SQL Server connector#

The SQL Server connector lets you query and create 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.

SEP includes additional enterprise features that are built on top of the existing Trino connector functionality. For more information on key feature differences between Trino and SEP, see the connectors feature matrix.

Requirements#

To connect to SQL Server, you need:

SQL Server 2012 or higher, or Azure SQL Database.
Network access from the SEP coordinator and workers to SQL Server. Port 1433 is the default port.
A valid Starburst Enterprise license.

Configuration#

To configure the SQL Server connector, 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 exposing actual values in the catalog properties files.

Connection security#

The JDBC driver and 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

Additional parameters like trustServerCertificate, hostNameInCertificate, trustStore, and trustStorePassword are detailed 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. 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. See also, Dynamic catalog selection

To add another catalog, add a new properties file to etc/catalog. For example, if you name the property file sales.properties, SEP creates a catalog named sales.

Another option to access multiple databases on a SQL Server cluster is to enable the sqlserver.database-prefix-for-schema.enabled catalog configuration property, as described in the following table:

Starburst SQL Server connector configuration properties#
Property name	Description	Default
`sqlserver.database-prefix-for-schema.enabled`	Allow access to other databases in SQL Server by including the database name in double quotes with the schema name: SELECT * FROM catalog."database.schema".table When enabled, `"database.schema"`, including the double quotes, is required at all times as part of the fully-qualified name.	false

Dynamic catalog selection#

The default configuration, similar to the etc/example.properties file, enables the connection to one database running on a SQL Server instance.

connector.name=sqlserver
connection-url=jdbc:sqlserver://dbserver.example.com:1443/exampledb;encrypt=false

The connector supports connecting to multiple SQL Server databases using a single catalog by setting an override_catalog session property. This support has to be enabled in the catalog properties file with the sqlserver.override-catalog.enabled property:

connector.name=sqlserver
connection-url=jdbc:sqlserver://dbserver.example.com:1443/exampledb;encrypt=false
sqlserver.override-catalog.enabled=true

The previously mentioned example lets you query any table in any schema in the database exampledb on the SQL Server dbserver. The user that was specified in connection-user must have sufficient access rights.

SELECT * FROM example.exampleschema.exampletable;

In order to query another database, such as testdb, you must override the database configured in the catalog. Then you can query that database in the current user session:

SET SESSION example.override_catalog=testdb;
SELECT * FROM example.testdbschema.testdbtable;

Note

The access rights to the databases, schemas, tables, and actual rows are determined by the configured user for the connection. This also applies to other security setups like impersonation or Apache Ranger integration. Make sure these access rights are as restrictive as required.

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 32 for this threshold.

Specific configuration properties#

The SQL Server connector supports additional catalog properties to configure the behavior of the connector.

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.

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.

Querying SQL Server#

The SQL Server connector provides access to all schemas visible to the specified user in the configured database.

Run SHOW SCHEMAS to see all the abvailable schemas in your SQL server database:

SHOW SCHEMAS FROM example;

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

Example:

If you have a schema named web, you can run SHOW TABLES to view the tables in the schema:

SHOW TABLES FROM example.web;

To see a list of the columns in the clicks table in the web database, run either of the following:

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

To access the clicks table in the web database, run the following:

SELECT * FROM example.web.clicks;

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`

See the Microsoft documentation for the 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 statements:

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

The =, !=, >, <, >=, <=, IN, NOT IN operators are supported in predicates. The following statement is not supported because the AND operator cannot be used in predicates:

UPDATE table SET col1 = 1 WHERE col3 = 1 AND col2 = 3

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 EXECUTE#

This connector supports the following commands for use with ALTER TABLE EXECUTE:

collect_statistics#

The collect_statistics command is used with Managed statistics to collect statistics for a table and its columns.

The following statement collects statistics for the example_table table and all of its columns:

ALTER TABLE example_table EXECUTE collect_statistics;

Collecting statistics for all columns in a table may be unnecessarily performance-intensive, especially for wide tables. To only collect statistics for a subset of columns, you can include the columns parameter with an array of column names. For example:

ALTER TABLE example_table
    EXECUTE collect_statistics(columns => ARRAY['customer','line_item']);

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

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 catalog property sqlserver.non-transactional-insert.enabled or the corresponding catalog session property non_transactional_insert to true.

