Apache Iceberg features
You can quickly build on your past experience with SQL to analyze Iceberg tables.
This documentation describes how to use Iceberg features in Open Data Lakehouse. From Impala, you can create and query Iceberg tables. Impala queries are table-format agnostic. For example, Impala options are supported in queries of Iceberg tables. You can run nested, correlated, or analytic queries on all supported table types.
This documentation does not attempt to show every possible query supported from Impala. For example, creating a view is not covered, but you can create a view of any table from Impala using SQL syntax. Many examples of how to run queries on Iceberg tables from Impala are covered.
Supported ACID transaction properties
Iceberg supports atomic and isolated database transaction properties. Writers work in isolation, not affecting the live table, and perform a metadata swap only when the write is complete, making the changes in one atomic commit.
Iceberg uses snapshots to guarantee isolated reads and writes. You see a consistent version of table data without locking the table. Readers always see a consistent version of the data without the need to lock the table. Writers work in isolation, not affecting the live table, and perform a metadata swap only when the write is complete, making the changes in one atomic commit.
The Iceberg partitioning technique has performance advantages over conventional partitioning, such as Apache Hive partitioning. Iceberg hidden partitioning is easier to use. Iceberg supports in-place partition evolution; to change a partition, you do not rewrite the entire table to add a new partition column, and queries do not need to be rewritten for the updated table. Iceberg continuously gathers data statistics, which supports additional optimizations, such as partition pruning.
Iceberg uses multiple layers of metadata files to find and prune data. Hive and Impala keep track of data at the folder level and not at the file level, performing file list operations when working with data in a table. Performance problems occur during the execution of multiple list operations. Iceberg keeps track of a complete list of files within a table using a persistent tree structure. Changes to an Iceberg table use an atomic object/file level commit to update the path to a new snapshot file. The snapshot points to the individual data files through manifest files.
The manifest files track several data files across many partitions. These files store partition information and column metrics for each data file. A manifest list is an additional index for pruning entire manifests. File pruning increases efficiency.
Iceberg relieves Hive metastore (HMS) pressure by storing partition information in metadata files on the file system/object store instead of within the HMS. This architecture supports rapid scaling without performance hits.