Practical Performance Tuning using Digested SQL Logs. Bob Burgess Salesforce Marketing Cloud
|
|
|
- Luke Bates
- 5 years ago
- Views:
Transcription
1 Practical Performance Tuning using Digested SQL Logs Bob Burgess Salesforce Marketing Cloud
2 Who?! Database Architect! Salesforce Marketing Cloud (Radian6 & Buddy Media stack)
3 Why?! I can t be the only one
4 Talking about today! Slow database: the problem & the solution! What is the MySQL Slow Log?! Making sense of log contents! Interpreting the digested log! Tuning examples
5 Slow Database: the problem & the solution
6 The Problem! Slow database. Why?! Long-running queries! Small fast queries (numerous & not tuned)! Small fast queries (tuned but extremely numerous)
7 The Solution! Watch everything then analyze! Catch all queries, even the fast queries! Look at total time spent on each query! Attack them from the top, down
8 Capture Everything! Slow Log! Detailed! Too detailed?
9 What is the MySQL Slow Log?
10 Slow Log: Purpose! Log all queries! Time threshold Capture information about each query! Execution time Row counts User & Host Others
11 Slow Log: Contents # Time: :52:13 # User@Host: localhost [] Id: 1 # Schema: sakila Last_errno: 0 Killed: 0 # Query_time: Lock_time: Rows_sent: 1 Rows_examined: 200 Rows_affected: 0 # Bytes_sent: 267 SET timestamp= ; select * from actor where first_name='john';
12 Slow Log: Contents # Time: :52:13 # User@Host: localhost [] Id: 1 # Schema: sakila Last_errno: 0 Killed: 0 # Query_time: Lock_time: Rows_sent: 1 Rows_examined: 200 Rows_affected: 0 # Bytes_sent: 267 SET timestamp= ; select * from actor where first_name='john';
13 Slow Log: Contents # Time: :52:13 # User@Host: localhost [] Id: 1 # Schema: sakila Last_errno: 0 Killed: 0 # Query_time: Lock_time: Rows_sent: 1 Rows_examined: 200 Rows_affected: 0 # Bytes_sent: 267 SET timestamp= ; select * from actor where first_name='john';
14 Slow Log: Contents # Time: :52:13 # User@Host: localhost [] Id: 1 # Schema: sakila Last_errno: 0 Killed: 0 # Query_time: Lock_time: Rows_sent: 1 Rows_examined: 200 Rows_affected: 0 # Bytes_sent: 267 SET timestamp= ; select * from actor where first_name='john';
15 Slow Log: Contents # Time: :52:13 # User@Host: localhost [] Id: 1 # Schema: sakila Last_errno: 0 Killed: 0 # Query_time: Lock_time: Rows_sent: 1 Rows_examined: 200 Rows_affected: 0 # Bytes_sent: 267 SET timestamp= ; select * from actor where first_name='john';
16 Slow Log: Contents # Time: :52:13 # User@Host: localhost [] Id: 1 # Schema: sakila Last_errno: 0 Killed: 0 # Query_time: Lock_time: Rows_sent: 1 Rows_examined: 200 Rows_affected: 0 # Bytes_sent: 267 SET timestamp= ; select * from actor where first_name='john';
17 Slow Log: Contents # Time: :52:13 # User@Host: localhost [] Id: 1 # Schema: sakila Last_errno: 0 Killed: 0 # Query_time: Lock_time: Rows_sent: 1 Rows_examined: 200 Rows_affected: 0 # Bytes_sent: 267 SET timestamp= ; select * from actor where first_name='john';
18 Slow Log: Contents # Time: :52:13 # User@Host: localhost [] Id: 1 # Schema: sakila Last_errno: 0 Killed: 0 # Query_time: Lock_time: Rows_sent: 1 Rows_examined: 200 Rows_affected: 0 # Bytes_sent: 267 SET timestamp= ; select * from actor where first_name='john';
19 Slow Log: Configuration! set global long_query_time = 0;! threshold execution time for inclusion in slow log
20 Slow Log: Configuration! set global slow_query_log_file = '/path/to/slow.log';! location of the slow log file
21 Slow Log: Configuration! set global log_slow_verbosity = 'microtime'; what to include in the slow log file! microtime! innodb! query_plan! profiling!
