  environment:  - MINIO_ROOT_USER=admin  - MINIO_ROOT_PASSWORD=password  - MINIO_DOMAIN=minio  networks:  iceberg_net:  aliases:  - warehouse.minio  ports:  - 9001:9001  - 9000:9000  command: ["server", "/data", "--console-address", ":9001"]  mc:  depends_on:  - minio  image: minio/mc  container_name: mc  networks:  iceberg_net:  environment:  - AWS_ACCESS_KEY_ID=admin  - AWS_SECRET_ACCESS_KEY=password  - AWS_REGION=us-east-1  entrypoint: |  /bin/sh -c "  until (/usr/bin/mc alias set minio http://minio:9000 admin password) do echo '...waiting...' && sleep 1; done;  /usr/bin/mc rm -r --force minio/warehouse;  /usr/bin/mc mb minio/warehouse;  /usr/bin/mc policy set public minio/warehouse;  tail -f /dev/null  " networks:  iceberg_net:

Next, start up the docker containers with this command: 

docker-compose up

You can then run any of the following commands to start a Spark session.

docker exec -it spark-iceberg spark-sql 
docker exec -it spark-iceberg spark-shell 
docker exec -it spark-iceberg pyspark

Note

You can also use the notebook server available at http://localhost:8888

Creating a table🔗

To create your first Iceberg table in Spark, run a CREATE TABLE command. Let's create a table using demo.nyc.taxis where demo is the catalog name, nyc is the database name, and taxis is the table name.

First, create the database if it doesn't already exist:

CREATE DATABASE IF NOT EXISTS demo.nyc; 
spark.sql("CREATE DATABASE IF NOT EXISTS demo.nyc") 
spark.sql("CREATE DATABASE IF NOT EXISTS demo.nyc")

Then create the table:

CREATE TABLE demo.nyc.taxis (  vendor_id bigint,  trip_id bigint,  trip_distance float,  fare_amount double,  store_and_fwd_flag string ) PARTITIONED BY (vendor_id); 
import org.apache.spark.sql.types._ import org.apache.spark.sql.Row val schema = StructType( Array(  StructField("vendor_id", LongType,true),  StructField("trip_id", LongType,true),  StructField("trip_distance", FloatType,true),  StructField("fare_amount", DoubleType,true),  StructField("store_and_fwd_flag", StringType,true) )) val df = spark.createDataFrame(spark.sparkContext.emptyRDD[Row],schema) df.writeTo("demo.nyc.taxis").create() 
from pyspark.sql.types import DoubleType, FloatType, LongType, StructType,StructField, StringType schema = StructType([  StructField("vendor_id", LongType(), True),  StructField("trip_id", LongType(), True),  StructField("trip_distance", FloatType(), True),  StructField("fare_amount", DoubleType(), True),  StructField("store_and_fwd_flag", StringType(), True) ])  df = spark.createDataFrame([], schema) df.writeTo("demo.nyc.taxis").create()

Iceberg catalogs support the full range of SQL DDL commands, including:

Writing Data to a Table🔗

Once your table is created, you can insert records.

INSERT INTO demo.nyc.taxis VALUES (1, 1000371, 1.8, 15.32, 'N'), (2, 1000372, 2.5, 22.15, 'N'), (2, 1000373, 0.9, 9.01, 'N'), (1, 1000374, 8.4, 42.13, 'Y'); 
import org.apache.spark.sql.Row  val schema = spark.table("demo.nyc.taxis").schema val data = Seq(  Row(1: Long, 1000371: Long, 1.8f: Float, 15.32: Double, "N": String),  Row(2: Long, 1000372: Long, 2.5f: Float, 22.15: Double, "N": String),  Row(2: Long, 1000373: Long, 0.9f: Float, 9.01: Double, "N": String),  Row(1: Long, 1000374: Long, 8.4f: Float, 42.13: Double, "Y": String) ) val df = spark.createDataFrame(spark.sparkContext.parallelize(data), schema) df.writeTo("demo.nyc.taxis").append() 
schema = spark.table("demo.nyc.taxis").schema data = [  (1, 1000371, 1.8, 15.32, "N"),  (2, 1000372, 2.5, 22.15, "N"),  (2, 1000373, 0.9, 9.01, "N"),  (1, 1000374, 8.4, 42.13, "Y")  ] df = spark.createDataFrame(data, schema) df.writeTo("demo.nyc.taxis").append()

Reading Data from a Table🔗

To read a table, simply use the Iceberg table's name.

SELECT * FROM demo.nyc.taxis; 
val df = spark.table("demo.nyc.taxis").show() 
df = spark.table("demo.nyc.taxis").show()

Adding A Catalog🔗

Iceberg has several catalog back-ends that can be used to track tables, like JDBC, Hive MetaStore and Glue. Catalogs are configured using properties under spark.sql.catalog.(catalog_name). In this guide, we use JDBC, but you can follow these instructions to configure other catalog types. To learn more, check out the Catalog page in the Spark section.

This configuration creates a path-based catalog named local for tables under $PWD/warehouse and adds support for Iceberg tables to Spark's built-in catalog.

spark-sql --packages org.apache.iceberg:iceberg-spark-runtime-4.0_2.13:1.10.1\  --conf spark.sql.extensions=org.apache.iceberg.spark.extensions.IcebergSparkSessionExtensions \  --conf spark.sql.catalog.spark_catalog=org.apache.iceberg.spark.SparkSessionCatalog \  --conf spark.sql.catalog.spark_catalog.type=hive \  --conf spark.sql.catalog.local=org.apache.iceberg.spark.SparkCatalog \  --conf spark.sql.catalog.local.type=hadoop \  --conf spark.sql.catalog.local.warehouse=$PWD/warehouse \  --conf spark.sql.defaultCatalog=local 
spark.jars.packages org.apache.iceberg:iceberg-spark-runtime-4.0_2.13:1.10.1 spark.sql.extensions org.apache.iceberg.spark.extensions.IcebergSparkSessionExtensions spark.sql.catalog.spark_catalog org.apache.iceberg.spark.SparkSessionCatalog spark.sql.catalog.spark_catalog.type hive spark.sql.catalog.local org.apache.iceberg.spark.SparkCatalog spark.sql.catalog.local.type hadoop spark.sql.catalog.local.warehouse $PWD/warehouse spark.sql.defaultCatalog local

Note

If your Iceberg catalog is not set as the default catalog, you will have to switch to it by executing USE local;

Next steps🔗

Adding Iceberg to Spark🔗

If you already have a Spark environment, you can add Iceberg, using the --packages option.

spark-sql --packages org.apache.iceberg:iceberg-spark-runtime-4.0_2.13:1.10.1 
spark-shell --packages org.apache.iceberg:iceberg-spark-runtime-4.0_2.13:1.10.1 
pyspark --packages org.apache.iceberg:iceberg-spark-runtime-4.0_2.13:1.10.1

Note

If you want to include Iceberg in your Spark installation, add the Iceberg Spark runtime to Spark's jars folder. You can download the runtime from the Releases page.

Learn More🔗

Now that you're up and running with Iceberg and Spark, check out the Iceberg-Spark docs to learn more!