In rare cases when exceptions occur during the insert operation, data in the target table can be corrupted. Since transactions have been 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.

Table functions#

The connector provides specific table functions to access SQL Server.

`query(varchar) -> table`#

The query function lets 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 SEP 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 lets you 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.experimental.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 following 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 features, detailed in the following sections.

Parallelism#

The connector is able to read data from SQL Server using multiple parallel connections for tables partitioned as described in the SQL Server partitioning documentation.

SQL Server parallelism configuration properties#
Property name	Description	Default
`sqlserver.parallel.connections_count`	Defines the maximum number of parallel splits during query execution. Use this property to limit the number of splits for tables with a large number of partitions to avoid opening a large number of connections. The default value of `1` disables parallelism. The maximum number of connections is automatically limited to the number of partitions in the table; if this number is set higher, it is ignored, and the number of partitions used instead. The equivalent catalog session property is `parallel_connections_count`.	1

In the event that multiple parallel connections result in a deadlocked state, the connector attempts to retry the operation up to 3 times.

Dynamic filtering#

Dynamic filtering is enabled by default. It causes the connector to wait for dynamic filtering to complete before starting a JDBC query.

You can disable dynamic filtering by setting the dynamic-filtering.enabled property in your catalog configuration file to false.

Wait timeout#

By default, table scans on the connector are delayed up to 20 seconds until dynamic filters are collected from the build side of joins. Using a large timeout can potentially result in more detailed dynamic filters. However, it can also increase latency for some queries.

You can configure the dynamic-filtering.wait-timeout property in your catalog properties file:

dynamic-filtering.wait-timeout=1m

You can use the dynamic_filtering_wait_timeout catalog session property in a specific session:

SET SESSION example.dynamic_filtering_wait_timeout = 1s;

Compaction#

The maximum size of dynamic filter predicate, that is pushed down to the connector during table scan for a column, is configured using the domain-compaction-threshold property in the catalog properties file:

domain-compaction-threshold=100

You can use the domain_compaction_threshold catalog session property:

SET SESSION domain_compaction_threshold = 10;

By default, domain-compaction-threshold is set to 32. When the dynamic predicate for a column exceeds this threshold, it is compacted into a single range predicate.

For example, if the dynamic filter collected for a date column dt on the fact table selects more than 32 days, the filtering condition is simplified from dt IN ('2020-01-10', '2020-01-12',..., '2020-05-30') to dt BETWEEN '2020-01-10' AND '2020-05-30'. Using a large threshold can result in increased table scan overhead due to a large IN list getting pushed down to the data source.

Metrics#

Metrics about dynamic filtering are reported in a JMX table for each catalog:

jmx.current."io.trino.plugin.jdbc:name=example,type=dynamicfilteringstats"

Metrics include information about the total number of dynamic filters, the number of completed dynamic filters, the number of available dynamic filters and the time spent waiting for dynamic filters.

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. To manually create statistics for a certain column, run the following statement:

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 manually update statistics. To manually update statistics, run 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.

Managed statistics#

The connector supports Managed statistics which lets SEP collect and store table and column statistics that can then be used for performance optimizations in query planning.

Statistics must be collected manually using the built-in collect_statistics command, see ALTER TABLE EXECUTE for details and examples.

Pushdown#

The connector supports pushdown for a number of operations:

In addition, the connector supportsAggregate 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#

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.

Starburst Cached Views#

The connector supports table scan redirection to improve performance and reduce load on the data source.

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'
);

Security#

The connector includes a number of security-related features, detailed in the following sections.

User impersonation#

The connector supports user impersonation.

To enable user impersonation in the catalog file, add the following property:

sqlserver.impersonation.enabled=true

User impersonation in SQL Server connector is based on EXECUTE AS USER. For more information, see the SQL Server documentation.