22 Slow Log: Configuration! set global slow_query_log_use_global_control = 'long_query_time, log_slow_verbosity';! make slow log configuration changes apply to all sessions, including open sessions
23 Slow Log: Configuration! set global log_slow_slave_statements = 1;! include replicated SQL in the slow log
24 Slow Log: Configuration! set global slow_query_log = 1;! turn on the slow log!! High activity / slow disk! General overhead
25 Slow Log with different MySQL versions
26 Slow-Log in Stock 5.1 / 5.5 / 5.6! set global slow_query_log_file = '/path/to/slow.log'; set global long_query_time=0; set global slow_query_log=1;! Logs only for sessions created after logging starts
27 Slow-Log in Percona 5.1 / 5.5 / 5.6! set global slow_query_log_file = '/path/to/slow.log'; set global long_query_time=0; set global slow_query_log=1;! Logs only for sessions created after logging starts
28 Slow-Log in Percona 5.1 set global slow_query_log_file = '/path/to/slow.log';! set global long_query_time=0; set global log_slow_verbosity='microtime,innodb'; set global use_global_log_slow_control=1; set global log_slow_slave_statements=1; set global slow_query_log=1;! Logs all sessions (and include innodb info & slave SQL)
29 Slow-Log in Percona 5.5 / 5.6 set global slow_query_log_file = '/path/to/slow.log';! set global long_query_time=0; set global log_slow_verbosity='microtime,innodb'; set global slow_query_log_use_global_control=1; set global log_slow_slave_statements=1; set global slow_query_log=1;! Logs all sessions (and include innodb info & slave SQL)
30 5.1 / 5.5 / 5.6 Stock 5.1 Percona 5.5 / 5.6 Percona long_query_time yes yes yes slow_query_log_file yes yes yes log_slow_verbosity no: microtime only yes yes slow_query_log _use_global_control no: session only use_global_log _slow_control yes log_slow_slave _statements no yes yes
31 5.1 / 5.5 / 5.6 Stock 5.1 Percona 5.5 / 5.6 Percona long_query_time yes yes yes slow_query_log_file yes yes yes log_slow_verbosity no: microtime only yes yes slow_query_log _use_global_control no: session only use_global_log _slow_control yes log_slow_slave _statements no yes yes
32 5.1 / 5.5 / 5.6 Stock 5.1 Percona 5.5 / 5.6 Percona long_query_time yes yes yes slow_query_log_file yes yes yes log_slow_verbosity no: microtime only yes yes slow_query_log _use_global_control no: session only use_global_log _slow_control yes log_slow_slave _statements no yes yes
33 5.1 / 5.5 / 5.6 Stock 5.1 Percona 5.5 / 5.6 Percona long_query_time yes yes yes slow_query_log_file yes yes yes log_slow_verbosity no: microtime only yes yes slow_query_log _use_global_control no: session only use_global_log _slow_control yes log_slow_slave _statements no yes yes
34 Making Sense of Log Contents! Millions of pieces of data! grep awk sort uniq -c! pt-query-digest! created by Baron Schwartz! primarily developed by him and Daniel Nichter
35 pt-query-digest! Aggregates log entry data by query fingerprint! Sorts output by whatever s important to you
36 pt-query-digest: query fingerprint select * from actor where first_name='john'; SELECT /* my query */ last_name From actor where first_name = 'Bob'; select * from actor where first_name=? select 50, last_name from actor where first_name='john' and last_name in ('SMITH', 'JONES'); select?, last_name from actor where first_name=? and last_name in(?+)
37 pt-query-digest: Output # s user time, 19.6s system time, M rss, M vsz # Current date: Tue Mar 10 12:16: # Hostname: db1234 # Files: slow.log.db5678 # Overall: 6.36M total, 775 unique, 10.90k QPS, 5.54x concurrency
38 pt-query-digest: Output # Time range: :34:01 to 11:43:44 # Attribute total min max avg 95% stddev median # ============ ======= ======= ======= ======= ======= ======= ======= # Exec time 3228s 1us 79s 507us 657us 52ms 131us # Lock time 303s 0 2s 47us 125us 1ms 30us # Rows sent M M k 0.99 # Rows examine M M k 0.99 # Rows affecte k # Rows read M M k 0.99 # Bytes sent 11.60G M 1.91k 2.62k k # Query size 1.48G k
39 pt-query-digest: Output # Time range: Query 11:34:01 to 11:43:44 # Attribute execution total time min max avg 95% stddev median # ============ ======= ======= ======= ======= ======= ======= ======= # Exec time 3228s 1us 79s 507us 657us 52ms 131us # Lock time 303s 0 2s 47us 125us 1ms 30us # Rows sent M M k 0.99 # Rows examine M M k 0.99 # Rows affecte k # Rows read M M k 0.99 # Bytes sent 11.60G M 1.91k 2.62k k # Query size 1.48G k
40 pt-query-digest: Output # Time range: :34:01 to 11:43:44 # Attribute Time before total min max avg 95% stddev median # ============ query executes ======= ======= ======= ======= ======= ======= ======= # Exec time 3228s 1us 79s 507us 657us 52ms 131us # Lock time 303s 0 2s 47us 125us 1ms 30us # Rows sent M M k 0.99 # Rows examine M M k 0.99 # Rows affecte k # Rows read M M k 0.99 # Bytes sent 11.60G M 1.91k 2.62k k # Query size 1.48G k
41 pt-query-digest: Output # Time range: :34:01 to 11:43:44 # Attribute total min max avg 95% stddev median # ============ Rows returned ======= ======= ======= ======= ======= ======= ======= # Exec time to the client 3228s 1us 79s 507us 657us 52ms 131us # Lock time 303s 0 2s 47us 125us 1ms 30us # Rows sent M M k 0.99 # Rows examine M M k 0.99 # Rows affecte k # Rows read M M k 0.99 # Bytes sent 11.60G M 1.91k 2.62k k # Query size 1.48G k
42 pt-query-digest: Output # Time range: :34:01 to 11:43:44 # Attribute total min max avg 95% stddev median # ============ ======= ======= ======= ======= ======= ======= ======= # Exec time Rows 3228s scanned 1us 79s 507us 657us 52ms 131us # Lock time for selects 303s 0 2s 47us 125us 1ms 30us # Rows sent M M k 0.99 # Rows examine M M k 0.99 # Rows affecte k # Rows read M M k 0.99 # Bytes sent 11.60G M 1.91k 2.62k k # Query size 1.48G k
