SQL Server connector#

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

Requirements#

To connect to SQL Server, you need:

SQL Server 2019 or higher, or Azure SQL Database.
Network access from the Trino coordinator and workers to SQL Server. Port 1433 is the default port.

Configuration#

The connector can query a single database on a given SQL Server instance. Create a catalog properties file that specifies the SQL server connector by setting the connector.name to sqlserver.

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

connector.name=sqlserver
connection-url=jdbc:sqlserver://<host>:<port>;databaseName=<databaseName>;encrypt=false
connection-user=root
connection-password=secret

The connection-url defines the connection information and parameters to pass to the SQL Server JDBC driver. The supported parameters for the URL are available in the SQL Server JDBC driver documentation.

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#

The JDBC driver, and therefore the connector, automatically use Transport Layer Security (TLS) encryption and certificate validation. This requires a suitable TLS certificate configured on your SQL Server database host.

If you do not have the necessary configuration established, you can disable encryption in the connection string with the encrypt property:

connection-url=jdbc:sqlserver://<host>:<port>;databaseName=<databaseName>;encrypt=false

Further parameters like trustServerCertificate, hostNameInCertificate, trustStore, and trustStorePassword are details in the TLS section of SQL Server 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 SQL Server databases or servers#

The SQL Server connector can only access a single SQL Server database within a single catalog. Thus, if you have multiple SQL Server databases, or want to connect to multiple SQL Server instances, you must configure multiple instances of the SQL Server 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
`case-insensitive-name-matching`	Support case insensitive schema and table names. Defaults to `false`.
`case-insensitive-name-matching.cache-ttl`	Duration for which case insensitive schema and table names are cached. Defaults to `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. Defaults to `null`.
`case-insensitive-name-matching.config-file.refresh-period`	Frequency with which Trino checks the name matching configuration file for changes. The duration value defaults to `0s` (refresh disabled).
`metadata.cache-ttl`	Duration for which metadata, including table and column statistics, is cached. Defaults to `0s` (caching disabled).
`metadata.cache-missing`	Cache the fact that metadata, including table and column statistics, is not available. Defaults to `false`.
`metadata.schemas.cache-ttl`	Duration for which schema metadata is cached. Defaults to the value of `metadata.cache-ttl`.
`metadata.tables.cache-ttl`	Duration for which table metadata is cached. Defaults to the value of `metadata.cache-ttl`.
`metadata.statistics.cache-ttl`	Duration for which tables statistics are cached. Defaults to the value of `metadata.cache-ttl`.
`metadata.cache-maximum-size`	Maximum number of objects stored in the metadata cache. Defaults to `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. Defaults to `1000`.
`dynamic-filtering.enabled`	Push down dynamic filters into JDBC queries. Defaults to `true`.
`dynamic-filtering.wait-timeout`	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 `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 256 for this threshold.

Specific configuration properties#

The SQL Server connector supports additional catalog properties to configure the behavior of the connector and the issues queries to the database.

Property name	Description
`sqlserver.snapshot-isolation.disabled`	Control the automatic use of snapshot isolation for transactions issued by Trino in SQL Server. Defaults to `false`, which means that snapshot isolation is enabled.

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.

Querying SQL Server#

The SQL Server connector provides access to all schemas visible to the specified user in the configured database. For the following examples, assume the SQL Server catalog is example.

You can see the available schemas by running SHOW SCHEMAS:

SHOW SCHEMAS FROM example;

If you have a 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 query 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.

Type mapping#

Because Trino and SQL Server 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.

SQL Server type to Trino type mapping#

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

SQL Server type to Trino type mapping#
SQL Server database type	Trino type	Notes
`BIT`	`BOOLEAN`
`TINYINT`	`SMALLINT`	SQL Server `TINYINT` is actually `unsigned TINYINT`
`SMALLINT`	`SMALLINT`
`INTEGER`	`INTEGER`
`BIGINT`	`BIGINT`
`DOUBLE PRECISION`	`DOUBLE`
`FLOAT[(n)]`	`REAL` or `DOUBLE`	See Numeric type mapping
`REAL`	`REAL`
`DECIMAL[(p[, s])]`, `NUMERIC[(p[, s])]`	`DECIMAL(p, s)`
`CHAR[(n)]`	`CHAR(n)`	`1 <= n <= 8000`
`NCHAR[(n)]`	`CHAR(n)`	`1 <= n <= 4000`
`VARCHAR[(n \| max)]`, `NVARCHAR[(n \| max)]`	`VARCHAR(n)`	`1 <= n <= 8000`, `max = 2147483647`
`TEXT`	`VARCHAR(2147483647)`
`NTEXT`	`VARCHAR(1073741823)`
`VARBINARY[(n \| max)]`	`VARBINARY`	`1 <= n <= 8000`, `max = 2147483647`
`DATE`	`DATE`
`TIME[(n)]`	`TIME(n)`	`0 <= n <= 7`
`DATETIME2[(n)]`	`TIMESTAMP(n)`	`0 <= n <= 7`
`SMALLDATETIME`	`TIMESTAMP(0)`
`DATETIMEOFFSET[(n)]`	`TIMESTAMP(n) WITH TIME ZONE`	`0 <= n <= 7`

Trino type to SQL Server type mapping#

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

Trino type to SQL Server type mapping#
Trino type	SQL Server type	Notes
`BOOLEAN`	`BIT`
`TINYINT`	`TINYINT`	Trino only supports writing values belonging to `[0, 127]`
`SMALLINT`	`SMALLINT`
`INTEGER`	`INTEGER`
`BIGINT`	`BIGINT`
`REAL`	`REAL`
`DOUBLE`	`DOUBLE PRECISION`
`DECIMAL(p, s)`	`DECIMAL(p, s)`
`CHAR(n)`	`NCHAR(n)` or `NVARCHAR(max)`	See Character type mapping
`VARCHAR(n)`	`NVARCHAR(n)` or `NVARCHAR(max)`	See Character type mapping
`VARBINARY`	`VARBINARY(max)`
`DATE`	`DATE`
`TIME(n)`	`TIME(n)`	`0 <= n <= 7`
`TIMESTAMP(n)`	`DATETIME2(n)`	`0 <= n <= 7`

Complete list of SQL Server data types.

