Amazon Athena Microsoft SQL Server connector - Amazon Athena
PrerequisitesLimitationsTermsParametersData type supportPartitions and splitsPerformancePassthrough queriesLicense informationAdditional resources

Amazon Athena Microsoft SQL Server connector

The Amazon Athena connector for Microsoft SQL Server enables Amazon Athena to run SQL queries on your data stored in Microsoft SQL Server using JDBC.

Prerequisites

Limitations

  • Write DDL operations are not supported.

  • In a multiplexer setup, the spill bucket and prefix are shared across all database instances.

  • Any relevant Lambda limits. For more information, see Lambda quotas in the AWS Lambda Developer Guide.

  • In filter conditions, you must cast the Date and Timestamp data types to the appropriate data type.

  • To search for negative values of type Real and Float, use the <= or >= operator.

  • The binary, varbinary, image, and rowversion data types are not supported.

Terms

The following terms relate to the SQL Server connector.

  • Database instance – Any instance of a database deployed on premises, on Amazon EC2, or on Amazon RDS.

  • Handler – A Lambda handler that accesses your database instance. A handler can be for metadata or for data records.

  • Metadata handler – A Lambda handler that retrieves metadata from your database instance.

  • Record handler – A Lambda handler that retrieves data records from your database instance.

  • Composite handler – A Lambda handler that retrieves both metadata and data records from your database instance.

  • Property or parameter – A database property used by handlers to extract database information. You configure these properties as Lambda environment variables.

  • Connection String – A string of text used to establish a connection to a database instance.

  • Catalog – A non-AWS Glue catalog registered with Athena that is a required prefix for the connection_string property.

  • Multiplexing handler – A Lambda handler that can accept and use multiple database connections.

Parameters

Use the Lambda environment variables in this section to configure the SQL Server connector.

Connection string

Use a JDBC connection string in the following format to connect to a database instance.

sqlserver://${jdbc_connection_string}

Using a multiplexing handler

You can use a multiplexer to connect to multiple database instances with a single Lambda function. Requests are routed by catalog name. Use the following classes in Lambda.

Handler Class
Composite handler SqlServerMuxCompositeHandler
Metadata handler SqlServerMuxMetadataHandler
Record handler SqlServerMuxRecordHandler

Multiplexing handler parameters

Parameter Description
$catalog_connection_string Required. A database instance connection string. Prefix the environment variable with the name of the catalog used in Athena. For example, if the catalog registered with Athena is mysqlservercatalog, then the environment variable name is mysqlservercatalog_connection_string.
default Required. The default connection string. This string is used when the catalog is lambda:${AWS_LAMBDA_FUNCTION_NAME}.

The following example properties are for a SqlServer MUX Lambda function that supports two database instances: sqlserver1 (the default), and sqlserver2.

Property Value
default sqlserver://jdbc:sqlserver://sqlserver1.hostname:port;databaseName=<database_name>;${secret1_name}
sqlserver_catalog1_connection_string sqlserver://jdbc:sqlserver://sqlserver1.hostname:port;databaseName=<database_name>;${secret1_name}
sqlserver_catalog2_connection_string sqlserver://jdbc:sqlserver://sqlserver2.hostname:port;databaseName=<database_name>;${secret2_name}

Providing credentials

To provide a user name and password for your database in your JDBC connection string, you can use connection string properties or AWS Secrets Manager.

  • Connection String – A user name and password can be specified as properties in the JDBC connection string.

    Important

    As a security best practice, do not use hardcoded credentials in your environment variables or connection strings. For information about moving your hardcoded secrets to AWS Secrets Manager, see Move hardcoded secrets to AWS Secrets Manager in the AWS Secrets Manager User Guide.

  • AWS Secrets Manager – To use the Athena Federated Query feature with AWS Secrets Manager, the VPC connected to your Lambda function should have internet access or a VPC endpoint to connect to Secrets Manager.

    You can put the name of a secret in AWS Secrets Manager in your JDBC connection string. The connector replaces the secret name with the username and password values from Secrets Manager.

    For Amazon RDS database instances, this support is tightly integrated. If you use Amazon RDS, we highly recommend using AWS Secrets Manager and credential rotation. If your database does not use Amazon RDS, store the credentials as JSON in the following format:

    {"username": "${username}", "password": "${password}"}
Example connection string with secret name

The following string has the secret name ${secret_name}.

sqlserver://jdbc:sqlserver://hostname:port;databaseName=<database_name>;${secret_name}

The connector uses the secret name to retrieve secrets and provide the user name and password, as in the following example.

sqlserver://jdbc:sqlserver://hostname:port;databaseName=<database_name>;user=<user>;password=<password>

Using a single connection handler

You can use the following single connection metadata and record handlers to connect to a single SQL Server instance.

