High Performance MySQL: Optimization, Backups, and Replication

By Baron Schwartz

How are you able to carry out MySQL’s complete energy? With High functionality MySQL, you’ll examine complex concepts for every little thing from designing schemas, indexes, and queries to tuning your MySQL server, working approach, and to their fullest power. This advisor additionally teaches you secure and sensible how one can scale purposes via replication, load balancing, excessive availability, and failover.

Updated to mirror fresh advances in MySQL and InnoDB functionality, positive factors, and instruments, this 3rd version not just bargains particular examples of ways MySQL works, it additionally teaches you why the program works because it does, with illustrative tales and case reviews that display MySQL’s rules in motion. With this booklet, you’ll research how to think in MySQL.

  • Learn the consequences of recent positive factors in MySQL 5.5, together with saved approaches, partitioned databases, triggers, and views
  • Implement advancements in replication, excessive availability, and clustering
  • Achieve excessive functionality while working MySQL within the cloud
  • Optimize complicated querying positive aspects, equivalent to full-text searches
  • Take benefit of smooth multi-core CPUs and solid-state disks
  • Explore backup and restoration strategies—including new instruments for warm on-line backups

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You don’t need to use command-line recommendations to discover difficulties like this. you could question the PROCESSLIST desk within the INFORMATION_SCHEMA in the event that your server is new sufficient, or use innotop with a quick refresh cost and watch the reveal for an strange buildup of queries. the instance we simply confirmed used to be of a server with InnoDB inner rivalry and flushing difficulties, however it will be way more mundane than that. The vintage instance will be loads of queries within the “Locked” country. That’s the unlovable trademark of MyISAM with its table-level locking, which speedy escalates into server-wide pileups while there’s sufficient write task at the tables. utilizing question logging to discover difficulties within the question log, activate the gradual question log and set long_query_time to zero globally, and ensure that each one of the connections see the hot surroundings. you have got to recycle connections in order that they decide up the hot international price, or use Percona Server’s function to strength it to take influence immediately with out disrupting latest connections. for those who can’t let the sluggish question log to trap all queries for a few cause, use tcpdump and pt-query-digest to emulate it. search for classes within the log the place the throughput drops abruptly. Queries are despatched to the sluggish question log at of completion time, so pileups mostly lead to a surprising drop of completions, until eventually the offender finishes and releases the source that’s blockading the opposite queries. the opposite queries will then entire. What’s worthy approximately this attribute habit is that it enables you to blame the 1st question that completes after a drop in throughput. (Sometimes it’s now not really the 1st question; different queries could be operating unaffected whereas a few are blocked, so this isn’t thoroughly trustworthy. ) back, strong instruments will help with this. You can’t be searching through enormous quantities of gigabytes of queries by means of hand. Here’s a one-liner that is determined by MySQL’s development of writing the present time to the log whilst the clock advances one moment: $ awk '/^# Time:/{print $3, $4, c;c=0}/^# User/{c++}' slow-query. log 080913 21:52:17 fifty one 080913 21:52:18 29 080913 21:52:19 34 080913 21:52:20 33 080913 21:52:21 38 080913 21:52:22 15 080913 21:52:23 forty seven 080913 21:52:24 ninety six 080913 21:52:25 6 080913 21:52:26 sixty six 080913 21:52:27 37 080913 21:52:28 59There was once a drop in throughput in that output, which was once curiously additionally preceded by way of a hurry of queries finishing. with no having a look into the log round those timestamps it’s not easy to claim what occurred, yet it’s attainable that the spike is said to the drop instantly later on. at the least, it’s transparent that whatever extraordinary occurred during this server, and digging into the log round the timestamps in query can be quite fruitful. (When we seemed into this log, we came across that the spike used to be as a result of connections being disconnected. probably an program server was once being restarted. now not every thing is a MySQL challenge. ) Making feel of the findings not anything beats visualization of the knowledge. We’ve proven in simple terms small examples right here, yet in fact lots of those innovations can lead to millions of traces of output.

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