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提交 f1528b51 编写于 作者: B Bruce Momjian
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Properly indent SGML file.

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doc/src/sgml/high-availability.sgml
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<!-- $PostgreSQL: pgsql/doc/src/sgml/high-availability.sgml,v 1.18 2007/11/08 19:16:30 momjian Exp $ -->
<!-- $PostgreSQL: pgsql/doc/src/sgml/high-availability.sgml,v 1.19 2007/11/08 19:18:23 momjian Exp $ -->
<chapter id="high-availability">
<title>High Availability, Load Balancing, and Replication</title>
......@@ -79,45 +79,45 @@
<variablelist>
<varlistentry>
<term>Shared Disk Failover</term>
<listitem>
<para>
Shared disk failover avoids synchronization overhead by having only one
copy of the database. It uses a single disk array that is shared by
multiple servers. If the main database server fails, the standby server
is able to mount and start the database as though it was recovering from
a database crash. This allows rapid failover with no data loss.
</para>
<para>
Shared hardware functionality is common in network storage devices.
Using a network file system is also possible, though care must be
taken that the file system has full POSIX behavior (see <xref
linkend="creating-cluster-nfs">). One significant limitation of this
method is that if the shared disk array fails or becomes corrupt, the
primary and standby servers are both nonfunctional. Another issue is
that the standby server should never access the shared storage while
the primary server is running.
</para>
</listitem>
</varlistentry>
<varlistentry>
<term>File System Replication</term>
<listitem>
<para>
A modified version of shared hardware functionality is file system
replication, where all changes to a file system are mirrored to a file
system residing on another computer. The only restriction is that
the mirroring must be done in a way that ensures the standby server
has a consistent copy of the file system &mdash; specifically, writes
to the standby must be done in the same order as those on the master.
DRBD is a popular file system replication solution for Linux.
</para>
<varlistentry>
<term>Shared Disk Failover</term>
<listitem>
<para>
Shared disk failover avoids synchronization overhead by having only one
copy of the database. It uses a single disk array that is shared by
multiple servers. If the main database server fails, the standby server
is able to mount and start the database as though it was recovering from
a database crash. This allows rapid failover with no data loss.
</para>
<para>
Shared hardware functionality is common in network storage devices.
Using a network file system is also possible, though care must be
taken that the file system has full POSIX behavior (see <xref
linkend="creating-cluster-nfs">). One significant limitation of this
method is that if the shared disk array fails or becomes corrupt, the
primary and standby servers are both nonfunctional. Another issue is
that the standby server should never access the shared storage while
the primary server is running.
</para>
</listitem>
</varlistentry>
<varlistentry>
<term>File System Replication</term>
<listitem>
<para>
A modified version of shared hardware functionality is file system
replication, where all changes to a file system are mirrored to a file
system residing on another computer. The only restriction is that
the mirroring must be done in a way that ensures the standby server
has a consistent copy of the file system &mdash; specifically, writes
to the standby must be done in the same order as those on the master.
DRBD is a popular file system replication solution for Linux.
</para>
<!--
https://forge.continuent.org/pipermail/sequoia/2006-November/004070.html
......@@ -128,150 +128,150 @@ only committed once to disk and there is a distributed locking
protocol to make nodes agree on a serializable transactional order.
-->
</listitem>
</varlistentry>
<varlistentry>
<term>Warm Standby Using Point-In-Time Recovery (<acronym>PITR</>)</term>
<listitem>
<para>
A warm standby server (see <xref linkend="warm-standby">) can
be kept current by reading a stream of write-ahead log (WAL)
records. If the main server fails, the warm standby contains
almost all of the data of the main server, and can be quickly
made the new master database server. This is asynchronous and
can only be done for the entire database server.
</para>
</listitem>
</varlistentry>
<varlistentry>
<term>Master-Slave Replication</term>
<listitem>
<para>
A master-slave replication setup sends all data modification
queries to the master server. The master server asynchronously
sends data changes to the slave server. The slave can answer
read-only queries while the master server is running. The
slave server is ideal for data warehouse queries.
