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documentation updates (CVS 112)

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FossilOrigin-Name: c686c6076abadcb715fe74436fa8bab48d013b26
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drh
2000-07-30 20:04:43 +00:00
parent a0c66f5cd3
commit 95e961beaa
5 changed files with 877 additions and 30 deletions
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16
manifest
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@@ -1,5 +1,5 @@
C better\scolumn\snames\sin\sthe\sshell\s(CVS\s111) C documentation\supdates\s(CVS\s112)
D 2000-07-29T13:20:21 D 2000-07-30T20:04:43
F COPYRIGHT 74a8a6531a42e124df07ab5599aad63870fa0bd4 F COPYRIGHT 74a8a6531a42e124df07ab5599aad63870fa0bd4
F Makefile.in 9e6dcd232e594fb599a5e9ba8bcf45e6c6e2fe72 F Makefile.in 9e6dcd232e594fb599a5e9ba8bcf45e6c6e2fe72
F README 51f6a4e7408b34afa5bc1c0485f61b6a4efb6958 F README 51f6a4e7408b34afa5bc1c0485f61b6a4efb6958
@@ -22,7 +22,7 @@ F src/tclsqlite.c 9f358618ae803bedf4fb96da5154fd45023bc1f7
F src/tokenize.c 77ff8164a8751994bc9926ce282847f653ac0c16 F src/tokenize.c 77ff8164a8751994bc9926ce282847f653ac0c16
F src/update.c 51b9ef7434b15e31096155da920302e9db0d27fc F src/update.c 51b9ef7434b15e31096155da920302e9db0d27fc
F src/util.c fcd7ac9d2be8353f746e52f665e6c4f5d6b3b805 F src/util.c fcd7ac9d2be8353f746e52f665e6c4f5d6b3b805
F src/vdbe.c b9ce1439931a56bdbec560d41d32f623b5d4b1c7 F src/vdbe.c 4308e226d5b33a72dfe2c88a44eb0a63381fe24b
F src/vdbe.h 6c5653241633c583549c2d8097394ab52550eb63 F src/vdbe.h 6c5653241633c583549c2d8097394ab52550eb63
F src/where.c 420f666a38b405cd58bd7af832ed99f1dbc7d336 F src/where.c 420f666a38b405cd58bd7af832ed99f1dbc7d336
F test/all.test 0950c135cab7e60c07bd745ccfad1476211e5bd7 F test/all.test 0950c135cab7e60c07bd745ccfad1476211e5bd7
@@ -63,10 +63,10 @@ F www/changes.tcl 4491a4c835a87945ec4b493d8fed8e31e7917db5
F www/fileformat.tcl f3a70650e942262f8285d53097d48f0b3aa59862 F www/fileformat.tcl f3a70650e942262f8285d53097d48f0b3aa59862
F www/index.tcl 58c9a33ceba12f5efee446c6b10b4f6523a214e1 F www/index.tcl 58c9a33ceba12f5efee446c6b10b4f6523a214e1
F www/lang.tcl 1645e9107d75709be4c6099b643db235bbe0a151 F www/lang.tcl 1645e9107d75709be4c6099b643db235bbe0a151
F www/opcode.tcl 401bdc639509c2f17d3bb97cbbdfdc22a61faa07 F www/opcode.tcl cb3a1abf8b7b9be9f3a228d097d6bf8b742c2b6f
F www/sqlite.tcl 69781eaffb02e17aa4af28b76a2bedb19baa8e9f F www/sqlite.tcl 69781eaffb02e17aa4af28b76a2bedb19baa8e9f
F www/vdbe.tcl 3330c700ef9c212a169f568a595361e4cce749ed F www/vdbe.tcl bcbfc33bcdd0ebad95eab31286adb9e1bc289520
P 3bf434d93a54a24f4882d0d9375f82ceee0b7602 P 57022a9d504e553d862f363b164c42ba53d8b489
R 62d5ed1a73773f8306d7f2d4bcc6586d R f7bf520ee9c56f3008ca4098d4e4be60
U drh U drh
Z 12cb62dbb78ef20fcb0d4c8ab57670db Z d5fb118e39f2c1e4c12a967c5d47998a

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@@ -1 +1 @@
57022a9d504e553d862f363b164c42ba53d8b489 c686c6076abadcb715fe74436fa8bab48d013b26

4
src/vdbe.c
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@@ -41,7 +41,7 @@
** But other routines are also provided to help in building up ** But other routines are also provided to help in building up
** a program instruction by instruction. ** a program instruction by instruction.
** **
** $Id: vdbe.c,v 1.36 2000/07/29 13:06:59 drh Exp $ ** $Id: vdbe.c,v 1.37 2000/07/30 20:04:43 drh Exp $
*/ */
#include "sqliteInt.h" #include "sqliteInt.h"
#include <unistd.h> #include <unistd.h>
@@ -1970,7 +1970,7 @@ int sqliteVdbeExec(
/* Opcode: KeyAsData P1 P2 * /* Opcode: KeyAsData P1 P2 *
** **
** Turn the key-as-data mode for cursor P1 either on (if P2==1) or ** Turn the key-as-data mode for cursor P1 either on (if P2==1) or
** off (if P2==0). In key-as-data mode, the OP_Fetch opcode pulls ** off (if P2==0). In key-as-data mode, the OP_Field opcode pulls
** data off of the key rather than the data. This is useful for ** data off of the key rather than the data. This is useful for
