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 11.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.
Access to system tables#
The PostgreSQL connector supports reading PostgreSQL catalog
tables, such as
pg_namespace. The functionality is turned off by default, and can be enabled
using the postgresql.include-system-tables configuration property.
You can see more details in the pg_catalog schema in the example catalog,
for example about the pg_namespace system table:
SHOW TABLES FROM example.pg_catalog;
SELECT * FROM example.pg_catalog.pg_namespace;
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 |
|---|---|
|
Type of the credential provider. Must be one of |
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Connection user name. |
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Connection password. |
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Name of the extra credentials property, whose value to use as the user
name. See |
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Name of the extra credentials property, whose value to use as the password. |
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Location of the properties file where credentials are present. It must
contain the |
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The location of the Java Keystore file, from which to read credentials. |
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File format of the keystore file, for example |
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Password for the key store. |
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Name of the key store entity to use as the user name. |
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Password for the user name key store entity. |
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Name of the key store entity to use as the password. |
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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 |
|---|---|
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Support case insensitive schema and table names. Defaults to |
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Duration for which case insensitive schema and table
names are cached. Defaults to |
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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. Defaults to |
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Frequency with which Trino checks the name matching configuration file
for changes. The duration value defaults to |
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Duration for which metadata, including table and
column statistics, is cached. Defaults to |
|
Cache the fact that metadata, including table and column statistics, is
not available. Defaults to |
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Duration for which schema metadata is cached.
Defaults to the value of |
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Duration for which table metadata is cached.
Defaults to the value of |
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Duration for which tables statistics are cached.
Defaults to the value of |
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Maximum number of objects stored in the metadata cache. Defaults to |
|
Maximum number of statements in a batched execution. Do not change
this setting from the default. Non-default values may negatively
impact performance. Defaults to |
|
Push down dynamic filters into JDBC queries. Defaults to |
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Maximum duration for which Trino waits 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.
Defaults to |
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.$SOURCE: The identifier of the client tool used to submit the query, for exampletrino-cli.$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
256 for this threshold.
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.
Fault-tolerant execution support#
The connector supports Fault-tolerant execution of query processing. Read and write operations are both supported with any retry policy.
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 |
Trino type |
Notes |
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Disabled, |
See Array type handling for more information. |
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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 |
PostgreSQL type |
Notes |
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No other types are supported.
Decimal type handling#
DECIMAL types with unspecified precision or scale are ignored unless the
decimal-mapping configuration property or the decimal_mapping session
property is set to allow_overflow. Then such types are mapped to a Trino
DECIMAL with a default precision of 38 and default scale of 0. To change the
scale of the resulting type, use the decimal-default-scale configuration
property or the decimal_default_scale 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 TrinoARRAYtype, for array columns with fixed dimensions.AS_JSON: array columns are interpreted as TrinoJSONtype, 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 |
|---|---|---|
|
Configure how unsupported column data types are handled:
The respective catalog session property is |
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|
Allow forced mapping of comma separated lists of data types to convert to
unbounded |
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:
UPDATE#
Only UPDATE statements with constant assignments and predicates are
supported. For example, the following statement is supported because the values
assigned are constants:
UPDATE table SET col1 = 1 WHERE col3 = 1
Arithmetic expressions, function calls, and other non-constant UPDATE
statements are not supported. For example, the following statement is not
supported because arithmetic expressions cannot be used with the SET
command:
UPDATE table SET col1 = col2 + 2 WHERE col3 = 1
All column values of a table row cannot be updated simultaneously. For a three column table, the following statement is not supported:
UPDATE table SET col1 = 1, col2 = 2, col3 = 3 WHERE col3 = 1
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.
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();
system.execute('query')#
The execute procedure allows you to execute a query in the underlying data
source directly. The query must use supported syntax of the connected data
source. Use the procedure to access features which are not available in Trino
or to execute queries that return no result set and therefore can not be used
with the query or raw_query pass-through table function. Typical use cases
are statements that create or alter objects, and require native feature such
as constraints, default values, automatic identifier creation, or indexes.
Queries can also invoke statements that insert, update, or delete data, and do
not return any data as a result.
The query text is not parsed by Trino, only passed through, and therefore only subject to any security or access control of the underlying data source.
The following example sets the current database to the example_schema of the
example catalog. Then it calls the procedure in that schema to drop the
default value from your_column on your_table table using the standard SQL
syntax in the parameter value assigned for query:
USE example.example_schema;
CALL system.execute(query => 'ALTER TABLE your_table ALTER COLUMN your_column DROP DEFAULT');
Verify that the specific database supports this syntax, and adapt as necessary based on the documentation for the specific connected database and database version.
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 |
|---|---|---|
|
Enable join pushdown. Equivalent catalog
session property is
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Strategy used to evaluate whether join operations are pushed down. Set to
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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.