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<chapter id="reliability">
 <title>Reliability</title>

  <para>
   Reliability is a major feature of any serious database system, and
   <productname>PostgreSQL</> does everything possible to guarantee
   reliable operation. One aspect of reliable operation is that all data
   recorded by a transaction should be stored in a non-volatile area
   that is safe from power loss, operating system failure, and hardware
   failure (unrelated to the non-volatile area itself). To accomplish
   this, <productname>PostgreSQL</> uses the magnetic platters of modern
   disk drives for permanent storage that is immune to the failures
   listed above. In fact, even if a computer is fatally damaged, if
   the disk drives survive they can be moved to another computer with
   similar hardware and all committed transactions will remain intact.
  </para>

  <para>
   While forcing data periodically to the disk platters might seem like
   a simple operation, it is not. Because disk drives are dramatically
   slower than main memory and CPUs, several layers of caching exist
   between the computer's main memory and the disk drive platters.
   First, there is the operating system kernel cache, which caches
   frequently requested disk blocks and delays disk writes. Fortunately,
   all operating systems give applications a way to force writes from
   the kernel cache to disk, and <productname>PostgreSQL</> uses those
   features. In fact, the <xref linkend="guc-wal-sync-method"> parameter
   controls how this is done.
  </para>
  <para>
   Secondly, there is an optional disk drive controller cache,
   particularly popular on <acronym>RAID</> controller cards. Some of
   these caches are <literal>write-through</>, meaning writes are passed
   along to the drive as soon as they arrive. Others are
   <literal>write-back</>, meaning data is passed on to the drive at
   some later time. Such caches can be a reliability problem because the
   disk controller card cache is volatile, unlike the disk driver
   platters, unless the disk drive controller has a battery-backed
   cache, meaning the card has a battery that maintains power to the
   cache in case of server power loss. When the disk drives are later
   accessible, the data is written to the drives.
  </para>

  <para>
   And finally, most disk drives have caches. Some are write-through
   (typically SCSI), and some are write-back(typically IDE), and the
   same concerns about data loss exist for write-back drive caches as
   exist for disk controller caches. To have reliability, all
   storage subsystems must be reliable in their storage characteristics.
   When the operating system sends a write request to the drive platters,
   there is little it can do to make sure the data has arrived at a
   non-volatile store area on the system. Rather, it is the
   administrator's responsibility to be sure that all storage components
   have reliable characteristics.
  </para>
  
  <para>
   One other area of potential data loss are the disk platter writes
   themselves. Disk platters are internally made up of 512-byte sectors.
   When a write request arrives at the drive, it might be for 512 bytes,
   1024 bytes, or 8192 bytes, and the process of writing could fail due
   to power loss at any time, meaning some of the 512-byte sectors were
   written, and others were not, or the first half of a 512-byte sector
   has new data, and the remainder has the original data. Obviously, on
   startup, <productname>PostgreSQL</> would not be able to deal with
   these partially written cases. To guard against that,
   <productname>PostgreSQL</> periodically writes full page images to
   permanent storage <emphasis>before</> modifying the actual page on
   disk. By doing this, during crash recovery <productname>PostgreSQL</> can
   restore partially-written pages. If you have a battery-backed disk
   controller or filesystem (e.g. Reiser4) that prevents partial page writes, 
   you can turn off this page imaging by using the 
   <xref linkend="guc-full-page-writes"> parameter. This parameter has no 
   effect on the successful use of Point in Time Recovery (PITR), 
   described in <xref linkend="backup-online">.
  </para>
 
  <para>
   The following sections explain how the Write-Ahead Log is used to 
   obtain efficient, reliable operation.
  </para>

  <sect1 id="wal">
   <title>Write-Ahead Logging (<acronym>WAL</acronym>)</title>

   <indexterm zone="wal">
    <primary>WAL</primary>
   </indexterm>

   <indexterm>
    <primary>transaction log</primary>
    <see>WAL</see>
   </indexterm>