Kerberos authentication#

The connector supports Kerberos authentication using a keytab. To configure Kerberos authentication, add the following properties to the catalog properties file:

sqlserver.authentication.type=KERBEROS
kerberos.client.principal=example@example.com
kerberos.client.keytab=etc/kerberos/example.keytab
kerberos.config=etc/kerberos/krb5.conf

In this configuration, the user example@example.com connects to the database. The related Kerberos service ticket is located in the etc/kerberos/example.keytab file defined in the kerberos.client.keytab property.

Kerberos credential pass-through#

You can configure SQL Server to pass through Kerberos credentials, received by SEP, to the SQL server database. To configure credential pass-through in Kerberos and SEP, see Kerberos credential pass-through.

After you configure Kerberos and SEP, edit the catalog properties file to enable the connector to pass the credentials to the SQL Server database.

Confirm the correct Kerberos client configuration properties in the catalog properties file. For example:

sqlserver.authentication.type=KERBEROS_PASS_THROUGH
http.authentication.krb5.config=/etc/krb5.conf
http-server.authentication.krb5.service-name=exampleServiceName
http-server.authentication.krb5.keytab=/path/to/Keytab/File

Note

When delegated Kerberos authentication is configured for the Starburst Enterprise web UI, make sure the http-server.authentication.krb5.service-name value is set to HTTP to match the configured Kerberos service name.

Now any SQL server database accessed using SEP is subject to the Kerberos-defined data access restrictions and permissions.

Password credential pass-through#

The connector supports password credential pass-through. To enable it, edit the catalog properties file to include the authentication type:

sqlserver.authentication.type=PASSWORD_PASS_THROUGH

NTLM authentication#

The connector supports NTLM authentication as an alternative to Kerberos authentication for environments where Kerberos auth is not feasible.

To enable NTLM authentication, configure the following properties in the catalog properties file:

connection-user=ad_username
connection-password=ad_password
sqlserver.authentication.type=NTLM_PASSWORD

In this configuration, the user ad_username and password ad_password are Active Directory credentials for a single user who has underlying access to the data source.

TLS/HTTPS settings#

Microsoft strongly suggests encrypting traffic using TLS when using NTLM authentication.

If you have globally-trusted certificates installed on both SEP and the data source, enable TLS by appending the encrypt=true parameter to the connection-url catalog configuration property:

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

If you do not have globally-trusted certificates installed, you can instead use certificates trusted by a custom truststore.

The following catalog configuration properties manage custom truststore configuration for the SQL Server connector:

TLS truststore configuration properties#
Property name	Description
`sqlserver.tls.truststore-path`	Path to the truststore file in SEP.
`sqlserver.tls.truststore-password`	Password used to generate the truststore file
`sqlserver.tls.truststore-type`	The type of truststore. Supports either `JKS` or `PKCS12` as values.

The following catalog properties configuration specifies a custom JKS truststore to enable TLS for NTLM authentication:

sqlserver.tls.truststore-path=path/to/truststore.jks
sqlserver.tls.truststore-password=insecurepassword
sqlserver.tls.truststore-type=JKS

NTLM credential pass-through#

The connector supports NTLM credential pass-through. To enable this, edit the catalog properties file to include the authentication type:

sqlserver.authentication.type=NTLM_PASSWORD_PASS_THROUGH

SQL Server connector#

Requirements#

Configuration#

Connection security#

Data source authentication#

Multiple SQL Server databases or servers#

Dynamic catalog selection#

General configuration properties#

Appending query metadata#

Domain compaction threshold#

Specific configuration properties#

Procedures#

Case insensitive matching#

Non-transactional INSERT#

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 EXECUTE#

collect_statistics#

ALTER TABLE RENAME TO#

INSERT#

Fault-tolerant execution support#

Table functions#

query(varchar) -> table#

procedure(varchar) -> table#

Performance#

Parallelism#

Dynamic filtering#

Wait timeout#

Compaction#

Metrics#

Table statistics#

Managed statistics#

Pushdown#

Cost-based join pushdown#

Predicate pushdown support#

Bulk insert#

Starburst Cached Views#

Data compression#

Security#

User impersonation#

Kerberos authentication#

Kerberos credential pass-through#

Password credential pass-through#

NTLM authentication#

TLS/HTTPS settings#

NTLM credential pass-through#

`query(varchar) -> table`#

`procedure(varchar) -> table`#