Numeric type mapping#

For SQL Server FLOAT[(n)]:

If n is not specified maps to Trino Double
If 1 <= n <= 24 maps to Trino REAL
If 24 < n <= 53 maps to Trino DOUBLE

Character type mapping#

For Trino CHAR(n):

If 1 <= n <= 4000 maps SQL Server NCHAR(n)
If n > 4000 maps SQL Server NVARCHAR(max)

For Trino VARCHAR(n):

If 1 <= n <= 4000 maps SQL Server NVARCHAR(n)
If n > 4000 maps SQL Server NVARCHAR(max)

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

SQL support#

The connector provides read access and write access to data and metadata in SQL Server. 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

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 SQL Server.

`query(varchar) -> table`#

The query function allows you to query the underlying database directly. It requires syntax native to SQL Server, because the full query is pushed down and processed in SQL Server. This can be useful for accessing native features which are not implemented 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.

For example, query the example catalog and select the top 10 percent of nations by population:

SELECT
  *
FROM
  TABLE(
    example.system.query(
      query => 'SELECT
        TOP(10) PERCENT *
      FROM
        tpch.nation
      ORDER BY
        population DESC'
    )
  );

`procedure(varchar) -> table`#

The procedure function allows you to run stored procedures on the underlying database directly. It requires syntax native to SQL Server, because the full query is pushed down and processed in SQL Server. In order to use this table function set sqlserver.stored-procedure-table-function-enabled to true.

Note

The procedure function does not support running StoredProcedures that return multiple statements, use a non-select statement, use output parameters, or use conditional statements.

Warning

This feature is experimental only. The function has security implication and syntax might change and be backward incompatible.

The follow example runs the stored procedure employee_sp in the example catalog and the example_schema schema in the underlying SQL Server database:

SELECT
  *
FROM
  TABLE(
    example.system.procedure(
      query => 'EXECUTE example_schema.employee_sp'
    )
  );

If the stored procedure employee_sp requires any input append the parameter value to the procedure statement:

SELECT
  *
FROM
  TABLE(
    example.system.procedure(
      query => 'EXECUTE example_schema.employee_sp 0'
    )
  );

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 SQL Server 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 SQL Server and retrieved by the connector.

The connector can use information stored in single-column statistics. SQL Server Database can automatically create column statistics for certain columns. If column statistics are not created automatically for a certain column, you can create them by executing the following statement in SQL Server Database.

CREATE STATISTICS example_statistics_name ON table_schema.table_name (column_name);

SQL Server Database routinely updates the statistics. In some cases, you may want to force statistics update (e.g. after defining new column statistics or after changing data in the table). You can do that by executing the following statement in SQL Server Database.

UPDATE STATISTICS table_schema.table_name;

Refer to SQL Server documentation for information about options, limitations and additional considerations.

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#

The connector supports pushdown of predicates on VARCHAR and NVARCHAR columns if the underlying columns in SQL Server use a case-sensitive collation.

The following operators are pushed down:

=
<>
IN
NOT IN

To ensure correct results, operators are not pushed down for columns using a case-insensitive collation.

Bulk insert#

You can optionally use the bulk copy API to drastically speed up write operations.

Enable bulk copying and a lock on the destination table to meet minimal logging requirements.

The following table shows the relevant catalog configuration properties and their default values:

Bulk load properties#
Property name	Description	Default
`sqlserver.bulk-copy-for-write.enabled`	Use the SQL Server bulk copy API for writes. The corresponding catalog session property is `bulk_copy_for_write`.	`false`
`sqlserver.bulk-copy-for-write.lock-destination-table`	Obtain a bulk update lock on the destination table for write operations. The corresponding catalog session property is `bulk_copy_for_write_lock_destination_table`. Setting is only used when `bulk-copy-for-write.enabled=true`.	`false`

Limitations:

Column names with leading and trailing spaces are not supported.

Data compression#

You can specify the data compression policy for SQL Server tables with the data_compression table property. Valid policies are NONE, ROW or PAGE.

Example:

CREATE TABLE example_schema.scientists (
  recordkey VARCHAR,
  name VARCHAR,
  age BIGINT,
  birthday DATE
)
WITH (
  data_compression = 'ROW'
);

SQL Server connector#

Requirements#

Configuration#

Connection security#

Data source authentication#

Multiple SQL Server databases or servers#

General configuration properties#

Appending query metadata#

Domain compaction threshold#

Specific configuration properties#

Case insensitive matching#

Non-transactional INSERT#

Fault-tolerant execution support#

Querying SQL Server#

Type mapping#

SQL Server type to Trino type mapping#

Trino type to SQL Server type mapping#

Numeric type mapping#

Character type mapping#

Type mapping configuration properties#

SQL support#

UPDATE#

SQL DELETE#

ALTER TABLE RENAME TO#

Procedures#

system.flush_metadata_cache()#

system.execute('query')#

Table functions#

query(varchar) -> table#

procedure(varchar) -> table#

Performance#

Table statistics#

Pushdown#

Cost-based join pushdown#

Predicate pushdown support#

Bulk insert#

Data compression#

`system.flush_metadata_cache()`#

`system.execute('query')`#

`query(varchar) -> table`#

`procedure(varchar) -> table`#