Spark and Iceberg Quickstart

This guide will get you up and running with an Iceberg and Spark environment, 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 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:

version: "3"

services:
  spark-iceberg:
    image: tabulario/spark-iceberg
    container_name: spark-iceberg
    build: spark/
    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
    links:
      - rest:rest
      - minio:minio
  rest:
    image: tabulario/iceberg-rest:0.1.0
    ports:
      - 8181:8181
    environment:
      - AWS_ACCESS_KEY_ID=admin
      - AWS_SECRET_ACCESS_KEY=password
      - AWS_REGION=us-east-1
      - CATALOG_WAREHOUSE=s3a://warehouse/wh/
      - 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
    ports:
      - 9001:9001
      - 9000:9000
    command: ["server", "/data", "--console-address", ":9001"]
  mc:
    depends_on:
      - minio
    image: minio/mc
    container_name: mc
    environment:
      - AWS_ACCESS_KEY_ID=admin
      - AWS_SECRET_ACCESS_KEY=password
      - AWS_REGION=us-east-1
    entrypoint: >
      /bin/sh -c "
      until (/usr/bin/mc config host add 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;
      exit 0;
      "      

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
You can also launch a notebook server by running docker exec -it spark-iceberg notebook. The notebook server will be 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.

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-3.2_2.12:1.1.0\
    --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.demo=org.apache.iceberg.spark.SparkCatalog \
    --conf spark.sql.catalog.demo.type=hadoop \
    --conf spark.sql.catalog.demo.warehouse=$PWD/warehouse \
    --conf spark.sql.defaultCatalog=demo
spark.jars.packages                                  org.apache.iceberg:iceberg-spark-runtime-3.2_2.12:1.1.0
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.demo                               org.apache.iceberg.spark.SparkCatalog
spark.sql.catalog.demo.type                          hadoop
spark.sql.catalog.demo.warehouse                     $PWD/warehouse
spark.sql.defaultCatalog                             demo
If your Iceberg catalog is not set as the default catalog, you will have to switch to it by executing USE demo;

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-3.2_2.12:1.1.0
spark-shell --packages org.apache.iceberg:iceberg-spark-runtime-3.2_2.12:1.1.0
pyspark --packages org.apache.iceberg:iceberg-spark-runtime-3.2_2.12:1.1.0
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 by visiting to the Releases page.

Learn More

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