SQLite Query Optimizer Overview

This document provides a terse overview of how the query optimizer for SQLite works. This is not a tutorial. The reader is likely to need some prior knowledge of how database engines operate in order to fully understand this text.

1.0 WHERE clause analysis

 The WHERE clause on a query is broken up into "terms" where each term is separated from the others by an AND operator.

 All terms of the WHERE clause are analyzed to see if they can be satisfied using indices. Terms that cannot be satisfied through the use of indices become tests that are evaluated against each row of the relevant input tables. No tests are done for terms that are completely satisfied by indices. Sometimes one or more terms will provide hints to indices but still must be evaluated against each row of the input tables.

 The analysis of a term might cause new "virtual" terms to be added to the WHERE clause. Virtual terms can be used with indices to restrict a search. But virtual terms never generate code that is tested against input rows.

 To be usable by an index a term must be of one of the following forms:

	

	  column = expression

	  column > expression

	  column >= expression

	  column < expression

	  column <= expression

	  expression = column

	  expression > column

	  expression >= column

	  expression < column

	  expression <= column

	  column IN (expression-list)

	  column IN (subquery)
	

 If an index is created using a statement like this:

	  CREATE INDEX idx_ex1 ON ex1(a,b,c,d,e,...,y,z);
	

 Then the index might be used if the initial columns of the index (columns a, b, and so forth) appear in WHERE clause terms. All index columns must be used with the = or IN operators except for the right-most column which can use inequalities. For the right-most column of an index that is used, there can be up to two inequalities that must sandwich the allowed values of the column between two extremes.

 It is not necessary for every column of an index to appear in a WHERE clause term in order for that index to be used. But there can not be gaps in the columns of the index that are used. Thus for the example index above, if there is no WHERE clause term that constraints column c, then terms that constraint columns a and b can be used with the index but not terms that constraint columns d through z. Similarly, no index column will be used (for indexing purposes) that is to the right of a column that is constrained only by inequalities. For the index above and WHERE clause like this:

	  ... WHERE a=5 AND b IN (1,2,3) AND c>12 AND d='hello'
	

 Only columns a, b, and c of the index would be usable. The d column would not be usable because it occurs to the right of c and c is constrained only by inequalities.

2.0 The BETWEEN optimization

 If a term of the WHERE clause is of the following form:

	

	  expr1 BETWEEN expr2 AND expr3
	

 Then two virtual terms are added as follows:

	

	  expr1 >= expr2 AND expr1 <= expr3
	

 If both virtual terms end up being used as constraints on an index, then the original BETWEEN term is omitted and the corresponding test is not performed on input rows. Thus if the BETWEEN term ends up being used as an index constraint no tests are ever performed on that term. On the other hand, the virtual terms themselves never causes tests to be performed on input rows. Thus if the BETWEEN term is not used as an index constraint and instead must be used to test input rows, the expr1 expression is only evaluated once.

3.0 The OR optimization

 If a term consists of multiple subterms containing a common column name and separated by OR, like this:

	

	  column = expr1 OR column = expr2 OR column = expr3 OR ...
	

 Then the term is rewritten as follows:

	

	  column IN (expr1,expr2,expr3,expr4,...)
	

 The rewritten term then might go on to constraint an index using the normal rules for IN operators. Note that column must be the same column in every OR-connected subterm, although the column can occur on either the left or the right side of the = operator.

4.0 The LIKE optimization

 Terms that are composed of the LIKE or GLOB operator can sometimes be used to constrain indices. There are many conditions on this use:

1. The left-hand side of the LIKE or GLOB operator must be the name of an indexed column.
2. The right-hand side of the LIKE or GLOB must be a string literal that does not begin with a wildcard character.
3. The ESCAPE clause cannot appear on the LIKE operator.
4. The build-in functions used to implement LIKE and GLOB must not have been overloaded using the sqlite3_create_function() API.
5. For the GLOB operator, the column must use the default BINARY collating sequence.
6. For the LIKE operator, if case_sensitive_like mode is enabled then the column must use the default BINARY collating sequence, or if case_sensitive_like mode is disabled then the column must use the built-in NOCASE collating sequence.