   <para>
    <firstterm>Write-Ahead Logging</firstterm> (<acronym>WAL</acronym>)
    is a standard approach to transaction logging.  Its detailed
    description may be found in most (if not all) books about
    transaction processing. Briefly, <acronym>WAL</acronym>'s central
    concept is that changes to data files (where tables and indexes
    reside) must be written only after those changes have been logged,
    that is, when log records describing the changes have been flushed
    to permanent storage. If we follow this procedure, we do not need
    to flush data pages to disk on every transaction commit, because we
    know that in the event of a crash we will be able to recover the
    database using the log: any changes that have not been applied to
    the data pages can be redone from the log records.  (This is
    roll-forward recovery, also known as REDO.)
   </para>

   <para>
    WAL brings three major benefits:
   </para>

   <para>
    The first major benefit of using <acronym>WAL</acronym> is a
    significantly reduced number of disk writes, because only the log
    file needs to be flushed to disk at the time of transaction
    commit, rather than every data file changed by the transaction.
    In multiuser environments, commits of many transactions
    may be accomplished with a single <function>fsync</function> of
    the log file. Furthermore, the log file is written sequentially,
    and so the cost of syncing the log is much less than the cost of
    flushing the data pages.   This is especially true for servers
    handling many small transactions touching different parts of the data
    store.
   </para>

   <para>
    The next benefit is crash recovery protection. The truth is
    that, before <acronym>WAL</acronym> was introduced back in release 7.1,
    <productname>PostgreSQL</productname> was never able to guarantee
    consistency in the case of a crash.  Now, 
    <acronym>WAL</acronym> protects fully against the following problems:

    <orderedlist>
     <listitem>
      <simpara>index rows pointing to nonexistent table rows</simpara>
     </listitem>

     <listitem>
      <simpara>index rows lost in split operations</simpara>
     </listitem>

     <listitem>
      <simpara>totally corrupted table or index page content, because
      of partially written data pages</simpara>
     </listitem>
    </orderedlist>
   </para>

   <para>
    Finally, <acronym>WAL</acronym> makes it possible to support on-line
    backup and point-in-time recovery, as described in <xref
    linkend="backup-online">.  By archiving the WAL data we can support
    reverting to any time instant covered by the available WAL data:
    we simply install a prior physical backup of the database, and
    replay the WAL log just as far as the desired time.  What's more,
    the physical backup doesn't have to be an instantaneous snapshot
    of the database state &mdash; if it is made over some period of time,
    then replaying the WAL log for that period will fix any internal
    inconsistencies.
   </para>
  </sect1>

 <sect1 id="wal-configuration">
  <title><acronym>WAL</acronym> Configuration</title>

  <para>
   There are several <acronym>WAL</acronym>-related configuration parameters that
   affect database performance. This section explains their use.
   Consult <xref linkend="runtime-config"> for general information about
   setting server configuration parameters.
  </para>

  <para>
   <firstterm>Checkpoints</firstterm><indexterm><primary>checkpoint</></>
   are points in the sequence of transactions at which it is guaranteed
   that the data files have been updated with all information logged before
   the checkpoint.  At checkpoint time, all dirty data pages are flushed to
   disk and a special checkpoint record is written to the log file. As a
   result, in the event of a crash, the crash recovery procedure knows from
   what point in the log (known as the redo record) it should start the
   REDO operation, since any changes made to data files before that point
   are already on disk. After a checkpoint has been made, any log segments
   written before the redo record are no longer needed and can be recycled
   or removed. (When <acronym>WAL</acronym> archiving is being done, the
   log segments must be archived before being recycled or removed.)
  </para>

  <para>
   The server's background writer process will automatically perform
   a checkpoint every so often.  A checkpoint is created every <xref
   linkend="guc-checkpoint-segments"> log segments, or every <xref
   linkend="guc-checkpoint-timeout"> seconds, whichever comes first.
   The default settings are 3 segments and 300 seconds respectively.
   It is also possible to force a checkpoint by using the SQL command
   <command>CHECKPOINT</command>.
  </para>

  <para>
   Reducing <varname>checkpoint_segments</varname> and/or
   <varname>checkpoint_timeout</varname> causes checkpoints to be done
   more often. This allows faster after-crash recovery (since less work
   will need to be redone). However, one must balance this against the
   increased cost of flushing dirty data pages more often. If 
   <xref linkend="guc-full-page-writes"> is set (the default), there is 
   another factor to consider. To ensure data page consistency, 
   the first modification of a data page after each checkpoint results in 
   logging the entire page content. In that case,
   a smaller checkpoint interval increases the volume of output to the WAL log,
   partially negating the goal of using a smaller interval, 
   and in any case causing more disk I/O.
  </para>

