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Add "High Availability, Load Balancing, and Replication Feature Matrix"
table to docs.
This commit is contained in:
Bruce Momjian
parent
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commit
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@ -1,4 +1,4 @@
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<!-- $PostgreSQL: pgsql/doc/src/sgml/high-availability.sgml,v 1.17 2007/11/04 19:23:24 momjian Exp $ -->
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<!-- $PostgreSQL: pgsql/doc/src/sgml/high-availability.sgml,v 1.18 2007/11/08 19:16:30 momjian Exp $ -->
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<chapter id="high-availability">
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<title>High Availability, Load Balancing, and Replication</title>
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@ -92,16 +92,23 @@
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</para>
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<para>
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Shared hardware functionality is common in network storage
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devices. Using a network file system is also possible, though
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care must be taken that the file system has full POSIX behavior.
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One significant limitation of this method is that if the shared
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disk array fails or becomes corrupt, the primary and standby
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servers are both nonfunctional. Another issue is that the
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standby server should never access the shared storage while
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Shared hardware functionality is common in network storage devices.
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Using a network file system is also possible, though care must be
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taken that the file system has full POSIX behavior (see <xref
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linkend="creating-cluster-nfs">). One significant limitation of this
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method is that if the shared disk array fails or becomes corrupt, the
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primary and standby servers are both nonfunctional. Another issue is
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that the standby server should never access the shared storage while
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the primary server is running.
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</para>
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</listitem>
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</varlistentry>
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<varlistentry>
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<term>File System Replication</term>
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<listitem>
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<para>
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A modified version of shared hardware functionality is file system
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replication, where all changes to a file system are mirrored to a file
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@ -125,7 +132,7 @@ protocol to make nodes agree on a serializable transactional order.
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</varlistentry>
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<varlistentry>
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<term>Warm Standby Using Point-In-Time Recovery</term>
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<term>Warm Standby Using Point-In-Time Recovery (<acronym>PITR</>)</term>
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<listitem>
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<para>
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@ -190,6 +197,21 @@ protocol to make nodes agree on a serializable transactional order.
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</listitem>
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</varlistentry>
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<varlistentry>
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<term>Asynchronous Multi-Master Replication</term>
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<listitem>
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<para>
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For servers that are not regularly connected, like laptops or
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remote servers, keeping data consistent among servers is a
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challenge. Using asynchronous multi-master replication, each
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server works independently, and periodically communicates with
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the other servers to identify conflicting transactions. The
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conflicts can be resolved by users or conflict resolution rules.
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</para>
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</listitem>
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</varlistentry>
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<varlistentry>
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<term>Synchronous Multi-Master Replication</term>
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<listitem>
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@ -222,21 +244,6 @@ protocol to make nodes agree on a serializable transactional order.
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</listitem>
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</varlistentry>
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<varlistentry>
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<term>Asynchronous Multi-Master Replication</term>
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<listitem>
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<para>
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For servers that are not regularly connected, like laptops or
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remote servers, keeping data consistent among servers is a
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challenge. Using asynchronous multi-master replication, each
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server works independently, and periodically communicates with
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the other servers to identify conflicting transactions. The
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conflicts can be resolved by users or conflict resolution rules.
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</para>
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</listitem>
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</varlistentry>
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<varlistentry>
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<term>Data Partitioning</term>
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<listitem>
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@ -253,23 +260,6 @@ protocol to make nodes agree on a serializable transactional order.
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</listitem>
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</varlistentry>
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<varlistentry>
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<term>Multi-Server Parallel Query Execution</term>
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<listitem>
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<para>
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Many of the above solutions allow multiple servers to handle
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multiple queries, but none allow a single query to use multiple
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servers to complete faster. This solution allows multiple
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servers to work concurrently on a single query. This is usually
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accomplished by splitting the data among servers and having
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each server execute its part of the query and return results
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to a central server where they are combined and returned to
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the user. Pgpool-II has this capability.
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</para>
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</listitem>
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</varlistentry>
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<varlistentry>
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<term>Commercial Solutions</term>
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<listitem>
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@ -285,4 +275,139 @@ protocol to make nodes agree on a serializable transactional order.
