Using lakeFS with Apache Hive

The Apache Hive ™ data warehouse software facilitates reading, writing, and managing large datasets residing in distributed storage using SQL. Structure can be projected onto data already in storage. A command line tool and JDBC driver are provided to connect users to Hive.

Configuration

To configure Hive to work with lakeFS, you need to set the lakeFS credentials in the corresponding S3 credential fields.

lakeFS endpoint: fs.s3a.endpoint

lakeFS access key: fs.s3a.access.key

lakeFS secret key: fs.s3a.secret.key

Note In the following examples, we set AWS credentials at runtime for clarity. In production, these properties should be set using one of Hadoop’s standard ways of Authenticating with S3.

For example, you can add the configurations to the file hdfs-site.xml:

<configuration>
    ...
    <property>
        <name>fs.s3a.secret.key</name>
        <value>wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY</value>
    </property>
    <property>
        <name>fs.s3a.access.key</name>
        <value>AKIAIOSFODNN7EXAMPLE</value>
    </property>
    <property>
        <name>fs.s3a.endpoint</name>
        <value>https://lakefs.example.com</value>
    </property>
    <property>
       <name>fs.s3a.path.style.access</name>
       <value>true</value>
    </property>
</configuration>

Note In this example, we set fs.s3a.path.style.access to true to remove the need for additional DNS records for virtual hosting fs.s3a.path.style.access that was introduced in Hadoop 2.8.0

Examples

Example with schema

CREATE  SCHEMA example LOCATION 's3a://example/main/' ;
CREATE TABLE example.request_logs (
    request_time timestamp,
    url string,
    ip string,
    user_agent string
);

Example with an external table

CREATE EXTERNAL TABLE request_logs (
    request_time timestamp,
    url string,
    ip string,
    user_agent string
) LOCATION 's3a://example/main/request_logs' ;