The following config properties must be removed from the
etc/config.properties file on both the coordinator and workers:
datasources property is now deprecated
and should also be removed (see Datasource Configuration).
Prevent Scheduling Work on Coordinator#
We have a new config property,
that allows or disallows scheduling work on the coordinator.
Previously, tasks like final aggregations could be scheduled on the
coordinator. For larger clusters, processing work on the coordinator
can impact query performance because the machine’s resources are not
available for the critical task of scheduling, managing and monitoring
We recommend setting this property to
false for the coordinator.
See Config Properties for an example.
datasources config property has been deprecated.
Please remove it from your
The datasources configuration is now automatically generated based
(see Prevent Scheduling Work on Coordinator).
Presto has an extremely experimental connector that was previously called
native connector and was intertwined with the main Presto code
(it was written before Presto had connectors). This connector is now
raptor and lives in a separate plugin.
As part of this refactoring, the
presto-metastore.db.filename config properties no longer exist
and must be removed from
The Raptor connector stores data on the Presto machines in a columnar format using the same layout that Presto uses for in-memory data. Currently, it has major limitations: lack of replication, dropping a table does not reclaim the storage, etc. It is only suitable for experimentation, temporary tables, caching of data from slower connectors, etc. The metadata and data formats are subject to change in incompatible ways between releases.
If you would like to experiment with the connector, create a catalog
properties file such as
etc/catalog/raptor.properties on both the
coordinator and workers that contains the following:
connector.name=raptor metadata.db.type=h2 metadata.db.filename=var/data/db/MetaStore
Machine Learning Functions#
Presto now has functions to train and use machine learning models (classifiers and regressors). This is currently only a proof of concept and is not ready for use in production. Example usage is as follows:
SELECT evaluate_classifier_predictions(label, classify(features, model)) FROM ( SELECT learn_classifier(label, features) AS model FROM training_data ) CROSS JOIN validation_data
In the above example, the column
label is a
bigint and the column
features is a map of feature identifiers to feature values. The feature
identifiers must be integers (encoded as strings because JSON only supports
strings for map keys) and the feature values are numbers (floating point).
Variable Length Binary Type#
Presto now supports the
varbinary type for variable length binary data.
Currently, the only supported function is
The Hive connector now maps the Hive
BINARY type to
- Add missing operator:
timestamp with time zone-
interval year to month
- Support explaining sampled queries
- Add JMX stats for abandoned and canceled queries
javax.injectto parent-first class list for plugins
- Improve error categorization in event logging