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

  <title>EXPLAIN Statement</title>
  <titlealts audience="PDF"><navtitle>EXPLAIN</navtitle></titlealts>
  <prolog>
    <metadata>
      <data name="Category" value="Impala"/>
      <data name="Category" value="SQL"/>
      <data name="Category" value="Querying"/>
      <data name="Category" value="Reports"/>
      <data name="Category" value="Planning"/>
      <data name="Category" value="Performance"/>
      <data name="Category" value="Troubleshooting"/>
      <data name="Category" value="Administrators"/>
      <data name="Category" value="Developers"/>
      <data name="Category" value="Data Analysts"/>
    </metadata>
  </prolog>

  <conbody>

    <p>
      <indexterm audience="hidden">EXPLAIN statement</indexterm>
      Returns the execution plan for a statement, showing the low-level mechanisms that Impala will use to read the
      data, divide the work among nodes in the cluster, and transmit intermediate and final results across the
      network. Use <codeph>explain</codeph> followed by a complete <codeph>SELECT</codeph> query. For example:
    </p>

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

<codeblock>EXPLAIN { <varname>select_query</varname> | <varname>ctas_stmt</varname> | <varname>insert_stmt</varname> }
</codeblock>

    <p>
      The <varname>select_query</varname> is a <codeph>SELECT</codeph> statement, optionally prefixed by a
      <codeph>WITH</codeph> clause. See <xref href="impala_select.xml#select"/> for details.
    </p>

    <p>
      The <varname>insert_stmt</varname> is an <codeph>INSERT</codeph> statement that inserts into or overwrites an
      existing table. It can use either the <codeph>INSERT ... SELECT</codeph> or <codeph>INSERT ...
      VALUES</codeph> syntax. See <xref href="impala_insert.xml#insert"/> for details.
    </p>

    <p>
      The <varname>ctas_stmt</varname> is a <codeph>CREATE TABLE</codeph> statement using the <codeph>AS
      SELECT</codeph> clause, typically abbreviated as a <q>CTAS</q> operation. See
      <xref href="impala_create_table.xml#create_table"/> for details.
    </p>

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

    <p>
      You can interpret the output to judge whether the query is performing efficiently, and adjust the query
      and/or the schema if not. For example, you might change the tests in the <codeph>WHERE</codeph> clause, add
      hints to make join operations more efficient, introduce subqueries, change the order of tables in a join, add
      or change partitioning for a table, collect column statistics and/or table statistics in Hive, or any other
      performance tuning steps.
    </p>

    <p>
      The <codeph>EXPLAIN</codeph> output reminds you if table or column statistics are missing from any table
      involved in the query. These statistics are important for optimizing queries involving large tables or
      multi-table joins. See <xref href="impala_compute_stats.xml#compute_stats"/> for how to gather statistics,
      and <xref href="impala_perf_stats.xml#perf_stats"/> for how to use this information for query tuning.
    </p>

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

    <p>
      If you come from a traditional database background and are not familiar with data warehousing, keep in mind
      that Impala is optimized for full table scans across very large tables. The structure and distribution of
      this data is typically not suitable for the kind of indexing and single-row lookups that are common in OLTP
      environments. Seeing a query scan entirely through a large table is common, not necessarily an indication of
      an inefficient query. Of course, if you can reduce the volume of scanned data by orders of magnitude, for
      example by using a query that affects only certain partitions within a partitioned table, then you might be
      able to optimize a query so that it executes in seconds rather than minutes.
    </p>

    <p>
      For more information and examples to help you interpret <codeph>EXPLAIN</codeph> output, see
      <xref href="impala_explain_plan.xml#perf_explain"/>.
    </p>

    <p rev="1.2">
      <b>Extended EXPLAIN output:</b>
    </p>

    <p rev="1.2">
      For performance tuning of complex queries, and capacity planning (such as using the admission control and
      resource management features), you can enable more detailed and informative output for the
      <codeph>EXPLAIN</codeph> statement. In the <cmdname>impala-shell</cmdname> interpreter, issue the command
      <codeph>SET EXPLAIN_LEVEL=<varname>level</varname></codeph>, where <varname>level</varname> is an integer
      from 0 to 3 or corresponding mnemonic values <codeph>minimal</codeph>, <codeph>standard</codeph>,
      <codeph>extended</codeph>, or <codeph>verbose</codeph>.
    </p>