** processing compound selects. ** processing compound selects.
*/ */

6
www/opcode.tcl
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@@ -1,7 +1,7 @@
# #
# Run this Tcl script to generate the sqlite.html file. # Run this Tcl script to generate the sqlite.html file.
# #
set rcsid {$Id: opcode.tcl,v 1.3 2000/06/23 17:02:09 drh Exp $} set rcsid {$Id: opcode.tcl,v 1.4 2000/07/30 20:04:43 drh Exp $}
puts {<html> puts {<html>
<head> <head>
@@ -55,8 +55,8 @@ by the SQLite library. This document describes the operation of that
virtual machine.</p> virtual machine.</p>
<p>This document is intended as a reference, not a tutorial. <p>This document is intended as a reference, not a tutorial.
A separate <a href="vdbe.html">Virtual Machine Tutorial</a> is currently A separate <a href="vdbe.html">Virtual Machine Tutorial</a> is
in preparation. If you are looking for a narrative description available. If you are looking for a narrative description
of how the virtual machine works, you should read the tutorial of how the virtual machine works, you should read the tutorial
and not this document. Once you have a basic idea of what the and not this document. Once you have a basic idea of what the
virtual machine does, you can refer back to this document for virtual machine does, you can refer back to this document for

879
www/vdbe.tcl
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@@ -1,7 +1,7 @@
# #
# Run this Tcl script to generate the vdbe.html file. # Run this Tcl script to generate the vdbe.html file.
# #
set rcsid {$Id: vdbe.tcl,v 1.4 2000/07/28 14:32:51 drh Exp $} set rcsid {$Id: vdbe.tcl,v 1.5 2000/07/30 20:04:43 drh Exp $}
puts {<html> puts {<html>
<head> <head>
@@ -14,11 +14,12 @@ The Virtual Database Engine of SQLite
puts "<p align=center> puts "<p align=center>
(This page was last modified on [lrange $rcsid 3 4] GMT) (This page was last modified on [lrange $rcsid 3 4] GMT)
</p>" </p>"
puts {
<blockquote><font color="red"><b>This document is # puts {
currently under development. It is incomplete and contains # <blockquote><font color="red"><b>This document is
errors. Use it accordingly.</b></font></blockquote> # currently under development. It is incomplete and contains
} # errors. Use it accordingly.</b></font></blockquote>
# }
puts { puts {
<p>If you want to know how the SQLite library works internally, <p>If you want to know how the SQLite library works internally,
@@ -96,6 +97,9 @@ INSERT INTO examp VALUES('Hello, World!',99);
<p>We can see the VDBE program that SQLite uses to implement this <p>We can see the VDBE program that SQLite uses to implement this
INSERT using the <b>sqlite</b> command-line utility. First start INSERT using the <b>sqlite</b> command-line utility. First start
up <b>sqlite</b> on a new, empty database, then create the table. up <b>sqlite</b> on a new, empty database, then create the table.
Next change the output format of <b>sqlite</b> to a form that
is designed to work with VDBE program dumps by entering the
".explain" command.
Finally, enter the INSERT statement shown above, but precede the Finally, enter the INSERT statement shown above, but precede the
INSERT with the special keyword "EXPLAIN". The EXPLAIN keyword INSERT with the special keyword "EXPLAIN". The EXPLAIN keyword
will cause <b>sqlite</b> to print the VDBE program rather than will cause <b>sqlite</b> to print the VDBE program rather than
@@ -153,7 +157,7 @@ another cursor open for writing that same file.</p>
<p>The second instruction, New, generates an integer key that <p>The second instruction, New, generates an integer key that
has not been previously used in the file "examp". The New instruction has not been previously used in the file "examp". The New instruction
uses its P1 operand as the handle of a cursor for the file uses its P1 operand as the handle of a cursor for the file
for which the new key will be generated. The new key is for which the new key will be generated. The generated key is
pushed onto the stack. The P2 and P3 operands are not used pushed onto the stack. The P2 and P3 operands are not used
by the New instruction.</p> by the New instruction.</p>
@@ -197,7 +201,7 @@ stack {A data record holding "Hello, World!" and 99} \
{A random integer key} {A random integer key}
puts {<p>The last instruction pops the top two elements from the stack puts {<p>The last instruction pops the top two elements from the stack
and uses them as data and key to make a new entry in database and uses them as data and key to make a new entry in the
database file pointed to by cursor P1. This instruction is where database file pointed to by cursor P1. This instruction is where
the insert actually occurs.</p> the insert actually occurs.</p>
@@ -301,7 +305,9 @@ int Callback(void *pUserData, int nColumn, char *azData[], char *azColumnName[])
</pre></blockquote> </pre></blockquote>
<p>The SQLite library supplies the VDBE with a pointer to the callback function <p>The SQLite library supplies the VDBE with a pointer to the callback function
itself, and the <b>pUserData</b> pointer. The job of the VDBE is to and the <b>pUserData</b> pointer. (Both the callback and the user data were
originally passed in as argument to the <b>sqlite_exec()</b> API function.)