  <para>
   Checkpoints are fairly expensive, first because they require writing
   out all currently dirty buffers, and second because they result in
   extra subsequent WAL traffic as discussed above.  It is therefore
   wise to set the checkpointing parameters high enough that checkpoints
   don't happen too often.  As a simple sanity check on your checkpointing
   parameters, you can set the <xref linkend="guc-checkpoint-warning">
   parameter.  If checkpoints happen closer together than
   <varname>checkpoint_warning</> seconds, 
   a message will be output to the server log recommending increasing 
   <varname>checkpoint_segments</varname>.  Occasional appearance of such
   a message is not cause for alarm, but if it appears often then the
   checkpoint control parameters should be increased. Bulk operations such
   as a COPY, INSERT SELECT etc. may cause a number of such warnings if you
   do not set <xref linkend="guc-checkpoint-segments"> high enough.
  </para>

  <para>
   There will be at least one WAL segment file, and will normally
   not be more than 2 * <varname>checkpoint_segments</varname> + 1
   files.  Each segment file is normally 16 MB (though this size can be
   altered when building the server).  You can use this to estimate space
   requirements for <acronym>WAL</acronym>.
   Ordinarily, when old log segment files are no longer needed, they
   are recycled (renamed to become the next segments in the numbered
   sequence). If, due to a short-term peak of log output rate, there
   are more than 2 * <varname>checkpoint_segments</varname> + 1
   segment files, the unneeded segment files will be deleted instead
   of recycled until the system gets back under this limit.
  </para>

  <para>
   There are two commonly used internal <acronym>WAL</acronym> functions:
   <function>LogInsert</function> and <function>LogFlush</function>.
   <function>LogInsert</function> is used to place a new record into
   the <acronym>WAL</acronym> buffers in shared memory. If there is no
   space for the new record, <function>LogInsert</function> will have
   to write (move to kernel cache) a few filled <acronym>WAL</acronym>
   buffers. This is undesirable because <function>LogInsert</function>
   is used on every database low level modification (for example, row
   insertion) at a time when an exclusive lock is held on affected
   data pages, so the operation needs to be as fast as possible.  What
   is worse, writing <acronym>WAL</acronym> buffers may also force the
   creation of a new log segment, which takes even more
   time. Normally, <acronym>WAL</acronym> buffers should be written
   and flushed by a <function>LogFlush</function> request, which is
   made, for the most part, at transaction commit time to ensure that
   transaction records are flushed to permanent storage. On systems
   with high log output, <function>LogFlush</function> requests may
   not occur often enough to prevent <function>LogInsert</function>
   from having to do writes.  On such systems
   one should increase the number of <acronym>WAL</acronym> buffers by
   modifying the configuration parameter <xref
   linkend="guc-wal-buffers">.  The default number of <acronym>WAL</acronym>
   buffers is 8.  Increasing this value will
   correspondingly increase shared memory usage.  When 
   <xref linkend="guc-full-page-writes"> is set and the system is very busy, 
   setting this value higher will help smooth response times during the 
   period immediately following each checkpoint.  As a guide, a setting of 1024 
   would be considered to be high.
  </para>

  <para>
   The <xref linkend="guc-commit-delay"> parameter defines for how many
   microseconds the server process will sleep after writing a commit
   record to the log with <function>LogInsert</function> but before
   performing a <function>LogFlush</function>. This delay allows other
   server processes to add their commit records to the log so as to have all
   of them flushed with a single log sync. No sleep will occur if
   <xref linkend="guc-fsync">
   is not enabled, nor if fewer than <xref linkend="guc-commit-siblings">
   other sessions are currently in active transactions; this avoids
   sleeping when it's unlikely that any other session will commit soon.
   Note that on most platforms, the resolution of a sleep request is
   ten milliseconds, so that any nonzero <varname>commit_delay</varname>
   setting between 1 and 10000 microseconds would have the same effect.
   Good values for these parameters are not yet clear; experimentation
   is encouraged.
  </para>