43 pt-query-digest: Output # Time range: :34:01 to 11:43:44 # Attribute total min max avg 95% stddev median # ============ ======= ======= ======= ======= ======= ======= ======= # Exec time 3228s 1us 79s 507us 657us 52ms 131us # Lock time Rows changed 303s 0 2s 47us 125us 1ms 30us # Rows sent by M ins/upd/del M k 0.99 # Rows examine M M k 0.99 # Rows affecte k # Rows read M M k 0.99 # Bytes sent 11.60G M 1.91k 2.62k k # Query size 1.48G k
44 pt-query-digest: Output # Time range: :34:01 to 11:43:44 # Attribute total min max avg 95% stddev median # ============ ======= ======= ======= ======= ======= ======= ======= # Exec time 3228s 1us 79s 507us 657us 52ms 131us # Lock time Rows read 303s to 0 2s 47us 125us 1ms 30us # Rows sent process M ins/upd/ M k 0.99 # Rows examine del M M k 0.99 # Rows affecte k # Rows read M M k 0.99 # Bytes sent 11.60G M 1.91k 2.62k k # Query size 1.48G k
45 pt-query-digest: Output # Time range: :34:01 to 11:43:44 # Attribute total min max avg 95% stddev median # ============ ======= ======= ======= ======= ======= ======= ======= # Exec time 3228s 1us 79s 507us 657us 52ms 131us # Lock time 303s 0 2s 47us 125us 1ms 30us # Rows sent M M k 0.99 # Rows examine Size M of result M k 0.99 # Rows affecte k set # Rows read M M k 0.99 # Bytes sent 11.60G M 1.91k 2.62k k # Query size 1.48G k
46 pt-query-digest: Output # Time range: :34:01 to 11:43:44 # Attribute total min max avg 95% stddev median # ============ ======= ======= ======= ======= ======= ======= ======= # Exec time 3228s 1us 79s 507us 657us 52ms 131us # Lock time 303s 0 2s 47us 125us 1ms 30us # Rows sent M M k 0.99 # Rows examine M M k 0.99 # Rows affecte Size of k incoming # Rows read M SQL M k 0.99 # Bytes sent 11.60G M 1.91k 2.62k k # Query size 1.48G k
47 pt-query-digest: Output # Profile # Rank Query ID Response time Calls R/Call V/M Item # ==== ================== =============== ======= ====== ====== ==================================================== # 1 0x2AEA6C2B72904C1E % SELECT UNION Roles RoleGrants DefaultRolePermissions # 2 0x97C874BC88FDB2E % SELECT Event EventAttribute # 3 0x4A106EF2450AEAC % SELECT ApplicationUser Client # 4 0x00694E9F0A0CD23D % SELECT ExtendedMediaTypeAttribute TopicFilter # 5 0x813031B8BBC3B % COMMIT # MISC 0xMISC % <769 ITEMS>
48 Rank in specified sort order pt-query-digest: Output # Profile # Rank Query ID Response time Calls R/Call V/M Item # ==== ================== =============== ======= ====== ====== ==================================================== # 1 0x2AEA6C2B72904C1E % SELECT UNION Roles RoleGrants DefaultRolePermissions # 2 0x97C874BC88FDB2E % SELECT Event EventAttribute # 3 0x4A106EF2450AEAC % SELECT ApplicationUser Client # 4 0x00694E9F0A0CD23D % SELECT ExtendedMediaTypeAttribute TopicFilter # 5 0x813031B8BBC3B % COMMIT # MISC 0xMISC % <769 ITEMS>
49 pt-query-digest: Output Hash of query fingerprint # Profile # Rank Query ID Response time Calls R/Call V/M Item # ==== ================== =============== ======= ====== ====== ==================================================== # 1 0x2AEA6C2B72904C1E % SELECT UNION Roles RoleGrants DefaultRolePermissions # 2 0x97C874BC88FDB2E % SELECT Event EventAttribute # 3 0x4A106EF2450AEAC % SELECT ApplicationUser Client # 4 0x00694E9F0A0CD23D % SELECT ExtendedMediaTypeAttribute TopicFilter # 5 0x813031B8BBC3B % COMMIT # MISC 0xMISC % <769 ITEMS>
50 pt-query-digest: Output Total time spent # Profile # Rank Query ID Response time Calls R/Call V/M Item # ==== ================== =============== ======= ====== ====== ==================================================== # 1 0x2AEA6C2B72904C1E % SELECT UNION Roles RoleGrants DefaultRolePermissions # 2 0x97C874BC88FDB2E % SELECT Event EventAttribute # 3 0x4A106EF2450AEAC % SELECT ApplicationUser Client # 4 0x00694E9F0A0CD23D % SELECT ExtendedMediaTypeAttribute TopicFilter # 5 0x813031B8BBC3B % COMMIT # MISC 0xMISC % <769 ITEMS>
51 pt-query-digest: Output How many times this SQL ran # Profile # Rank Query ID Response time Calls R/Call V/M Item # ==== ================== =============== ======= ====== ====== ==================================================== # 1 0x2AEA6C2B72904C1E % SELECT UNION Roles RoleGrants DefaultRolePermissions # 2 0x97C874BC88FDB2E % SELECT Event EventAttribute # 3 0x4A106EF2450AEAC % SELECT ApplicationUser Client # 4 0x00694E9F0A0CD23D % SELECT ExtendedMediaTypeAttribute TopicFilter # 5 0x813031B8BBC3B % COMMIT # MISC 0xMISC % <769 ITEMS>
52 pt-query-digest: Output Mean response time per call # Profile # Rank Query ID Response time Calls R/Call V/M Item # ==== ================== =============== ======= ====== ====== ==================================================== # 1 0x2AEA6C2B72904C1E % SELECT UNION Roles RoleGrants DefaultRolePermissions # 2 0x97C874BC88FDB2E % SELECT Event EventAttribute # 3 0x4A106EF2450AEAC % SELECT ApplicationUser Client # 4 0x00694E9F0A0CD23D % SELECT ExtendedMediaTypeAttribute TopicFilter # 5 0x813031B8BBC3B % COMMIT # MISC 0xMISC % <769 ITEMS>
53 pt-query-digest: Output Variance to Mean ratio (indicates spread of values) # Profile # Rank Query ID Response time Calls R/Call V/M Item # ==== ================== =============== ======= ====== ====== ==================================================== # 1 0x2AEA6C2B72904C1E % SELECT UNION Roles RoleGrants DefaultRolePermissions # 2 0x97C874BC88FDB2E % SELECT Event EventAttribute # 3 0x4A106EF2450AEAC % SELECT ApplicationUser Client # 4 0x00694E9F0A0CD23D % SELECT ExtendedMediaTypeAttribute TopicFilter # 5 0x813031B8BBC3B % COMMIT # MISC 0xMISC % <769 ITEMS>
