Amazon Athena Hortonworks connector - Amazon Athena
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Amazon Athena Hortonworks connector

The Amazon Athena connector for Hortonworks enables Amazon Athena to run SQL queries on the Cloudera Hortonworks data platform. The connector transforms your Athena SQL queries to their equivalent HiveQL syntax.



  • 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 Amazon Lambda Developer Guide.


The following terms relate to the Hortonworks Hive 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-Amazon 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.


Use the Lambda environment variables in this section to configure the Hortonworks Hive connector.

Connection string

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


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 HiveMuxCompositeHandler
Metadata handler HiveMuxMetadataHandler
Record handler HiveMuxRecordHandler

Multiplexing handler parameters

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

The following example properties are for a Hive MUX Lambda function that supports two database instances: hive1 (the default), and hive2.

Property Value
default hive://jdbc:hive2://hive1:10000/default?${Test/RDS/hive1}
hive_catalog1_connection_string hive://jdbc:hive2://hive1:10000/default?${Test/RDS/hive1}
hive_catalog2_connection_string hive://jdbc:hive2://hive2:10000/default?UID=sample&PWD=sample

Providing credentials

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

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


    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 Amazon Secrets Manager, see Move hardcoded secrets to Amazon Secrets Manager in the Amazon Secrets Manager User Guide.

  • Amazon Secrets Manager – To use the Athena Federated Query feature with Amazon 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 Amazon 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 Amazon 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/hive1host}.


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


Currently, the Hortonworks Hive connector recognizes the UID and PWD JDBC properties.

Using a single connection handler

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

Handler type Class
Composite handler HiveCompositeHandler
Metadata handler HiveMetadataHandler
Record handler HiveRecordHandler

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 Hortonworks Hive instance supported by a Lambda function.

Property Value
default hive://jdbc:hive2://hive1host:10000/default?secret=${Test/RDS/hive1host}

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, Hortonworks Hive, and Arrow.

JDBC Hortonworks Hive Arrow
Boolean Boolean Bit
Integer TINYINT Tiny
Short SMALLINT Smallint
Integer INT Int
Long BIGINT Bigint
float float4 Float4
Double float8 Float8
Date date DateDay
Timestamp timestamp DateMilli
String VARCHAR Varchar
Bytes bytes Varbinary
BigDecimal Decimal Decimal
ARRAY N/A (see note) List

Currently, Hortonworks Hive does not support the aggregate types ARRAY, MAP, STRUCT, or UNIONTYPE. Columns of aggregate types are treated as VARCHAR columns in SQL.

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.


Hortonworks Hive supports static partitions. The Athena Hortonworks Hive connector can retrieve data from these partitions in parallel. If you want to query very large datasets with uniform partition distribution, static partitioning is highly recommended. Selecting a subset of columns significantly speeds up query runtime and reduces data scanned. The Hortonworks Hive connector is resilient to throttling due to concurrency.

The Athena Hortonworks Hive 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.


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

The following Athena Hortonworks Hive connector operators support predicate pushdown:

  • Boolean: AND, OR, NOT




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 Hortonworks Hive 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 Hortonworks Hive, 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 Hortonworks Hive. 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

Additional resources

For the latest JDBC driver version information, see the pom.xml file for the Hortonworks Hive connector on

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