Replication Documentation

Here's a patch to add in the material on replication recently
discussed on pgsql.docs. I'm not thrilled that there were only a few
comments made; I'd be happy to see "slicing and dicing" to see this
made more useful.

Index: filelist.sgml
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RCS file: /projects/cvsroot/pgsql/doc/src/sgml/filelist.sgml,v
retrieving revision 1.44
diff -c -u -r1.44 filelist.sgml
--- filelist.sgml 12 Sep 2005 22:11:38 -0000 1.44
+++ filelist.sgml 1 Aug 2006 20:00:00 -0000
@@ -44,6 +44,7 @@
<!entity config SYSTEM "config.sgml">
<!entity user-manag SYSTEM "user-manag.sgml">
<!entity wal SYSTEM "wal.sgml">
+<!entity replication SYSTEM "replication.sgml">

<!-- programmer's guide -->
<!entity dfunc SYSTEM "dfunc.sgml">
Index: postgres.sgml
===================================================================
RCS file: /projects/cvsroot/pgsql/doc/src/sgml/postgres.sgml,v
retrieving revision 1.77
diff -c -u -r1.77 postgres.sgml
--- postgres.sgml 10 Mar 2006 19:10:48 -0000 1.77
+++ postgres.sgml 1 Aug 2006 20:00:00 -0000
@@ -155,6 +155,7 @@
&diskusage;
&wal;
&regress;
+ &replication;

</part>

---- Then add the following as .../doc/src/sgml/replication.sgml

<!-- $PostgreSQL$ -->

<chapter id="replication"> <title> Replication </title>

<indexterm><primary>replication</primary></indexterm>

<para> People frequently ask about what replication options are
available for <productname>PostgreSQL</productname>. Unfortunately,
there are so many approaches and models to this that are useful for
different purposes that things tend to get confusing.
</para>

<para> At perhaps the most primitive level, one might use <xref
linkend="backup"> tools, whether <xref linkend="app-pgdump"> or
<xref linkend="continuous-archiving"> to create additional copies of
databases. This <emphasis>doesn't</emphasis> provide any way to
keep the replicas up to date; to bring the state of things to a
different point in time requires bringing up another copy. There is
no way, with these tools, for updates on a <quote>master</quote>
system to automatically propagate to the replicas.</para>

<sect1> <title> Categorization of Replication Systems </title>

<para> Looking at replication systems, there are a number of ways in
which they may be viewed:

<itemizedlist>

<listitem><para> Single master versus multimaster.</para>

<para> That is, whether there is a single database considered
<quote>master</quote>, where all update operations are required
to be submitted, or the alternative, multimaster, where updates
may be submitted to any of several databases.</para>

<para> Multimaster replication is vastly more complex and
expensive, because of the need to deal with the possibility of
conflicting updates. The simplest example of this is where a
replicated database manages inventory; the question is, what
happens when requests go to different database nodes requesting
a particular piece of inventory?</para>

<para> Synchronous multimaster replication introduces the need
to distribute locks across the systems, which, in research work
done with Postgres-R and Slony-II, has proven to be very
expensive. </para></listitem>

<listitem><para> Synchronous versus asynchronous</para>

<para>Synchronous systems are ones where updates must be
accepted on all the databases before they are permitted to
<command>COMMIT</command>. </para>

<para> Asynchronous systems propagate updates to the other
databases later. This permits the possibility that one database
may have data significantly behind others. Whether or not being
behind is acceptable or not will depend on the nature of the
application.</para>

<para> Asynchronous multimaster replication introduces the
possibility that conflicting updates will be accepted by
multiple nodes, as they don't know, at <command>COMMIT</command>
time, that the updates conflict. It is then necessary to have
some sort of conflict resolution system, which can't really be
generalized as a generic database facility. An instance of this
that is commonly seen is in the <productname>PalmOS
HotSync</productname> system; the <quote>general policy</quote>
when conflicts are noticed is to allow both conflicting records
to persist until a human can intervene. That may be quite
acceptable for an address book; it's <emphasis>not</emphasis>
fine for OLTP systems. </para>

</listitem>

<listitem><para> Update capture methods </para>

<para> Common methods include having triggers on tables,
capturing SQL statements, and capturing transaction log (WAL)
updates </para>

