PostgreSQL connector#

The PostgreSQL connector allows querying and creating tables in an external PostgreSQL database. This can be used to join data between different systems like PostgreSQL and Hive, or between different PostgreSQL instances.

Requirements#

To connect to PostgreSQL, you need:

PostgreSQL 10.x or higher.
Network access from the Trino coordinator and workers to PostgreSQL. Port 5432 is the default port.

Configuration#

The connector can query a database on a PostgreSQL server. Create a catalog properties file that specifies the PostgreSQL connector by setting the connector.name to postgresql.

For example, to access a database as the example catalog, create the file etc/catalog/example.properties. Replace the connection properties as appropriate for your setup:

connector.name=postgresql
connection-url=jdbc:postgresql://example.net:5432/database
connection-user=root
connection-password=secret

The connection-url defines the connection information and parameters to pass to the PostgreSQL JDBC driver. The parameters for the URL are available in the PostgreSQL JDBC driver documentation. Some parameters can have adverse effects on the connector behavior or not work with the connector.

The connection-user and connection-password are typically required and determine the user credentials for the connection, often a service user. You can use secrets to avoid actual values in the catalog properties files.

Connection security#

If you have TLS configured with a globally-trusted certificate installed on your data source, you can enable TLS between your cluster and the data source by appending a parameter to the JDBC connection string set in the connection-url catalog configuration property.

For example, with version 42 of the PostgreSQL JDBC driver, enable TLS by appending the ssl=true parameter to the connection-url configuration property:

connection-url=jdbc:postgresql://example.net:5432/database?ssl=true

For more information on TLS configuration options, see the PostgreSQL JDBC driver documentation.

Data source authentication#

The connector can provide credentials for the data source connection in multiple ways:

inline, in the connector configuration file
in a separate properties file
in a key store file
as extra credentials set when connecting to Trino

You can use secrets to avoid storing sensitive values in the catalog properties files.

The following table describes configuration properties for connection credentials:

Property name	Description
`credential-provider.type`	Type of the credential provider. Must be one of `INLINE`, `FILE`, or `KEYSTORE`; defaults to `INLINE`.
`connection-user`	Connection user name.
`connection-password`	Connection password.
`user-credential-name`	Name of the extra credentials property, whose value to use as the user name. See `extraCredentials` in Parameter reference.
`password-credential-name`	Name of the extra credentials property, whose value to use as the password.
`connection-credential-file`	Location of the properties file where credentials are present. It must contain the `connection-user` and `connection-password` properties.
`keystore-file-path`	The location of the Java Keystore file, from which to read credentials.
`keystore-type`	File format of the keystore file, for example `JKS` or `PEM`.
`keystore-password`	Password for the key store.
`keystore-user-credential-name`	Name of the key store entity to use as the user name.
`keystore-user-credential-password`	Password for the user name key store entity.
`keystore-password-credential-name`	Name of the key store entity to use as the password.
`keystore-password-credential-password`	Password for the password key store entity.

Multiple PostgreSQL databases or servers#

The PostgreSQL connector can only access a single database within a PostgreSQL server. Thus, if you have multiple PostgreSQL databases, or want to connect to multiple PostgreSQL servers, you must configure multiple instances of the PostgreSQL connector.

To add another catalog, simply add another properties file to etc/catalog with a different name, making sure it ends in .properties. For example, if you name the property file sales.properties, Trino creates a catalog named sales using the configured connector.

General configuration properties#

The following table describes general catalog configuration properties for the connector:

