Evaluate/projects/iceberg/java/site/docs/spark-quickstart.md

---
title: "Spark and Iceberg Quickstart"
---
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## 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](docs/latest/spark-ddl.md) section.

- [Docker-Compose](#docker-compose)
- [Creating a table](#creating-a-table)
- [Writing Data to a Table](#writing-data-to-a-table)
- [Reading Data from a Table](#reading-data-from-a-table)
- [Adding A Catalog](#adding-a-catalog)
- [Next Steps](#next-steps)

### Docker-Compose

The fastest way to get started is to use a docker-compose file that uses the [tabulario/spark-iceberg](https://hub.docker.com/r/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](https://docs.docker.com/get-docker/) as well as the [Docker Compose CLI](https://github.com/docker/compose-cli/blob/main/INSTALL.md).

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

```yaml
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:
```sh
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`](docs/latest/spark-ddl.md#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:

* [`CREATE TABLE ... PARTITIONED BY`](docs/latest/spark-ddl.md#create-table)
* [`CREATE TABLE ... AS SELECT`](docs/latest/spark-ddl.md#create-table--as-select)
* [`ALTER TABLE`](docs/latest/spark-ddl.md#alter-table)
* [`DROP TABLE`](docs/latest/spark-ddl.md#drop-table)

### 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](docs/latest/spark-configuration.md#catalogs) 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](releases.md) page.

<!-- markdown-link-check-disable-next-line -->
[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](docs/latest/spark-ddl.md) to learn more!