impala/docs/topics/impala_performance.xml

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<!DOCTYPE concept PUBLIC "-//OASIS//DTD DITA Concept//EN" "concept.dtd">
<concept id="performance">

  <title>Tuning Impala for Performance</title>
  <titlealts audience="PDF"><navtitle>Performance Tuning</navtitle></titlealts>
  <prolog>
    <metadata>
      <data name="Category" value="Impala"/>
      <data name="Category" value="Performance"/>
      <data name="Category" value="Databases"/>
      <data name="Category" value="SQL"/>
      <data name="Category" value="Querying"/>
      <data name="Category" value="Developers"/>
      <!-- Like Impala Administration, this page has a fair bit of info already, but it could benefit from wiki-style embedded of intro text from those other pages. -->
      <data name="Category" value="Stub Pages"/>
    </metadata>
  </prolog>

  <conbody>

    <p>
      The following sections explain the factors affecting the performance of Impala features, and procedures for
      tuning, monitoring, and benchmarking Impala queries and other SQL operations.
    </p>

    <p>
      This section also describes techniques for maximizing Impala scalability. Scalability is tied to performance:
      it means that performance remains high as the system workload increases. For example, reducing the disk I/O
      performed by a query can speed up an individual query, and at the same time improve scalability by making it
      practical to run more queries simultaneously. Sometimes, an optimization technique improves scalability more
      than performance. For example, reducing memory usage for a query might not change the query performance much,
      but might improve scalability by allowing more Impala queries or other kinds of jobs to run at the same time
      without running out of memory.
    </p>

    <note>
      <p>
        Before starting any performance tuning or benchmarking, make sure your system is configured with all the
        recommended minimum hardware requirements from <xref href="impala_prereqs.xml#prereqs_hardware"/> and
        software settings from <xref href="impala_config_performance.xml#config_performance"/>.
      </p>
    </note>

    <ul>
      <li>
        <xref href="impala_partitioning.xml#partitioning"/>. This technique physically divides the data based on
        the different values in frequently queried columns, allowing queries to skip reading a large percentage of
        the data in a table.
      </li>

      <li>
        <xref href="impala_perf_joins.xml#perf_joins"/>. Joins are the main class of queries that you can tune at
        the SQL level, as opposed to changing physical factors such as the file format or the hardware
        configuration. The related topics <xref href="impala_perf_stats.xml#perf_column_stats"/> and
        <xref href="impala_perf_stats.xml#perf_table_stats"/> are also important primarily for join performance.
      </li>

      <li>
        <xref href="impala_perf_stats.xml#perf_table_stats"/> and
        <xref href="impala_perf_stats.xml#perf_column_stats"/>. Gathering table and column statistics, using the
        <codeph>COMPUTE STATS</codeph> statement, helps Impala automatically optimize the performance for join
        queries, without requiring changes to SQL query statements. (This process is greatly simplified in Impala
        1.2.2 and higher, because the <codeph>COMPUTE STATS</codeph> statement gathers both kinds of statistics in
        one operation, and does not require any setup and configuration as was previously necessary for the
        <codeph>ANALYZE TABLE</codeph> statement in Hive.)
      </li>

      <li>
        <xref href="impala_perf_testing.xml#performance_testing"/>. Do some post-setup testing to ensure Impala is
        using optimal settings for performance, before conducting any benchmark tests.
      </li>

      <li>
        <xref href="impala_perf_benchmarking.xml#perf_benchmarks"/>. The configuration and sample data that you use
        for initial experiments with Impala is often not appropriate for doing performance tests.
      </li>

      <li>
        <xref href="impala_perf_resources.xml#mem_limits"/>. The more memory Impala can utilize, the better query
        performance you can expect. In a cluster running other kinds of workloads as well, you must make tradeoffs
        to make sure all Hadoop components have enough memory to perform well, so you might cap the memory that
        Impala can use.
      </li>

      <li rev="1.2" audience="hidden">
        <xref href="impala_perf_hdfs_caching.xml#hdfs_caching"/>. Impala can use the HDFS caching feature to pin
        frequently accessed data in memory, reducing disk I/O.
      </li>

      <li rev="2.2.0">
        <xref href="impala_s3.xml#s3"/>. Queries against data stored in the Amazon Simple Storage Service (S3)
        have different performance characteristics than when the data is stored in HDFS.
      </li>
    </ul>

    <p outputclass="toc"/>

    <p conref="../shared/impala_common.xml#common/cookbook_blurb"/>

  </conbody>

<!-- Empty/hidden stub sections that might be worth expanding later. -->

  <concept id="perf_network" audience="hidden">

    <title>Network Traffic</title>

    <conbody/>
  </concept>

  <concept id="perf_partition_schema" audience="hidden">

    <title>Designing Partitioned Tables</title>

    <conbody/>
  </concept>

  <concept id="perf_partition_query" audience="hidden">

    <title>Queries on Partitioned Tables</title>

    <conbody/>
  </concept>

  <concept id="perf_monitoring" audience="hidden">

    <title>Monitoring Performance through the Impala Web Interface</title>
  <prolog>
    <metadata>
      <data name="Category" value="Monitoring"/>
    </metadata>
  </prolog>

    <conbody/>
  </concept>

  <concept id="perf_query_coord" audience="hidden">

    <title>Query Coordination</title>

    <conbody/>
  </concept>

  <concept id="perf_bottlenecks" audience="hidden">

    <title>Performance Bottlenecks</title>

    <conbody/>
  </concept>

  <concept id="perf_long_queries" audience="hidden">

    <title>Managing Long-Running Queries</title>

    <conbody/>
  </concept>

  <concept id="perf_load" audience="hidden">

    <title>Performance Considerations for Loading Data</title>

    <conbody/>
  </concept>

  <concept id="perf_file_formats" audience="hidden">

    <title>Performance Considerations for File Formats</title>

    <conbody/>
  </concept>

  <concept id="perf_compression" audience="hidden">

    <title>Performance Considerations for Compression</title>
  <prolog>
    <metadata>
      <data name="Category" value="Compression"/>
    </metadata>
  </prolog>

    <conbody/>
  </concept>

  <concept id="perf_codegen" audience="hidden">

    <title>Native Code Generation</title>

    <conbody/>
  </concept>
</concept>