Property name	Description	Default value
`case-insensitive-name-matching`	Support case insensitive schema and table names.	`false`
`case-insensitive-name-matching.cache-ttl`	This value should be a duration.	`1m`
`case-insensitive-name-matching.config-file`	Path to a name mapping configuration file in JSON format that allows Trino to disambiguate between schemas and tables with similar names in different cases.	`null`
`case-insensitive-name-matching.config-file.refresh-period`	Frequency with which Trino checks the name matching configuration file for changes. This value should be a duration.	(refresh disabled)
`metadata.cache-ttl`	The duration for which metadata, including table and column statistics, is cached.	`0s` (caching disabled)
`metadata.cache-missing`	Cache the fact that metadata, including table and column statistics, is not available	`false`
`metadata.cache-maximum-size`	Maximum number of objects stored in the metadata cache	`10000`
`write.batch-size`	Maximum number of statements in a batched execution. Do not change this setting from the default. Non-default values may negatively impact performance.	`1000`
`dynamic-filtering.enabled`	Push down dynamic filters into JDBC queries	`true`
`dynamic-filtering.wait-timeout`	Maximum duration for which Trino will wait for dynamic filters to be collected from the build side of joins before starting a JDBC query. Using a large timeout can potentially result in more detailed dynamic filters. However, it can also increase latency for some queries.	`20s`

Appending query metadata#

The optional parameter query.comment-format allows you to configure a SQL comment that is sent to the datasource with each query. The format of this comment can contain any characters and the following metadata:

$QUERY_ID: The identifier of the query.
$USER: The name of the user who submits the query to Trino.
$SOURCE: The identifier of the client tool used to submit the query, for example trino-cli.
$TRACE_TOKEN: The trace token configured with the client tool.

The comment can provide more context about the query. This additional information is available in the logs of the datasource. To include environment variables from the Trino cluster with the comment , use the ${ENV:VARIABLE-NAME} syntax.

The following example sets a simple comment that identifies each query sent by Trino:

query.comment-format=Query sent by Trino.

With this configuration, a query such as SELECT * FROM example_table; is sent to the datasource with the comment appended:

SELECT * FROM example_table; /*Query sent by Trino.*/

The following example improves on the preceding example by using metadata:

query.comment-format=Query $QUERY_ID sent by user $USER from Trino.

If Jane sent the query with the query identifier 20230622_180528_00000_bkizg, the following comment string is sent to the datasource:

SELECT * FROM example_table; /*Query 20230622_180528_00000_bkizg sent by user Jane from Trino.*/

Note

Certain JDBC driver settings and logging configurations might cause the comment to be removed.

Domain compaction threshold#

Pushing down a large list of predicates to the data source can compromise performance. Trino compacts large predicates into a simpler range predicate by default to ensure a balance between performance and predicate pushdown. If necessary, the threshold for this compaction can be increased to improve performance when the data source is capable of taking advantage of large predicates. Increasing this threshold may improve pushdown of large dynamic filters. The domain-compaction-threshold catalog configuration property or the domain_compaction_threshold catalog session property can be used to adjust the default value of 32 for this threshold.

Procedures#

system.flush_metadata_cache()

Flush JDBC metadata caches. For example, the following system call flushes the metadata caches for all schemas in the example catalog
```
USE example.example_schema;
CALL system.flush_metadata_cache();
```

Case insensitive matching#

When case-insensitive-name-matching is set to true, Trino is able to query non-lowercase schemas and tables by maintaining a mapping of the lowercase name to the actual name in the remote system. However, if two schemas and/or tables have names that differ only in case (such as “customers” and “Customers”) then Trino fails to query them due to ambiguity.

In these cases, use the case-insensitive-name-matching.config-file catalog configuration property to specify a configuration file that maps these remote schemas/tables to their respective Trino schemas/tables:

{
  "schemas": [
    {
      "remoteSchema": "CaseSensitiveName",
      "mapping": "case_insensitive_1"
    },
    {
      "remoteSchema": "cASEsENSITIVEnAME",
      "mapping": "case_insensitive_2"
    }],
  "tables": [
    {
      "remoteSchema": "CaseSensitiveName",
      "remoteTable": "tablex",
      "mapping": "table_1"
    },
    {
      "remoteSchema": "CaseSensitiveName",
      "remoteTable": "TABLEX",
      "mapping": "table_2"
    }]
}

Queries against one of the tables or schemes defined in the mapping attributes are run against the corresponding remote entity. For example, a query against tables in the case_insensitive_1 schema is forwarded to the CaseSensitiveName schema and a query against case_insensitive_2 is forwarded to the cASEsENSITIVEnAME schema.

