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<chapter id="high-availability">
 <title>High Availability and Load Balancing</title>

 <indexterm><primary>high availability</></>
 <indexterm><primary>failover</></>
 <indexterm><primary>replication</></>
 <indexterm><primary>load balancing</></>
 <indexterm><primary>clustering</></>
 <indexterm><primary>data partitioning</></>

 <para>
  Database servers can work together to allow a second server to
  quickly take over quickly if the primary server fails (high
  availability), or to allow several computers to serve the same
  data (load balancing).  Ideally, database servers could work
  together seamlessly.  Web servers serving static web pages can
  be combined quite easily by merely load-balancing web requests
  to multiple machines.  In fact, read-only database servers can
  be combined relatively easily too.  Unfortunately, most database
  servers have a read/write mix of requests, and read/write servers
  are much harder to combine.  This is because though read-only
  data needs to be placed on each server only once, a write to any
  server has to be propagated to all servers so that future read
  requests to those servers return consistent results.
 </para>

 <para>
  This synchronization problem is the fundamental difficulty for
  servers working together.  Because there is no single solution
  that eliminates the impact of the sync problem for all use cases,
  there are multiple solutions.  Each solution addresses this
  problem in a different way, and minimizes its impact for a specific
  workload.
 </para>

 <para>
  Some solutions deal with synchronization by allowing only one
  server to modify the data.  Servers that can modify data are
  called read/write or "master" servers.  Servers that can reply
  to read-only queries are called "slave" servers.  Servers that
  cannot be accessed until they are changed to master servers are
  called "standby" servers.
 </para>

 <para>
  Some failover and load balancing solutions are synchronous, meaning that
  a data-modifying transaction is not considered committed until all
  servers have committed the transaction.  This guarantees that a failover
  will not lose any data and that all load-balanced servers will return
  consistent results with no propagation delay. Asynchronous updating has
  a small delay between the time of commit and its propagation to the
  other servers, opening the possibility that some transactions might be
  lost in the switch to a backup server, and that load balanced servers
  might return slightly stale results.  Asynchronous communication is used
  when synchronous would be too slow.
 </para>

 <para>
  Solutions can also be categorized by their granularity.  Some solutions
  can deal only with an entire database server, while others allow control
  at the per-table or per-database level.
 </para>

 <para>
  Performance must be considered in any failover or load balancing
  choice.  There is usually a tradeoff between functionality and
  performance.  For example, a full synchronous solution over a slow
  network might cut performance by more than half, while an asynchronous
  one might have a minimal performance impact.
 </para>

 <para>
  The remainder of this section outlines various failover, replication,
  and load balancing solutions.
 </para>

 <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.
    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>Warm Standby Using Point-In-Time Recovery</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>Query Broadcasting</term>
  <listitem>

   <para>
    In query broadcasting, a program intercepts every SQL query
    and sends it to all servers.  Each server operates independently.
    Read-only queries can be sent to a single server because there
    is no need for all servers to process it.
   </para>

   <para>
    One limitation of this solution is that functions like
    <function>random()</>, <function>CURRENT_TIMESTAMP</>, and
    sequences can 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, applications 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 is an example of this type of replication.
   </para>
  </listitem>
 </varlistentry>

 <varlistentry>
  <term>Multi-Master Replication Using Clustering</term>
  <listitem>

   <para>
    In clustering, 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.  Clustering
    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 changes are sent from one server to another,
    there is not a problem with non-deterministic functions like
    <function>random()</>.
   </para>

   <para>
    Clustering is implemented by <productname>Oracle</> in their
    <productname><acronym>RAC</></> product.  <productname>PostgreSQL</>
    does not offer this type of load balancing, 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>Clustering For Parallel Query Execution</term>
  <listitem>

   <para>
    This allows multiple servers to work concurrently on a single
    query.  One possible way this could work is for the data to be
    split among servers and for each server to execute its part of
    the query and results sent to a central server to be combined
    and returned to the user.  There currently is no
    <productname>PostgreSQL</> open source solution for this.
   </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>

</chapter>
