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Restructure performance tips into a single chapter ('populating a

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database' was way too small to make a chapter).  Add a section about
using JOIN syntax to direct the planner.
This commit is contained in:
Tom Lane 2000-12-16 02:29:36 +00:00
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<!entity inherit SYSTEM "inherit.sgml">
<!entity manage SYSTEM "manage.sgml">
<!entity mvcc SYSTEM "mvcc.sgml">
<!entity plan SYSTEM "plan.sgml">
<!entity perform SYSTEM "perform.sgml">
<!entity plperl SYSTEM "plperl.sgml">
<!entity plsql SYSTEM "plsql.sgml">
<!entity pltcl SYSTEM "pltcl.sgml">
<!entity populate SYSTEM "populate.sgml">
<!entity psql SYSTEM "psql.sgml">
<!entity query-ug SYSTEM "query-ug.sgml">
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<chapter id="performance-tips">
<title>Performance Tips</title>
<para>
Query performance can be affected by many things. Some of these can
be manipulated by the user, while others are fundamental to the underlying
design of the system. This chapter provides some hints about understanding
and tuning <productname>Postgres</productname> performance.
</para>
<sect1 id="using-explain">
<title>Using <command>EXPLAIN</command></title>
<note>
<title>Author</title>
<para>
Written by Tom Lane, from e-mail dated 2000-03-27.
</para>
</note>
<para>
<productname>Postgres</productname> devises a <firstterm>query
plan</firstterm> for each query it is given. Choosing the right
plan to match the query structure and the properties of the data
is absolutely critical for good performance. You can use the
<command>EXPLAIN</command> command to see what query plan the system
creates for any query. Unfortunately,
plan-reading is an art that deserves a tutorial, and I haven't
had time to write one. Here is some quick &amp; dirty explanation.
</para>
<para>
The numbers that are currently quoted by EXPLAIN are:
<itemizedlist>
<listitem>
<para>
Estimated start-up cost (time expended before output scan can start,
eg, time to do the sorting in a SORT node).
</para>
</listitem>
<listitem>
<para>
Estimated total cost (if all tuples are retrieved, which they may not
be --- a query with a LIMIT will stop short of paying the total cost,
for example).
</para>
</listitem>
<listitem>
<para>
Estimated number of rows output by this plan node (again, without
regard for any LIMIT).
</para>
</listitem>
<listitem>
<para>
Estimated average width (in bytes) of rows output by this plan
node.
</para>
</listitem>
</itemizedlist>
</para>
<para>
The costs are measured in units of disk page fetches. (CPU effort
estimates are converted into disk-page units using some
fairly arbitrary fudge-factors. If you want to experiment with these
factors, see the list of run-time configuration parameters in the
<citetitle>Administrator's Guide</citetitle>.)
</para>
<para>
It's important to note that the cost of an upper-level node includes
the cost of all its child nodes. It's also important to realize that
the cost only reflects things that the planner/optimizer cares about.
In particular, the cost does not consider the time spent transmitting
result tuples to the frontend --- which could be a pretty dominant
factor in the true elapsed time, but the planner ignores it because
it cannot change it by altering the plan. (Every correct plan will
output the same tuple set, we trust.)
</para>
<para>
Rows output is a little tricky because it is <emphasis>not</emphasis> the
number of rows
processed/scanned by the query --- it is usually less, reflecting the
estimated selectivity of any WHERE-clause constraints that are being
applied at this node. Ideally the top-level rows estimate will
approximate the number of rows actually returned, updated, or deleted
by the query (again, without considering the effects of LIMIT).
</para>
<para>
Average width is pretty bogus because the thing really doesn't have
any idea of the average length of variable-length columns. I'm thinking
about improving that in the future, but it may not be worth the trouble,
because the width isn't used for very much.
</para>
<para>
Here are some examples (using the regress test database after a
vacuum analyze, and almost-7.0 sources):
<programlisting>
regression=# explain select * from tenk1;
NOTICE: QUERY PLAN:
Seq Scan on tenk1 (cost=0.00..333.00 rows=10000 width=148)
</programlisting>
</para>
<para>
This is about as straightforward as it gets. If you do
<programlisting>
select * from pg_class where relname = 'tenk1';
</programlisting>
you'll find out that tenk1 has 233 disk
pages and 10000 tuples. So the cost is estimated at 233 block
reads, defined as 1.0 apiece, plus 10000 * cpu_tuple_cost which is
currently 0.01 (try <command>show cpu_tuple_cost</command>).