The job of the VDBE is to
come up with values for <b>nColumn</b>, <b>azData[]</b>, come up with values for <b>nColumn</b>, <b>azData[]</b>,
and <b>azColumnName[]</b>. and <b>azColumnName[]</b>.
<b>nColumn</b> is the number of columns in the results, of course. <b>nColumn</b> is the number of columns in the results, of course.
@@ -755,18 +761,859 @@ table. This text is fed back into the SQLite parser
and used to reconstruct the and used to reconstruct the
internal data structures describing the index or table.</p> internal data structures describing the index or table.</p>
<h2>Using Indexes To Speed Searches</h2> <h2>Using Indexes To Speed Searching</h2>
<i>TBD</i>
<p>In the example queries above, every row of the table being
queried must be loaded off of the disk and examined, even if only
a small percentage of the rows end up in the result. This can
take a long time on a big table. To speed things up, SQLite
can use an index.</p>
<p>An GDBM file associates a key with some data. For a SQLite
table, the GDBM file is set up so that the key is a integer
and the data is the information for one row of the table.
Indices in SQLite reverse this arrangement. The GDBM key
is (some of) the information being stored and the GDBM data
is an integer.
To access a table row that has some particular
content, we first look up the content in the GDBM index file to find
its integer index, then we use that integer to look up the
complete record in the GDBM table file.</p>
<p>Note that because GDBM uses hashing instead of b-trees, indices
are only helpful when the WHERE clause of the SELECT statement
contains tests for equality. Inequalities will not work since there
is no way to ask GDBM to fetch records that do not match a key.
So, in other words, queries like the following will use an index
if it is available:</p>
<blockquote><pre>
SELECT * FROM examp WHERE two==50;
</pre></blockquote>
<p>If there exists an index that maps the "two" column of the "examp"
table into integers, then SQLite will use that index to find the integer
keys of all rows in examp that have a value of 50 for column two.
But the following query will not use an index:</p>
<blockquote><pre>
SELECT * FROM examp WHERE two<50;
</pre></blockquote>
<p>GDBM does not have the ability to select records based on
a magnitude comparison, and so there is no way to use an index
to speed the search in this case.</p>
<p>To understand better how indices work, lets first look at how
they are created. Let's go ahead and put an index on the two
column of the examp table. We have:</p>
<blockquote><pre>
CREATE INDEX examp_idx1 ON examp(two);
</pre></blockquote>
<p>The VDBE code generated by the above statement looks like the
following:</p>
}
Code {
addr opcode p1 p2 p3
---- ------------ ----- ----- ----------------------------------------
0 Open 0 0 examp
1 Open 1 1 examp_idx1
2 Open 2 1 sqlite_master
3 New 2 0
4 String 0 0 index
5 String 0 0 examp_idx1
6 String 0 0 examp
7 String 0 0 CREATE INDEX examp_idx1 ON examp(two)
8 MakeRecord 4 0
9 Put 2 0
10 Close 2 0
11 Next 0 17
12 Key 0 0
13 Field 0 1
14 MakeKey 1 0
15 PutIdx 1 0
16 Goto 0 11
17 Noop 0 0
18 Close 1 0
19 Close 0 0
}
puts {
<p>Remember that every table (except sqlite_master) and every named
index has an entry in the sqlite_master table. Since we are creating
a new index, we have to add a new entry to sqlite_master. This is
handled by instructions 2 through 10. Adding an entry to sqlite_master
works just like any other INSERT statement so we will not say anymore
about it here. In this example, we want to focus on populating the
new index with valid data, which happens on instructions 0 and 1 and
on instructions 11 through 19.</p>
<p>The first thing that happens is that we open the table being
indexed for reading. In order to construct an index for a table,
we have to know what is in that table. The second instruction
opens the index file for writing.</p>
<p>Instructions 11 through 16 implement a loop over every row
of the table being indexed. For each table row, we first extract
the integer key for that row in instruction 12, then get the
value of the two column in instruction 13. The MakeKey instruction
at 14 converts data from the two column (which is on the top of
the stack) into a valid index key. For an index on a single column,
this is basically a no-op. But if the P1 operand to MakeKey had
been greater than one multiple entries would have been popped from
the stack and converted into a single index key. The PutIdx
instruction at 15 is what actually creates the index entry. PutIdx
pops two elements from the stack. The top of the stack is used as
a key to fetch an entry from the GDBM index file. Then the integer
which was second on stack is added to the set of integers for that
index and the new record is written back to the GDBM file. Note
that the same index entry can store multiple integers if there
are two or more table entries with the same value for the two
column.
