docs/topics/impala_perf_ddl.xml

<?xml version="1.0" encoding="UTF-8"?>
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<!DOCTYPE concept PUBLIC "-//OASIS//DTD DITA Concept//EN" "concept.dtd">
<concept id="perf_ddl">

  <title>Performance Considerations for DDL Statements</title>
  <prolog>
    <metadata>
      <data name="Category" value="Performance"/>
      <data name="Category" value="Impala"/>
      <data name="Category" value="DDL"/>
      <data name="Category" value="SQL"/>
      <data name="Category" value="Developers"/>
      <data name="Category" value="Data Analysts"/>
    </metadata>
  </prolog>

  <conbody>

    <p>
      These tips and guidelines apply to the Impala DDL statements, which are listed in
      <xref href="impala_ddl.xml#ddl"/>.
    </p>

    <p>
      Because Impala DDL statements operate on the metastore database, the performance considerations for those
      statements are totally different than for distributed queries that operate on HDFS
      <ph rev="2.2.0">or S3</ph> data files, or on HBase tables.
    </p>

    <p>
      Each DDL statement makes a relatively small update to the metastore database. The overhead for each statement
      is proportional to the overall number of Impala and Hive tables, and (for a partitioned table) to the overall
      number of partitions in that table. Issuing large numbers of DDL statements (such as one for each table or
      one for each partition) also has the potential to encounter a bottleneck with access to the metastore
      database. Therefore, for efficient DDL, try to design your application logic and ETL pipeline to avoid a huge
      number of tables and a huge number of partitions within each table. In this context, <q>huge</q> is in the
      range of tens of thousands or hundreds of thousands.
    </p>

    <note conref="../shared/impala_common.xml#common/add_partition_set_location"/>
  </conbody>
</concept>