Getting Started🔗
The latest version of Iceberg is 1.6.1.
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:
Info
If you want to include Iceberg in your Spark installation, add the iceberg-spark-runtime-3.5_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 $PWD/warehouse
and adds support for Iceberg tables to Spark's built-in catalog:
spark-sql --packages org.apache.iceberg:iceberg-spark-runtime-3.5_2.12:1.6.1\
--conf spark.sql.extensions=org.apache.iceberg.spark.extensions.IcebergSparkSessionExtensions \
--conf spark.sql.catalog.spark_catalog=org.apache.iceberg.spark.SparkSessionCatalog \
--conf spark.sql.catalog.spark_catalog.type=hive \
--conf spark.sql.catalog.local=org.apache.iceberg.spark.SparkCatalog \
--conf spark.sql.catalog.local.type=hadoop \
--conf spark.sql.catalog.local.warehouse=$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:
The old write
API is supported, but not recommended.
Reading🔗
To read with SQL, use the Iceberg table's name in a SELECT
query:
SQL is also the recommended way to inspect tables. To view all snapshots in a table, use the snapshots
metadata table:
+-------------------------+----------------+-----------+-----------+----------------------------------------------------+-----+
| 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
:
Type compatibility🔗
Spark and Iceberg support different set of types. Iceberg does the type conversion automatically, but not for all combinations, so you may want to understand the type conversion in Iceberg in prior to design the types of columns in your tables.
Spark type to Iceberg type🔗
This type conversion table describes how Spark types are converted to the Iceberg types. The conversion applies on both creating Iceberg table and writing to Iceberg table via Spark.
Spark | Iceberg | Notes |
---|---|---|
boolean | boolean | |
short | integer | |
byte | integer | |
integer | integer | |
long | long | |
float | float | |
double | double | |
date | date | |
timestamp | timestamp with timezone | |
timestamp_ntz | timestamp without timezone | |
char | string | |
varchar | string | |
string | string | |
binary | binary | |
decimal | decimal | |
struct | struct | |
array | list | |
map | map |
Info
The table is based on representing conversion during creating table. In fact, broader supports are applied on write. Here're some points on write:
- Iceberg numeric types (
integer
,long
,float
,double
,decimal
) support promotion during writes. e.g. You can write Spark typesshort
,byte
,integer
,long
to Iceberg typelong
. - You can write to Iceberg
fixed
type using Sparkbinary
type. Note that assertion on the length will be performed.
Iceberg type to Spark type🔗
This type conversion table describes how Iceberg types are converted to the Spark types. The conversion applies on reading from Iceberg table via Spark.
Iceberg | Spark | Note |
---|---|---|
boolean | boolean | |
integer | integer | |
long | long | |
float | float | |
double | double | |
date | date | |
time | Not supported | |
timestamp with timezone | timestamp | |
timestamp without timezone | timestamp_ntz | |
string | string | |
uuid | string | |
fixed | binary | |
binary | binary | |
decimal | decimal | |
struct | struct | |
list | array | |
map | map |
Next steps🔗
Next, you can learn more about Iceberg tables in Spark:
- DDL commands:
CREATE
,ALTER
, andDROP
- Querying data:
SELECT
queries and metadata tables - Writing data:
INSERT INTO
andMERGE INTO
- Maintaining tables with stored procedures