</p>
<p>Now let's look at how this index will be used. Consider the
following query:</p>
<blockquote><pre>
SELECT * FROM examp WHERE two==50;
</pre></blockquote>
<p>SQLite generates the following VDBE code to handle this query:</p>
}
Code {
addr opcode p1 p2 p3
---- ------------ ----- ----- ----------------------------------------
0 ColumnCount 2 0
1 ColumnName 0 0 one
2 ColumnName 1 0 two
3 Open 0 0 examp
4 Open 1 0 examp_idx1
5 Integer 50 0
6 MakeKey 1 0
7 Fetch 1 0
8 NextIdx 1 14
9 Fetch 0 0
10 Field 0 0
11 Field 0 1
12 Callback 2 0
13 Goto 0 8
14 Close 0 0
15 Close 1 0
}
puts {
<p>The SELECT begins in a familiar fashion. First the column
names are initialized and the table being queried is opened.
Things become different beginning with instruction 4 where
the index file is also opened. Instructions 5 and 6 make
a key with the value of 50 and instruction 7 fetches the
record of the GDBM index file that has this key. This will
be the only fetch from the index file.</p>
<p>Instructions 8 through 13 implement a loop over all
integers in the payload of the index record that was fetched
by instruction 7. The NextIdx operation works much like
the Next and ListRead operations that are discussed above.
Each NextIdx instruction reads a single integer from the
payload of the index record and falls through, except that
if there are no more records it jumps immediately to 14.</p>
<p>The Fetch instruction at 9 loads a single record from
the GDBM file that holds the table. Then there are two
Field instructions to construct the result and the callback
is invoked. All this is the same as we have seen before.
The only difference is that the loop is now constructed using
NextIdx instead of Next.</p>
<p>Since the index is used to look up values in the table,
it is important that the index and table be kept consistent.
Now that there is an index on the examp table, we will have
to update that index whenever data is inserted, deleted, or
changed in the examp table. Remember the first example above
how we were able to insert a new row into the examp table using
only 6 VDBE instructions. Now that this table is indexed, 10
instructions are required. The SQL statement is this:</p>
<blockquote><pre>
INSERT INTO examp VALUES('Hello, World!',99);
</pre></blockquote>
<p>And the generated code looks like this:</p>
}
Code {
addr opcode p1 p2 p3
---- ------------ ----- ----- ----------------------------------------
0 Open 0 1 examp
1 Open 1 1 examp_idx1
2 New 0 0
3 Dup 0 0
4 String 0 0 Hello, World!
5 Integer 99 0
6 MakeRecord 2 0
7 Put 0 0
8 Integer 99 0
9 MakeKey 1 0
10 PutIdx 1 0
}
puts {
<p>At this point, you should understand the VDBE well enough to
figure out on your own how the above program works. So we will
not discuss it further in this text.</p>
<h2>Joins</h2> <h2>Joins</h2>
<i>TBD</i>
<p>In a join, two or more tables are combined to generate a single
result. The result table consists of every possible combination
of rows from the tables being joined. The easiest and most natural
way to implement this is with nested loops.</p>
<p>Recall the query template discussed above where there was a
single loop that searched through every record of the table.
In a join we have basically the same thing except that there
are nested loops. For example, to join two tables, the query
template might look something like this:</p>
<p>
<ol>
<li>Initialize the <b>azColumnName[]</b> array for the callback.</li>
<li>Open two cursors, one to each of the two tables being queried.</li>
<li>For each record in the first table, do:
<ol type="a">
<li>For each record in the second table do:
<ol type="i">
<li>If the WHERE clause evaluates to FALSE, then skip the steps that
follow and continue to the next record.</li>
<li>Compute all columns for the current row of the result.</li>
<li>Invoke the callback function for the current row of the result.</li>
</ol></li>
</ol>
<li>Close both cursors.</li>
</ol>
</p>
<p>This template will work, but it is likely to be slow since we
are now dealing with an O(N<sup>2</sup>) loop. But it often works
out that the WHERE clause can be factored into terms and that one or
more of those terms will involve only columns in the first table.
