Prerequisites Limitations Terms Parameters Data type support Data type conversions Partitions and splits Performance Passthrough queries License information Additional resources

Amazon Athena Snowflake connector

The Amazon Athena connector for Snowflake enables Amazon Athena to run SQL queries on data stored in your Snowflake SQL database or RDS instances using JDBC.

This connector can be registered with Glue Data Catalog as a federated catalog. It supports data access controls defined in Lake Formation at the catalog, database, table, column, row, and tag levels. This connector uses Glue Connections to centralize configuration properties in Glue.

Prerequisites

Deploy the connector to your AWS account using the Athena console or the AWS Serverless Application Repository. For more information, see Create a data source connection or Use the AWS Serverless Application Repository to deploy a data source connector.

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.
Currently, Snowflake views with single split are supported.
In Snowflake, because object names are case sensitive, two tables can have the same name in lower and upper case (for example, EMPLOYEE and employee). In Athena Federated Query, schema table names are provided to the Lambda function in lower case. To work around this issue, you can provide @schemaCase query hints to retrieve the data from the tables that have case sensitive names. Following are two sample queries with query hints.
```
SELECT *
                    FROM "lambda:snowflakeconnector".SYSTEM."MY_TABLE@schemaCase=upper&tableCase=upper"     
```
```
SELECT *
                    FROM "lambda:snowflakeconnector".SYSTEM."MY_TABLE@schemaCase=upper&tableCase=lower"     
```
If you migrate your Snowflake connections to Glue Catalog and Lake Formation, Athena will not default all requests to upper case or support annotation. The default behavior for Glue Connection will not adjust casing.

Snowflake supports the following casing modes:
- NONE (default for connector with Glue Connection)
- CASE_INSENSITIVE_SEARCH
- ANNOTATION (default for connector without Glue Connection)

Terms

The following terms relate to the Snowflake 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 parameters in this section to configure the Snowflake connector.

Glue connections (recommended)

We recommended that you configure a Snowflake connector by using a Glue connections object. To do this, set the glue_connection environment variable of the Snowflake connector Lambda to the name of the Glue connection to use.

Glue connections properties

Use the following command to get the schema for a Glue connection object. This schema contains all the parameters that you can use to control your connection.


aws glue describe-connection-type --connection-type SNOWFLAKE

Lambda environment properties

glue_connection – Specifies the name of the Glue connection associated with the federated connector.
casing_mode – (Optional) Specifies how to handle casing for schema and table names. The casing_mode parameter uses the following values to specify the behavior of casing:
- none – Do not change case of the given schema and table names. This is the default for connectors that have an associated glue connection.
- annotation – Adjusts the table name if an annotation is present. This is the default for connectors that do not have an associated glue connection.
- case_insensitive_search – Perform case insensitive searches against schema and tables names in Snowflake. Use this value if your query contains schema or table names that do not match the default casing for your connector.
- upper – Upper case all given schema and table names.
- lower – Lower case all given schema and table names.

Note

A Snowflake connector created using Glue connections does not support the use of a multiplexing handler.

Legacy connections

Note

Athena data source connectors created on December 3, 2024 and later use AWS Glue connections.

The parameter names and definitions listed below are for Athena data source connectors created without an associated Glue connection. Use the following parameters only when you manually deploy an earlier version of an Athena data source connector or when the glue_connection environment property is not specified.

Lambda environment properties

default – The JDBC connection string to use to connect to the Snowflake database instance. For example, snowflake://${jdbc_connection_string}
catalog_connection_string – Used by the Multiplexing handler (not supported when using a glue connection). 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 mysnowflakecatalog, then the environment variable name is mysnowflakecatalog_connection_string.
spill_bucket – Specifies the Amazon S3 bucket for data that exceeds Lambda function limits.
spill_prefix – (Optional) Defaults to a subfolder in the specified spill_bucket called athena-federation-spill. We recommend that you configure an Amazon S3 storage lifecycle on this location to delete spills older than a predetermined number of days or hours.
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.
kms_key_id – (Optional) By default, any data that is spilled to Amazon S3 is encrypted using the AES-GCM authenticated encryption mode and a randomly generated key. To have your Lambda function use stronger encryption keys generated by KMS like a7e63k4b-8loc-40db-a2a1-4d0en2cd8331, you can specify a KMS key ID.
disable_spill_encryption – (Optional) When set to True, disables spill encryption. Defaults to False so that data that is spilled to S3 is encrypted using AES-GCM – either using a randomly generated key or KMS to generate keys. Disabling spill encryption can improve performance, especially if your spill location uses server-side encryption.

