title: Spark and Iceberg Quickstart
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
- Creating a table
- Writing Data to a Table
- Reading Data from a Table
- Adding A Catalog
- Next Steps
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:
version: "3"
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: tabulario/iceberg-rest
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 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;
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.
=== "SparkSQL"
``` sh
docker exec -it spark-iceberg spark-sql
```
=== "Spark-Shell"
``` sh
docker exec -it spark-iceberg spark-shell
```
=== "PySpark"
``` sh
docker exec -it spark-iceberg pyspark
```
!!! note
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](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.
=== "SparkSQL"
```sql
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);
```
=== "Spark-Shell"
```scala
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()
```
=== "PySpark"
```py
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.
=== "SparkSQL"
```sql
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');
```
=== "Spark-Shell"
```scala
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()
```
=== "PySpark"
```py
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.
=== "SparkSQL"
```sql
SELECT * FROM demo.nyc.taxis;
```
=== "Spark-Shell"
```scala
val df = spark.table("demo.nyc.taxis").show()
```
=== "PySpark"
```py
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 $PWD/warehouse and adds support for Iceberg tables to Spark's built-in catalog.
=== "CLI"
```sh
spark-sql --packages org.apache.iceberg:iceberg-spark-runtime-3.5_2.12:{{ icebergVersion }}\
--conf spark.sql.extensions=org.apache.iceberg.spark.extensions.IcebergSparkSessionExtensions \
--conf spark.sql.catalog.spark_catalog=org.apache.iceberg.spark.SparkSessionCatalog \
--conf spark.sql.catalog.spark_catalog.type=hive \
--conf spark.sql.catalog.local=org.apache.iceberg.spark.SparkCatalog \
--conf spark.sql.catalog.local.type=hadoop \
--conf spark.sql.catalog.local.warehouse=$PWD/warehouse \
--conf spark.sql.defaultCatalog=local
```
=== "spark-defaults.conf"
```sh
spark.jars.packages org.apache.iceberg:iceberg-spark-runtime-3.5_2.12:{{ icebergVersion }}
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.
=== "SparkSQL"
```sh
spark-sql --packages org.apache.iceberg:iceberg-spark-runtime-3.5_2.12:{{ icebergVersion }}
```
=== "Spark-Shell"
```sh
spark-shell --packages org.apache.iceberg:iceberg-spark-runtime-3.5_2.12:{{ icebergVersion }}
```
=== "PySpark"
```sh
pyspark --packages org.apache.iceberg:iceberg-spark-runtime-3.5_2.12:{{ icebergVersion }}
```
!!! 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 by visiting to the Releases page.
[spark-runtime-jar]: https://search.maven.org/remotecontent?filepath=org/apache/iceberg/iceberg-spark-runtime-3.5_2.12/{{ icebergVersion }}/iceberg-spark-runtime-3.5_2.12-{{ icebergVersion }}.jar
Learn More
Now that you're up an running with Iceberg and Spark, check out the Iceberg-Spark docs to learn more!