EXPLAIN QUERY PLAN

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EXPLAIN QUERY PLAN

# 1. The EXPLAIN QUERY PLAN Command

Warning: The data returned by the EXPLAIN QUERY PLAN command is intended for interactive debugging only. The output format may change between SQLite releases. Applications should not depend on the output format of the EXPLAIN QUERY PLAN command.

Alert: As warned above, the EXPLAIN QUERY PLAN output format did change substantially with the version 3.24.0 release (2018-06-04). Further changes are possible in subsequent releases.

The EXPLAIN QUERY PLAN SQL command is used to obtain a high-level description of the strategy or plan that SQLite uses to implement a specific SQL query. Most significantly, EXPLAIN QUERY PLAN reports on the way in which the query uses database indices. This document is a guide to understanding and interpreting the EXPLAIN QUERY PLAN output. Background information is available separately:

- Notes on the query optimizer.
- How indexing works.
- The next generation query planner.

A query plan is represented as a tree. In raw form, as returned by sqlite3_step(), each node of the tree consists of four fields: A integer node id, an integer parent id, an auxiliary integer field that is not currently used, and a description of the node. The entire tree is therefore a table with four columns and zero or more rows. The command-line shell will usually intercept this table and renders it as an ASCII-art graph for more convenient viewing. To defeat the shells automatic graph rendering, simply include extra white space in between any of the "EXPLAIN", "QUERY", and/or "PLAN" keywords and the output will appear in a (less helpful) tabular format.

One can also set the CLI into automatic EXPLAIN QUERY PLAN mode using the ".eqp on" command:

    sqlite> .eqp on

In automatic EXPLAIN QUERY PLAN mode, the shell automatically runs a separate EXPLAIN QUERY PLAN query for each statement you enter and displays the result before actually running the query. Use the ".eqp off" command to turn automatic EXPLAIN QUERY PLAN mode back off.

EXPLAIN QUERY PLAN is most useful on a SELECT statement, but may also appear with other statements that read data from database tables (e.g. UPDATE, DELETE, INSERT INTO ... SELECT).

## 1.1. Table and Index Scans

When processing a SELECT (or other) statement, SQLite may retrieve data from database tables in a variety of ways. It may scan through all the records in a table (a full-table scan), scan a contiguous subset of the records in a table based on the rowid index, scan a contiguous subset of the entries in a database index, or use a combination of the above strategies in a single scan. The various ways in which SQLite may retrieve data from a table or index are described in detail here.

For each table read by the query, the output of EXPLAIN QUERY PLAN includes a record for which the value in the "detail" column begins with either "SCAN" or "SEARCH". "SCAN" is used for a full-table scan, including cases where SQLite iterates through all records in a table in an order defined by an index. "SEARCH" indicates that only a subset of the table rows are visited. Each SCAN or SEARCH record includes the following information:

- The name of the table data is read from.
- Whether or not an index or automatic index is used.
- Whether or not the covering index optimization applies.
- Which terms of the WHERE clause are used for indexing.

For example, the following EXPLAIN QUERY PLAN command operates on a SELECT statement that is implemented by performing a full-table scan on table t1:

    sqlite> EXPLAIN QUERY PLAN SELECT a, b FROM t1 WHERE a=1;
    QUERY PLAN
    `--SCAN TABLE t1

The example above shows SQLite picking full-table scan will visit all rows in the table. If the query were able to use an index, then the SCAN/SEARCH record would include the name of the index and, for a SEARCH record, an indication of how the subset of rows visited is identified. For example:

    sqlite> CREATE INDEX i1 ON t1(a);
    sqlite> EXPLAIN QUERY PLAN SELECT a, b FROM t1 WHERE a=1;
    QUERY PLAN
    `--SEARCH TABLE t1 USING INDEX i1 (a=?)