54 pt-query-digest: Output Command and tables # Profile # Rank Query ID Response time Calls R/Call V/M Item # ==== ================== =============== ======= ====== ====== ==================================================== # 1 0x2AEA6C2B72904C1E % SELECT UNION Roles RoleGrants DefaultRolePermissions # 2 0x97C874BC88FDB2E % SELECT Event EventAttribute # 3 0x4A106EF2450AEAC % SELECT ApplicationUser Client # 4 0x00694E9F0A0CD23D % SELECT ExtendedMediaTypeAttribute TopicFilter # 5 0x813031B8BBC3B % COMMIT # MISC 0xMISC % <769 ITEMS>
55 pt-query-digest: Output per Query # Query 3: QPS, 0.20x concurrency, ID 0x4A106EF2450AEAC1 at byte # Scores: V/M = 0.03 # Time range: :34:01 to 11:43:44 # Attribute pct total min max avg 95% stddev median # ============ === ======= ======= ======= ======= ======= ======= ======= # Count # Exec time 3 119s 100us 922ms 217us 424us 2ms 159us # Lock time 10 31s 18us 10ms 56us 131us 97us 36us # Rows sent k # Rows examine k # Rows affecte # Rows read k # Bytes sent G 2.62k 2.89k 2.71k 2.62k k # Query size M
56 Rank in sort order pt-query-digest: Output per Query # Query 3: QPS, 0.20x concurrency, ID 0x4A106EF2450AEAC1 at byte # Scores: V/M = 0.03 # Time range: :34:01 to 11:43:44 # Attribute pct total min max avg 95% stddev median # ============ === ======= ======= ======= ======= ======= ======= ======= # Count # Exec time 3 119s 100us 922ms 217us 424us 2ms 159us # Lock time 10 31s 18us 10ms 56us 131us 97us 36us # Rows sent k # Rows examine k # Rows affecte # Rows read k # Bytes sent G 2.62k 2.89k 2.71k 2.62k k # Query size M
57 How many per second pt-query-digest: Output per Query # Query 3: QPS, 0.20x concurrency, ID 0x4A106EF2450AEAC1 at byte # Scores: V/M = 0.03 # Time range: :34:01 to 11:43:44 # Attribute pct total min max avg 95% stddev median # ============ === ======= ======= ======= ======= ======= ======= ======= # Count # Exec time 3 119s 100us 922ms 217us 424us 2ms 159us # Lock time 10 31s 18us 10ms 56us 131us 97us 36us # Rows sent k # Rows examine k # Rows affecte # Rows read k # Bytes sent G 2.62k 2.89k 2.71k 2.62k k # Query size M
58 pt-query-digest: Output per Query Query ID # Query 3: QPS, 0.20x concurrency, ID 0x4A106EF2450AEAC1 at byte # Scores: V/M = 0.03 # Time range: :34:01 to 11:43:44 # Attribute pct total min max avg 95% stddev median # ============ === ======= ======= ======= ======= ======= ======= ======= # Count # Exec time 3 119s 100us 922ms 217us 424us 2ms 159us # Lock time 10 31s 18us 10ms 56us 131us 97us 36us # Rows sent k # Rows examine k # Rows affecte # Rows read k # Bytes sent G 2.62k 2.89k 2.71k 2.62k k # Query size M
59 pt-query-digest: Output per Query Percent of # Query 3: QPS, 0.20x concurrency, ID 0x4A106EF2450AEAC1 at byte Total # Scores: V/M = 0.03 # Time range: :34:01 to 11:43:44 # Attribute pct total min max avg 95% stddev median # ============ === ======= ======= ======= ======= ======= ======= ======= # Count # Exec time 3 119s 100us 922ms 217us 424us 2ms 159us # Lock time 10 31s 18us 10ms 56us 131us 97us 36us # Rows sent k # Rows examine k # Rows affecte # Rows read k # Bytes sent G 2.62k 2.89k 2.71k 2.62k k # Query size M
60 pt-query-digest: Output per Query Total this # Query 3: QPS, 0.20x concurrency, ID 0x4A106EF2450AEAC1 at byte query # Scores: V/M = 0.03 # Time range: :34:01 to 11:43:44 # Attribute pct total min max avg 95% stddev median # ============ === ======= ======= ======= ======= ======= ======= ======= # Count # Exec time 3 119s 100us 922ms 217us 424us 2ms 159us # Lock time 10 31s 18us 10ms 56us 131us 97us 36us # Rows sent k # Rows examine k # Rows affecte # Rows read k # Bytes sent G 2.62k 2.89k 2.71k 2.62k k # Query size M
61 pt-query-digest: Output per Query # Query 3: QPS, 0.20x concurrency, ID 0x4A106EF2450AEAC1 at byte # Scores: V/M = 0.03 # Time range: :34:01 to 11:43:44 Stats # Attribute pct total min max avg 95% stddev median # ============ === ======= ======= ======= ======= ======= ======= ======= # Count # Exec time 3 119s 100us 922ms 217us 424us 2ms 159us # Lock time 10 31s 18us 10ms 56us 131us 97us 36us # Rows sent k # Rows examine k # Rows affecte # Rows read k # Bytes sent G 2.62k 2.89k 2.71k 2.62k k # Query size M
62 pt-query-digest: Output per Query # String: # Databases sphere # Hosts dm1234 (90486/36%), dm2345 (79213/24%), ap3456 (77959/23%) # Last errno 0 # Users appuser01 (478323/87%), appuser02 (64465/11%), appuser03(5049/0%)
63 pt-query-digest: Output per Query # Query_time distribution # 1us # 10us ## # 100us ################################################################ # 1ms #### # 10ms # # 100ms # # 1s # 10s+
64 pt-query-digest: Output per Query # Tables # SHOW TABLE STATUS FROM `sphere` LIKE 'ApplicationUser'\G # SHOW CREATE TABLE `sphere`.`applicationuser`\g # SHOW TABLE STATUS FROM `sphere` LIKE 'Client'\G # SHOW CREATE TABLE `sphere`.`client`\g # EXPLAIN /*!50100 PARTITIONS*/ SELECT au.*, c.active, c.clientenddate FROM ApplicationUser au JOIN Client c ON au.clientid = c.clientid WHERE userid = 48954\G
65 pt-query-digest: Useful Switches! --limit! Top-n limit! Percent and/or number of results! --limit 95%:20! --limit 5! --limit 10%
66 pt-query-digest: Useful Switches! --show-all! Show all values in lists, not just what fits on one line
67 pt-query-digest: Useful Switches! --order-by! Specify how to sort the output! Parameter is {metric}:{function} --order-by Query_time:sum (default)! --order-by Bytes_sent:sum (interface saturation?)! --order-by Rows_examined:sum (Find table scans)! --order-by Query-time:cnt (find high QPS)!