</para>
<para>
Slony-I is an example of this type of replication, with per-table
granularity, and support for multiple slaves. Because it
updates the slave server asynchronously (in batches), there is
possible data loss during fail over.
</para>
</listitem>
</varlistentry>
<varlistentry>
<term>Statement-Based Replication Middleware</term>
<listitem>
<para>
With statement-based replication middleware, a program intercepts
every SQL query and sends it to one or all servers. Each server
operates independently. Read-write queries are sent to all servers,
while read-only queries can be sent to just one server, allowing
the read workload to be distributed.
</para>
<para>
If queries are simply broadcast unmodified, functions like
<function>random()</>, <function>CURRENT_TIMESTAMP</>, and
sequences would have different values on different servers.
This is because each server operates independently, and because
SQL queries are broadcast (and not actual modified rows). If
this is unacceptable, either the middleware or the application
must query such values from a single server and then use those
values in write queries. Also, care must be taken that all
transactions either commit or abort on all servers, perhaps
using two-phase commit (<xref linkend="sql-prepare-transaction"
endterm="sql-prepare-transaction-title"> and <xref
linkend="sql-commit-prepared" endterm="sql-commit-prepared-title">.
Pgpool and Sequoia are an example of this type of replication.
</para>
</listitem>
</varlistentry>
<varlistentry>
<term>Asynchronous Multi-Master Replication</term>
<listitem>
<para>
For servers that are not regularly connected, like laptops or
remote servers, keeping data consistent among servers is a
challenge. Using asynchronous multi-master replication, each
server works independently, and periodically communicates with
the other servers to identify conflicting transactions. The
conflicts can be resolved by users or conflict resolution rules.
</para>
</listitem>
</varlistentry>
<varlistentry>
<term>Synchronous Multi-Master Replication</term>
<listitem>
<para>
In synchronous multi-master replication, each server can accept
write requests, and modified data is transmitted from the
original server to every other server before each transaction
commits. Heavy write activity can cause excessive locking,
leading to poor performance. In fact, write performance is
often worse than that of a single server. Read requests can
be sent to any server. Some implementations use shared disk
to reduce the communication overhead. Synchronous multi-master
replication is best for mostly read workloads, though its big
advantage is that any server can accept write requests &mdash;
there is no need to partition workloads between master and
slave servers, and because the data changes are sent from one
server to another, there is no problem with non-deterministic
functions like <function>random()</>.
</para>
<para>
<productname>PostgreSQL</> does not offer this type of replication,
though <productname>PostgreSQL</> two-phase commit (<xref
linkend="sql-prepare-transaction"
endterm="sql-prepare-transaction-title"> and <xref
linkend="sql-commit-prepared" endterm="sql-commit-prepared-title">)
can be used to implement this in application code or middleware.
</para>
</listitem>
</varlistentry>
<varlistentry>
<term>Data Partitioning</term>
<listitem>
<para>
Data partitioning splits tables into data sets. Each set can
be modified by only one server. For example, data can be
partitioned by offices, e.g. London and Paris, with a server
in each office. If queries combining London and Paris data
are necessary, an application can query both servers, or
master/slave replication can be used to keep a read-only copy
of the other office's data on each server.
</para>
</listitem>
</varlistentry>
<varlistentry>
<term>Commercial Solutions</term>
<listitem>
<para>
Because <productname>PostgreSQL</> is open source and easily
extended, a number of companies have taken <productname>PostgreSQL</>
and created commercial closed-source solutions with unique
failover, replication, and load balancing capabilities.
</para>
</listitem>
</varlistentry>
</listitem>
</varlistentry>
<varlistentry>
<term>Warm Standby Using Point-In-Time Recovery (<acronym>PITR</>)</term>
<listitem>
<para>
A warm standby server (see <xref linkend="warm-standby">) can
be kept current by reading a stream of write-ahead log (WAL)
records. If the main server fails, the warm standby contains
almost all of the data of the main server, and can be quickly
made the new master database server. This is asynchronous and
can only be done for the entire database server.
