Reliability is a major feature of any serious database system, and 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, 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. 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 PostgreSQL uses those features. In fact, the parameter controls how this is done. Secondly, there is an optional disk drive controller cache, particularly popular on RAID controller cards. Some of these caches are write-through, meaning writes are passed along to the drive as soon as they arrive. Others are 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. 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. 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, PostgreSQL would not be able to deal with these partially written cases. To guard against that, PostgreSQL periodically writes full page images to permanent storage before modifying the actual page on disk. By doing this, during crash recovery 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 parameter. This parameter has no effect on the successful use of Point in Time Recovery (PITR), described in . The following sections explain how the Write-Ahead Log is used to obtain efficient, reliable operation. WAL transaction log WAL Write-Ahead Logging (WAL) is a standard approach to transaction logging. Its detailed description may be found in most (if not all) books about transaction processing. Briefly, WAL'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.) WAL brings three major benefits: The first major benefit of using WAL 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 fsync 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. The next benefit is crash recovery protection. The truth is that, before WAL was introduced back in release 7.1, PostgreSQL was never able to guarantee consistency in the case of a crash. Now, WAL protects fully against the following problems: index rows pointing to nonexistent table rows index rows lost in split operations totally corrupted table or index page content, because of partially written data pages Finally, WAL makes it possible to support on-line backup and point-in-time recovery, as described in . 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 — if it is made over some period of time, then replaying the WAL log for that period will fix any internal inconsistencies. There are several WAL-related configuration parameters that affect database performance. This section explains their use. Consult for general information about setting server configuration parameters. Checkpointscheckpoint 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 WAL archiving is being done, the log segments must be archived before being recycled or removed.) The server's background writer process will automatically perform a checkpoint every so often. A checkpoint is created every log segments, or every 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 CHECKPOINT. Reducing checkpoint_segments and/or checkpoint_timeout 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 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. 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 parameter. If checkpoints happen closer together than checkpoint_warning seconds, a message will be output to the server log recommending increasing checkpoint_segments. 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 high enough. There will be at least one WAL segment file, and will normally not be more than 2 * checkpoint_segments + 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 WAL. 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 * checkpoint_segments + 1 segment files, the unneeded segment files will be deleted instead of recycled until the system gets back under this limit. There are two commonly used internal WAL functions: LogInsert and LogFlush. LogInsert is used to place a new record into the WAL buffers in shared memory. If there is no space for the new record, LogInsert will have to write (move to kernel cache) a few filled WAL buffers. This is undesirable because LogInsert 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 WAL buffers may also force the creation of a new log segment, which takes even more time. Normally, WAL buffers should be written and flushed by a LogFlush 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, LogFlush requests may not occur often enough to prevent LogInsert from having to do writes. On such systems one should increase the number of WAL buffers by modifying the configuration parameter . The default number of WAL buffers is 8. Increasing this value will correspondingly increase shared memory usage. When 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. The parameter defines for how many microseconds the server process will sleep after writing a commit record to the log with LogInsert but before performing a LogFlush. 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 is not enabled, nor if fewer than 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 commit_delay setting between 1 and 10000 microseconds would have the same effect. Good values for these parameters are not yet clear; experimentation is encouraged. The parameter determines how PostgreSQL will ask the kernel to force WAL 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 fsync has been turned off. Enabling the configuration parameter (provided that PostgreSQL has been compiled with support for it) will result in each LogInsert and LogFlush WAL 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. WAL is automatically enabled; no action is required from the administrator except ensuring that the disk-space requirements for the WAL logs are met, and that any necessary tuning is done (see ). WAL logs are stored in the directory pg_xlog 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 access/xlog.h; 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 000000010000000000000000. The numbers do not wrap, at present, but it should take a very very long time to exhaust the available stock of numbers. The WAL 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 WAL buffers is 8 buffers of 8 kB each, or 64 kB total. 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 pg_xlog 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. The aim of WAL, to ensure that the log is written before database records are altered, may be subverted by disk drivesdisk 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 PostgreSQL's WAL log files do not make such false reports. After a checkpoint has been made and the log flushed, the checkpoint's position is saved in the file pg_control. Therefore, when recovery is to be done, the server first reads pg_control 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. To deal with the case where pg_control is corrupted, we should support the possibility of scanning existing log segments in reverse order — newest to oldest — in order to find the latest checkpoint. This has not been implemented yet. pg_control 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 pg_control itself. So while it is theoretically a weak spot, pg_control does not seem to be a problem in practice.