Starburst Redshift connector#
The Starburst Redshift connector is an improved version of the Trino Redshift connector that allows querying and creating tables in an external Amazon Redshift cluster.
Requirements#
To connect to Redshift, you need:
Network access from the Trino coordinator and workers to Redshift. Port 5439 is the default port.
A valid Starburst Enterprise license.
Configuration#
To configure a Redshift catalog, create a catalog properties file in
etc/catalog
named, for example, example.properties
. The following
is a minimal configuration for a Redshift catalog properties file:
connector.name=redshift
connection-url=jdbc:redshift://example.net:5439/database
connection-user=redshift_username
connection-password=redshift_password
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 using 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, in version 2.1 of the Redshift JDBC driver, TLS/SSL is enabled by
default with the SSL
parameter. You can disable or further configure TLS
by appending parameters to the connection-url
configuration property:
connection-url=jdbc:redshift://example.net:5439/database;SSL=TRUE;
For more information on TLS configuration options, see the Redshift 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 |
|
Connection user name. |
|
Connection password. |
|
Name of the extra credentials property, whose value to use as the user
name. See |
|
Name of the extra credentials property, whose value to use as the password. |
|
Location of the properties file where credentials are present. It must
contain the |
|
The location of the Java Keystore file, from which to read credentials. |
|
File format of the keystore file, for example |
|
Password for the key store. |
|
Name of the key store entity to use as the user name. |
|
Password for the user name key store entity. |
|
Name of the key store entity to use as the password. |
|
Password for the password key store entity. |
Multiple Redshift databases or clusters#
By default, the Redshift connector can only access a single database within
a Redshift cluster. Enable the
redshift.database-prefix-for-schema.enabled
catalog configuration property
to access multiple databases on a Redshift cluster as described in the
following table:
Property name |
Description |
Default |
---|---|---|
|
Allow access to other databases in Redshift by including the database name in double quotes with the schema name: SELECT *
FROM catalog."database.schema".table
When enabled, |
false |
To connect to multiple Redshift clusters, you must configure additional catalogs using the Redshift connector for each cluster.
General configuration properties#
The following table describes general catalog configuration properties for the connector:
Property name |
Description |
Default value |
---|---|---|
|
Support case insensitive schema and table names. |
|
|
This value should be a duration. |
|
|
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. |
|
|
Frequency with which Trino checks the name matching configuration file for changes. This value should be a duration. |
(refresh disabled) |
|
The duration for which metadata, including table and column statistics, is cached. |
|
|
Cache the fact that metadata, including table and column statistics, is not available |
|
|
Maximum number of objects stored in the metadata cache |
|
|
Maximum number of statements in a batched execution. Do not change this setting from the default. Non-default values may negatively impact performance. |
|
|
Push down dynamic filters into JDBC queries |
|
|
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. |
|
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
catalogUSE 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 Redshift#
The Redshift connector provides a schema for every Redshift schema.
See the available Redshift schemas by running SHOW SCHEMAS
. The following
example shows the Redshift schemas available in a catalog named example
:
SHOW SCHEMAS FROM example;
If you have a Redshift schema named web
, view the tables in this schema by
running SHOW TABLES
:
SHOW TABLES FROM example.web;
See a list of the columns in the clicks
table in the web
database using either DESCRIBE
or SHOW COLUMNS
:
DESCRIBE example.web.clicks;
SHOW COLUMNS FROM example.web.clicks;
Finally, 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.
Type mapping#
Because SEP and Redshift each support types that the other does not, this connector modifies some types when reading or writing data.
Redshift to SEP type mapping#
This connector supports reading the following Redshift types and performs conversion to SEP types with the detailed mappings as shown in the following table.
Redshift database type |
SEP type |
Notes |
---|---|---|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Redshift’s |
|
|
Redshift’s |
|
|
|
|
|
|
|
|
No other types are supported.
SEP to Redshift type mapping#
This connector supports writing the following SEP types and performs conversion to Redshift types with the detailed mappings as shown in the following table.
SEP type |
Redshift type |
Notes |
---|---|---|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
For |
|
|
For |
|
|
For |
|
|
For |
|
|
For |
|
|
When no bound is given. |
|
|
|
|
|
|
|
|
No other types are supported.
Mapping datetime types#
Redshift’s TIME
and TIMESTAMP
types only support microsecond precision
(6 digits). When writing data with higher precision from SEP to Redshift,
the time is rounded to the nearest microsecond before being inserted.
SQL support#
The connector provides read and write access to data and metadata in Redshift. In addition to the globally available and read operation statements, the connector supports the following features:
When the redshift.database-prefix-for-schema.enabled
catalog configuration
property is enabled, the connector only supports globally available and read operation SQL
statements.