68 pt-query-digest: Useful Switches! --explain! Explain each query! Specify where to connect for the Explain Plan queries! --explain h=myserver,p=3306,u=myuser,p=secret
69 Generating Log Digests First: Get pt-query-digest wget percona.com/get/pt-query-digest chmod +x pt-query-digest
70 Generating Log Digests Then: Enable the slow log for a limited time mysql e'set global {all the settings from earlier}' mysql e'set global slow_query_log=1' sleep 60 mysql e'set global slow_query_log=0'
71 Generating Log Digests Digest the resulting log./pt-query-digest /path/to/slow.log >digest.txt
72 Generating Log Digests (Periodic) For 10 minutes: Enable the slow log Wait 30 seconds Disable the slow log Wait 30 seconds Then pt-query-digest --limit 5 --explain h=,p= /path/to/slow.log >digest.txt
73 Generating Log Digests (Emergency) Enable the slow log Wait 30 seconds Disable the slow log Then pt-query-digest --show-all /path/to/slow.log >digest.txt
74 Interpreting Digested Logs! Most important metric: total execution time 4 s 100 ms
75 Interpreting Digested Logs! Most important metric: total execution time 4 s 8 s
76 pt-query-digest: Output # Profile # Rank Query ID Response time Calls R/Call V/M Item # ==== ================== =============== ======= ====== ====== ==================================================== # 1 0x2AEA6C2B72904C1E % SELECT UNION Roles RoleGrants DefaultRolePermissions # 2 0x97C874BC88FDB2E % SELECT Event EventAttribute # 3 0x4A106EF2450AEAC % SELECT ApplicationUser Client # 4 0x00694E9F0A0CD23D % SELECT ExtendedMediaTypeAttribute TopicFilter # 5 0x813031B8BBC3B % COMMIT # MISC 0xMISC % <769 ITEMS>
77 pt-query-digest: Output Single Most Important Metric # Profile # Rank Query ID Response time Calls R/Call V/M Item # ==== ================== =============== ======= ====== ====== ==================================================== # 1 0x2AEA6C2B72904C1E % SELECT UNION Roles RoleGrants DefaultRolePermissions # 2 0x97C874BC88FDB2E % SELECT Event EventAttribute # 3 0x4A106EF2450AEAC % SELECT ApplicationUser Client # 4 0x00694E9F0A0CD23D % SELECT ExtendedMediaTypeAttribute TopicFilter # 5 0x813031B8BBC3B % COMMIT # MISC 0xMISC % <769 ITEMS> THEN look at each query, starting from the top
78 Interpreting Digested Logs (Steps)! Generate Digest, sorted by total execution time per query! Start with Query #1; work your way down! Don t get distracted by other queries in the list, or not in the list! Ignore the actual SQL until you start tuning
79 Interpreting Digested Logs (Steps) Two main reasons for appearing on the list:! High queries-per-second (high enough that fast queries are making the list)! Expensive query (not running often, but when they do ) And also! Sort of expensive fast enough, except when we run a lot of them
80 Interpreting Digested Logs (examples) # Rank Query ID Response time Calls R/Call V/M Item # ==== ================== ============== ======= ======= ====== ================================== # 1 0xA F8ED32B % SELECT UserAttributes # 2 0x2AEA6C2B72904C1E % SELECT UNION Roles RoleGrants # 3 0xFB33D80D68CDD % SELECT PostStatusException # 4 0xC1EDA7581D372A3F % SELECT TopicFilter FilterGroup FilterQuery
81 Interpreting Digested Logs (example 1) Reality-based
82 Interpreting Digested Logs (example 1) # Rank Query ID Response time Calls R/Call V/M Item # ==== ================== ============== ======= ======= ====== ================================== # 1 0xA F8ED32B % SELECT UserAttributes # 2 0x2AEA6C2B72904C1E % SELECT UNION Roles RoleGrants # 3 0xFB33D80D68CDD % SELECT PostStatusException # 4 0xC1EDA7581D372A3F % SELECT TopicFilter FilterGroup FilterQuery
83 Interpreting Digested Logs (example 1) # Query 1: 0.45 QPS, 1.08x concurrency, ID 0xA F8ED32B2 at byte # Scores: V/M = 2.40 # Time range: :55:10 to 10:04:31 # Attribute pct total min max avg 95% stddev median # ============ === ======= ======= ======= ======= ======= ======= ======= # Count # Exec time s 8ms 6s 2s 5s 2s 2s # Lock time 0 135ms 21us 4ms 529us 2ms 793us 64us # Rows sent k # Rows examine M 10.73k 10.73k 10.73k 10.73k k # Rows affecte # Rows read M 10.73k 10.73k 10.73k 10.73k k # Bytes sent M k 4.44k 7.31k 3.24k 7.31k # Query size k
84 Interpreting Digested Logs (example 1) # Query 1: 0.45 QPS, 1.08x concurrency, ID 0xA F8ED32B2 at byte # Scores: V/M = 2.40 # Time range: :55:10 to 10:04:31 # Attribute pct total min max avg 95% stddev median # ============ === ======= ======= ======= ======= ======= ======= ======= # Count # Exec time s 8ms 6s 2s 5s 2s 2s # Lock time 0 135ms 21us 4ms 529us 2ms 793us 64us # Rows sent k # Rows examine M 10.73k 10.73k 10.73k 10.73k k # Rows affecte # Rows read M 10.73k 10.73k 10.73k 10.73k k # Bytes sent M k 4.44k 7.31k 3.24k 7.31k # Query size k
85 Interpreting Digested Logs (example 1) # Query 1: 0.45 QPS, 1.08x concurrency, ID 0xA F8ED32B2 at byte # Scores: V/M = 2.40 # Time range: :55:10 to 10:04:31 # Attribute pct total min max avg 95% stddev median # ============ === ======= ======= ======= ======= ======= ======= ======= # Count # Exec time s 8ms 6s 2s 5s 2s 2s # Lock time 0 135ms 21us 4ms 529us 2ms 793us 64us # Rows sent k # Rows examine M 10.73k 10.73k 10.73k 10.73k k # Rows affecte # Rows read M 10.73k 10.73k 10.73k 10.73k k # Bytes sent M k 4.44k 7.31k 3.24k 7.31k # Query size k
