EXPLAIN statement

The EXPLAIN statement 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 explain followed by a complete SELECT query. For example:

Syntax:

EXPLAIN { select_query | ctas_stmt | insert_stmt }

The select_query is a SELECT statement, optionally prefixed by a WITH clause.

The insert_stmt is an INSERT statement that inserts into or overwrites an existing table. It can use either the INSERT ... SELECT or INSERT ... VALUES syntax.

The ctas_stmt is a CREATE TABLE statement using the AS SELECT clause, typically abbreviated as a CTAS operation.

Usage notes:

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 WHERE 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.

The EXPLAIN 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.

Read the EXPLAIN plan from bottom to top:

The last part of the plan shows the low-level details such as the expected amount of data that will be read, where you can judge the effectiveness of your partitioning strategy and estimate how long it will take to scan a table based on total data size and the size of the cluster.
As you work your way up, next you see the operations that will be parallelized and performed on each Impala node.
At the higher levels, you see how data flows when intermediate result sets are combined and transmitted from one node to another.
The EXPLAIN_LEVEL query option lets you customize how much detail to show in the EXPLAIN plan depending on whether you are doing high-level or low-level tuning, dealing with logical or physical aspects of the query.

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.

Extended EXPLAIN output:

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 EXPLAIN statement. In the impala-shell interpreter, issue the command SET EXPLAIN_LEVEL=level, where level is an integer from 0 to 3 or corresponding mnemonic values minimal, standard, extended, or verbose.

When extended EXPLAIN output is enabled, EXPLAIN statements print information about estimated memory requirements, minimum number of virtual cores, and so on.

Starting in Impala 3.2, if the EXPLAIN_LEVEL option is set to EXTENDED level or VERBOSE, the output contains the following additional information.

The analyzed query, in the output header.
The analyzed query may have been rewritten to include various optimizations and implicit casts. See the example below.
The predicates in the plan output includes the same implicit casts and literals printed with a cast to show the type.

Examples:

This example shows how the standard EXPLAIN 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 COUNT(*)) 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 EXCHANGE node); lastly, the intermediate results are summed to display the final result.

[impalad-host:21000] > 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                            |
+----------------------------------------------------------+

The following example shows an extended EXPLAIN output. Note that the analyzed query was rewritten to include:

The 'constant folding' optimization, which simplified the expression in the original query, '1000 / 100' to '10'.
The implicit casts in the WHERE clause.

EXPLAIN SELECT * FROM functional_kudu.alltypestiny WHERE bigint_col < 1000 / 100;
+----------------------------------------------------------+
| Explain String                                           |
+----------------------------------------------------------+
| ...
| Analyzed query: SELECT * FROM mytable WHERE CAST(bigint_col AS DOUBLE) < CAST(10 AS DOUBLE)
| ...
| 00:SCAN KUDU [functional_kudu.alltypestiny]
| predicates: CAST(bigint_col AS DOUBLE) < CAST(10 AS DOUBLE)
...

Security considerations:

If these statements in your environment contain sensitive literal values such as credit card numbers or tax identifiers, Impala can redact this sensitive information when displaying the statements in log files and other administrative contexts.

Cancellation: Cannot be cancelled.

HDFS permissions:

The user ID that the impalad daemon runs under, typically the impala user, must have read and execute permissions for all applicable directories in all source tables for the query that is being explained. (A SELECT operation could read files from multiple different HDFS directories if the source table is partitioned.)

Kudu considerations:

The EXPLAIN statement displays equivalent plan information for queries against Kudu tables as for queries against HDFS-based tables.

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


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

Only binary predicates, IS NULL and IS NOT NULL (in Impala 2.9 and higher), and IN predicates containing literal values that exactly match the types in the Kudu table, and do not require any casting, can be pushed to Kudu.