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.
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.
=== “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 use the notebook server available at [http://localhost:8888](http://localhost:8888)
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:
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()
```
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()
```
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;
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
Now that you're up an running with Iceberg and Spark, check out the Iceberg-Spark docs to learn more!