Measuring PostgreSQL Checkpoint Statistics

標籤：

　　Checkpoints can be a major drag on write-heavy PostgreSQL installations. The first step toward identifying issues in this area is to monitor how often they happen, which just got an easier to use interface added to the database recently.

　　Checkpoints are periodic maintenance operations the database performs to make sure that everything it’s been caching in memory has been synchronized with the disk. The idea is that once you’ve finished one, you can eliminate needing to worry about older entries placed into the write-ahead log of the database. That means less time to recover after a crash.

　　The problem with checkpoints is that they can be very intensive, because to complete one requires writing every single bit of changed data in the database’s buffer cache out to disk. There were a number of features added to PostgreSQL 8.3 that allow you to better monitor the checkpoint overhead, and to lower it by spreading the activity over a longer period of time. I wrote a long article about those changes called Checkpoints and the Background Writer that goes over what changed, but it’s pretty dry reading.
　　

　　What you probably want to know is how to monitor checkpoints on your production system, and how to tell if they’re happening too often. Even though things have improved, “checkpoint spikes” where disk I/O becomes really heavy are still possible even in current PostgreSQL versions. And it doesn’t help that the default configuration is tuned for very low disk space and fast crash recovery rather than performance. The checkpoint_segments parameter that’s one input on how often a checkpoint happens defaults to 3, which forces a checkpoint after only 48MB of writes.
　　

　　You can find out checkpoint frequency two ways. You can turn on log_checkpoints and watch what happens in the logs. You can also use the pg_stat_bgwriter view, which gives a count of each of the two sources for checkpoints (time passing and writes occurring) as well as statistics about how much work they did.
　　

　　The main problem with making that easier to do is that until recently, it’s been impossible to reset the counters inside of pg_stat_bgwriter. That means you have to take a snapshot with a timestamp on it, wait a while, take another snapshot, then subtract all the values to derive any useful statistics from the data. That’s a pain.

　　Enough of a pain that I wrote a patch to make it easier. With the current development version of the database, you can now call pg_stat_reset_shared(‘bgwriter’) and pop all these values back to 0 again. This allows following a practice that used to be common on PostgreSQL. Before 8.3, there was a parameter named stats_reset_on_server_start you could turn on. That reset all of the server’s internal statistics each time you started it. That meant that you could call the handy pg_postmaster_start_time() function, compare with the current time, and always have an accurate count in terms of operations/second of any statistic available on the system.
It’s still not automatic, but now that resetting these shared pieces is possible you can do it yourself. The first key is to integrate statistics clearing into your server startup sequence. A script like this will work:

pg_ctl start -l $PGLOG -wpsql -c "select pg_stat_reset();"psql -c "select pg_stat_reset_shared(‘bgwriter‘);"

 

　　Note the “-w” on the start command there–that will make pg_ctl wait until the server is finished starting before it returns, which is vital if you want to immediately execute a statement against it.
If you’ve done that, and your server start time is essentially the same as when the background writer stats started collection, you can now use this fun query:

SELECTtotal_checkpoints,seconds_since_start / total_checkpoints / 60 AS minutes_between_checkpointsFROM(SELECTEXTRACT(EPOCH FROM (now() - pg_postmaster_start_time())) AS seconds_since_start(checkpoints_timed+checkpoints_req) AS total_checkpointsFROM pg_stat_bgwriter) AS sub;

 

And get a simple report of exactly how often checkpoints are happening on your system. The output looks like this:

total_checkpoints           | 9minutes_between_checkpoints | 3.82999310740741

 

　　What you do with this information is stare at the average time interval and see if it seems too fast. Normally, you’d want a checkpoint to happen no more than every five minutes, and on a busy system you might need to push it to ten minutes or more to have a hope of keeping up. With this example, every 3.8 minutes is probably too fast–this is a system that needs checkpoint_segments to be higher.
　　Using this technique to measure the checkpoint interval lets you know if you need to increase the checkpoint_segments and checkpoint_timeout parameters in order to achieve that goal. You can compute the numbers manually right now, and once 9.0 ships it’s something you can consider making completely automatic–so long as you don’t mind your stats going away each time the server restarts.
　　There are some other interesting ways to analyze the data the background writer provides for you in pg_stat_bgwriter, but I’m not going to give away all of my tricks today.

 

註：

1、上面給了一個查詢checkpoint執行時間長度的sql，當然在計算之前要清掉記錄。直接運行select pg_stat_reset()是清不掉的，需要執行select pg_stat_reset_shared(‘bgwriter‘)，這可以將視圖pg_stat_bgwriter中的各值除掉stats_reset均置為0；

2、視圖pg_stat_bgwriter 欄位：

swrd=# \d pg_stat_bgwriter               View "pg_catalog.pg_stat_bgwriter"        Column         |           Type           | Modifiers -----------------------+--------------------------+----------- checkpoints_timed     | bigint                   |  checkpoints_req       | bigint                   |  checkpoint_write_time | double precision         |  checkpoint_sync_time  | double precision         |  buffers_checkpoint    | bigint                   |  buffers_clean         | bigint                   |  maxwritten_clean      | bigint                   |  buffers_backend       | bigint                   |  buffers_backend_fsync | bigint                   |  buffers_alloc         | bigint                   |  stats_reset           | timestamp with time zone | 

其中checkpoints_timed表示由於checkpoint_timeout 引起的checkpoint的次數，checkpoints_req表示由於checkpoint_segments引起的checkpoint的次數。手動執行checkpoint命令，會將次數計算到checkpoints_req欄位中，根據這兩個的大小情況，可以來決定修改checkpoint_timeout 和checkpoint_segments值的大小。

兩欄位數值相加就是總的checkpoint數，可以結合buffers_checkpoint值計算出平均每次checkpoint的buffer大小。

3、計算checkpoint時間的sql：

SELECTtotal_checkpoints,seconds_since_start / total_checkpoints / 60 AS minutes_between_checkpointsFROM(SELECTEXTRACT(EPOCH FROM (now() - pg_postmaster_start_time())) AS seconds_since_start(checkpoints_timed+checkpoints_req) AS total_checkpointsFROM pg_stat_bgwriter) AS sub;

 

EPOCH：

The  Unix epoch   (or  Unix time   or  POSIX time   or  Unix timestamp ) is the number of seconds that have elapsed since January 1, 1970 (midnight UTC/GMT), not counting leap seconds (in ISO 8601: 1970-01-01T00:00:00Z). Literally speaking the epoch is Unix time 0 (midnight 1-1-1970), but ‘epoch‘ is often used as a synonym for ‘Unix time‘. Many Unix systems store epoch dates as a signed 32-bit integer, which might cause problems on January 19, 2038 (known as the Year 2038 problem or Y2038).  

 

EXTRACT：

EXTRACT(field FROM source)

The extract function retrieves subfields such as year or hour from date/time values. source must be a value expression of type timestamp, time, or interval. (Expressions of type date are cast to timestamp and can therefore be used as well.) field is an identifier or string that selects what field to extract from the source value. The extract function returns values of type double precision.

 

參考：

http://blog.2ndquadrant.com/measuring_postgresql_checkpoin/

http://www.westnet.com/~gsmith/content/postgresql/chkp-bgw-83.htm

http://yao.iteye.com/blog/628941

http://www.postgresql.org/docs/9.4/static/functions-datetime.html

Measuring PostgreSQL Checkpoint Statistics