86 Interpreting Digested Logs (example 1) # Query 1: 0.45 QPS, 1.08x concurrency, ID 0xA F8ED32B2 at byte # Scores: V/M = 2.40 # Time range: :55:10 to 10:04:31 # Attribute pct total min max avg 95% stddev median # ============ === ======= ======= ======= ======= ======= ======= ======= # Count # Exec time s 8ms 6s 2s 5s 2s 2s # Lock time 0 135ms 21us 4ms 529us 2ms 793us 64us # Rows sent k # Rows examine M 10.73k 10.73k 10.73k 10.73k k # Rows affecte # Rows read M 10.73k 10.73k 10.73k 10.73k k # Bytes sent M k 4.44k 7.31k 3.24k 7.31k # Query size k
87 Interpreting Digested Logs (example 1) # Query_time distribution # 1us # 10us # 100us # 1ms # # 10ms ################################################## # 100ms ####### # 1s ################################################################ # 10s+
88 Interpreting Digested Logs (example 1) select userid, clientid, userattributetypeid, value from UserAttributes where clientid = and userattributetypeid = 1\G # *************************** 1. row *************************** # id: 1 # select_type: SIMPLE # table: UserAttributes # partitions: NULL # type: ALL # possible_keys: NULL # key: NULL # key_len: NULL # ref: NULL # rows: # Extra: Using where
89 Interpreting Digested Logs (example 1) select userid, clientid, userattributetypeid, value from UserAttributes where clientid = and userattributetypeid = 1\G # *************************** 1. row *************************** # id: 1 # select_type: SIMPLE # table: UserAttributes # partitions: NULL # type: ALL # possible_keys: NULL # key: NULL # key_len: NULL # ref: NULL # rows: # Extra: Using where
90 Interpreting Digested Logs (example 1) select userid, clientid, userattributetypeid, value from UserAttributes where clientid = and userattributetypeid = 1\G # *************************** 1. row *************************** # id: 1 # select_type: SIMPLE # table: UserAttributes # partitions: NULL # type: ALL # possible_keys: NULL # key: NULL # key_len: NULL # ref: NULL # rows: # Extra: Using where New index required.
91 Interpreting Digested Logs (example 2) Reality-based
92 Interpreting Digested Logs (example 2) # Rank Query ID Response time Calls R/Call V/M Item # ==== ================== ============== ======= ======= ====== ================================== # 1 0xA F8ED32B % SELECT UserAttributes # 2 0x2AEA6C2B72904C1E % SELECT UNION Roles RoleGrants # 3 0xFB33D80D68CDD % SELECT PostStatusException # 4 0xC1EDA7581D372A3F % SELECT TopicFilter FilterGroup FilterQuery
93 Interpreting Digested Logs (example 2) # Query 2: 2.38 QPS, 0.69x concurrency, ID 0x2AEA6C2B72904C1E at byte # Scores: V/M = 0.24 # Time range: :55:08 to 10:04:34 # Attribute pct total min max avg 95% stddev median # ============ === ======= ======= ======= ======= ======= ======= ======= # Count # Exec time s 60ms 2s 289ms 740ms 265ms 141ms # Lock time 0 762ms 228us 16ms 566us 1ms 1ms 348us # Rows sent k k k 9.83 # Rows examine M M k k k 9.33k # Rows affecte # Rows read M M k k k 9.33k # Bytes sent M k 3.17k 3.69k 18.78k # Query size M 2.58k 2.63k 2.62k 2.62k k
94 Interpreting Digested Logs (example 2) # Query 2: 2.38 QPS, 0.69x concurrency, ID 0x2AEA6C2B72904C1E at byte # Scores: V/M = 0.24 # Time range: :55:08 to 10:04:34 # Attribute pct total min max avg 95% stddev median # ============ === ======= ======= ======= ======= ======= ======= ======= # Count # Exec time s 60ms 2s 289ms 740ms 265ms 141ms # Lock time 0 762ms 228us 16ms 566us 1ms 1ms 348us # Rows sent k k k 9.83 # Rows examine M M k k k 9.33k # Rows affecte # Rows read M M k k k 9.33k # Bytes sent M k 3.17k 3.69k 18.78k # Query size M 2.58k 2.63k 2.62k 2.62k k
95 Interpreting Digested Logs (example 2) # Query 2: 2.38 QPS, 0.69x concurrency, ID 0x2AEA6C2B72904C1E at byte # Scores: V/M = 0.24 # Time range: :55:08 to 10:04:34 # Attribute pct total min max avg 95% stddev median # ============ === ======= ======= ======= ======= ======= ======= ======= # Count # Exec time s 60ms 2s 289ms 740ms 265ms 141ms # Lock time 0 762ms 228us 16ms 566us 1ms 1ms 348us # Rows sent k k k 9.83 # Rows examine M M k k k 9.33k # Rows affecte # Rows read M M k k k 9.33k # Bytes sent M k 3.17k 3.69k 18.78k # Query size M 2.58k 2.63k 2.62k 2.62k k
96 Interpreting Digested Logs (example 2) # Query 2: 2.38 QPS, 0.69x concurrency, ID 0x2AEA6C2B72904C1E at byte # Scores: V/M = 0.24 # Time range: :55:08 to 10:04:34 # Attribute pct total min max avg 95% stddev median # ============ === ======= ======= ======= ======= ======= ======= ======= # Count # Exec time s 60ms 2s 289ms 740ms 265ms 141ms # Lock time 0 762ms 228us 16ms 566us 1ms 1ms 348us # Rows sent k k k 9.83 # Rows examine M M k k k 9.33k # Rows affecte # Rows read M M k k k 9.33k # Bytes sent M k 3.17k 3.69k 18.78k # Query size M 2.58k 2.63k 2.62k 2.62k k
97 Interpreting Digested Logs (example 2) # Query_time distribution # 1us # 10us # 100us # 1ms # 10ms ############################# # 100ms ################################################################ # 1s # # 10s+
