Release 0.76

    Apache Kafka 0.8+ is supported although Apache Kafka 0.8.1+ is recommended. There is extensive documentation about configuring the connector and a to get started.

    This release adds the MySQL Connector and for querying and creating tables in external relational databases. These can be used to join or copy data between different systems like MySQL and Hive, or between two different MySQL or PostgreSQL instances, or any combination.

    The Cassandra Connector configuration properties and cassandra.client.connect-timeout are now specified using a duration rather than milliseconds (this makes them consistent with all other such properties in Presto). If you were previously specifying a value such as 25, change it to 25ms.

    The retry policy for the Cassandra client is now configurable via the property. In particular, the custom BACKOFF retry policy may be useful.

    Hive allows the partitions in a table to have a different schema than the table. In particular, it allows changing the type of a column without changing the column type of existing partitions. The Hive connector does not support this and could previously return garbage data for partitions stored using the RCFile Text format if the column type was converted from a non-numeric type such as STRING to a numeric type such as BIGINT and the actual data in existing partitions was not numeric. The Hive connector now detects this scenario and fails the query after the partition metadata has been read.

    The property hive.storage-format is broken and has been disabled. It sets the storage format on the metadata but always writes the table using . This will be implemented in a future release.

    • Fix hang in verifier when an exception occurs.

    • Fix function to work with Unicode code points instead of ASCII code points.

    • Fix incorrect parsing of function arguments.

    • The bytecode compiler now caches generated code for join and group byqueries, which should improve performance and CPU efficiency for these types of queries.

    • Improve planning performance for certain trivial queries over tables with lots of partitions.