</para>
<para>
Now let's modify the query to add a qualification clause:
<programlisting>
regression=# explain select * from tenk1 where unique1 &lt; 1000;
NOTICE: QUERY PLAN:
Seq Scan on tenk1 (cost=0.00..358.00 rows=1000 width=148)
</programlisting>
The estimate of output rows has gone down because of the WHERE clause.
(This estimate is uncannily accurate because tenk1 is a particularly
simple case --- the unique1 column has 10000 distinct values ranging
from 0 to 9999, so the estimator's linear interpolation between min and
max column values is dead-on.) However, the scan will still have to
visit all 10000 rows, so the cost hasn't decreased; in fact it has gone
up a bit to reflect the extra CPU time spent checking the WHERE
condition.
</para>
<para>
Modify the query to restrict the qualification even more:
<programlisting>
regression=# explain select * from tenk1 where unique1 &lt; 100;
NOTICE: QUERY PLAN:
Index Scan using tenk1_unique1 on tenk1 (cost=0.00..89.35 rows=100 width=148)
</programlisting>
and you will see that if we make the WHERE condition selective
enough, the planner will
eventually decide that an indexscan is cheaper than a sequential scan.
This plan will only have to visit 100 tuples because of the index,
so it wins despite the fact that each individual fetch is expensive.
</para>
<para>
Add another condition to the qualification:
<programlisting>
regression=# explain select * from tenk1 where unique1 &lt; 100 and
regression-# stringu1 = 'xxx';
NOTICE: QUERY PLAN:
Index Scan using tenk1_unique1 on tenk1 (cost=0.00..89.60 rows=1 width=148)
</programlisting>
The added clause "stringu1 = 'xxx'" reduces the output-rows estimate,
but not the cost because we still have to visit the same set of tuples.
</para>
<para>
Let's try joining two tables, using the fields we have been discussing:
<programlisting>
regression=# explain select * from tenk1 t1, tenk2 t2 where t1.unique1 &lt; 100
regression-# and t1.unique2 = t2.unique2;
NOTICE: QUERY PLAN:
Nested Loop (cost=0.00..144.07 rows=100 width=296)
-&gt; Index Scan using tenk1_unique1 on tenk1 t1
(cost=0.00..89.35 rows=100 width=148)
-&gt; Index Scan using tenk2_unique2 on tenk2 t2
(cost=0.00..0.53 rows=1 width=148)
</programlisting>
</para>
<para>
In this nested-loop join, the outer scan is the same indexscan we had
in the example before last, and so its cost and row count are the same
because we are applying the "unique1 &lt; 100" WHERE clause at that node.
The "t1.unique2 = t2.unique2" clause isn't relevant yet, so it doesn't
affect the outer scan's row count. For the inner scan, the
current
outer-scan tuple's unique2 value is plugged into the inner indexscan
to produce an indexqual like
"t2.unique2 = <replaceable>constant</replaceable>". So we get the
same inner-scan plan and costs that we'd get from, say, "explain select
* from tenk2 where unique2 = 42". The loop node's costs are then set
on the basis of the outer scan's cost, plus one repetition of the
inner scan for each outer tuple (100 * 0.53, here), plus a little CPU
time for join processing.
</para>
<para>
In this example the loop's output row count is the same as the product
of the two scans' row counts, but that's not true in general, because
in general you can have WHERE clauses that mention both relations and
so can only be applied at the join point, not to either input scan.
For example, if we added "WHERE ... AND t1.hundred &lt; t2.hundred",
that'd decrease the output row count of the join node, but not change
either input scan.
</para>
<para>
One way to look at variant plans is to force the planner to disregard
whatever strategy it thought was the winner, using the enable/disable
flags for each plan type. (This is a crude tool, but useful. See
also <xref linkend="explicit-joins">.)