</para>
</listitem>
</varlistentry>
<varlistentry>
<term>Master-Slave Replication</term>
<listitem>
<para>
A master-slave replication setup sends all data modification
queries to the master server. The master server asynchronously
sends data changes to the slave server. The slave can answer
read-only queries while the master server is running. The
slave server is ideal for data warehouse queries.
</para>
<para>
Slony-I is an example of this type of replication, with per-table
granularity, and support for multiple slaves. Because it
updates the slave server asynchronously (in batches), there is
possible data loss during fail over.
</para>
</listitem>
</varlistentry>
<varlistentry>
<term>Statement-Based Replication Middleware</term>
<listitem>
<para>
With statement-based replication middleware, a program intercepts
every SQL query and sends it to one or all servers. Each server
operates independently. Read-write queries are sent to all servers,
while read-only queries can be sent to just one server, allowing
the read workload to be distributed.
</para>
<para>
If queries are simply broadcast unmodified, functions like
<function>random()</>, <function>CURRENT_TIMESTAMP</>, and
sequences would have different values on different servers.
This is because each server operates independently, and because
SQL queries are broadcast (and not actual modified rows). If
this is unacceptable, either the middleware or the application
must query such values from a single server and then use those
values in write queries. Also, care must be taken that all
transactions either commit or abort on all servers, perhaps
using two-phase commit (<xref linkend="sql-prepare-transaction"
endterm="sql-prepare-transaction-title"> and <xref
linkend="sql-commit-prepared" endterm="sql-commit-prepared-title">.
Pgpool and Sequoia are an example of this type of replication.
</para>
</listitem>
</varlistentry>
<varlistentry>
<term>Asynchronous Multi-Master Replication</term>
<listitem>
<para>
For servers that are not regularly connected, like laptops or
remote servers, keeping data consistent among servers is a
challenge. Using asynchronous multi-master replication, each
server works independently, and periodically communicates with
the other servers to identify conflicting transactions. The
conflicts can be resolved by users or conflict resolution rules.
</para>
</listitem>
</varlistentry>
<varlistentry>
<term>Synchronous Multi-Master Replication</term>
<listitem>
<para>
In synchronous multi-master replication, each server can accept
write requests, and modified data is transmitted from the
original server to every other server before each transaction
commits. Heavy write activity can cause excessive locking,
leading to poor performance. In fact, write performance is
often worse than that of a single server. Read requests can
be sent to any server. Some implementations use shared disk
to reduce the communication overhead. Synchronous multi-master
replication is best for mostly read workloads, though its big
advantage is that any server can accept write requests &mdash;
there is no need to partition workloads between master and
slave servers, and because the data changes are sent from one
server to another, there is no problem with non-deterministic
functions like <function>random()</>.
</para>
<para>
<productname>PostgreSQL</> does not offer this type of replication,
though <productname>PostgreSQL</> two-phase commit (<xref
linkend="sql-prepare-transaction"
endterm="sql-prepare-transaction-title"> and <xref
linkend="sql-commit-prepared" endterm="sql-commit-prepared-title">)
can be used to implement this in application code or middleware.
</para>
</listitem>
</varlistentry>
<varlistentry>
<term>Data Partitioning</term>
<listitem>
<para>
Data partitioning splits tables into data sets. Each set can
be modified by only one server. For example, data can be
partitioned by offices, e.g. London and Paris, with a server
in each office. If queries combining London and Paris data
are necessary, an application can query both servers, or
master/slave replication can be used to keep a read-only copy
of the other office's data on each server.
</para>
</listitem>
</varlistentry>
<varlistentry>
<term>Commercial Solutions</term>
<listitem>
<para>
Because <productname>PostgreSQL</> is open source and easily
extended, a number of companies have taken <productname>PostgreSQL</>
and created commercial closed-source solutions with unique
failover, replication, and load balancing capabilities.
</para>
</listitem>
</varlistentry>
</variablelist>
......
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