When this happens, we can factor part of the WHERE clause test out of
the inner loop and gain a lot of efficiency. So a better template
would be something like this:</p>
<p>
<ol>
<li>Initialize the <b>azColumnName[]</b> array for the callback.</li>
<li>Open two cursors, one to each of the two tables being queried.</li>
<li>For each record in the first table, do:
<ol type="a">
<li>Evaluate terms of the WHERE clause that only involve columns from
the first table. If any term is false (meaning that the whole
WHERE clause must be false) then skip the rest of this loop and
continue to the next record.</li>
<li>For each record in the second table do:
<ol type="i">
<li>If the WHERE clause evaluates to FALSE, then skip the steps that
follow and continue to the next record.</li>
<li>Compute all columns for the current row of the result.</li>
<li>Invoke the callback function for the current row of the result.</li>
</ol></li>
</ol>
<li>Close both cursors.</li>
</ol>
</p>
<p>Additional speed-up can occur if an index can be used to speed
the search of either or the two loops.</p>
<p>SQLite always constructs the loops in the same order as the
tables appear in the FROM clause of the SELECT statement. The
left-most table becomes the outer loop and the right-most table
becomes the inner loop. It is possible, in theory, to reorder
the loops in some circumstances to speed the evaluation of the
join. But SQLite does not attempt this optimization.</p>
<p>You can see how SQLite constructs nested loops in the following
example:</p>
<blockquote><pre>
CREATE TABLE examp2(three int, four int);
SELECT * FROM examp, examp2 WHERE two<50 AND four==two;
</pre></blockquote>
}
Code {
addr opcode p1 p2 p3
---- ------------ ----- ----- ----------------------------------------
0 ColumnCount 4 0
1 ColumnName 0 0 examp.one
2 ColumnName 1 0 examp.two
3 ColumnName 2 0 examp2.three
4 ColumnName 3 0 examp2.four
5 Open 0 0 examp
6 Open 1 0 examp2
7 Next 0 21
8 Field 0 1
9 Integer 50 0
10 Ge 0 7
11 Next 1 7
12 Field 1 1
13 Field 0 1
14 Ne 0 11
15 Field 0 0
16 Field 0 1
17 Field 1 0
18 Field 1 1
19 Callback 4 0
20 Goto 0 11
21 Close 0 0
22 Close 1 0
}
puts {
<p>The outer loop over table examp is implement by instructions
7 through 20. The inner loop is instructions 11 through 20.
Notice that the "two<50" term of the WHERE expression involves
only columns from the first table and can be factored out of
the inner loop. SQLite does this and implements the "two<50"
test in instructions 8 through 10. The "four==two" test is
implement by instructions 12 through 14 in the inner loop.</p>
<p>SQLite does not impose any arbitrary limits on the tables in
a join. It also allows a table to be joined with itself.</p>
<h2>The ORDER BY clause</h2> <h2>The ORDER BY clause</h2>
<i>TBD</i>
<p>As noted previously, GDBM does not have any facility for
handling inequalities. A consequence of this is that we cannot
sort on disk using GDBM. All sorted must be done in memory.</p>
<p>SQLite implements the ORDER BY clause using a special
set of instruction control an object called a sorter. In the
inner-most loop of the query, where there would normally be
a Callback instruction, instead a record is constructed that
contains both callback parameters and a key. This record
is added to a linked list. After the query loop finishes,
the list of records is sort and this walked. For each record
on the list, the callback is invoked. Finally, the sorter
is closed and memory is deallocated.</p>
<p>We can see the process in action in the following query:</p>
<blockquote><pre>
SELECT * FROM examp ORDER BY one DESC, two;
</pre></blockquote>
}
Code {
addr opcode p1 p2 p3
---- ------------ ----- ----- ----------------------------------------
0 SortOpen 0 0
1 ColumnCount 2 0
2 ColumnName 0 0 one
3 ColumnName 1 0 two
4 Open 0 0 examp
5 Next 0 14
6 Field 0 0
7 Field 0 1
8 SortMakeRec 2 0
9 Field 0 0
10 Field 0 1
11 SortMakeKey 2 0 -+
12 SortPut 0 0
13 Goto 0 5
14 Close 0 0
15 Sort 0 0
16 SortNext 0 19
17 SortCallback 2 0
18 Goto 0 16
19 SortClose 0 0
}
puts {
<p>The sorter is opened on the first instruction. The VDBE allows
any number of sorters, but in practice no more than one is every used.</p>
<p>The query loop is built from instructions 5 through 13. Instructions
6 through 8 build a record that contains the azData[] values for a single
invocation of the callback. A sort key is generated by instructions
9 through 11. Instruction 12 combines the invocation record and the
sort key into a single entry and puts that entry on the sort list.<p>
<p>The P3 argument of instruction 11 is of particular interest. The
sort key is formed by prepending one character from P3 to each string
and concatenating all the strings. The sort comparison function will
look at this character to determine whether the sort order is
ascending or descending. In this example, the first column should be
sorted in descending order so its prefix is "-" and the second column
should sort in ascending order so its prefix is "+".</p>
<p>After the query loop ends, the table being queried is closed at
instruction 14. This is done early in order to allow other processes
or threads to access that table, if desired. The list of records
that was built up inside the query loop is sorted by the instruction
at 15. Instructions 16 through 18 walk through the record list
(which is now in sorted order) and invoke the callback once for
each record. Finally, the sorter is closed at instruction 19.</p>
<h2>Aggregate Functions And The GROUP BY and HAVING Clauses</h2> <h2>Aggregate Functions And The GROUP BY and HAVING Clauses</h2>
<i>TBD</i>
<p>To compute aggregate functions, the VDBE implements a special
data structure and instructions for controlling that data structure.