<programlisting>
regression=# set enable_nestloop = off;
SET VARIABLE
regression=# explain select * from tenk1 t1, tenk2 t2 where t1.unique1 < 100
regression-# and t1.unique2 = t2.unique2;
NOTICE: QUERY PLAN:
Hash Join (cost=89.60..574.10 rows=100 width=296)
-&gt; Seq Scan on tenk2 t2
(cost=0.00..333.00 rows=10000 width=148)
-&gt; Hash (cost=89.35..89.35 rows=100 width=148)
-&gt; Index Scan using tenk1_unique1 on tenk1 t1
(cost=0.00..89.35 rows=100 width=148)
</programlisting>
This plan proposes to extract the 100 interesting rows of tenk1
using ye same olde indexscan, stash them into an in-memory hash table,
and then do a sequential scan of tenk2, probing into the hash table
for possible matches of "t1.unique2 = t2.unique2" at each tenk2 tuple.
The cost to read tenk1 and set up the hash table is entirely start-up
cost for the hash join, since we won't get any tuples out until we can
start reading tenk2. The total time estimate for the join also
includes a pretty hefty charge for CPU time to probe the hash table
10000 times. Note, however, that we are NOT charging 10000 times 89.35;
the hash table setup is only done once in this plan type.
</para>
</sect1>
<sect1 id="explicit-joins">
<title>Controlling the Planner with Explicit JOINs</title>
<para>
Beginning with <productname>Postgres</productname> 7.1 it is possible
to control the query planner to some extent by using explicit JOIN
syntax. To see why this matters, we first need some background.
</para>
<para>
In a simple join query, such as
<programlisting>
SELECT * FROM a,b,c WHERE a.id = b.id AND b.ref = c.id;
</programlisting>
the planner is free to join the given tables in any order. For example,
it could generate a query plan that joins A to B, using the WHERE clause
a.id = b.id, and then joins C to this joined table, using the other
WHERE clause. Or it could join B to C and then join A to that result.
Or it could join A to C and then join them with B --- but that would
be inefficient, since the full Cartesian product of A and C would have
to be formed, there being no applicable WHERE clause to allow optimization
of the join.
(All joins in the <productname>Postgres</productname> executor happen
between two input tables, so it's necessary to build up the result in one
or another of these fashions.) The important point is that these different
join possibilities give semantically equivalent results but may have hugely
different execution costs. Therefore, the planner will explore all of them
to try to find the most efficient query plan.
</para>
<para>
When a query only involves two or three tables, there aren't many join
orders to worry about. But the number of possible join orders grows
exponentially as the number of tables expands. Beyond ten or so input
tables it's no longer practical to do an exhaustive search of all the
possibilities, and even for six or seven tables planning may take an
annoyingly long time. When there are too many input tables, the
<productname>Postgres</productname> planner will switch from exhaustive
search to a <firstterm>genetic</firstterm> probabilistic search
through a limited number of possibilities. (The switchover threshold is
set by the GEQO_THRESHOLD run-time
parameter described in the <citetitle>Administrator's Guide</citetitle>.)
The genetic search takes less time, but it won't
necessarily find the best possible plan.
</para>
<para>
When the query involves outer joins, the planner has much less freedom
than it does for plain (inner) joins. For example, consider
<programlisting>
SELECT * FROM a LEFT JOIN (b JOIN c ON (b.ref = c.id)) ON (a.id = b.id);
</programlisting>
Although this query's restrictions are superficially similar to the
previous example, the semantics are different because a row must be
emitted for each row of A that has no matching row in the join of B and C.
Therefore the planner has no choice of join order here: it must join
B to C and then join A to that result. Accordingly, this query takes
less time to plan than the previous query.
</para>
<para>
In <productname>Postgres</productname> 7.1, the planner treats all
explicit JOIN syntaxes as constraining the join order, even though
it is not logically necessary to make such a constraint for inner
joins. Therefore, although all of these queries give the same result:
<programlisting>
SELECT * FROM a,b,c WHERE a.id = b.id AND b.ref = c.id;
SELECT * FROM a CROSS JOIN b CROSS JOIN c WHERE a.id = b.id AND b.ref = c.id;
SELECT * FROM a JOIN (b JOIN c ON (b.ref = c.id)) ON (a.id = b.id);
</programlisting>
the second and third take less time to plan than the first. This effect
is not worth worrying about for only three tables, but it can be a
lifesaver with many tables.
</para>
<para>
You do not need to constrain the join order completely in order to
cut search time, because it's OK to use JOIN operators in a plain
FROM list. For example,
<programlisting>
SELECT * FROM a CROSS JOIN b, c, d, e WHERE ...;
</programlisting>
forces the planner to join A to B before joining them to other tables,
but doesn't constrain its choices otherwise. In this example, the
number of possible join orders is reduced by a factor of 5.