The previous example, SQLite uses index "i1" to optimize a WHERE clause term of the form (a=?) - in this case "a=1". The previous example could not use a covering index, but the following example can, and that fact is reflected in the output:

    sqlite> CREATE INDEX i2 ON t1(a, b);
    sqlite> EXPLAIN QUERY PLAN SELECT a, b FROM t1 WHERE a=1;
    QUERY PLAN
    `--SEARCH TABLE t1 USING COVERING INDEX i2 (a=?)

All joins in SQLite are implemented using nested scans. When a SELECT query that features a join is analyzed using EXPLAIN QUERY PLAN, one SCAN or SEARCH record is output for each nested loop. For example:

    sqlite> EXPLAIN QUERY PLAN SELECT t1.*, t2.* FROM t1, t2 WHERE t1.a=1 AND t1.b>2;
    QUERY PLAN
    |--SEARCH TABLE t1 USING INDEX i2 (a=? AND b>?)
    `--SCAN TABLE t2

The order of the entries indicates the nesting order. In this case, the scan of table t1 using index i2 is the outer loop (since it appears first) and the full-table scan of table t2 is the inner loop (since it appears last). In the following example, the positions of t1 and t2 in the FROM clause of the SELECT are reversed. The query strategy remains the same. The output from EXPLAIN QUERY PLAN shows how the query is actually evaluated, not how it is specified in the SQL statement.

    sqlite> EXPLAIN QUERY PLAN SELECT t1.*, t2.* FROM t2, t1 WHERE t1.a=1 AND t1.b>2;
    QUERY PLAN
    |--SEARCH TABLE t1 USING INDEX i2 (a=? AND b>?)
    `--SCAN TABLE t2

If the WHERE clause of a query contains an OR expression, then SQLite might use the "OR by union" strategy (also known as the OR optimization). In this case there will be single top-level record for the search, with two sub-records, one for each index:

    sqlite> CREATE INDEX i3 ON t1(b);
    sqlite> EXPLAIN QUERY PLAN SELECT * FROM t1 WHERE a=1 OR b=2;
    QUERY PLAN
    `--MULTI-INDEX OR
       |--SEARCH TABLE t1 USING COVERING INDEX i2 (a=?)
       `--SEARCH TABLE t1 USING INDEX i3 (b=?)

## 1.2. Temporary Sorting B-Trees

If a SELECT query contains an ORDER BY, GROUP BY or DISTINCT clause, SQLite may need to use a temporary b-tree structure to sort the output rows. Or, it might use an index. Using an index is almost always much more efficient than performing a sort. If a temporary b-tree is required, a record is added to the EXPLAIN QUERY PLAN output with the "detail" field set to a string value of the form "USE TEMP B-TREE FOR xxx", where xxx is one of "ORDER BY", "GROUP BY" or "DISTINCT". For example:

    sqlite> EXPLAIN QUERY PLAN SELECT c, d FROM t2 ORDER BY c;
    QUERY PLAN
    |--SCAN TABLE t2
    `--USE TEMP B-TREE FOR ORDER BY

In this case using the temporary b-tree can be avoided by creating an index on t2(c), as follows:

    sqlite> CREATE INDEX i4 ON t2(c);
    sqlite> EXPLAIN QUERY PLAN SELECT c, d FROM t2 ORDER BY c;
    QUERY PLAN
    `--SCAN TABLE t2 USING INDEX i4

## 1.3. Subqueries

In all the examples above, there has only been a single SELECT statement. If a query contains sub-selects, those are shown as being children of the outer SELECT. For example:

    sqlite> EXPLAIN QUERY PLAN SELECT (SELECT b FROM t1 WHERE a=0), (SELECT a FROM t1 WHERE b=t2.c) FROM t2;
    |--SCAN TABLE t2 USING COVERING INDEX i4
    |--SCALAR SUBQUERY
    |  `--SEARCH TABLE t1 USING COVERING INDEX i2 (a=?)
    `--CORRELATED SCALAR SUBQUERY
       `--SEARCH TABLE t1 USING INDEX i3 (b=?)

The example above contains two "SCALAR" subqueries. The subqueries are SCALAR in the sense that they return a single value - a one-row, one-column table. If the actual query returns more than that, then only the first column of the first row is used.