The data structure is an unordered set of buckets, where each bucket
has a key and one or more memory locations. Within the query
loop, the GROUP BY clause is used to construct a key and the bucket
with that key is brought into focus. A new bucket is created with
the key if one did not previously exist. Once the bucket is in
focus, the memory locations of the bucket are used to accumulate
the values of the various aggregate functions. After the query
loop terminates, the each bucket is visited once to generate a
single row of the results.</p>
<p>An example will help to clarify this concept. Consider the
following query:</p>
<blockquote><pre>
SELECT three, min(three+four)+avg(four)
FROM examp2
GROUP BY three;
</pre></blockquote>
}
puts {
<p>The VDBE code generated for this query is as follows:</p>
}
Code {
addr opcode p1 p2 p3
---- ------------ ----- ----- ----------------------------------------
0 ColumnCount 2 0
1 ColumnName 0 0 three
2 ColumnName 1 0 min(three+four)+avg(four)
3 AggReset 0 4
4 Open 0 0 examp2
5 Next 0 23
6 Field 0 0
7 MakeKey 1 0
8 AggFocus 0 11
9 Field 0 0
10 AggSet 0 0
11 Field 0 0
12 Field 0 1
13 Add 0 0
14 AggGet 0 1
15 Min 0 0
16 AggSet 0 1
17 AggIncr 1 2
18 Field 0 1
19 AggGet 0 3
20 Add 0 0
21 AggSet 0 3
22 Goto 0 5
23 Close 0 0
24 AggNext 0 33
25 AggGet 0 0
26 AggGet 0 1
27 AggGet 0 3
28 AggGet 0 2
29 Divide 0 0
30 Add 0 0
31 Callback 2 0
32 Goto 0 24
33 Noop 0 0
}
puts {
<p>The first instruction of interest is the AggReset at 3.
The AggReset instruction initializes the set of buckets to be the
empty set and specifies the number of memory slots available in each
bucket. In this example, each bucket will hold four memory slots.
It is not obvious, but if you look closely at the rest of the program
you can figure out what each of these four slots is intended for.</p>
<blockquote><table border="2" cellpadding="5">
<tr><th>Memory Slot</th><th>Intended Use Of This Memory Slot</th></tr>
<tr><td>0</td><td>The "three" column -- the key to the bucket</td></tr>
<tr><td>1</td><td>The minimum "three+four" value</td></tr>
<tr><td>2</td><td>The number of records with the same key. This value
divides the value in slot 3 to compute "avg(four)".</td></tr>
<tr><td>3</td><td>The sum of all "four" values. This is used to compute
"avg(four)".</td></tr>
</table></blockquote>
<p>The query loop is implement by instructions 5 through 22.
The aggregate key specified by the GROUP BY clause is computed
by instructions 6 and 7. Instruction 8 causes the appropriate
bucket to come into focus. If a bucket with the given key does
not already exists, a new bucket is created and control falls
through to instructions 9 and 10 which initialize the bucket.
If the bucket does already exist, then a jump is made to instruction
11. The values of aggregate functions are updated by the instructions
between 11 and 21. Instructions 11 through 16 update memory
slot 1 to hold the next value "min(three+four)". The counter in
slot 2 is incremented by instruction 17. Finally the sum of
the "four" column is updated by instructions 18 through 21.</p>
<p>After the query loop is finished, the GDBM table is closed at
instruction 23 so that its lock will be released and it can be
used by other threads or processes. The next step is to loop
over all aggregate buckets and output one row of the result for
each bucket. This is done by the loop at instructions 24
through 32. The AggNext instruction at 24 brings the next bucket
into focus, or jumps to the end of the loop if all buckets have
been examined already. The first column of the result ("three")
is computed by instruction 25. The second result column
("min(three+four)+avg(four)") is computed by instructions
26 through 30. Notice how the avg() function is computed
as if it where sum()/count(). Finally, the callback is invoked
at instruction 31.</p>
<p>In summary then, any query with aggregate functions is implemented
by two loops. The first loop scans the input table and computes
aggregate information into buckets and the second loop scans through
all the buckets to compute the final result.</p>
<p>The realization that an aggregate query is really two consequtive
loops makes it much easier to understand the difference between
a WHERE clause and a HAVING clause in SQL query statement. The
WHERE clause is a restriction on the first loop and the HAVING
clause is a restriction on the second loop. You can see this
by adding both a WHERE and a HAVING clause to our example query:</p>
<blockquote><pre>
SELECT three, min(three+four)+avg(four)
FROM examp2
WHERE three>four
GROUP BY three
HAVING avg(four)<10;
</pre></blockquote>
}
Code {
addr opcode p1 p2 p3
---- ------------ ----- ----- ----------------------------------------
0 ColumnCount 2 0
1 ColumnName 0 0 three
2 ColumnName 1 0 min(three+four)+avg(four)
3 AggReset 0 4
4 Open 0 0 examp2
5 Next 0 26
6 Field 0 0
7 Field 0 1
8 Le 0 5
9 Field 0 0
10 MakeKey 1 0
11 AggFocus 0 14
12 Field 0 0
13 AggSet 0 0
14 Field 0 0
15 Field 0 1
16 Add 0 0
17 AggGet 0 1
18 Min 0 0
19 AggSet 0 1
20 AggIncr 1 2
21 Field 0 1
22 AggGet 0 3
23 Add 0 0
24 AggSet 0 3
25 Goto 0 5
26 Close 0 0
27 AggNext 0 41
28 AggGet 0 3
29 AggGet 0 2
30 Divide 0 0
31 Integer 10 0
32 Ge 0 27
33 AggGet 0 0
34 AggGet 0 1
35 AggGet 0 3
36 AggGet 0 2
37 Divide 0 0
38 Add 0 0
39 Callback 2 0
40 Goto 0 27
41 Noop 0 0
}
puts {
<p>The code generated in this last example is the same as the
previous except for the addition of two conditional jumps used
to implement the extra WHERE and HAVING clauses. The WHERE
clause is implemented by instructions 6 through 8 in the query
loop. The HAVING clause is implemented by instruction 28 through
32 in the output loop.</p>
<h2>Using SELECT Statements As Terms In An Expression</h2> <h2>Using SELECT Statements As Terms In An Expression</h2>
<i>TBD</i>
<p>The very name "Structured Query Language" tells us that SQL should
support nested queries. And, in fact, two different kinds of nesting
are supported. Any SELECT statement that returns a single-row, single-column
result can be used as a term in an expression of another SELECT statement.