</para>
<para>
If you have a mix of outer and inner joins in a complex query, you
might not want to constrain the planner's search for a good ordering
of inner joins inside an outer join. You can't do that directly in the
JOIN syntax, but you can get around the syntactic limitation by using
views. For example,
<programlisting>
CREATE VIEW v1 AS SELECT ... FROM a, b, c WHERE ...;
SELECT * FROM d LEFT JOIN v1 ON (...);
</programlisting>
Here, joining D must be the last step in the query plan, but the
planner is free to consider various join orders for A,B,C.
</para>
<para>
Constraining the planner's search in this way is a useful technique
both for reducing planning time and for directing the planner to a
good query plan. If the planner chooses a bad join order by default,
you can force it to choose a better order via JOIN syntax --- assuming
that you know of a better order, that is. Experimentation is recommended.
</para>
</sect1>
<sect1 id="populate">
<title>Populating a Database</title>
<para>
One may need to do a large number of table insertions when first
populating a database. Here are some tips and techniques for making that as
efficient as possible.
</para>
<sect2 id="disable-autocommit">
<title>Disable Auto-commit</title>
<para>
Turn off auto-commit and just do one commit at
the end. Otherwise <productname>Postgres</productname> is doing a
lot of work for each record
added. In general when you are doing bulk inserts, you want
to turn off some of the database features to gain speed.
</para>
</sect2>
<sect2 id="populate-copy-from">
<title>Use COPY FROM</title>
<para>
Use <command>COPY FROM STDIN</command> to load all the records in one
command, instead
of a series of INSERT commands. This reduces parsing, planning, etc
overhead a great deal. If you do this then it's not necessary to fool
around with autocommit.
</para>
</sect2>
<sect2 id="populate-rm-indices">
<title>Remove Indices</title>
<para>
If you are loading a freshly created table, the fastest way is to
create the table, bulk-load with COPY, then create any indexes needed
for the table. Creating an index on pre-existing data is quicker than
updating it incrementally as each record is loaded.
</para>
<para>
If you are augmenting an existing table, you can <command>DROP
INDEX</command>, load the table, then recreate the index. Of
course, the database performance for other users may be adversely
affected during the time that the index is missing.
</para>
</sect2>
</sect1>
</chapter>
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<chapter id="understand-performance">
<title>Understanding Performance</title>
<para>
Query performance can be affected by many things. Some of these can
be manipulated by the user, while others are fundamental to the underlying
design of the system.
</para>
<para>
Some performance issues, such as index creation and bulk data
loading, are covered elsewhere. This chapter will discuss the
<command>EXPLAIN</command> command, and will show how the details
of a query can affect the query plan, and hence overall
performance.
</para>
<sect1 id="using-explain">
<title>Using <command>EXPLAIN</command></title>
<note>
<title>Author</title>
<para>
Written by Tom Lane, from e-mail dated 2000-03-27.
</para>
</note>
<para>
Plan-reading is an art that deserves a tutorial, and I haven't
had time to write one. Here is some quick &amp; dirty explanation.
</para>
<para>
The numbers that are currently quoted by EXPLAIN are:
<itemizedlist>
<listitem>
<para>
Estimated start-up cost (time expended before output scan can start,
eg, time to do the sorting in a SORT node).
</para>
</listitem>
<listitem>
<para>
Estimated total cost (if all tuples are retrieved, which they may not
be --- LIMIT will stop short of paying the total cost, for
example).
</para>
</listitem>
<listitem>
<para>
Estimated number of rows output by this plan node.
</para>
</listitem>
<listitem>
<para>
Estimated average width (in bytes) of rows output by this plan
node.
</para>
</listitem>
</itemizedlist>
</para>
<para>
The costs are measured in units of disk page fetches. (CPU effort
estimates are converted into disk-page units using some
fairly arbitrary fudge-factors. See the <command>SET</command>
reference page if you want to experiment with these.)
It's important to note that the cost of an upper-level node includes
the cost of all its child nodes. It's also important to realize that
the cost only reflects things that the planner/optimizer cares about.
In particular, the cost does not consider the time spent transmitting
result tuples to the frontend --- which could be a pretty dominant
factor in the true elapsed time, but the planner ignores it because
it cannot change it by altering the plan. (Every correct plan will
output the same tuple set, we trust.)