 The LIKE operator has two modes that can be set by a pragma. The default mode is for LIKE comparisons to be insensitive to differences of case for latin1 characters. Thus, by default, the following expression is true:

	  'a' LIKE 'A'
	

 By turned on the case_sensitive_like pragma as follows:

	  PRAGMA case_sensitive_like=ON;
	

 Then the LIKE operator pays attention to case and the example above would evaluate to false. Note that case insensitivity only applies to latin1 characters - basically the upper and lower case letters of English in the lower 127 byte codes of ASCII. International character sets are case sensitive in SQLite unless a user-supplied collating sequence is used. But if you employ a user-supplied collating sequence, the LIKE optimization describe here will never be taken.

 The LIKE operator is case insensitive by default because this is what the SQL standard requires. You can change the default behavior at compile time by using the -DSQLITE_CASE_SENSITIVE_LIKE command-line option to the compiler.

 The LIKE optimization might occur if the column named on the left of the operator uses the BINARY collating sequence (which is the default) and case_sensitive_like is turned on. Or the optimization might occur if the column uses the built-in NOCASE collating sequence and the case_sensitive_like mode is off. These are the only two combinations under which LIKE operators will be optimized. If the column on the right-hand side of the LIKE operator uses any collating sequence other than the built-in BINARY and NOCASE collating sequences, then no optimizations will ever be attempted on the LIKE operator.

 The GLOB operator is always case sensitive. The column on the left side of the GLOB operator must always use the built-in BINARY collating sequence or no attempt will be made to optimize that operator with indices.

 The right-hand side of the GLOB or LIKE operator must be a literal string value that does not begin with a wildcard. If the right-hand side is a parameter that is bound to a string, then no optimization is attempted. If the right-hand side begins with a wildcard character then no optimization is attempted.

 Suppose the initial sequence of non-wildcard characters on the right-hand side of the LIKE or GLOB operator is x. We are using a single character to denote this non-wildcard prefix but the reader should understand that the prefix can consist of more than 1 character. Let y the smallest string that is the same length as /x/ but which compares greater than x. For example, if x is hello then y would be hellp. The LIKE and GLOB optimizations consist of adding two virtual terms like this:

	

	  column >= x AND column < y
	

 Under most circumstances, the original LIKE or GLOB operator is still tested against each input row even if the virtual terms are used to constrain an index. This is because we do not know what additional constraints may be imposed by characters to the right of the x prefix. However, if there is only a single global wildcard to the right of x, then the original LIKE or GLOB test is disabled. In other words, if the pattern is like this:

	

	  column LIKE x%

	  column GLOB x*
	

 Then the original LIKE or GLOB tests are disabled when the virtual terms constrain an index because in that case we know that all of the rows selected by the index will pass the LIKE or GLOB test.

5.0 Joins

 The current implementation of SQLite uses only loop joins. That is to say, joins are implemented as nested loops.

 The default order of the nested loops in a join is for the left-most table in the FROM clause to form the outer loop and the right-most table to form the inner loop. However, SQLite will nest the loops in a different order if doing so will help it to select better indices.

 Inner joins can be freely reordered. However a left outer join is neither commutative nor associative and hence will not be reordered. Inner joins to the left and right of the outer join might be reordered if the optimizer thinks that is advantageous but the outer joins are always evaluated in the order in which they occur.

 When selecting the order of tables in a join, SQLite uses a greedy algorithm that runs in polynomial time.

 The ON and USING clauses of a join are converted into additional terms of the WHERE clause prior to WHERE clause analysis described above in paragraph 1.0. Thus with SQLite, there is no advantage to use the newer SQL92 join syntax over the older SQL89 comma-join syntax. They both end up accomplishing exactly the same thing.

 Join reordering is automatic and usually works well enough that programmer do not have to think about it. But occasionally some hints from the programmer are needed. For a description of when hints might be necessary and how to provide those hints, see the QueryPlans page in the Wiki.

6.0 Choosing between multiple indices

 Each table in the FROM clause of a query can use at most one index, and SQLite strives to use at least one index on each table. Sometimes, two or more indices might be candidates for use on a single table. For example:

	  CREATE TABLE ex2(x,y,z);

	  CREATE INDEX ex2i1 ON ex2(x);

	  CREATE INDEX ex2i2 ON ex2(y);

	  SELECT z FROM ex2 WHERE x=5 AND y=6;
	

 For the SELECT statement above, the optimizer can use the ex2i1 index to lookup rows of ex2 that contain x=5 and then test each row against the y=6 term. Or it can use the ex2i2 index to lookup rows of ex2 that contain y=6 then test each of those rows against the x=5 term.

 When faced with a choice of two or more indices, SQLite tries to estimate the total amount of work needed to perform the query using each option. It then selects the option that gives the least estimated work.