And, a SELECT statement that returns a single-column, multi-row result
can be used as the right-hand operand of the IN and NOT IN operators.
We will begin this section with an example of the first kind of nesting,
where a single-row, single-column SELECT is used as a term in an expression
of another SELECT. Here is our example:</p>
<blockquote><pre>
SELECT * FROM examp
WHERE two!=(SELECT three FROM examp2
WHERE four=5);
</pre></blockquote>
<p>The way SQLite deals with this is to first run the inner SELECT
(the one against examp2) and store its result in a private memory
cell. SQLite then substitutes the value of this private memory
cell for the inner SELECT when it evaluations the outer SELECT.
The code looks like this:</p>
}
Code {
addr opcode p1 p2 p3
---- ------------ ----- ----- ----------------------------------------
0 Null 0 0
1 MemStore 0 0
2 Open 0 0 examp2
3 Next 0 11
4 Field 0 1
5 Integer 5 0
6 Ne 0 3
7 Field 0 0
8 MemStore 0 0
9 Goto 0 11
10 Goto 0 3
11 Close 0 0
12 ColumnCount 2 0
13 ColumnName 0 0 one
14 ColumnName 1 0 two
15 Open 0 0 examp
16 Next 0 24
17 Field 0 1
18 MemLoad 0 0
19 Eq 0 16
20 Field 0 0
21 Field 0 1
22 Callback 2 0
23 Goto 0 16
24 Close 0 0
}
puts {
<p>The private memory cell is initialized to NULL by the first
two instructions. Instructions 2 through 11 implement the inner
SELECT statement against the examp2 table. Notice that instead of
sending the result to a callback or storing the result on a sorter,
the result of the query is pushed into the memory cell by instruction
8 and the loop is abandoned by the jump at instruction 9.
The jump at instruction at 10 is vestigial and
never executes.</p>
<p>The outer SELECT is implemented by instructions 12 through 24.
In particular, the WHERE clause that contains the nested select
is implemented by instructions 17 through 19. You can see that
the result of the inner select is loaded onto the stack by instruction
18 and used by the conditional jump at 19.</p>
<p>When the result of a sub-select is a scalar, a single private memory
cell can be used, as shown in the previous
example. But when the result of a sub-select is a vector, such
as when the sub-select is the right-hand operand of IN or NOT IN,
a different approach is needed. In this case,
the result of the sub-select is
stored in a temporary GDBM table and the contents of that table
are tested using the Found or NotFound operators. Consider this
example:</p>
<blockquote><pre>
SELECT * FROM examp
WHERE two IN (SELECT three FROM examp2);
</pre></blockquote>
<p>The code generated to implement this last query is as follows:</p>
}
Code {
addr opcode p1 p2 p3
---- ------------ ----- ----- ----------------------------------------
0 Open 0 1
1 Open 1 0 examp2
2 Next 1 7
3 Field 1 0
4 String 0 0
5 Put 0 0
6 Goto 0 2
7 Close 1 0
8 ColumnCount 2 0
9 ColumnName 0 0 one
10 ColumnName 1 0 two
11 Open 1 0 examp
12 Next 1 19
13 Field 1 1
14 NotFound 0 12
15 Field 1 0
16 Field 1 1
17 Callback 2 0
18 Goto 0 12
19 Close 1 0
}
puts {
<p>The temporary table in which the results of the inner SELECT are
stored is created by instruction 0. Notice that the P3 field of
this Open instruction is empty. An empty P3 field on an Open
instruction tells the VDBE to create a temporary table. This temporary
table will be automatically deleted from the disk when the
VDBE halts.</p>
<p>The inner SELECT statement is implemented by instructions 1 through 7.
All this code does is make an entry in the temporary table for each
row of the examp2 table. The key for each temporary table entry
is the "three" column of examp2 and the data
entries is an empty string since it is never used.</p>
<p>The outer SELECT is implemented by instructions 8 through 19. In
particular, the WHERE clause containing the IN operator is implemented
by two instructions at 13 and 14. Instruction 13 pushes the value of
the "two" column for the current row onto the stack and instruction 14
tests to see if top of the stack matches any key in the temporary table.