Connection string

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


snowflake://${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	`SnowflakeMuxCompositeHandler`
Metadata handler	`SnowflakeMuxMetadataHandler`
Record handler	`SnowflakeMuxRecordHandler`

Multiplexing handler parameters

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

The following example properties are for a Snowflake MUX Lambda function that supports two database instances: snowflake1 (the default), and snowflake2.

Property	Value
`default`	`snowflake://jdbc:snowflake://snowflake1.host:port/?warehouse=warehousename&db=db1&schema=schema1&${Test/RDS/Snowflake1}`
`snowflake_catalog1_connection_string`	`snowflake://jdbc:snowflake://snowflake1.host:port/?warehouse=warehousename&db=db1&schema=schema1${Test/RDS/Snowflake1}`
`snowflake_catalog2_connection_string`	`snowflake://jdbc:snowflake://snowflake2.host:port/?warehouse=warehousename&db=db1&schema=schema1&user=sample2&password=sample2`

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, don't 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 ${Test/RDS/Snowflake1}.


snowflake://jdbc:snowflake://snowflake1.host:port/?warehouse=warehousename&db=db1&schema=schema1${Test/RDS/Snowflake1}&...

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


snowflake://jdbc:snowflake://snowflake1.host:port/warehouse=warehousename&db=db1&schema=schema1&user=sample2&password=sample2&...

Currently, Snowflake recognizes the user and password JDBC properties. It also accepts the user name and password in the format username/password without the keys user or password.

Using a single connection handler

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

Handler type	Class
Composite handler	`SnowflakeCompositeHandler`
Metadata handler	`SnowflakeMetadataHandler`
Record handler	`SnowflakeRecordHandler`

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 Snowflake instance supported by a Lambda function.

Property	Value
`default`	`snowflake://jdbc:snowflake://snowflake1.host:port/?secret=Test/RDS/Snowflake1`

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 JDBC and Apache Arrow.

JDBC	Arrow
Boolean	Bit
Integer	Tiny
Short	Smallint
Integer	Int
Long	Bigint
float	Float4
Double	Float8
Date	DateDay
Timestamp	DateMilli
String	Varchar
Bytes	Varbinary
BigDecimal	Decimal
ARRAY	List

Data type conversions

In addition to the JDBC to Arrow conversions, the connector performs certain other conversions to make the Snowflake source and Athena data types compatible. These conversions help ensure that queries get executed successfully. The following table shows these conversions.

Source data type (Snowflake)	Converted data type (Athena)
TIMESTAMP	TIMESTAMPMILLI
DATE	TIMESTAMPMILLI
INTEGER	INT
DECIMAL	BIGINT
TIMESTAMP_NTZ	TIMESTAMPMILLI

All other unsupported data types are converted to VARCHAR.

Partitions and splits

Partitions are used to determine how to generate splits for the connector. Athena constructs a synthetic column of type varchar that represents the partitioning scheme for the table to help the connector generate splits. The connector does not modify the actual table definition.

To create this synthetic column and the partitions, Athena requires a primary key to be defined. However, because Snowflake does not enforce primary key constraints, you must enforce uniqueness yourself. Failure to do so causes Athena to default to a single split.

Performance

For optimal performance, use filters in queries whenever possible. In addition, we highly recommend native partitioning to retrieve huge datasets that have uniform partition distribution. Selecting a subset of columns significantly speeds up query runtime and reduces data scanned. The Snowflake connector is resilient to throttling due to concurrency.

The Athena Snowflake connector performs predicate pushdown to decrease the data scanned by the query. LIMIT clauses, simple predicates, and complex expressions are pushed down to the connector to reduce the amount of data scanned and decrease query execution run time.

LIMIT clauses

A LIMIT N statement reduces the data scanned by the query. With LIMIT N pushdown, the connector returns only N rows to Athena.

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 Snowflake connector can combine these expressions and push them directly to Snowflake for enhanced functionality and to reduce the amount of data scanned.

The following Athena Snowflake 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%') 
LIMIT 10;

Passthrough queries

The Snowflake 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 Snowflake, 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 Snowflake. 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 Snowflake connector on GitHub.com.

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

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Document Conventions

SAP HANA

SQL Server

Amazon Athena Snowflake connector

Prerequisites

Limitations

Terms

Parameters

Note

Note

Connection string

Using a multiplexing handler

Multiplexing handler parameters

Providing credentials

Important

Example connection string with secret name

Using a single connection handler

Single connection handler parameters

Spill parameters

Data type support

Data type conversions

Partitions and splits

Performance

LIMIT clauses

Predicates

Combined pushdown example

Passthrough queries

License information

Additional resources