Apache Hive is a data warehouse infrastructure built on top of Hadoop that
supports data summarization, query, and analysis. Hive provides an SQL-like
language called HiveQL that transparently converts queries to MapReduce for
execution on large datasets stored in Hadoop's HDFS.

Learn more at http://hive.apache.org

Overview

Apache Hive is a data warehouse infrastructure built on top of Hadoop that
supports data summarization, query, and analysis. Hive provides an SQL-like
language called HiveQL that transparently converts queries to MapReduce for
execution on large datasets stored in Hadoop's HDFS. Learn more at
hive.apache.org.

This charm provides the Hive command line interface and the HiveServer2 service.

Usage

This charm is intended to be deployed via one of the
apache bundles.
For example:

juju quickstart apache-analytics-sql

This will deploy the Apache Hadoop platform with Apache Hive available to
perform SQL-like queries against your data.

Status and Smoke Test

The services provide extended status reporting to indicate when they are ready:

juju status --format=tabular

This is particularly useful when combined with watch to track the on-going
progress of the deployment:

watch -n 0.5 juju status --format=tabular

The message for each unit will provide information about that unit's state.
Once they all indicate that they are ready, you can perform a "smoke test"
to verify that Hive is working as expected using the built-in smoke-test
action:

juju action do hive/0 smoke-test

After a few seconds or so, you can check the results of the smoke test:

juju action status

You will see status: completed if the smoke test was successful, or
status: failed if it was not. You can get more information on why it failed
via:

juju action fetch <action-id>

Contact Information

Help

Configuration

resources_mirror
(string)
                            URL from which to fetch resources (e.g., Hadoop binaries) instead of Launchpad.

                        
heap
(int)
                            The maximum heap size (in MB) used by the hadoop client jvm. If you
experience out of memory (OOM) errors when running jobs, consider
increasing this value.

                        
1024