At the table mapping level, a query on case_insensitive_1.table_1 as configured above is forwarded to CaseSensitiveName.tablex, and a query on case_insensitive_1.table_2 is forwarded to CaseSensitiveName.TABLEX.

By default, when a change is made to the mapping configuration file, Trino must be restarted to load the changes. Optionally, you can set the case-insensitive-name-mapping.refresh-period to have Trino refresh the properties without requiring a restart:

case-insensitive-name-mapping.refresh-period=30s

Non-transactional INSERT#

The connector supports adding rows using INSERT statements. By default, data insertion is performed by writing data to a temporary table. You can skip this step to improve performance and write directly to the target table. Set the insert.non-transactional-insert.enabled catalog property or the corresponding non_transactional_insert catalog session property to true.

Note that with this property enabled, data can be corrupted in rare cases where exceptions occur during the insert operation. With transactions disabled, no rollback can be performed.

Type mapping#

Because Trino and PostgreSQL each support types that the other does not, this connector modifies some types when reading or writing data. Data types may not map the same way in both directions between Trino and the data source. Refer to the following sections for type mapping in each direction.

PostgreSQL type to Trino type mapping#

The connector maps PostgreSQL types to the corresponding Trino types following this table:

PostgreSQL type to Trino type mapping#
PostgreSQL type	Trino type	Notes
`BIT`	`BOOLEAN`
`BOOLEAN`	`BOOLEAN`
`SMALLINT`	`SMALLINT`
`INTEGER`	`INTEGER`
`BIGINT`	`BIGINT`
`REAL`	`REAL`
`DOUBLE`	`DOUBLE`
`NUMERIC(p, s)`	`DECIMAL(p, s)`	`DECIMAL(p, s)` is an alias of `NUMERIC(p, s)`. See Decimal type handling for more information.
`CHAR(n)`	`CHAR(n)`
`VARCHAR(n)`	`VARCHAR(n)`
`ENUM`	`VARCHAR`
`BYTEA`	`VARBINARY`
`DATE`	`DATE`
`TIME(n)`	`TIME(n)`
`TIMESTAMP(n)`	`TIMESTAMP(n)`
`TIMESTAMPTZ(n)`	`TIMESTAMP(n) WITH TIME ZONE`
`MONEY`	`VARCHAR`
`UUID`	`UUID`
`JSON`	`JSON`
`JSONB`	`JSON`
`HSTORE`	`MAP(VARCHAR, VARCHAR)`
`ARRAY`	Disabled, `ARRAY`, or `JSON`	See Array type handling for more information.

No other types are supported.

Trino type to PostgreSQL type mapping#

The connector maps Trino types to the corresponding PostgreSQL types following this table:

Trino type to PostgreSQL type mapping#
Trino type	PostgreSQL type	Notes
`BOOLEAN`	`BOOLEAN`
`SMALLINT`	`SMALLINT`
`TINYINT`	`SMALLINT`
`INTEGER`	`INTEGER`
`BIGINT`	`BIGINT`
`DOUBLE`	`DOUBLE`
`DECIMAL(p, s)`	`NUMERIC(p, s)`	`DECIMAL(p, s)` is an alias of `NUMERIC(p, s)`. See Decimal type handling for more information.
`CHAR(n)`	`CHAR(n)`
`VARCHAR(n)`	`VARCHAR(n)`
`VARBINARY`	`BYTEA`
`DATE`	`DATE`
`TIME(n)`	`TIME(n)`
`TIMESTAMP(n)`	`TIMESTAMP(n)`
`TIMESTAMP(n) WITH TIME ZONE`	`TIMESTAMPTZ(n)`
`UUID`	`UUID`
`JSON`	`JSONB`
`ARRAY`	`ARRAY`	See Array type handling for more information.

No other types are supported.

Decimal type handling#

DECIMAL types with unspecified precision or scale are mapped to a Trino DECIMAL with a default precision of 38 and default scale of 0. The scale can be changed by setting the decimal-mapping configuration property or the decimal_mapping session property to allow_overflow. The scale of the resulting type is controlled via the decimal-default-scale configuration property or the decimal-rounding-mode session property. The precision is always 38.