Handler type Class
Composite handler SqlServerCompositeHandler
Metadata handler SqlServerMetadataHandler
Record handler SqlServerRecordHandler

Single connection handler parameters

Parameter Description
default Required. The default connection string.

The single connection handlers support one database instance and must provide a default connection string parameter. All other connection strings are ignored.

The following example property is for a single SQL Server instance supported by a Lambda function.

Property Value
default sqlserver://jdbc:sqlserver://hostname:port;databaseName=<database_name>;${secret_name}

Spill parameters

The Lambda SDK can spill data to Amazon S3. All database instances accessed by the same Lambda function spill to the same location.

Parameter Description
spill_bucket Required. Spill bucket name.
spill_prefix Required. Spill bucket key prefix.
spill_put_request_headers (Optional) A JSON encoded map of request headers and values for the Amazon S3 putObject request that is used for spilling (for example, {"x-amz-server-side-encryption" : "AES256"}). For other possible headers, see PutObject in the Amazon Simple Storage Service API Reference.

Data type support

The following table shows the corresponding data types for SQL Server and Apache Arrow.

SQL Server Arrow
bit TINYINT
tinyint SMALLINT
smallint SMALLINT
int INT
bigint BIGINT
decimal DECIMAL
numeric FLOAT8
smallmoney FLOAT8
money DECIMAL
float[24] FLOAT4
float[53] FLOAT8
real FLOAT4
datetime Date(MILLISECOND)
datetime2 Date(MILLISECOND)
smalldatetime Date(MILLISECOND)
date Date(DAY)
time VARCHAR
datetimeoffset Date(MILLISECOND)
char[n] VARCHAR
varchar[n/max] VARCHAR
nchar[n] VARCHAR
nvarchar[n/max] VARCHAR
text VARCHAR
ntext VARCHAR

Partitions and splits

A partition is represented by a single partition column of type varchar. In case of the SQL Server connector, a partition function determines how partitions are applied on the table. The partition function and column name information are retrieved from the SQL Server metadata table. A custom query then gets the partition. Splits are created based upon the number of distinct partitions received.

Performance

Selecting a subset of columns significantly speeds up query runtime and reduces data scanned. The SQL Server connector is resilient to throttling due to concurrency.

The Athena SQL Server connector performs predicate pushdown to decrease the data scanned by the query. Simple predicates and complex expressions are pushed down to the connector to reduce the amount of data scanned and decrease query execution run time.

Predicates

A predicate is an expression in the WHERE clause of a SQL query that evaluates to a Boolean value and filters rows based on multiple conditions. The Athena SQL Server connector can combine these expressions and push them directly to SQL Server for enhanced functionality and to reduce the amount of data scanned.

The following Athena SQL Server connector operators support predicate pushdown:

  • Boolean: AND, OR, NOT

  • Equality: EQUAL, NOT_EQUAL, LESS_THAN, LESS_THAN_OR_EQUAL, GREATER_THAN, GREATER_THAN_OR_EQUAL, IS_DISTINCT_FROM, NULL_IF, IS_NULL

  • Arithmetic: ADD, SUBTRACT, MULTIPLY, DIVIDE, MODULUS, NEGATE

  • Other: LIKE_PATTERN, IN

Combined pushdown example

For enhanced querying capabilities, combine the pushdown types, as in the following example:

SELECT * 
FROM my_table 
WHERE col_a > 10 
    AND ((col_a + col_b) > (col_c % col_d)) 
    AND (col_e IN ('val1', 'val2', 'val3') OR col_f LIKE '%pattern%');

Passthrough queries

The SQL Server connector supports passthrough queries. Passthrough queries use a table function to push your full query down to the data source for execution.

To use passthrough queries with SQL Server, you can use the following syntax:

SELECT * FROM TABLE(
        system.query(
            query => 'query string'
        ))

The following example query pushes down a query to a data source in SQL Server. The query selects all columns in the customer table, limiting the results to 10.

SELECT * FROM TABLE(
        system.query(
            query => 'SELECT * FROM customer LIMIT 10'
        ))

License information

By using this connector, you acknowledge the inclusion of third party components, a list of which can be found in the pom.xml file for this connector, and agree to the terms in the respective third party licenses provided in the LICENSE.txt file on GitHub.com.

Additional resources

For the latest JDBC driver version information, see the pom.xml file for the SQL Server connector on GitHub.com.

For additional information about this connector, visit the corresponding site on GitHub.com.