</para>
<para>
Rows output is a little tricky because it is <emphasis>not</emphasis> the number of rows
processed/scanned by the query --- it is usually less, reflecting the
estimated selectivity of any WHERE-clause constraints that are being
applied at this node.
</para>
<para>
Average width is pretty bogus because the thing really doesn't have
any idea of the average length of variable-length columns. I'm thinking
about improving that in the future, but it may not be worth the trouble,
because the width isn't used for very much.
</para>
<para>
Here are some examples (using the regress test database after a
vacuum analyze, and almost-7.0 sources):
<programlisting>
regression=# explain select * from tenk1;
NOTICE: QUERY PLAN:
Seq Scan on tenk1 (cost=0.00..333.00 rows=10000 width=148)
</programlisting>
</para>
<para>
This is about as straightforward as it gets. If you do
<programlisting>
select * from pg_class where relname = 'tenk1';
</programlisting>
you'll find out that tenk1 has 233 disk
pages and 10000 tuples. So the cost is estimated at 233 block
reads, defined as 1.0 apiece, plus 10000 * cpu_tuple_cost which is
currently 0.01 (try <command>show cpu_tuple_cost</command>).
</para>
<para>
Now let's modify the query to add a qualification clause:
<programlisting>
regression=# explain select * from tenk1 where unique1 &lt; 1000;
NOTICE: QUERY PLAN:
Seq Scan on tenk1 (cost=0.00..358.00 rows=1000 width=148)
</programlisting>
The estimate of output rows has gone down because of the WHERE clause.
(The uncannily accurate estimate is just because tenk1 is a particularly
simple case --- the unique1 column has 10000 distinct values ranging
from 0 to 9999, so the estimator's linear interpolation between min and
max column values is dead-on.) However, the scan will still have to
visit all 10000 rows, so the cost hasn't decreased; in fact it has gone
up a bit to reflect the extra CPU time spent checking the WHERE
condition.
</para>
<para>
Modify the query to restrict the qualification even more:
<programlisting>
regression=# explain select * from tenk1 where unique1 &lt; 100;
NOTICE: QUERY PLAN:
Index Scan using tenk1_unique1 on tenk1 (cost=0.00..89.35 rows=100 width=148)
</programlisting>
and you will see that if we make the WHERE condition selective
enough, the planner will
eventually decide that an indexscan is cheaper than a sequential scan.
This plan will only have to visit 100 tuples because of the index,
so it wins despite the fact that each individual fetch is expensive.
</para>
<para>
Add another condition to the qualification:
<programlisting>
regression=# explain select * from tenk1 where unique1 &lt; 100 and
regression-# stringu1 = 'xxx';
NOTICE: QUERY PLAN:
Index Scan using tenk1_unique1 on tenk1 (cost=0.00..89.60 rows=1 width=148)
</programlisting>
The added clause "stringu1 = 'xxx'" reduces the output-rows estimate,
but not the cost because we still have to visit the same set of tuples.
</para>
<para>
Let's try joining two tables, using the fields we have been discussing:
<programlisting>
regression=# explain select * from tenk1 t1, tenk2 t2 where t1.unique1 &lt; 100
regression-# and t1.unique2 = t2.unique2;
NOTICE: QUERY PLAN:
Nested Loop (cost=0.00..144.07 rows=100 width=296)
-&gt; Index Scan using tenk1_unique1 on tenk1 t1
(cost=0.00..89.35 rows=100 width=148)
-&gt; Index Scan using tenk2_unique2 on tenk2 t2
(cost=0.00..0.53 rows=1 width=148)
</programlisting>
</para>
<para>
In this nested-loop join, the outer scan is the same indexscan we had
in the example before last, and so its cost and row count are the same
because we are applying the "unique1 &lt; 100" WHERE clause at that node.
The "t1.unique2 = t2.unique2" clause isn't relevant yet, so it doesn't
affect the outer scan's row count. For the inner scan, the
current
outer-scan tuple's unique2 value is plugged into the inner indexscan
to produce an indexqual like
"t2.unique2 = <replaceable>constant</replaceable>". So we get the
same inner-scan plan and costs that we'd get from, say, "explain select
* from tenk2 where unique2 = 42". The loop node's costs are then set
on the basis of the outer scan's cost, plus one repetition of the
inner scan for each outer tuple (100 * 0.53, here), plus a little CPU
time for join processing.