  <para>
   The <xref linkend="guc-wal-sync-method"> parameter determines how
   <productname>PostgreSQL</productname> will ask the kernel to force
    <acronym>WAL</acronym> updates out to disk. 
   All the options should be the same as far as reliability goes,
   but it's quite platform-specific which one will be the fastest.
   Note that this parameter is irrelevant if <varname>fsync</varname>
   has been turned off.
  </para>

  <para>
   Enabling the <xref linkend="guc-wal-debug"> configuration parameter
   (provided that <productname>PostgreSQL</productname> has been
   compiled with support for it) will result in each
   <function>LogInsert</function> and <function>LogFlush</function>
   <acronym>WAL</acronym> call being logged to the server log. The output
   is too verbose for use as a guide to performance tuning. This
   option may be replaced by a more general mechanism in the future.
  </para>
 </sect1>

 <sect1 id="wal-internals">
  <title>WAL Internals</title>

  <para>
   <acronym>WAL</acronym> is automatically enabled; no action is
   required from the administrator except ensuring that the
   disk-space requirements for the <acronym>WAL</acronym> logs are met,
   and that any necessary tuning is done (see <xref
   linkend="wal-configuration">).
  </para>

  <para>
   <acronym>WAL</acronym> logs are stored in the directory
   <filename>pg_xlog</filename> under the data directory, as a set of
   segment files, normally each 16 MB in size.  Each segment is divided into
   pages, normally 8 KB each.  The log record headers are described in
   <filename>access/xlog.h</filename>; the record content is dependent
   on the type of event that is being logged.  Segment files are given
   ever-increasing numbers as names, starting at
   <filename>000000010000000000000000</filename>.  The numbers do not wrap, at
   present, but it should take a very very long time to exhaust the
   available stock of numbers.
  </para>

  <para>
   The <acronym>WAL</acronym> buffers and control structure are in
   shared memory and are handled by the server child processes; they
   are protected by lightweight locks.  The demand on shared memory is
   dependent on the number of buffers.  The default size of the
   <acronym>WAL</acronym> buffers is 8 buffers of 8 kB each, or 64 kB
   total.
  </para>

  <para>
   It is of advantage if the log is located on another disk than the
   main database files.  This may be achieved by moving the directory
   <filename>pg_xlog</filename> to another location (while the server
   is shut down, of course) and creating a symbolic link from the
   original location in the main data directory to the new location.
  </para>

  <para>
   The aim of <acronym>WAL</acronym>, to ensure that the log is
   written before database records are altered, may be subverted by
   disk drives<indexterm><primary>disk drive</></> that falsely report a
   successful write to the kernel, 
   when in fact they have only cached the data and not yet stored it
   on the disk.  A power failure in such a situation may still lead to
   irrecoverable data corruption.  Administrators should try to ensure
   that disks holding <productname>PostgreSQL</productname>'s
   <acronym>WAL</acronym> log files do not make such false reports.
  </para>

  <para>
   After a checkpoint has been made and the log flushed, the
   checkpoint's position is saved in the file
   <filename>pg_control</filename>. Therefore, when recovery is to be
   done, the server first reads <filename>pg_control</filename> and
   then the checkpoint record; then it performs the REDO operation by
   scanning forward from the log position indicated in the checkpoint
   record.  Because the entire content of data pages is saved in the
   log on the first page modification after a checkpoint, all pages
   changed since the checkpoint will be restored to a consistent
   state.
  </para>

  <para>
   To deal with the case where <filename>pg_control</filename> is
   corrupted, we should support the possibility of scanning existing log
   segments in reverse order &mdash; newest to oldest &mdash; in order to find the
   latest checkpoint.  This has not been implemented yet.
   <filename>pg_control</filename> is small enough (less than one disk page)
   that it is not subject to partial-write problems, and as of this writing
   there have been no reports of database failures due solely to inability
   to read <filename>pg_control</filename> itself.  So while it is
   theoretically a weak spot, <filename>pg_control</filename> does not
   seem to be a problem in practice.
  </para>
 </sect1>
</chapter>

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