By default, values that require rounding or truncation to fit will cause a failure at runtime. This behavior is controlled via the decimal-rounding-mode configuration property or the decimal_rounding_mode session property, which can be set to UNNECESSARY (the default), UP, DOWN, CEILING, FLOOR, HALF_UP, HALF_DOWN, or HALF_EVEN (see RoundingMode).

Array type handling#

The PostgreSQL array implementation does not support fixed dimensions whereas Trino support only arrays with fixed dimensions. You can configure how the PostgreSQL connector handles arrays with the postgresql.array-mapping configuration property in your catalog file or the array_mapping session property. The following values are accepted for this property:

DISABLED (default): array columns are skipped.
AS_ARRAY: array columns are interpreted as Trino ARRAY type, for array columns with fixed dimensions.
AS_JSON: array columns are interpreted as Trino JSON type, with no constraint on dimensions.

Type mapping configuration properties#

The following properties can be used to configure how data types from the connected data source are mapped to Trino data types and how the metadata is cached in Trino.

Property name

Description

Default value

unsupported-type-handling

Configure how unsupported column data types are handled:

IGNORE, column is not accessible.
CONVERT_TO_VARCHAR, column is converted to unbounded VARCHAR.

The respective catalog session property is unsupported_type_handling.

IGNORE

jdbc-types-mapped-to-varchar

Allow forced mapping of comma separated lists of data types to convert to unbounded VARCHAR

Querying PostgreSQL#

The PostgreSQL connector provides a schema for every PostgreSQL schema. You can see the available PostgreSQL schemas by running SHOW SCHEMAS:

SHOW SCHEMAS FROM example;

If you have a PostgreSQL schema named web, you can view the tables in this schema by running SHOW TABLES:

SHOW TABLES FROM example.web;

You can see a list of the columns in the clicks table in the web database using either of the following:

DESCRIBE example.web.clicks;
SHOW COLUMNS FROM example.web.clicks;

Finally, you can access the clicks table in the web schema:

SELECT * FROM example.web.clicks;

If you used a different name for your catalog properties file, use that catalog name instead of example in the above examples.

SQL support#

The connector provides read access and write access to data and metadata in PostgreSQL. In addition to the globally available and read operation statements, the connector supports the following features:

SQL DELETE#

If a WHERE clause is specified, the DELETE operation only works if the predicate in the clause can be fully pushed down to the data source.

ALTER TABLE RENAME TO#

The connector does not support renaming tables across multiple schemas. For example, the following statement is supported:

ALTER TABLE example.schema_one.table_one RENAME TO example.schema_one.table_two

The following statement attempts to rename a table across schemas, and therefore is not supported:

ALTER TABLE example.schema_one.table_one RENAME TO example.schema_two.table_two

ALTER SCHEMA#

The connector supports renaming a schema with the ALTER SCHEMA RENAME statement. ALTER SCHEMA SET AUTHORIZATION is not supported.

Fault-tolerant execution support#

The connector supports Fault-tolerant execution of query processing. Read and write operations are both supported with any retry policy.

Table functions#

The connector provides specific table functions to access PostgreSQL.

`query(varchar) -> table`#

The query function allows you to query the underlying database directly. It requires syntax native to PostgreSQL, because the full query is pushed down and processed in PostgreSQL. This can be useful for accessing native features which are not available in Trino or for improving query performance in situations where running a query natively may be faster.

The native query passed to the underlying data source is required to return a table as a result set. Only the data source performs validation or security checks for these queries using its own configuration. Trino does not perform these tasks. Only use passthrough queries to read data.

