Post-Installation Configuration for Impala

This section describes the mandatory and recommended configuration settings for Impala. If Impala is installed using Cloudera Manager, some of these configurations are completed automatically; you must still configure short-circuit reads manually. If you installed Impala without Cloudera Manager, or if you want to customize your environment, consider making the changes described in this topic.

In some cases, depending on the level of Impala, CDH, and Cloudera Manager, you might need to add particular component configuration details in one of the free-form fields on the Impala configuration pages within Cloudera Manager. In Cloudera Manager 4, these fields are labelled Safety Valve; in Cloudera Manager 5, they are called Advanced Configuration Snippet.

  • You must enable short-circuit reads, whether or not Impala was installed through Cloudera Manager. This setting goes in the Impala configuration settings, not the Hadoop-wide settings.
  • If you installed Impala in an environment that is not managed by Cloudera Manager, you must enable block location tracking, and you can optionally enable native checksumming for optimal performance.
  • If you deployed Impala using Cloudera Manager see Testing Impala Performance to confirm proper configuration.

Mandatory: Short-Circuit Reads

Enabling short-circuit reads allows Impala to read local data directly from the file system. This removes the need to communicate through the DataNodes, improving performance. This setting also minimizes the number of additional copies of data. Short-circuit reads requires libhadoop.so (the Hadoop Native Library) to be accessible to both the server and the client. You must install it from an .rpm, .deb, or parcel to use short-circuit local reads.

To configure DataNodes for short-circuit reads:

  1. Copy the client core-site.xml and hdfs-site.xml configuration files from the Hadoop configuration directory to the Impala configuration directory. The default Impala configuration location is /etc/impala/conf.
  2. On all Impala nodes, configure the following properties in Impala's copy of hdfs-site.xml as shown: 
    <property>
    <name>dfs.client.read.shortcircuit</name>
    <value>true</value>
</property>

<property>
    <name>dfs.domain.socket.path</name>
    <value>/var/run/hdfs-sockets/dn</value>
</property>

<property>
    <name>dfs.client.file-block-storage-locations.timeout.millis</name>
    <value>10000</value>
</property>

  3. If /var/run/hadoop-hdfs/ is group-writable, make sure its group is root.

  4. After applying these changes, restart all DataNodes.

Mandatory: Block Location Tracking

Enabling block location metadata allows Impala to know which disk data blocks are located on, allowing better utilization of the underlying disks. Impala will not start unless this setting is enabled.

To enable block location tracking:

  1. For each DataNode, adding the following to the hdfs-site.xml file: 
    <property>
  <name>dfs.datanode.hdfs-blocks-metadata.enabled</name>
  <value>true</value>
</property>
  2. Copy the client core-site.xml and hdfs-site.xml configuration files from the Hadoop configuration directory to the Impala configuration directory. The default Impala configuration location is /etc/impala/conf.
  3. After applying these changes, restart all DataNodes.

Optional: Native Checksumming

Enabling native checksumming causes Impala to use an optimized native library for computing checksums, if that library is available.

To enable native checksumming:

If you installed CDH from packages, the native checksumming library is installed and setup correctly. In such a case, no additional steps are required. Conversely, if you installed by other means, native checksumming may not be available due to missing shared objects. Finding the message "Unable to load native-hadoop library for your platform... using builtin-java classes where applicable" in the Impala logs indicates native checksumming may be unavailable. To enable native checksumming, you must build and install libhadoop.so (the Hadoop Native Library).