<itemizedlist>

<listitem><para> Triggers, as used in eRServer and Slony-I,
have the advantage of capturing updates at the end of
processing when all column values have been finalized. The use
of transaction visibility (MVCC) and ordering can provide
strong guarantees on consistency. </para>

<para> Of course, firing a trigger for each tuple update comes
at a not inconsiderable cost: a statement that touches 10,000
tuples will fire the trigger 10,000 times, and transform, on
the subscriber, into 10,000 SQL statements.</para></listitem>

<listitem><para> Statement capture almost exactly reverses the
issues, as compared to triggers.</para>

<para> There are no strong guarantees on consistency: any sort
of nondeterministic query can <quote>corrupt</quote> things by
introducing differences between nodes. Here are four examples
of cases where naive statement capture is sure to get things
wrong:</para>

<itemizedlist>
<listitem><para><command>INSERT INTO mytable (txntime,
product, quantity, taxes, total) values (now(), 'AB-275', 10,
45, 250.00);</command></para> <para> Some replication systems
parse the queries, replacing date requests with
timestamps. </para>
</listitem>
<listitem><para><command>INSERT INTO table2 (random() *
50);</command></para> <para> In this case, nondeterminism is
fairly much the point!</para>
</listitem>
<listitem><para>Any use of sequnce values as defaults,
particularly with per-connection value cacheing, will open up
occasions for values to diverge between
nodes.</para></listitem>

<listitem><para><command>INSERT INTO tab1 (txn_type, tdate,
quantity, units, price) SELECT * FROM tab2 ORDER BY txn_type
limit 50;</command></para>

<para> There are many variations on this which will turn out
badly: </para>
<itemizedlist>
<listitem><para>If there are default fields in tab1
that are set using sequences, the only way to even
hope for the same ordering is to have
an <command>ORDER BY</command> clause that ensures
identical ordering on both hosts.</para></listitem>
<listitem><para> If the ordering isn't a suitable
total ordering, the requests for data from tab2 may
find different data on different
hosts.</para></listitem>
<listitem><para>Columns with a default
of <function>now()</function> will be troublesome as
mentioned earlier, and this makes the problem harder
because unlike in the earlier query, where one might
substitute '2006-09-02 04:42:23-00'
for <function>now()</function>, this requires a
substantial rewriting of the query.</para></listitem>
</itemizedlist>
</listitem>
</itemizedlist>

</listitem>
</itemizedlist>

</listitem>

</itemizedlist>

</para>

</sect1>

<sect1 id="replicationsystems"> <title> PostgreSQL Replication Systems and Their Uses </title>

<para> Based on the preceding taxonomy, we may categorize various
replication systems, which should be helpful in determining what
they may be best used for, and whether they are compatible with
your <quote>use case.</quote></para>

<sect2><title> Slony-I</title>

<para> Slony-I is a single-master to multiple subscriber
asynchronous replication system that captures updates using
triggers. </para>

<para> For many systems, it is not clear how to initialize
replication on a new node some time after a system has been set up
in production. Slony-I was specifically designed to provide the
ability to introduce new nodes without the need to interrupt
activity on the master node. </para>

<para> It has, a particular merit, that, by only using components
internal to PostgreSQL, it is compatible with multiple versions of
PostgreSQL. This lends it especially to assisting at upgrading
systems from one version of PostgreSQL to another without
requiring a long outage. </para>

<para> It suffers from three particular problems:</para>

<itemizedlist>
<listitem><para> Despite improvements from earlier versions, it
is fairly complex to configure and administer.</para></listitem>
<listitem><para> It can only replicate changes that can be
captured using triggers. </para>

<para> There is a handling for sequences, which comes via
polling, but Slony-I <emphasis>does not</emphasis> provide an
automatic way to replicate other sorts of objects. </listitem>

<listitem><para> The handling of DDL changes is somewhat fragile,
and exists as something of a bag on the side. </para>

<para> There has been loose discussion as to how to address
that; useful comprehensive answers have not emerged.
</listitem>
</itemizedlist>

<sect3> <title> Use Cases </title>

<para> Slony-I has proven useful for the following sorts of usages: </para>
<itemizedlist>

<listitem><para> Upgrading from one PostgreSQL release to
another with only brief downtime. </para></listitem>

