Capturing data is a critical part of performing a query analysis, or even just to have an idea of what’s going on inside the database.
There are several known ways to achieve this. For example:
However, these methods can add significant overhead and might even have negative performance consequences, such as:
Now, sometimes you just need to sneak a peek at the traffic. Nothing fancy. In that case, probably the faster and easiest way to gather some traffic data is to use pt-query-digest with the –processlist. It doesn’t require any change in the server’s configuration nor critical handling of files. It doesn’t even require access to the server, just a user with the proper permissions to run “show full processlist”. But, and this is a significantly big “but,” you have to take into account that polling the SHOW PROCESSLIST command misses quite a number of queries and gives very poor timing resolution, among other things (like the processlist Mutex).
What’s the alternative? Use the Performance Schema. Specifically: The events_statements_* and threads tables.
First, we have to make sure that we have the correspondent consumers enabled:mysql> select * from setup_consumers where name like 'events%statement%' and enabled = 'yes'; +--------------------------------+---------+ | NAME | ENABLED | +--------------------------------+---------+ | events_statements_current | YES | | events_statements_history | YES | | events_statements_history_long | YES | +--------------------------------+---------+ 3 rows in set (0.00 sec)
Additionally, for statistics to be collected for statements, it is not sufficient to enable only the final statement/sql/* instruments used for individual statement types. The abstract statement/abstract/* instruments must be enabled as well. This should not normally be an issue because all statement instruments are enabled by default.
If you can’t see the event_statements_* consumers on your setup_consumers tables, you’re probably running a MySQL version prior to 5.6.3. Before that version, the events_statements_* tables didn’t exists. MySQL 5.6 might not be more widely used, as was already pointed out in this same blog.
Before continuing, it’s important to note that the most important condition at the moment of capture data is that:
The statements must have ended.
If the statement is still being executed, it can’t be part of the collected traffic. For the ones out there that want to know what’s running inside MySQL, there’s already a detailed non-blocking processlist view to replace [INFORMATION_SCHEMA. | SHOW FULL] PROCESSLIST available with Sys Schema (that will come as default in MySQL 5.7).
Our options to capture data are: get it from one of the three available tables: events_statements_current, events_statements_history or events_statements_history_long.
First option: use the events_statements_current table, which contains current statement events. Since we only want to get statements that have ended, the query will need to add the condition END_EVENT_ID IS NOT NULL to the query. This column is set to NULL when the event starts and updated to the thread current event number when the event ends, but when testing, there were too many missing queries. This is probably because between iterations, the associated threads were removed from the threads table or simply because the time between END_EVENT_ID being updated and the row being removed from the table is too short. This option is discarded.
Second option: The events_statements_history table contains the most recent statement events per thread and since statement events are not added to the events_statements_history table until they have ended, using this table will do the trick without additional conditions in order to know if the event is still running or not. Also, as new events are added, older events are discarded if the table is full.
That means that this table size is fixed. You can change the table size by modifying the variable performance_schema_events_statements_history_size. In the server version I used (5.6.25-73.1-log Percona Server (GPL), Release 73.1, Revision 07b797f) the table size is, by default, defined as autosized (-1) and can have 10 rows per thread. For example: if you are running 5 threads, the table will have 50 rows.
Since it is a fixed size, chances are that some events might be lost between iterations.
Third option: The events_statements_history_long table, which is kind of an extended version of events_statements_history table. Depending on the MySQL version, by default it can hold up to 10000 rows or be autosized (also modifiable with the variable performance_schema_events_statements_history_long_size)
One major -and not cool at all- drawback for this table is that “When a thread ends, its rows are removed from the table”. So it is not history-history data. It will go as far as the oldest thread, with the older event still alive.
The logical option to choose would be the third one: use the events_statements_history_long table. I’ve created a small script (available here) to collect infinite iterations on all the events per thread between a range of event_id’s. The idea of the range is to avoid capturing the same event more than once. Turns out that the execute a query against this table is pretty slow, something between 0.53 seconds and 1.96 seconds. It can behave in a quite invasive way.
Which leave us with the second option: The events_statements_history table.