</para>
<para>
In this example the loop's output row count is the same as the product
of the two scans' row counts, but that's not true in general, because
in general you can have WHERE clauses that mention both relations and
so can only be applied at the join point, not to either input scan.
For example, if we added "WHERE ... AND t1.hundred &lt; t2.hundred",
that'd decrease the output row count of the join node, but not change
either input scan.
</para>
<para>
We can look at variant plans by forcing the planner to disregard
whatever strategy it thought was the winner (a pretty crude tool,
but it's what we've got at the moment):
<programlisting>
regression=# set enable_nestloop = off;
SET VARIABLE
regression=# explain select * from tenk1 t1, tenk2 t2 where t1.unique1 < 100
regression-# and t1.unique2 = t2.unique2;
NOTICE: QUERY PLAN:
Hash Join (cost=89.60..574.10 rows=100 width=296)
-&gt; Seq Scan on tenk2 t2
(cost=0.00..333.00 rows=10000 width=148)
-&gt; Hash (cost=89.35..89.35 rows=100 width=148)
-&gt; Index Scan using tenk1_unique1 on tenk1 t1
(cost=0.00..89.35 rows=100 width=148)
</programlisting>
This plan proposes to extract the 100 interesting rows of tenk1
using ye same olde indexscan, stash them into an in-memory hash table,
and then do a sequential scan of tenk2, probing into the hash table
for possible matches of "t1.unique2 = t2.unique2" at each tenk2 tuple.
The cost to read tenk1 and set up the hash table is entirely start-up
cost for the hash join, since we won't get any tuples out until we can
start reading tenk2. The total time estimate for the join also
includes a pretty hefty charge for CPU time to probe the hash table
10000 times. Note, however, that we are NOT charging 10000 times 89.35;
the hash table setup is only done once in this plan type.
</para>
</sect1>
</chapter>
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@ -1,79 +0,0 @@
<!--
$Header: /cvsroot/pgsql/doc/src/sgml/Attic/populate.sgml,v 2.3 2000/09/29 20:21:34 petere Exp $
-->
<chapter id="populate">
<title>Populating a Database</title>
<note>
<title>Author</title>
<para>
Written by Tom Lane, from an e-mail message dated 1999-12-05.
</para>
</note>
<para>
One may need to do a large number of table insertions when first
populating a database. Here are some tips and techniques for making that as
efficient as possible.
</para>
<sect1 id="disable-autocommit">
<title>Disable Auto-commit</title>
<para>
Turn off auto-commit and just do one commit at
the end. Otherwise <productname>Postgres</productname> is doing a
lot of work for each record
added. In general when you are doing bulk inserts, you want
to turn off some of the database features to gain speed.
</para>
</sect1>
<sect1 id="populate-copy-from">
<title>Use COPY FROM</title>
<para>
Use <command>COPY FROM STDIN</command> to load all the records in one
command, instead
of a series of INSERT commands. This reduces parsing, planning, etc
overhead a great deal. If you do this then it's not necessary to fool
around with autocommit.
</para>
</sect1>
<sect1 id="populate-rm-indices">
<title>Remove Indices</title>
<para>
If you are loading a freshly created table, the fastest way is to
create the table, bulk-load with COPY, then create any indexes needed
for the table. Creating an index on pre-existing data is quicker than
updating it incrementally as each record is loaded.
</para>
<para>
If you are augmenting an existing table, you can <command>DROP
INDEX</command>, load the table, then recreate the index. Of
course, the database performance for other users may be adversely
affected during the time that the index is missing.
</para>
</sect1>
</chapter>
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<!--
$Header: /cvsroot/pgsql/doc/src/sgml/Attic/user.sgml,v 1.21 2000/12/14 22:30:56 petere Exp $
$Header: /cvsroot/pgsql/doc/src/sgml/Attic/user.sgml,v 1.22 2000/12/16 02:29:36 tgl Exp $
-->
<book id="user">
@ -57,8 +57,7 @@ $Header: /cvsroot/pgsql/doc/src/sgml/Attic/user.sgml,v 1.21 2000/12/14 22:30:56
&environ;
&manage;
&storage;
&plan;
&populate
&perform;
<!-- appendices -->