Amazon EMR
Developer Guide



Amazon EMR installing Impala at cluster creation time is not supported in current release versions of Amazon EMR. The examples and tutorial in this section require Amazon EMR release versions 3.11.0, 3.10.0, and 3.9.0 and an older version of Impala (1.2.4) is installed.

Impala on Amazon EMR supports the following:

  • Large subset of SQL and HiveQL commands

  • Querying data in HDFS and HBase

  • Use of ODBC and JDBC drivers

  • Concurrent client requests for each Impala daemon

  • Kerberos authentication

  • Partitioned tables

  • Appending and inserting data into tables using the INSERT statement

  • Multiple HDFS file formats and compression codecs. For more information, see Impala-supported File and Compression Formats.

For more information about Impala, see Apache Impala (incubating).

What Can I Do With Impala?

Similar to using Hive with Amazon EMR, leveraging Impala with Amazon EMR can implement sophisticated data-processing applications with SQL syntax. However, Impala is built to perform faster in certain use cases (see below). With Amazon EMR, you can use Impala as a reliable data warehouse to execute tasks such as data analytics, monitoring, and business intelligence. Here are three use cases:

  • Use Impala instead of Hive on long-running clusters to perform ad hoc queries. Impala reduces interactive queries to seconds, making it an excellent tool for fast investigation. You could run Impala on the same cluster as your batch MapReduce work flows, use Impala on a long-running analytics cluster with Hive and Pig, or create a cluster specifically tuned for Impala queries.

  • Use Impala instead of Hive for batch ETL jobs on transient Amazon EMR clusters. Impala is faster than Hive for many queries, which provides better performance for these workloads. Like Hive, Impala uses SQL, so queries can easily be modified from Hive to Impala.

  • Use Impala in conjunction with a third-party business intelligence tool. Connect a client ODBC or JDBC driver with your cluster to use Impala as an engine for powerful visualization tools and dashboards.

Both batch and interactive Impala clusters can be created in Amazon EMR. For instance, you can have a long-running Amazon EMR cluster running Impala for ad hoc, interactive querying or use transient Impala clusters for quick ETL workflows.

Differences from Traditional Relational Databases

Traditional relational database systems provide transaction semantics and database atomicity, consistency, isolation, and durability (ACID) properties. They also allow tables to be indexed and cached so that small amounts of data can be retrieved very quickly and provide for fast update of small amounts of data and for enforcement of referential integrity constraints. Typically, they run on a single large machine and do not provide support for acting over complex user defined data types.

Impala uses a similar distributed query system to that found in RDBMSs, but queries data stored in HDFS and uses the Hive metastore to hold information about the input data. As with Hive, the schema for a query is provided at runtime, allowing for easier schema changes. Also, Impala can query a variety of complex data types and execute user defined functions. However, because Impala processes data in-memory, it is important to understand the hardware limitations of your cluster and optimize your queries for the best performance.

Differences from Hive

Impala executes SQL queries using a massively parallel processing (MPP) engine, while Hive executes SQL queries using MapReduce. Impala avoids Hive's overhead from creating MapReduce jobs, giving it faster query times than Hive. However, Impala uses significant memory resources and the cluster's available memory places a constraint on how much memory any query can consume. Hive is not limited in the same way, and can successfully process larger data sets with the same hardware.

Generally, you should use Impala for fast, interactive queries, while Hive is better for ETL workloads on large datasets. Impala is built for speed and is great for ad hoc investigation, but requires a significant amount of memory to execute expensive queries or process very large datasets. Because of these limitations, Hive is recommended for workloads where speed is not as crucial as completion.


With Impala, you may experience performance gains over Hive, even when using standard instance types. For more information, see Impala Performance Testing and Query Optimization.