Since the goal is to capture data in a slow log format manner, additional information needs to be obtained from the threads table, which has a row for each server thread. The most important thing to remember: access to threads does not require a mutex and has minimal impact on server performance.
Combined, these two tables give us enough information to simulate a very comprehensive slow log format. We just need the proper query:SELECT CONCAT_WS( '','# Time: ', date_format(CURDATE(),'%y%m%d'),' ',TIME_FORMAT(NOW(6),'%H:%i:%s.%f'),'n' ,'# User@Host: ',t.PROCESSLIST_USER,'[',t.PROCESSLIST_USER,'] @ ',PROCESSLIST_HOST,'  Id: ',t.PROCESSLIST_ID,'n' ,'# Schema: ',CURRENT_SCHEMA,' Last_errno: ',MYSQL_ERRNO,' ','n' ,'# Query_time: ',ROUND(s.TIMER_WAIT / 1000000000000, 6),' Lock_time: ',ROUND(s.LOCK_TIME / 1000000000000, 6),' Rows_sent: ',ROWS_SENT,' Rows_examined: ',ROWS_EXAMINED,' Rows_affected: ',ROWS_AFFECTED,'n' ,'# Tmp_tables: ',CREATED_TMP_TABLES,' Tmp_disk_tables: ',CREATED_TMP_DISK_TABLES,' ','n' ,'# Full_scan: ',IF(SELECT_SCAN=0,'No','Yes'),' Full_join: ',IF(SELECT_FULL_JOIN=0,'No','Yes'),' Tmp_table: ',IF(CREATED_TMP_TABLES=0,'No','Yes'),' Tmp_table_on_disk: ',IF(CREATED_TMP_DISK_TABLES=0,'No','Yes'),'n' , t.PROCESSLIST_INFO,';') FROM performance_schema.events_statements_history s JOIN performance_schema.threads t using(thread_id) WHERE t.TYPE = 'FOREGROUND' AND t.PROCESSLIST_INFO IS NOT NULL AND t.PROCESSLIST_ID != connection_id() ORDER BY t.PROCESSLIST_TIME desc;
The idea of this query is to get a Slow Log format as close as possible to the one that can be obtained by using all the options from the log_slow_filter variable.
The other conditions are:
The output of the query will look like a proper Slow Log output:# Time: 150928 18:13:59.364770 # User@Host: root[root] @ localhost  Id: 58918 # Schema: test Last_errno: 0 # Query_time: 0.000112 Lock_time: 0.000031 Rows_sent: 1 Rows_examined: 1 Rows_affected: 0 # Tmp_tables: 0 Tmp_disk_tables: 0 # Full_scan: No Full_join: No Tmp_table: No Tmp_table_on_disk: No INSERT INTO sbtest1 (id, k, c, pad) VALUES (498331, 500002, '94319277193-32425777628-16873832222-63349719430-81491567472-95609279824-62816435936-35587466264-28928538387-05758919296' , '21087155048-49626128242-69710162312-37985583633-69136889432');
And this file can be used with pt-query-digest to aggregate similar queries, just as it was a regular slow log output. I ran a small test which consists of:
The results were:
– Slow Log:# Profile # Rank Query ID Response time Calls R/Call V/M Item # ==== ================== ============= ====== ====== ===== ============== # 1 0x813031B8BBC3B329 47.7743 18.4% 15319 0.0031 0.01 COMMIT # 2 0x737F39F04B198EF6 39.4276 15.2% 15320 0.0026 0.00 SELECT sbtest? # 3 0x558CAEF5F387E929 37.8536 14.6% 153220 0.0002 0.00 SELECT sbtest? # 4 0x84D1DEE77FA8D4C3 30.1610 11.6% 15321 0.0020 0.00 SELECT sbtest? # 5 0x6EEB1BFDCCF4EBCD 24.4468 9.4% 15322 0.0016 0.00 SELECT sbtest? # 6 0x3821AE1F716D5205 22.4813 8.7% 15322 0.0015 0.00 SELECT sbtest? # 7 0x9270EE4497475EB8 18.9363 7.3% 3021 0.0063 0.00 SELECT performance_schema.events_statements_history performance_schema.threads # 8 0xD30AD7E3079ABCE7 12.8770 5.0% 15320 0.0008 0.01 UPDATE sbtest? # 9 0xE96B374065B13356 8.4475 3.3% 15319 0.0006 0.00 UPDATE sbtest? # 10 0xEAB8A8A8BEEFF705 8.0984 3.1% 15319 0.0005 0.00 DELETE sbtest? # MISC 0xMISC 8.5077 3.3% 42229 0.0002 0.0 <10 ITEMS>