All the rest of the code is the same as what has been shown before.</p>
<h2>Compound SELECT Statements</h2> <h2>Compound SELECT Statements</h2>
<i>TBD</i>
<p>SQLite also allows two or more SELECT statements to be joined as
peers using operators UNION, UNION ALL, INTERSECT, and EXCEPT. These
compound select statements are implemented using temporary tables.
The implementation is slightly different for each operator, but the
basic ideas are the same. For an example we will use the EXCEPT
operator.</p>
<blockquote><pre>
SELECT two FROM examp
EXCEPT
SELECT four FROM examp2;
</pre></blockquote>
<p>The result of this last example should be every unique value
of the two column in the examp table except any value that is
in the four column of examp2 is removed. The code to implement
this query is as follows:</p>
}
Code {
addr opcode p1 p2 p3
---- ------------ ----- ----- ----------------------------------------
0 Open 0 1
1 KeyAsData 0 1
2 Open 1 0 examp
3 Next 1 9
4 Field 1 1
5 MakeRecord 1 0
6 String 0 0
7 Put 0 0
8 Goto 0 3
9 Close 1 0
10 Open 1 0 examp2
11 Next 1 16
12 Field 1 1
13 MakeRecord 1 0
14 Delete 0 0
15 Goto 0 11
16 Close 1 0
17 ColumnCount 1 0
18 ColumnName 0 0 four
19 Next 0 23
20 Field 0 0
21 Callback 1 0
22 Goto 0 19
23 Close 0 0
}
puts {
<p>The temporary table in which the result is built is created by
instruction 0. Three loops then follow. The loop at instructions
3 through 8 implements the first SELECT statement. The second
SELECT statement is implemented by the loop at instructions 11 through
15. Finally, a loop at instructions 19 through 22 reads the temporary
table and invokes the callback once for each row in the result.</p>
<p>Instruction 1 is of particular importance in this example. Normally,
the Field opcode extracts the value of a column from a larger
record in the data of a GDBM file entry. Instructions 1 sets a flag on
the temporary table so that Field will instead treat the key of the
GDBM file entry as if it were data and extract column information from
the key.</p>
<p>Here is what is going to happen: The first SELECT statement
will construct rows of the result and save each row as the key of
an entry in the temporary table. The data for each entry in the
temporary table is a never used so we fill it in with an empty string.
The second SELECT statement also constructs rows, but the rows
constructed by the second SELECT are removed from the temporary table.
That is why we want the rows to be stored in the key of the GDBM file
instead of in the data -- so they can be easily located and deleted.</p>
<p>Let's look more closely at what is happening here. The first
SELECT is implemented by the loop at instructions 3 through 8.
Instruction 4 extracts the value of the "two" column from "examp"
and instruction 5 converts this into a row. Instruction 6 pushes
an empty string onto the stack. Finally, instruction 7 writes the
row into the temporary table. But remember, the Put opcode uses
the top of the stack as the GDBM data and the next on stack as the
GDBM key. For an INSERT statement, the row generated by the
MakeRecord opcode is the GDBM data and the GDBM key is an integer
created by the New opcode. But here the roles are reversed and
the row created by MakeRecord is the GDBM key and the GDBM data is
just an empty string.</p>
<p>The second SELECT is implemented by instructions 11 through 15.
A new result row is created from the "four" column of table "examp2"
by instructions 12 and 13. But instead of using Put to write this
new row into the temporary table, we instead call Delete to remove
it from the temporary table if it exists.</p>
<p>The result of the compound select is sent to the callback routine
by the loop at instructions 19 through 22. There is nothing new
or remarkable about this loop, except for the fact that the Field
instruction at 20 will be extracting a column out of the GDBM key
rather than the GDBM data.</p>
<h2>Summary</h2>
<p>This article has reviewed all of the major techniques used by
SQLite's VDBE to implement SQL statements. What has not been shown
is that most of these techniques can be used in combination to
generate code for an appropriately complex query statement. For
example, we have shown how sorting is accomplished on a simple query
and we have shown how to implement a compound query. But we did
not give an example of sorting in a compound query. This is because
sorting a compound query does not introduce any new concepts: it
merely combines two previous ideas (sorting and compounding)
in the same VDBE program.</p>
<p>For additional information on how the SQLite library
functions, the reader is directed to look at the SQLite source
code directly. If you understand the material in this article,
you should not have much difficulty in following the sources.
Serious students of the internals of SQLite will probably
also what to make a careful study of the VDBE opcodes
as documented <a href="opcode.html">here</a>. Most of the
opcode documentation is extracted from comments in the source
code using a script so you can also get information about the
various opcodes directly from the <b>vdbe.c</b> source file.
If you have successfully read this far, you should have little
difficulty understanding the rest.</p>
<p>If you find errors in either the documentation or the code,
feel free to fix them and/or contact the author at
<a href="drh@hwaci.com">drh@hwaci.com</a>. Your bug fixes or
suggestions are always welcomed.</p>
} }
puts { puts {