98 Interpreting Digested Logs (example 2) Index problem.
99 Interpreting Digested Logs (example 3) Reality-based
100 Interpreting Digested Logs (example 3) # Rank Query ID Response time Calls R/Call V/M Item # ==== ================== ============== ======= ======= ====== ================================== # 1 0xA F8ED32B % SELECT UserAttributes # 2 0x2AEA6C2B72904C1E % SELECT UNION Roles RoleGrants # 3 0xFB33D80D68CDD % SELECT PostStatusException # 4 0xC1EDA7581D372A3F % SELECT TopicFilter FilterGroup FilterQuery
101 Interpreting Digested Logs (example 3) # Query 3: QPS, 0.26x concurrency, ID 0xFB33D80D68CDD066 at byte # Scores: V/M = 9.27 # Time range: :55:01 to 10:04:34 # Attribute pct total min max avg 95% stddev median # ============ === ======= ======= ======= ======= ======= ======= ======= # Count # Exec time 6 147s 105us 18s 10ms 1ms 299ms 152us # Lock time 0 704ms 19us 11ms 46us 57us 232us 26us # Rows sent M M 3.11k k 0 # Rows examine M M 3.55k k 0 # Rows affecte # Rows read M M 3.55k k 0 # Bytes sent M M 59.37k M # Query size M
102 Interpreting Digested Logs (example 3) # Query 3: QPS, 0.26x concurrency, ID 0xFB33D80D68CDD066 at byte # Scores: V/M = 9.27 # Time range: :55:01 to 10:04:34 # Attribute pct total min max avg 95% stddev median # ============ === ======= ======= ======= ======= ======= ======= ======= # Count # Exec time 6 147s 105us 18s 10ms 1ms 299ms 152us # Lock time 0 704ms 19us 11ms 46us 57us 232us 26us # Rows sent M M 3.11k k 0 # Rows examine M M 3.55k k 0 # Rows affecte # Rows read M M 3.55k k 0 # Bytes sent M M 59.37k M # Query size M
103 Interpreting Digested Logs (example 3) # Query 3: QPS, 0.26x concurrency, ID 0xFB33D80D68CDD066 at byte # Scores: V/M = 9.27 # Time range: :55:01 to 10:04:34 # Attribute pct total min max avg 95% stddev median # ============ === ======= ======= ======= ======= ======= ======= ======= # Count # Exec time 6 147s 105us 18s 10ms 1ms 299ms 152us # Lock time 0 704ms 19us 11ms 46us 57us 232us 26us # Rows sent M M 3.11k k 0 # Rows examine M M 3.55k k 0 # Rows affecte # Rows read M M 3.55k k 0 # Bytes sent M M 59.37k M # Query size M
104 Interpreting Digested Logs (example 3) # Query_time distribution # 1us # 10us # 100us ################################################################ # 1ms ### # 10ms # # 100ms # # 1s # # 10s+ #
105 Interpreting Digested Logs (example 3) select blogpostid, poststatusid from PostStatusException where topicfilterid in (560938,549993) and publisheddate between ' :34:22' and ' :02:12 \G # *************************** 1. row *************************** # id: 1 # select_type: SIMPLE # table: PostStatusException # partitions: NULL # type: range # possible_keys: PRIMARY,topicFilterPublishedDate # key: PRIMARY # key_len: 4 # ref: NULL # rows: # Extra: Using where
106 Interpreting Digested Logs (example 3) Acceptable index in this case.! For this query, some predicates naturally return large result sets.!
