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Getting Started🔗

The latest version of Iceberg is 1.7.1.

Spark is currently the most feature-rich compute engine for Iceberg operations. We recommend you to get started with Spark to understand Iceberg concepts and features with examples. You can also view documentations of using Iceberg with other compute engine under the Multi-Engine Support page.

Using Iceberg in Spark 3🔗

To use Iceberg in a Spark shell, use the --packages option:

spark-shell --packages org.apache.iceberg:iceberg-spark-runtime-3.2_2.12:1.7.1

Info

If you want to include Iceberg in your Spark installation, add the iceberg-spark-runtime-3.2_2.12 Jar to Spark's jars folder.

Adding catalogs🔗

Iceberg comes with catalogs that enable SQL commands to manage tables and load them by name. Catalogs are configured using properties under spark.sql.catalog.(catalog_name).

This command 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_2.12:1.7.1\
    --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

Creating a table🔗

To create your first Iceberg table in Spark, use the spark-sql shell or spark.sql(...) to run a CREATE TABLE command:

-- local is the path-based catalog defined above
CREATE TABLE local.db.table (id bigint, data string) USING iceberg;

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

Writing🔗

Once your table is created, insert data using INSERT INTO:

INSERT INTO local.db.table VALUES (1, 'a'), (2, 'b'), (3, 'c');
INSERT INTO local.db.table SELECT id, data FROM source WHERE length(data) = 1;

Iceberg also adds row-level SQL updates to Spark, MERGE INTO and DELETE FROM:

MERGE INTO local.db.target t USING (SELECT * FROM updates) u ON t.id = u.id
WHEN MATCHED THEN UPDATE SET t.count = t.count + u.count
WHEN NOT MATCHED THEN INSERT *;

Iceberg supports writing DataFrames using the new v2 DataFrame write API:

spark.table("source").select("id", "data")
     .writeTo("local.db.table").append()

The old write API is supported, but not recommended.

Reading🔗

To read with SQL, use the Iceberg table's name in a SELECT query:

SELECT count(1) as count, data
FROM local.db.table
GROUP BY data;

SQL is also the recommended way to inspect tables. To view all snapshots in a table, use the snapshots metadata table: 

SELECT * FROM local.db.table.snapshots;

+-------------------------+----------------+-----------+-----------+----------------------------------------------------+-----+
| committed_at            | snapshot_id    | parent_id | operation | manifest_list                                      | ... |
+-------------------------+----------------+-----------+-----------+----------------------------------------------------+-----+
| 2019-02-08 03:29:51.215 | 57897183625154 | null      | append    | s3://.../table/metadata/snap-57897183625154-1.avro | ... |
|                         |                |           |           |                                                    | ... |
|                         |                |           |           |                                                    | ... |
| ...                     | ...            | ...       | ...       | ...                                                | ... |
+-------------------------+----------------+-----------+-----------+----------------------------------------------------+-----+

DataFrame reads are supported and can now reference tables by name using spark.table:

val df = spark.table("local.db.table")
df.count()

Next steps🔗

Next, you can learn more about Iceberg tables in Spark: