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zon emr amazon data firehose amazon redshift google bigquery snowflake impala doris druid kafka connect integrations integrations aws dell jdbc nessie api api java quickstart java api java custom catalog javadoc pyiceberg icebergrust iceberggo 1 7 2 1 7 2 introduction tables tables branching and tagging configuration evolution maintenance metrics reporting partitioning performance reliability schemas views views configuration spark spark getting started configuration ddl procedures queries structured streaming writes flink flink flink getting started flink connector flink ddl flink queries flink writes flink actions flink configuration hive trino daft clickhouse presto dremio starrocks amazon athena amazon emr amazon data firehose amazon redshift google bigquery snowflake impala doris druid kafka connect integrations integrations aws dell jdbc nessie api api java quickstart java api java custom catalog javadoc pyiceberg icebergrust 1 7 1 1 7 1 introduction tables tables branching and tagging configuration evolution maintenance metrics reporting partitioning performance reliability schemas views views configuration spark spark getting started configuration ddl procedures queries structured streaming writes flink flink flink getting started flink connector flink ddl flink queries flink writes flink actions flink configuration hive trino daft clickhouse presto dremio starrocks amazon athena amazon emr amazon data firehose amazon redshift google bigquery snowflake impala doris druid kafka connect integrations integrations aws dell jdbc nessie api api java quickstart java api java custom catalog javadoc pyiceberg icebergrust 1 7 0 1 7 0 introduction tables tables branching and tagging configuration evolution maintenance metrics reporting partitioning performance reliability schemas views views configuration spark spark getting started configuration ddl procedures queries structured streaming writes flink flink flink getting started flink connector flink ddl flink queries flink writes flink actions flink configuration hive trino daft clickhouse presto dremio starrocks amazon athena amazon emr amazon data firehose amazon redshift google bigquery snowflake impala doris druid kafka connect integrations integrations aws dell jdbc nessie api api java quickstart java api java custom catalog javadoc pyiceberg icebergrust 1 6 1 1 6 1 introduction tables tables branching and tagging configuration evolution maintenance metrics reporting partitioning performance reliability schemas views views configuration spark spark getting started configuration ddl procedures queries structured streaming writes flink flink flink getting started flink connector flink ddl flink queries flink writes flink actions flink configuration hive trino daft clickhouse presto dremio starrocks amazon athena amazon emr google bigquery snowflake impala doris integrations integrations aws dell jdbc nessie api api java quickstart java api java custom catalog javadoc pyiceberg icebergrust 1 6 0 1 6 0 introduction tables tables branching and tagging configuration evolution maintenance metrics reporting partitioning performance reliability schemas views views configuration spark spark getting started configuration ddl procedures queries structured streaming writes flink flink flink getting started flink connector flink ddl flink queries flink writes flink actions flink configuration hive trino daft clickhouse presto dremio starrocks amazon athena amazon emr google bigquery snowflake impala doris integrations integrations aws dell jdbc nessie api api java quickstart java api java custom catalog javadoc pyiceberg icebergrust 1 5 2 1 5 2 introduction tables tables branching and tagging configuration evolution maintenance partitioning performance reliability schemas views views configuration spark spark getting started configuration ddl procedures queries structured streaming writes flink flink flink getting started flink connector flink ddl flink queries flink writes flink actions flink configuration hive trino clickhouse presto dremio starrocks amazon athena amazon emr snowflake impala doris integrations integrations aws dell jdbc nessie api api java quickstart java api java custom catalog javadoc pyiceberg icebergrust 1 5 1 1 5 1 introduction tables tables branching and tagging configuration evolution maintenance partitioning performance reliability schemas views views configuration spark spark getting started configuration ddl procedures queries structured streaming writes flink flink flink getting started flink connector flink ddl flink queries flink writes flink actions flink configuration hive trino clickhouse presto dremio starrocks amazon athena amazon emr snowflake impala doris integrations integrations aws dell jdbc nessie api api java quickstart java api java custom catalog javadoc pyiceberg icebergrust 1 5 0 1 5 0 introduction tables tables branching and tagging configuration evolution maintenance partitioning performance reliability schemas views views configuration spark spark getting started configuration ddl procedures queries structured streaming writes flink flink flink getting started flink connector flink ddl flink queries flink writes flink actions flink configuration hive trino clickhouse presto dremio starrocks amazon athena amazon emr snowflake impala doris integrations integrations aws dell jdbc nessie api api java quickstart java api java custom catalog javadoc pyiceberg icebergrust 1 4 3 1 4 3 introduction tables tables branching and tagging configuration evolution maintenance metrics reporting partitioning performance reliability schemas spark spark getting started configuration ddl procedures queries structured streaming writes flink flink flink getting started flink connector flink ddl flink queries flink writes flink actions flink configuration hive trino clickhouse presto dremio starrocks amazon athena amazon emr impala doris integrations integrations aws dell jdbc nessie api api java quickstart java api java custom catalog migration migration overview hive migration delta lake migration javadoc pyiceberg 1 4 2 1 4 2 introduction tables tables branching and tagging configuration evolution maintenance metrics reporting partitioning performance reliability schemas spark spark getting started configuration ddl procedures queries structured streaming writes flink flink flink getting started flink connector flink ddl flink queries flink writes flink actions flink configuration hive trino clickhouse presto dremio starrocks amazon athena amazon emr impala doris integrations integrations aws dell jdbc nessie api api java quickstart java api java custom catalog migration migration overview hive migration delta lake migration javadoc pyiceberg 1 4 1 1 4 1 introduction tables tables branching and tagging configuration evolution maintenance metrics reporting partitioning performance reliability schemas spark spark getting started configuration ddl procedures queries structured streaming writes flink flink flink getting started flink connector flink ddl flink queries flink writes flink actions flink configuration hive trino clickhouse presto dremio starrocks amazon athena amazon emr impala doris integrations integrations aws dell jdbc nessie api api java quickstart java api java custom catalog migration migration overview hive migration delta lake migration javadoc pyiceberg 1 4 0 1 4 0 introduction tables tables branching and tagging configuration evolution maintenance metrics reporting partitioning performance reliability schemas spark spark getting started configuration ddl procedures queries structured streaming writes flink flink flink getting started flink connector flink ddl flink queries flink writes flink actions flink configuration hive trino clickhouse presto dremio starrocks amazon athena amazon emr impala doris integrations integrations aws dell jdbc nessie api api java quickstart java api java custom catalog migration migration overview hive migration delta lake migration javadoc pyiceberg archive other implementations other implementations python rust go c third party third party catalogs catalogs apache gravitino apache polaris boring catalog datahub google biglake metastore lakekeeper integrations integrations amazon athena amazon data firehose amazon emr amazon redshift apache amoro apache doris apache druid apache fluss bladepipe clickhouse daft databend dremio duckdb estuary firebolt google bigquery impala memiiso debezium microsoft onelake nimtable olake presto redpanda risingwave ryft sail snowflake stackable starburst starrocks tinybird trino releases project project contributing multi engine support developer snapshot testing benchmarks security how to release asf asf sponsorship events privacy license security sponsors community community community talks vendors blog specification specification terms rest catalog spec table spec view spec puffin spec aes gcm stream spec udf spec implementation status table of contents overview use cases historical tags audit branch usage schema selection with branches and tags home docs java previous 1 9 1 tables branching and tagging overview iceberg table metadata maintains a snapshot log which represents the changes applied to a table snapshots are fundamental in iceberg as they are the basis for reader isolation and time travel queries for controlling metadata size and storage costs iceberg provides snapshot lifecycle management procedures such as expire_snapshots for removing unused snapshots and no longer necessary data files based on table snapshot retention properties for more sophisticated snapshot lifecycle management iceberg supports branches and tags which are named references to snapshots with their own independent lifecycles this lifecycle is controlled by branch and tag level retention policies branches are independent lineages of snapshots and point to the head of the lineage branches and tags have a maximum reference age property which control when the reference to the snapshot itself should be expired branches have retention properties which define the minimum number of snapshots to retain on a branch as well as the maximum age of individual snapshots to retain on the branch these properties are used when the expiresnapshots procedure is run for details on the algorithm for expiresnapshots refer to the spec use cases branching and tagging can be used for handling gdpr requirements and retaining important historical snapshots for auditing branches can also be used as part of data engineering workflows for enabling experimental branches for testing and validating new jobs see below for some examples of how branching and tagging can facilitate these use cases historical tags tags can be used for retaining important historical snapshots for auditing purposes the above diagram demonstrates retaining important historical snapshot with the following retention policy defined via spark sql retain 1 snapshot per week for 1 month this can be achieved by tagging the weekly snapshot and setting the tag retention to be a month snapshots will be kept and the branch reference itself will be retained for 1 week create a tag for the first end of week snapshot retain the snapshot for a week alter table prod db table create tag eow 01 as of version 7 retain 7 days retain 1 snapshot per month for 6 months this can be achieved by tagging the monthly snapshot and setting the tag retention to be 6 months create a tag for the first end of month snapshot retain the snapshot for 6 months alter table prod db table create tag eom 01 as of version 30 retain 180 days retain 1 snapshot per year forever this can be achieved by tagging the annual snapshot the default retention for branches and tags is forever create a tag for the end of the year and retain it forever alter table prod db table create tag eoy 2023 as of version 365 create a temporary test branch which is retained for 7 days and the latest 2 snapshots on the branch are retained create a branch test branch which will be retained for 7 days along with the latest 2 snapshots alter table prod db table create branch test branch retain 7 days with snapshot retention 2 snapshots audit branch the above diagram shows an example of using an audit branch for validating a write workflow first ensure write wap enabled is set alter table db table set tblproperties write wap enabled true create audit branch starting from snapshot 3 which will be written to and retained for 1 week alter table db table create branch audit branch as of version 3 retain 7 days writes are performed on a separate audit branch independent from the main table history wap branch write set spark wap branch audit branch insert into prod db table values 3 c a validation workflow can validate e g data quality the state of audit branch after validation the main branch can be fastforward to the head of audit branch to update the main table state call catalog_name system fast_forward prod db table main audit branch the branch reference will be removed when expiresnapshots is run 1 week later usage creating querying and writing to branches and tags are supported in the iceberg java library and in spark and flink engine integrations iceberg java library spark ddls spark reads spark branch writes flink reads flink branch writes schema selection with branches and tags it is important to understand that the schema tracked for a table is valid across all branches when working with branches the table s schema is used as that s the schema being validated when writing data to a branch on the other hands querying a tag uses the snapshot s schema which is the schema id that snapshot pointed to when the snapshot was created the below examples show which schema is being used when working with branches create a table and insert some data create table db table id bigint data string col float insert into db table values 1 a 1 0 2 b 2 0 3 c 3 0 select from db table 1 a 1 0 2 b 2 0 3 c 3 0 create a branch test_branch that points to the current snapshot and read data from the branch alter table db table create branch test_branch select from db table branch_test_branch 1 a 1 0 2 b 2 0 3 c 3 0 modify the table s schema by dropping the col column and adding a new column named new_col alter table db table drop column col alter table db table add column new_col date insert into db table values 4 d date 2024 04 04 5 e date 2024 05 05 select from db table 1 a null 2 b null 3 c null 4 d 2024 04 04 5 e 2024 05 05 querying the head of the branch using one of the below statements will return data using the table s schema select from db table branch_test_branch 1 a null 2 b null 3 c null select from db table version as of test_branch 1 a null 2 b null 3 c null performing a time travel query using the snapshot id uses the snapshot s schema select from db table refs test_branch branch 8109744798576441359 null null null main branch 6910357365743665710 null null null select from db table version as of 8109744798576441359...