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.
SEP includes additional enterprise features that are built on top of the existing Trino connector functionality. For more information on connector key feature differences between Trino and SEP, see the connectors feature matrix.
Requirements#
To connect to PostgreSQL, you need:
PostgreSQL 11.x or higher.
Network access from the SEP coordinator and workers to PostgreSQL. Port 5432 is the default port.
A valid Starburst Enterprise license.
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 |
---|---|
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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, 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
, SEP 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 |
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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 |
|
Duration for which tables statistics are cached.
Defaults to the value of |
|
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 |
|
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
32
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. |
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
SEP 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 TrinoARRAY
type, for array columns with fixed dimensions.AS_JSON
: array columns are interpreted as TrinoJSON
type, with no constraint on dimensions.
Type mapping configuration properties#
The following properties can be used to configure how data types from the connected data source are mapped to Trino data types and how the metadata is cached in Trino.
Property name |
Description |
Default value |
---|---|---|
|
Configure how unsupported column data types are handled:
The respective catalog session property is |
|
|
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 use 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 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
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 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.
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 PostgresSQL 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 features 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.
Managed statistics#
The connector supports Managed statistics allowing SEP to 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 collect_statistics for details
and examples.
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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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.
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 SEP.
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.
Starburst Cached Views#
The connector supports table scan redirection to improve performance and reduce load on the data source.
JDBC connection pooling#
When JDBC connection pooling is enabled, each node creates and maintains a connection pool instead of opening and closing separate connections to the data source. Each connection is available to connect to the data source and retrieve data. After completion of an operation, the connection is returned to the pool and can be reused. This improves performance by a small amount, reduces the load on any required authentication system used for establishing the connection, and helps avoid running into connection limits on data sources.
JDBC connection pooling is disabled by default. You can enable JDBC connection
pooling by setting the connection-pool.enabled
property to true
in your
catalog configuration file:
connection-pool.enabled=true
The following catalog configuration properties can be used to tune connection pooling:
Property name |
Description |
Default value |
---|---|---|
|
Enable connection pooling for the catalog. |
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The maximum number of idle and active connections in the pool. |
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The maximum lifetime of a connection. When a connection reaches this lifetime it is removed, regardless of how recently it has been active. |
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The maximum size of the JDBC data source cache. |
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The expiration time of a cached data source when it is no longer accessed. |
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Security#
The connector includes a number of security-related features, detailed in the following sections.
User impersonation#
The PostgreSQL connector supports user impersonation.
User impersonation can be enabled in the catalog file:
postgresql.impersonation.enabled=true
User impersonation in PostgreSQL connector is based on SET ROLE
. For more
details visit the PostgreSQL
documentation.
Kerberos authentication#
The connector supports Kerberos authentication using either a keytab or credential cache.
To configure Kerberos authentication with a keytab, add the following catalog configuration properties to the catalog properties file:
postgresql.authentication.type=KERBEROS
kerberos.client.principal=example@example.com
kerberos.client.keytab=etc/kerberos/example.keytab
kerberos.config=etc/kerberos/krb5.conf
With this configuration the user example@example.com
, defined in the principal
property, is used to connect to the database, and the related Kerberos service
ticket is located in the example.keytab
file.
To configure Kerberos authentication with a credential cache, add the following catalog configuration properties to the catalog properties file:
postgresql.authentication.type=KERBEROS
kerberos.client.principal=example@example.com
kerberos.client.credential-cache.location=etc/kerberos/example.cache
kerberos.config=etc/kerberos/krb5.conf
In these configurations the user example@example.com
, as defined in the
principal property, connects to the database. The related Kerberos service
ticket is located in the etc/kerberos/example.keytab
file, or cache
credentials in the etc/kerberos/example.cache
file.
Kerberos credential pass-through#
The PostgreSQL connector can be configured to pass through Kerberos credentials, received by SEP, to the PostgreSQL database.
Configure Kerberos and SEP, following the instructions in Kerberos credential pass-through.
Then configure the connector to pass through the credentials from the server to the database in your catalog properties file and ensure the Kerberos client configuration properties are in place on all nodes.
postgresql.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 database access via SEP is subject to the data access restrictions and permissions of the user supplied via Kerberos.
Password credential pass-through#
The connector supports password credential pass-through. To enable it, edit the catalog properties file to include the authentication type:
postgresql.authentication.type=PASSWORD_PASS_THROUGH
For more information about configurations and limitations, see Password credential pass-through.
AWS IAM authentication#
When the PostgreSQL database is deployed as an AWS RDS instance, the connector can use IAM authentication. 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:
postgresql.authentication.type=AWS
connection-user=<RDS username>
aws.region-name=<AWS region>
aws.token-expiration-timeout=10m
You can also configure the connector to assume a specific IAM role for authentication before creating the access token, in order to apply policies specific to SEP. Alongside this role, you must include an (informal) external identifier of a user to assume this role.
To apply an IAM role to the connector, add the following configuration properties:
aws.iam-role=<role_arn>
aws.external-id=<external_id>
This table describes the configuration properties for IAM authentication:
Property name |
Description |
---|---|
|
The database account used to access the RDS database instance. |
|
The name of the AWS region in which the RDS instance is deployed. |
|
(Optional) Set an IAM role to assume for authentication before creating
the access token. If set, |
|
(Optional) The informal identifier of the user who assumes
the IAM role set in |
|
The amount of time to keep the generated RDS access tokens for each user
before they are regenerated. The maximum value is 15 minutes. Defaults to
|
|
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 delievered 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>