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.
ALTER TABLE EXECUTE#
The 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']);
Table functions#
The connector provides specific table functions to access Redshift.
query(VARCHAR) -> table
#
The query
function allows you to query the underlying database directly. It
requires syntax native to Redshift, because the full query is pushed down and
processed in Redshift. 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.
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.
For example, select the top 10 nations by population:
SELECT
*
FROM
TABLE(
example.system.query(
query => 'SELECT
TOP 10 *
FROM
tpch.nation
ORDER BY
population DESC'
)
);
Fault-tolerant execution support#
The connector supports Fault-tolerant execution of query processing. Read and write operations are both supported with any retry policy.
Performance#
The connector includes a number of performance improvements, detailed in the following sections.
Pushdown#
The connector supports pushdown for a number of operations:
Aggregate pushdown for the following functions:
count()
, alsocount(distinct x)
variance()
andvar_samp()
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
|
|
|
Strategy used to evaluate whether join operations are pushed down. Set to
|
|
Table statistics#
The 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 Redshift and retrieved by the connector.
ANALYZE
may be run automatically depending on your Redshift configuration.
To manually collect statistics for a table, execute the following statement in
Redshift.
ANALYZE table_schema.table_name;
Refer to Redshift documentation for additional ANALYZE
options.
Managed statistics#
The connector supports Managed statistics allowing SEP to collect and store its own 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
collect_statistics for details and examples.
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.
Starburst Cached Views#
The connector supports table scan redirection to improve performance and reduce load on the data source.
Security#
The connector includes a number of security-related features, detailed in the following sections.
User impersonation#
The connector supports user impersonation. Enable user impersonation in the catalog properties file:
redshift.impersonation.enabled=true
User impersonation in the Redshift connector is based on SET SESSION AUTHORIZATION command supported in Redshift.
Note
Running SET SESSION AUTHORIZATION
in Redshift requires the initial
connection user to be a superuser.
Password credential pass-through#
The connector supports password credential pass-through. To enable it, edit the catalog properties file to include the authentication type:
redshift.authentication.type=PASSWORD_PASS_THROUGH
For more information about configurations and limitations, see Password credential pass-through.
AWS IAM authentication#
The connector supports IAM user authentication with an access key ID and secret access key. This enhancement allows you to manage access control from SEP with IAM policies.
Configuration#
To enable IAM authentication, add the following configuration properties to the catalog configuration file:
redshift.authentication.type=AWS
aws.region-name=<AWS region>
This table describes the configuration properties for IAM authentication:
Property name |
Description |
---|---|
|
The name of the AWS region in which the Redshift instance is deployed. |
|
The access key of the principal to authenticate with for the token generator service. Used for fixed authentication, setting this property disables automatic authentication. |
|
The secret key of the principal to authenticate with for the token generator service. Used for fixed authentication, setting this property disables automatic authentication. |
|
(Optional) A session token for temporary credentials, such as credentials obtained from SSO. Used for fixed authentication, setting this property disables automatic authentication. |
Authentication#
By default the connector attempts to automatically obtain its authentication credentials from the environment. The default credential provider chain attempts to obtain credentials from the following sources, in order:
Environment variables:
AWS_ACCESS_KEY_ID
andAWS_SECRET_ACCESS_KEY
, orAWS_ACCESS_KEY
andAWS_SECRET_KEY
.Java system properties:
aws.accessKeyId
andaws.secretKey
.Web identity token: credentials from the environment or container.
Credential profiles file: a profiles file at the default location (
~/.aws/credentials
) shared by all AWS SDKs and the AWS CLI.EC2 service credentials: credentials delivered through the Amazon EC2 container service, assuming the security manager has permission to access the value of the
AWS_CONTAINER_CREDENTIALS_RELATIVE_URI
environment variable.Instance profile credentials: credentials delivered through the Amazon EC2 metadata service.
If the SEP cluster is running on an EC2 instance, these credentials most likely come from the metadata service.
Alternatively, you can set fixed credentials for authentication. This option disables the container’s automatic attempt to locate credentials. To use fixed credentials for authentication, set the following configuration properties:
aws.access-key=<access_key>
aws.secret-key=<secret_key>
# (Optional) You can use temporary credentials. For example, you can use temporary credentials from SSO
aws.session-token=<session_token>
Limitations#
The Starburst Redshift connector does not push down queries with a
GROUP BY
andWHERE
clause on the same column for tables usingALL
orAUTO(ALL)
distribution styles due to a limitation in Redshift. You can work around this by changing the table to use anEVEN
orKEY
distribution style as described in the Redshift documentation about distribution styles.