 To help the optimizer get a more accurate estimate of the work involved in using various indices, the user may optional run the ANALYZE command. The ANALYZE command scans all indices of database where there might be a choice between two or more indices and gathers statistics on the selectiveness of those indices. The results of this scan are stored in the sqlite_stat1 table. The contents of the sqlite_stat1 table are not updated as the database changes so after making significant changes it might be prudent to rerun ANALYZE. The results of an ANALYZE command are only available to database connections that are opened after the ANALYZE command completes.

 Once created, the sqlite_stat1 table cannot be dropped. But its content can be viewed, modified, or erased. Erasing the entire content of the sqlite_stat1 table has the effect of undoing the ANALYZE command. Changing the content of the sqlite_stat1 table can get the optimizer deeply confused and cause it to make silly index choices. Making updates to the sqlite_stat1 table (except by running ANALYZE) is not recommended.

 Terms of the WHERE clause can be manually disqualified for use with indices by prepending a unary + operator to the column name. The unary + is a no-op and will not slow down the evaluation of the test specified by the term. But it will prevent the term from constraining an index. So, in the example above, if the query were rewritten as:

	  SELECT z FROM ex2 WHERE +x=5 AND y=6;
	

 The + operator on the x column would prevent that term from constraining an index. This would force the use of the ex2i2 index.

7.0 Avoidance of table lookups

 When doing an indexed lookup of a row, the usual procedure is to do a binary search on the index to find the index entry, then extract the rowid from the index and use that rowid to do a binary search on the original table. Thus a typical indexed lookup involves two binary searches. If, however, all columns that were to be fetched from the table are already available in the index itself, SQLite will use the values contained in the index and will never look up the original table row. This saves one binary search for each row and can make many queries run twice as fast.

8.0 ORDER BY optimizations

 SQLite attempts to use an index to satisfy the ORDER BY clause of a query when possible. When faced with the choice of using an index to satisfy WHERE clause constraints or satisfying an ORDER BY clause, SQLite does the same work analysis described in section 6.0 and chooses the index that it believes will result in the fastest answer.

9.0 Subquery flattening

 When a subquery occurs in the FROM clause of a SELECT, the default behavior is to evaluate the subquery into a transient table, then run the outer SELECT against the transient table. This is problematic since the transient table will not have any indices and the outer query (which is likely a join) will be forced to do a full table scan on the transient table.

 To overcome this problem, SQLite attempts to flatten subqueries in the FROM clause of a SELECT. This involves inserting the FROM clause of the subquery into the FROM clause of the outer query and rewriting expressions in the outer query that refer to the result set of the subquery. For example:

	  SELECT a FROM (SELECT x+y AS a FROM t1 WHERE z<100) WHERE a>5
	

 Would be rewritten using query flattening as:

	  SELECT x+y AS a FROM t1 WHERE z<100 AND a>5
	

 There is a long list of conditions that must all be met in order for query flattening to occur.

1.  The subquery and the outer query do not both use aggregates.
2.  The subquery is not an aggregate or the outer query is not a join.
3.  The subquery is not the right operand of a left outer join, or the subquery is not itself a join.
4.  The subquery is not DISTINCT or the outer query is not a join.
5.  The subquery is not DISTINCT or the outer query does not use aggregates.
6.  The subquery does not use aggregates or the outer query is not DISTINCT.
7.  The subquery has a FROM clause.
8.  The subquery does not use LIMIT or the outer query is not a join.
9.  The subquery does not use LIMIT or the outer query does not use aggregates.
10.  The subquery does not use aggregates or the outer query does not use LIMIT.
11.  The subquery and the outer query do not both have ORDER BY clauses.
12.  The subquery is not the right term of a LEFT OUTER JOIN or the subquery has no WHERE clause.

 The proof that query flattening may safely occur if all of the the above conditions are met is left as an exercise to the reader.

 Query flattening is an important optimization when views are used as each use of a view is translated into a subquery.

10.0 The MIN/MAX optimization

 Queries of the following forms will be optimized to run in logarithmic time assuming appropriate indices exist:

	  SELECT MIN(x) FROM table;

	  SELECT MAX(x) FROM table;
	

 In order for these optimizations to occur, they must appear in exactly the form shown above - changing only the name of the table and column. It is not permissible to add a WHERE clause or do any arithmetic on the result. The result set must contain a single column. The column in the MIN or MAX function must be an indexed column.

This mail is by Raj Patil.

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