To perform as expected, Impala depends on the availability of the software, hardware, and configurations described in the following sections.
Product Compatibility Matrix
The ultimate source of truth about compatibility between various versions of CDH, Cloudera Manager, and various CDH components is the Product Compatibility Matrix for CDH and Cloudera Manager.
Supported Operating Systems
The relevant supported operating systems and versions for Impala are the same as for the corresponding CDH 5 platforms. For details, see the Supported Operating Systems page for CDH 5.
Hive Metastore and Related Configuration
Impala can interoperate with data stored in Hive, and uses the same infrastructure as Hive for tracking metadata about schema objects such as tables and columns. The following components are prerequisites for Impala:
- MySQL or PostgreSQL, to act as a metastore database for both Impala and Hive.
- Optional: Hive. Although only the Hive metastore database is required for Impala to function, you might install Hive on some client machines to create and load data into tables that use certain file formats. See How Impala Works with Hadoop File Formats for details. Hive does not need to be installed on the same DataNodes as Impala; it just needs access to the same metastore database.
Although Impala is primarily written in C++, it does use Java to communicate with various Hadoop components:
- The officially supported JVM for Impala is the Oracle JVM. Other JVMs might cause issues, typically resulting in a failure at impalad startup. In particular, the JamVM used by default on certain levels of Ubuntu systems can cause impalad to fail to start.
- Internally, the impalad daemon relies on the JAVA_HOME environment variable to locate the system Java libraries. Make sure the impalad service is not run from an environment with an incorrect setting for this variable.
- All Java dependencies are packaged in the impala-dependencies.jar file, which is located at /usr/lib/impala/lib/. These map to everything that is built under fe/target/dependency.
Networking Configuration Requirements
As part of ensuring best performance, Impala attempts to complete tasks on local data, as opposed to using network connections to work with remote data. To support this goal, Impala matches the hostname provided to each Impala daemon with the IP address of each DataNode by resolving the hostname flag to an IP address. For Impala to work with local data, use a single IP interface for the DataNode and the Impala daemon on each machine. Ensure that the Impala daemon's hostname flag resolves to the IP address of the DataNode. For single-homed machines, this is usually automatic, but for multi-homed machines, ensure that the Impala daemon's hostname resolves to the correct interface. Impala tries to detect the correct hostname at start-up, and prints the derived hostname at the start of the log in a message of the form:
Using hostname: impala-daemon-1.example.com
In the majority of cases, this automatic detection works correctly. If you need to explicitly set the hostname, do so by setting the --hostname flag.
The memory allocation should be consistent across Impala executor nodes. A single Impala executor with a lower memory limit than the rest can easily become a bottleneck and lead to suboptimal performance.
This guideline does not apply to coordinator-only nodes.
Hardware Requirements for Optimal Join Performance
During join operations, portions of data from each joined table are loaded into memory. Data sets can be very large, so ensure your hardware has sufficient memory to accommodate the joins you anticipate completing.
While requirements vary according to data set size, the following is generally recommended:
Impala version 2.2 and higher uses the SSSE3 instruction set, which is included in newer processors.
128 GB or more recommended, ideally 256 GB or more. If the intermediate results during query processing on a particular node exceed the amount of memory available to Impala on that node, the query writes temporary work data to disk, which can lead to long query times. Note that because the work is parallelized, and intermediate results for aggregate queries are typically smaller than the original data, Impala can query and join tables that are much larger than the memory available on an individual node.
DataNodes with 12 or more disks each. I/O speeds are often the limiting factor for disk performance with Impala. Ensure that you have sufficient disk space to store the data Impala will be querying.
User Account Requirements
Impala creates and uses a user and group named impala. Do not delete this account or group and do not modify the account's or group's permissions and rights. Ensure no existing systems obstruct the functioning of these accounts and groups. For example, if you have scripts that delete user accounts not in a white-list, add these accounts to the list of permitted accounts.
For correct file deletion during DROP TABLE operations, Impala must be able to move files to the HDFS trashcan. You might need to create an HDFS directory /user/impala, writeable by the impala user, so that the trashcan can be created. Otherwise, data files might remain behind after a DROP TABLE statement.
Impala should not run as root. Best Impala performance is achieved using direct reads, but root is not permitted to use direct reads. Therefore, running Impala as root negatively affects performance.
By default, any user can connect to Impala and access all the associated databases and tables. You can enable authorization and authentication based on the Linux OS user who connects to the Impala server, and the associated groups for that user. Overview of Impala Security for details. These security features do not change the underlying file permission requirements; the impala user still needs to be able to access the data files.