    <p rev="1.2">
      When extended <codeph>EXPLAIN</codeph> output is enabled, <codeph>EXPLAIN</codeph> statements print
      information about estimated memory requirements, minimum number of virtual cores, and so on.
      <!--
      that you can use to fine-tune the resource management options explained in <xref href="impala_resource_management.xml#rm_options"/>.
      (The estimated memory requirements are intentionally on the high side, to allow a margin for error,
      to avoid cancelling a query unnecessarily if you set the <codeph>MEM_LIMIT</codeph> option to the estimated memory figure.)
      -->
    </p>

    <p>
      See <xref href="impala_explain_level.xml#explain_level"/> for details and examples.
    </p>

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

    <p>
      This example shows how the standard <codeph>EXPLAIN</codeph> output moves from the lowest (physical) level to
      the higher (logical) levels. The query begins by scanning a certain amount of data; each node performs an
      aggregation operation (evaluating <codeph>COUNT(*)</codeph>) on some subset of data that is local to that
      node; the intermediate results are transmitted back to the coordinator node (labelled here as the
      <codeph>EXCHANGE</codeph> node); lastly, the intermediate results are summed to display the final result.
    </p>

<codeblock id="explain_plan_simple">[impalad-host:21000] &gt; explain select count(*) from customer_address;
+----------------------------------------------------------+
| Explain String                                           |
+----------------------------------------------------------+
| Estimated Per-Host Requirements: Memory=42.00MB VCores=1 |
|                                                          |
| 03:AGGREGATE [MERGE FINALIZE]                            |
| |  output: sum(count(*))                                 |
| |                                                        |
| 02:EXCHANGE [PARTITION=UNPARTITIONED]                    |
| |                                                        |
| 01:AGGREGATE                                             |
| |  output: count(*)                                      |
| |                                                        |
| 00:SCAN HDFS [default.customer_address]                  |
|    partitions=1/1 size=5.25MB                            |
+----------------------------------------------------------+
</codeblock>

    <p>
      These examples show how the extended <codeph>EXPLAIN</codeph> output becomes more accurate and informative as
      statistics are gathered by the <codeph>COMPUTE STATS</codeph> statement. Initially, much of the information
      about data size and distribution is marked <q>unavailable</q>. Impala can determine the raw data size, but
      not the number of rows or number of distinct values for each column without additional analysis. The
      <codeph>COMPUTE STATS</codeph> statement performs this analysis, so a subsequent <codeph>EXPLAIN</codeph>
      statement has additional information to use in deciding how to optimize the distributed query.
    </p>

    <!-- To do:
      Re-run these examples with more substantial tables populated with data.
    -->

<codeblock rev="1.2">[localhost:21000] &gt; set explain_level=extended;
EXPLAIN_LEVEL set to extended
[localhost:21000] &gt; explain select x from t1;
[localhost:21000] &gt; explain select x from t1;
+----------------------------------------------------------+
| Explain String                                           |
+----------------------------------------------------------+
| Estimated Per-Host Requirements: Memory=32.00MB VCores=1 |
|                                                          |
| 01:EXCHANGE [PARTITION=UNPARTITIONED]                    |
| |  hosts=1 per-host-mem=unavailable                      |
<b>| |  tuple-ids=0 row-size=4B cardinality=unavailable       |</b>
| |                                                        |
| 00:SCAN HDFS [default.t2, PARTITION=RANDOM]              |
|    partitions=1/1 size=36B                               |
<b>|    table stats: unavailable                              |</b>
<b>|    column stats: unavailable                             |</b>
|    hosts=1 per-host-mem=32.00MB                          |
<b>|    tuple-ids=0 row-size=4B cardinality=unavailable       |</b>
+----------------------------------------------------------+
</codeblock>