The first subquery above is constant with respect to the outer query. The value for the first subquery can be computed once and then reused for each row of the outer SELECT. The second subquery, however, is "CORRELATED". The value of the second subquery changes depending on values in the current row of the outer query. Hence, the second subquery must be run once for each output row in the outer SELECT.

Unless the flattening optimization is applied, if a subquery appears in the FROM clause of a SELECT statement, SQLite can either run the subquery and stores the results in a temporary table, or it can run the subquery as a co-routine. The following query is an example of the latter. The subquery is run by a co-routine. The outer query blocks whenever it needs another row of input from the subquery. Control switches to the co-routine which produces the desired output row, then control switches back to the main routine which continues processing.

    sqlite> EXPLAIN QUERY PLAN SELECT count(*) FROM (SELECT max(b) AS x FROM t1 GROUP BY a) GROUP BY x;
    QUERY PLAN
    |--CO-ROUTINE 0x20FC3E0
    |  `--SCAN TABLE t1 USING COVERING INDEX i2
    |--SCAN SUBQUERY 0x20FC3E0
    `--USE TEMP B-TREE FOR GROUP BY

If the flattening optimization is used on a subquery in the FROM clause of a SELECT statement, that effectively merges the subquery into the outer query. The output of EXPLAIN QUERY PLAN reflects this, as in the following example:

    sqlite> EXPLAIN QUERY PLAN SELECT * FROM (SELECT * FROM t2 WHERE c=1), t1;
    QUERY PLAN
    |--SEARCH TABLE t2 USING INDEX i4 (c=?)
    `--SCAN TABLE t1

If the content of a subquery might need to be visited more than once, then the use of a co-routine is undesirable, as the co-routine would then have to compute the data more than once. And if the subquery cannot be flattened, that means the subquery must be manifested into a transient table.

    sqlite> SELECT * FROM
          >   (SELECT * FROM t1 WHERE a=1 ORDER BY b LIMIT 2) AS x,
          >   (SELECT * FROM t2 WHERE c=1 ORDER BY d LIMIT 2) AS y;
    QUERY PLAN
    |--MATERIALIZE 0x18F06F0
    |  `--SEARCH TABLE t1 USING COVERING INDEX i2 (a=?)
    |--MATERIALIZE 0x18F80D0
    |  |--SEARCH TABLE t2 USING INDEX i4 (c=?)
    |  `--USE TEMP B-TREE FOR ORDER BY
    |--SCAN SUBQUERY 0x18F06F0 AS x
    `--SCAN SUBQUERY 0x18F80D0 AS y

## 1.4. Compound Queries

Each component query of a compound query (UNION, UNION ALL, EXCEPT or INTERSECT) is assigned computed separately and is given its own line in the EXPLAIN QUERY PLAN output.

    sqlite> EXPLAIN QUERY PLAN SELECT a FROM t1 UNION SELECT c FROM t2;
    QUERY PLAN
    `--COMPOUND QUERY
       |--LEFT-MOST SUBQUERY
       |  `--SCAN TABLE t1 USING COVERING INDEX i1
       `--UNION USING TEMP B-TREE
          `--SCAN TABLE t2 USING COVERING INDEX i4

The "USING TEMP B-TREE" clause in the above output indicates that a temporary b-tree structure is used to implement the UNION of the results of the two sub-selects. An alternative method of computing a compound is to run each subquery as a co-routine, arrange for their outputs to appear in sorted order, and merge the results together. When the query planner chooses this latter approach, the EXPLAIN QUERY PLAN output looks like this:

    sqlite> EXPLAIN QUERY PLAN SELECT a FROM t1 EXCEPT SELECT d FROM t2 ORDER BY 1;
    QUERY PLAN
    `--MERGE (EXCEPT)
       |--LEFT
       |  `--SCAN TABLE t1 USING COVERING INDEX i1
       `--RIGHT
          |--SCAN TABLE t2
          `--USE TEMP B-TREE FOR ORDER BY