<listitem><para> Providing extra database copies that are nearly
up to date that may be used to offload read activity from the
<quote>master</quote> database system. </para></listitem>

<listitem><para> Providing extra database copies that are nearly
up to date that may be used as failover targets. </para>
</listitem>

</itemizedlist>

</sect2>

<sect2><title> pgpool </title>

<para> <application>pgpool</application> was initially created by
Tatsuo Isshii as a portable alternative to Java connection pool
modules. He subsequently observed that it wouldn't take very much
effort to extend it to create a simple replication system: if it
is forwarding SQL queries to a PostgreSQL instance, extending that
to two databases is very straightforward. </para>

<para> It suffers, by nature, from the problems associated with
replicating using capture of SQL statements; any sort of
nondeterminism in the replicated statements will cause the
databases to diverge. </para>

<para> On the other hand, it is very easy to install and
configure; for users with simple requirements, that can
suffice. </para>

<para> A <application>pgpool-2</application> is under way which
introduces a more sophisticated query parser to try to address the
nondeterminism issues; that may limit ongoing support for the
legacy version.</para>

<sect3> <title> Use Cases </title>

<para> pgpool has proven useful for the following sorts of usages: </para>
<itemizedlist>

<listitem><para> Dividing read-only database activity between
two database instances. </para></listitem>

<listitem><para> Providing a simple replication system for
systems that do not make use of nondeterministic update
queries. </para></listitem>

</itemizedlist>

</sect3>

</sect2>

<sect2> <title> PITR - Point In Time Recovery </title>

<para> If you have a database cluster that supports a large number
of database instances (<emphasis>e.g.</emphasis> - varying values
for PGDATABASE), connection-managing systems like pgpool and
systems like Slony-I which require a manager process for each
database for each node that is replicated will turn out quite
badly.</para>

<para> For instance, if you have a database cluster that hosts 300
databases, as would be the case in a "web hosting" situation, for
Slony-I to replicate all of this data, it would have to have 300
slon processes for each node. </para>

<para> PITR is likely to be more suitable in this case; that
doesn't provide you with a usable replica running, but it can
recover <emphasis>all</emphasis> of the tables in
<emphasis>all</emphasis> of the databases on the backend.</para>

</sect2>

<sect2> <title> Postgres-R </title>

<para> This has been a research project at McGill University,
building a multimaster synchronous replication system which uses a
group communications system (<emphasis>e.g.</emphasis> - <ulink
url="http://www.spread.org/"> Spread</ulink>) to control
propagation of update requests, which it captures via adding
<quote>hooks</quote> to the database engine to detect
changes. </para>

<para> Being a research project, the key has been to learn about
replication as opposed to provide a <quote> production grade
</quote> replication system. For a considerable period of time it
was only at all usable on rather old releases of PostgreSQL; it is
now available for recent releases. </para>

<para> The handling of DDL changes has long been somewhat
controversial; several attempts to implement DDL handlers have
been made, none of which has yet <quote>stuck.</quote> </para>

<para> The Slony-II project inherited directly from Postgres-R,
with an intent to create a multimaster synchronous replication
system atop a group communications system, but then to proceed to
something more of <quote>production grade</quote>. </para>

<para> The notable distinction from Postgres-R was that, in order
to find conflicts earlier, and to diminish the amount of work
needing to be done at the synchronization point, Slony-II would
try to publish and promote lock requests as soon as possible. (It
is possible for this to worsen behaviour in some cases.)</para>

<para> Unfortunately several problems emerged: </para>

<itemizedlist>

<listitem><para> The available open source group communications
systems turn out to neither be fast enough nor reliable enough
for the purpose. </para></listitem>

<listitem><para> One of the goals was for there to be as little
need as possible to modify applications to deal with
replication. </para>

<para> Unfortunately, there turn out to be some cases where
competing updates (e.g. - for updates to account balances) would
cause multimaster replication to reject transactions due to
concurrency problems with high frequency. </para>
</listitem>

</itemizedlist>

<para> As a result of those problems, Slony-II efforts have fallen
off somewhat. </para>

<para> The remaining developers plan to join together efforts for
these two projects. There are working prototypes, but it is not
clear when <quote>production grade</quote> versions will
emerge. </para>

</sect2>

</sect1>

</chapter>

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sgml-local-ecat-files:nil
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