As a simple example, query the example catalog and select an entire table:

SELECT
  *
FROM
  TABLE(
    example.system.query(
      query => 'SELECT
        *
      FROM
        tpch.nation'
    )
  );

As a practical example, you can leverage frame exclusion from PostgresQL when using window functions:

SELECT
  *
FROM
  TABLE(
    example.system.query(
      query => 'SELECT
        *,
        array_agg(week) OVER (
          ORDER BY
            week
          ROWS
            BETWEEN 2 PRECEDING
            AND 2 FOLLOWING
            EXCLUDE GROUP
        ) AS week,
        array_agg(week) OVER (
          ORDER BY
            day
          ROWS
            BETWEEN 2 PRECEDING
            AND 2 FOLLOWING
            EXCLUDE GROUP
        ) AS all
      FROM
        test.time_data'
    )
  );

Note

The query engine does not preserve the order of the results of this function. If the passed query contains an ORDER BY clause, the function result may not be ordered as expected.

Performance#

The connector includes a number of performance improvements, detailed in the following sections.

Table statistics#

The PostgreSQL connector can use table and column statistics for cost based optimizations, to improve query processing performance based on the actual data in the data source.

The statistics are collected by PostgreSQL and retrieved by the connector.

To collect statistics for a table, execute the following statement in PostgreSQL.

ANALYZE table_schema.table_name;

Refer to PostgreSQL documentation for additional ANALYZE options.

Pushdown#

The connector supports pushdown for a number of operations:

Aggregate pushdown for the following functions:

Note

The connector performs pushdown where performance may be improved, but in order to preserve correctness an operation may not be pushed down. When pushdown of an operation may result in better performance but risks correctness, the connector prioritizes correctness.

Cost-based join pushdown#

The connector supports cost-based Join pushdown to make intelligent decisions about whether to push down a join operation to the data source.

When cost-based join pushdown is enabled, the connector only pushes down join operations if the available Table statistics suggest that doing so improves performance. Note that if no table statistics are available, join operation pushdown does not occur to avoid a potential decrease in query performance.

The following table describes catalog configuration properties for join pushdown:

Property name	Description	Default value
`join-pushdown.enabled`	Enable join pushdown. Equivalent catalog session property is `join_pushdown_enabled`.	`true`
`join-pushdown.strategy`	Strategy used to evaluate whether join operations are pushed down. Set to `AUTOMATIC` to enable cost-based join pushdown, or `EAGER` to push down joins whenever possible. Note that `EAGER` can push down joins even when table statistics are unavailable, which may result in degraded query performance. Because of this, `EAGER` is only recommended for testing and troubleshooting purposes.	`AUTOMATIC`

Predicate pushdown support#

Predicates are pushed down for most types, including UUID and temporal types, such as DATE.

The connector does not support pushdown of range predicates, such as >, <, or BETWEEN, on columns with character string types like CHAR or VARCHAR. Equality predicates, such as IN or =, and inequality predicates, such as != on columns with textual types are pushed down. This ensures correctness of results since the remote data source may sort strings differently than Trino.

In the following example, the predicate of the first query is not pushed down since name is a column of type VARCHAR and > is a range predicate. The other queries are pushed down.

-- Not pushed down
SELECT * FROM nation WHERE name > 'CANADA';
-- Pushed down
SELECT * FROM nation WHERE name != 'CANADA';
SELECT * FROM nation WHERE name = 'CANADA';

There is experimental support to enable pushdown of range predicates on columns with character string types which can be enabled by setting the postgresql.experimental.enable-string-pushdown-with-collate catalog configuration property or the corresponding enable_string_pushdown_with_collate session property to true. Enabling this configuration will make the predicate of all the queries in the above example get pushed down.

PostgreSQL connector#

Requirements#

Configuration#

Connection security#

Data source authentication#

Multiple PostgreSQL databases or servers#

General configuration properties#

Appending query metadata#

Domain compaction threshold#

Procedures#

Case insensitive matching#

Non-transactional INSERT#

Type mapping#

PostgreSQL type to Trino type mapping#

Trino type to PostgreSQL type mapping#

Decimal type handling#

Array type handling#

Type mapping configuration properties#

Querying PostgreSQL#

SQL support#

SQL DELETE#

ALTER TABLE RENAME TO#

ALTER SCHEMA#

Fault-tolerant execution support#

Table functions#

query(varchar) -> table#

Performance#

Table statistics#

Pushdown#

Cost-based join pushdown#

Predicate pushdown support#

`query(varchar) -> table`#