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</variablelist>
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<para>
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The table below (<xref linkend="high-availability-matrix">) summarizes
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the capabilities of the various solutions listed above.
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</para>
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<table id="high-availability-matrix">
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<title>High Availability, Load Balancing, and Replication Feature Matrix</title>
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<tgroup cols="9">
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<thead>
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<row>
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<entry>Feature</entry>
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<entry>Shared Disk Failover</entry>
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<entry>File System Replication</entry>
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<entry>Warm Standby Using PITR</entry>
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<entry>Master-Slave Replication</entry>
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<entry>Statement-Based Replication Middleware</entry>
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<entry>Asynchronous Multi-Master Replication</entry>
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<entry>Synchronous Multi-Master Replication</entry>
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<entry>Data Partitioning</entry>
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</row>
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</thead>
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<tbody>
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<row>
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<entry>No special hardware required</entry>
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<entry align="center"></entry>
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<entry align="center">•</entry>
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<entry align="center">•</entry>
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<entry align="center">•</entry>
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<entry align="center">•</entry>
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<entry align="center">•</entry>
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<entry align="center">•</entry>
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<entry align="center">•</entry>
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</row>
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<row>
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<entry>Allows multiple master servers</entry>
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<entry align="center"></entry>
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<entry align="center"></entry>
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<entry align="center"></entry>
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<entry align="center"></entry>
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<entry align="center">•</entry>
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<entry align="center">•</entry>
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<entry align="center">•</entry>
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<entry align="center"></entry>
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</row>
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<row>
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<entry>No master server overhead</entry>
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<entry align="center">•</entry>
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<entry align="center"></entry>
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<entry align="center">•</entry>
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<entry align="center"></entry>
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<entry align="center"></entry>
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<entry align="center"></entry>
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<entry align="center"></entry>
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<entry align="center"></entry>
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</row>
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<row>
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<entry>Master server never locks others</entry>
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<entry align="center">•</entry>
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<entry align="center">•</entry>
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<entry align="center">•</entry>
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<entry align="center">•</entry>
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<entry align="center">•</entry>
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<entry align="center">•</entry>
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<entry align="center"></entry>
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<entry align="center">•</entry>
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</row>
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<row>
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<entry>Master failure will never lose data</entry>
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<entry align="center">•</entry>
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<entry align="center">•</entry>
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<entry align="center"></entry>
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<entry align="center"></entry>
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<entry align="center">•</entry>
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<entry align="center"></entry>
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<entry align="center">•</entry>
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<entry align="center"></entry>
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</row>
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<row>
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<entry>Slaves accept read-only queries</entry>
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<entry align="center"></entry>
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<entry align="center"></entry>
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<entry align="center"></entry>
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<entry align="center">•</entry>
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<entry align="center">•</entry>
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<entry align="center">•</entry>
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<entry align="center">•</entry>
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<entry align="center">•</entry>
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</row>
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<row>
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<entry>Per-table granularity</entry>
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<entry align="center"></entry>
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<entry align="center"></entry>
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<entry align="center"></entry>
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<entry align="center">•</entry>
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<entry align="center"></entry>
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<entry align="center">•</entry>
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<entry align="center">•</entry>
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<entry align="center">•</entry>
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</row>
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<row>
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<entry>No conflict resolution necessary</entry>
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<entry align="center">•</entry>
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<entry align="center">•</entry>
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<entry align="center">•</entry>
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<entry align="center">•</entry>
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<entry align="center"></entry>
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<entry align="center"></entry>
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<entry align="center">•</entry>
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<entry align="center">•</entry>
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</row>
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</tbody>
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</tgroup>
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</table>
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<para>
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Many of the above solutions allow multiple servers to handle multiple
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queries, but none allow a single query to use multiple servers to
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complete faster. Multi-server parallel query execution allows multiple
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servers to work concurrently on a single query. This is usually
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accomplished by splitting the data among servers and having each server
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execute its part of the query and return results to a central server
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where they are combined and returned to the user. Pgpool-II has this
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capability.
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</para>
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</chapter>
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