– pt-query-digest –processlist# Profile # Rank Query ID Response time Calls R/Call V/M Item # ==== ================== ============= ===== ====== ===== =============== # 1 0x737F39F04B198EF6 53.4780 16.7% 3676 0.0145 0.20 SELECT sbtest? # 2 0x813031B8BBC3B329 50.7843 15.9% 3577 0.0142 0.10 COMMIT # 3 0x558CAEF5F387E929 50.7241 15.8% 4024 0.0126 0.08 SELECT sbtest? # 4 0x84D1DEE77FA8D4C3 35.8314 11.2% 2753 0.0130 0.11 SELECT sbtest? # 5 0x6EEB1BFDCCF4EBCD 32.3391 10.1% 2196 0.0147 0.21 SELECT sbtest? # 6 0x3821AE1F716D5205 28.1566 8.8% 2013 0.0140 0.17 SELECT sbtest? # 7 0x9270EE4497475EB8 22.1537 6.9% 1381 0.0160 0.22 SELECT performance_schema.events_statements_history performance_schema.threads # 8 0xD30AD7E3079ABCE7 15.4540 4.8% 1303 0.0119 0.00 UPDATE sbtest? # 9 0xE96B374065B13356 11.3250 3.5% 885 0.0128 0.09 UPDATE sbtest? # 10 0xEAB8A8A8BEEFF705 10.2592 3.2% 792 0.0130 0.09 DELETE sbtest? # MISC 0xMISC 9.7642 3.0% 821 0.0119 0.0 <3 ITEMS>
– Performance Schema# Profile # Rank Query ID Response time Calls R/Call V/M Item # ==== ================== ============= ===== ====== ===== ============== # 1 0x813031B8BBC3B329 14.6698 24.8% 12380 0.0012 0.00 COMMIT # 2 0x558CAEF5F387E929 12.0447 20.4% 10280 0.0012 0.00 SELECT sbtest? # 3 0x737F39F04B198EF6 7.9803 13.5% 10280 0.0008 0.00 SELECT sbtest? # 4 0x3821AE1F716D5205 4.6945 7.9% 5520 0.0009 0.00 SELECT sbtest? # 5 0x84D1DEE77FA8D4C3 4.6906 7.9% 7350 0.0006 0.00 SELECT sbtest? # 6 0x6EEB1BFDCCF4EBCD 4.1018 6.9% 6310 0.0007 0.00 SELECT sbtest? # 7 0xD30AD7E3079ABCE7 3.7983 6.4% 3710 0.0010 0.00 UPDATE sbtest? # 8 0xE96B374065B13356 2.3878 4.0% 2460 0.0010 0.00 UPDATE sbtest? # 9 0xEAB8A8A8BEEFF705 2.2231 3.8% 2220 0.0010 0.00 DELETE sbtest? # MISC 0xMISC 2.4961 4.2% 2460 0.0010 0.0 <7 ITEMS>
The P_S data is closer to the Slow Log one than the captured with regular SHOW FULL PROCESSLIST, but it is still far from being accurate. Remember that this is an alternative for a fast and easy way to capture traffic without too much trouble, so that’s a trade-off that you might have to accept.
Summary: Capture traffic always comes with a tradeoff, but if you’re willing to sacrifice accuracy it can be done with minimal impact on server performance, using the Performance Schema. Because P_S is enabled by default since MySQL 5.6.6 you might already be living with the overhead (if using 5.6). If you are one of the lucky ones that have P_S on production, don’t be afraid to use it. There’s a lot of data already in there.
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