<codeblock rev="1.2">[localhost:21000] &gt; compute stats t1;
+-----------------------------------------+
| summary                                 |
+-----------------------------------------+
| Updated 1 partition(s) and 1 column(s). |
+-----------------------------------------+
[localhost:21000] &gt; explain select x from t1;
+----------------------------------------------------------+
| Explain String                                           |
+----------------------------------------------------------+
| Estimated Per-Host Requirements: Memory=64.00MB VCores=1 |
|                                                          |
| 01:EXCHANGE [PARTITION=UNPARTITIONED]                    |
| |  hosts=1 per-host-mem=unavailable                      |
| |  tuple-ids=0 row-size=4B cardinality=0                 |
| |                                                        |
| 00:SCAN HDFS [default.t1, PARTITION=RANDOM]              |
|    partitions=1/1 size=36B                               |
<b>|    table stats: 0 rows total                             |</b>
<b>|    column stats: all                                     |</b>
|    hosts=1 per-host-mem=64.00MB                          |
<b>|    tuple-ids=0 row-size=4B cardinality=0                 |</b>
+----------------------------------------------------------+
</codeblock>

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

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

    <p conref="../shared/impala_common.xml#common/permissions_blurb"/>
    <p rev="">
      <!-- Doublecheck these details. Does EXPLAIN really need any permissions? -->
      The user ID that the <cmdname>impalad</cmdname> daemon runs under,
      typically the <codeph>impala</codeph> user, must have read
      and execute permissions for all applicable directories in all source tables
      for the query that is being explained.
      (A <codeph>SELECT</codeph> operation could read files from multiple different HDFS directories
      if the source table is partitioned.)
    </p>

    <p rev="kudu" conref="../shared/impala_common.xml#common/kudu_blurb"/>
    <p>
      The <codeph>EXPLAIN</codeph> statement displays equivalent plan
      information for queries against Kudu tables as for queries
      against HDFS-based tables.
    </p>

    <p>
      To see which predicates Impala can <q>push down</q> to Kudu for
      efficient evaluation, without transmitting unnecessary rows back
      to Impala, look for the <codeph>kudu predicates</codeph> item in
      the scan phase of the query. The label <codeph>kudu predicates</codeph>
      indicates a condition that can be evaluated efficiently on the Kudu
      side. The label <codeph>predicates</codeph> in a <codeph>SCAN KUDU</codeph>
      node indicates a condition that is evaluated by Impala.
      For example, in a table with primary key column <codeph>X</codeph>
      and non-primary key column <codeph>Y</codeph>, you can see that
      some operators in the <codeph>WHERE</codeph> clause are evaluated
      immediately by Kudu and others are evaluated later by Impala:
    </p>

<codeblock rev="2.9.0 IMPALA-4859">
EXPLAIN SELECT x,y from kudu_table WHERE
  x = 1 AND y NOT IN (2,3) AND z = 1
  AND a IS NOT NULL AND b > 0 AND length(s) > 5;
+----------------
| Explain String
+----------------
...
| 00:SCAN KUDU [kudu_table]
|    predicates: y NOT IN (2, 3), length(s) > 5
|    kudu predicates: a IS NOT NULL, b > 0, x = 1, z = 1
</codeblock>

    <p rev="2.9.0 IMPALA-4859">
      Only binary predicates, <codeph>IS NULL</codeph> and <codeph>IS NOT NULL</codeph>
      (in <keyword keyref="impala29"/> and higher), and <codeph>IN</codeph> predicates
      containing literal values that exactly match the types in the Kudu table, and do not
      require any casting, can be pushed to Kudu.
    </p>

    <p conref="../shared/impala_common.xml#common/related_info"/>
    <p>
      <xref href="impala_select.xml#select"/>,
      <xref href="impala_insert.xml#insert"/>,
      <xref href="impala_create_table.xml#create_table"/>,
      <xref href="impala_explain_plan.xml#explain_plan"/>
    </p>

  </conbody>
</concept>