107 Interpreting Digested Logs (example 4) Not reality-based But sometimes happens!
108 Interpreting Digested Logs (example 4) # Query 2: QPS, 8.26x concurrency, ID 0x4B33D80D68CDD066 at byte # Scores: V/M = 1.27 # Time range: :55:01 to 10:04:34 # Attribute pct total min max avg 95% stddev median # ============ === ======= ======= ======= ======= ======= ======= ======= # Count # Exec time s 105us 802us 224us 118us 99us 198us # Lock time 0 704ms 19us 91us 46us 57us 232us 26us # Rows sent M # Rows examine M # Rows affecte # Rows read M M # Bytes sent M k k # Query size M
109 Interpreting Digested Logs (example 4) # Query 2: QPS, 8.26x concurrency, ID 0x4B33D80D68CDD066 at byte # Scores: V/M = 1.27 # Time range: :55:01 to 10:04:34 # Attribute pct total min max avg 95% stddev median # ============ === ======= ======= ======= ======= ======= ======= ======= # Count # Exec time s 105us 802us 224us 118us 99us 198us # Lock time 0 704ms 19us 91us 46us 57us 232us 26us # Rows sent M # Rows examine M # Rows affecte # Rows read M M # Bytes sent M k k # Query size M
110 Interpreting Digested Logs (example 4) # Query 2: QPS, 8.26x concurrency, ID 0x4B33D80D68CDD066 at byte # Scores: V/M = 0.27 # Time range: :55:01 to 10:04:34 # Attribute pct total min max avg 95% stddev median # ============ === ======= ======= ======= ======= ======= ======= ======= # Count # Exec time s 105us 802us 224us 118us 99us 198us # Lock time 0 704ms 19us 91us 46us 57us 232us 26us # Rows sent M # Rows examine M # Rows affecte # Rows read M M # Bytes sent M k k # Query size M
111 Interpreting Digested Logs (example 4) # Query 2: QPS, 8.26x concurrency, ID 0x4B33D80D68CDD066 at byte # Scores: V/M = 0.27 # Time range: :55:01 to 10:04:34 # Attribute pct total min max avg 95% stddev median # ============ === ======= ======= ======= ======= ======= ======= ======= # Count # Exec time s 105us 802us 224us 118us 99us 198us # Lock time 0 704ms 19us 91us 46us 57us 232us 26us # Rows sent M # Rows examine M # Rows affecte # Rows read M M # Bytes sent M k k # Query size M
112 Interpreting Digested Logs (example 4) # Query 2: QPS, 8.26x concurrency, ID 0x4B33D80D68CDD066 at byte # Scores: V/M = 0.27 # Time range: :55:01 to 10:04:34 # Attribute pct total min max avg 95% stddev median # ============ === ======= ======= ======= ======= ======= ======= ======= # Count # Exec time s 105us 802us 224us 118us 99us 198us # Lock time 0 704ms 19us 91us 46us 57us 232us 26us # Rows sent M # Rows examine M # Rows affecte # Rows read M M # Bytes sent M k k # Query size M
113 Interpreting Digested Logs (other cases) # Databases schema1 (813422/89%), schema2 (101753/11%) # Hosts ap1234 (813323/89%), ap2345 (89429/10%), ap3456 (12324/1%) # Last errno 0 # Users user1 (813422/89%), user2 (101753/11%)
114 Interpreting Digested Logs (other cases) # Databases schema1 (813422/89%), schema2 (101753/11%) # Hosts ap1234 (813323/89%), ap2345 (89429/10%), ap3456 (12324/1%) # Last errno 0 # Users user1 (813422/89%), user2 (101753/11%)
115 Interpreting Digested Logs (other cases) # Databases schema1 (813422/89%), schema2 (101753/11%) # Hosts ap1234 (813323/89%), ap2345 (89429/10%), ap3456 (12324/1%) # Last errno 0 # Users user1 (813422/89%), user2 (101753/11%)
116 Interpreting Digested Logs (other cases) # Databases schema1 (813422/89%), schema2 (101753/11%) # Hosts ap1234 (813323/89%), ap2345 (89429/10%), ap3456 (12324/1%) # Last errno 0 # Users user1 (813422/89%), user2 (101753/11%) Talk to developers, verify logic Talk to Operations, verify traffic pattern
117 Trending! Spreadsheet! Set up database to accept pt-query-digest output (see --review and --history switches)
118 Other Slow Log Analyses Finding Outliers!! Reports of a slow query that doesn t make it in the log digest # Query_time: Lock_time: Rows_sent: 1 Rows_examined: 200 Rows_affected: 0 # Bytes_sent: 267 SET timestamp= ; select * from actor where first_name='john';! Dump out all access times from the slow log for this query: grep -B 3 'select \* from actor where first_name' slow.log \ grep Query cut -f3 -d' ' tr -d. >file
119 Other Slow Log Analyses Finding Outliers!! Reports of a slow query that doesn t make it in the log digest # Query_time: Lock_time: Rows_sent: 1 Rows_examined: 200 Rows_affected: 0 # Bytes_sent: 267 SET timestamp= ; select * from actor where first_name='john';! Go right nuts: Dump out all access times from the slow log for this query, including timestamps: grep -B 3 'select \* from actor where first_name' slow.log \ grep -E 'Query timestamp' cut -f2,3 -d' ' tr '=' ' ' \ (while read Q T; do read TS; echo $TS $T; done) \ cut -f2,3 -d' ' tr -d ';' >file
120 Extra Reading / Resources! Percona Docs! slow_extended_55.html! id=percona-server:features:slow_extended_51! MySQL Docs! Slides, and script to safely do periodic slow log digests! bit.ly/bobburgesspercona2015
121 Thank You! Bob Burgess bit.ly/bobburgesspercona2015 Please fill out the session evaluation!
Maximizing SQL reviews with pt-query-digest
PALOMINODB OPERATIONAL EXCELLENCE FOR DATABASES Maximizing SQL reviews with pt-query-digest Mark Filipi www.palominodb.com What is pt-query-digest Analyzes MySQL queries from slow, general and binary log
Troubleshooting Best Practices
Troubleshooting Best Practices Monitoring the Production Database Without Killing Performance June, 27, 2018 Sveta Smirnova Table of Contents Introduction: Between Desire and Reality Why Monitoring is
Analyze MySQL Query Performance with Percona Cloud Tools
Analyze MySQL Query Performance with Percona Cloud Tools Vadim Tkachenko Percona, Co-founder /CTO www.percona.com cloud.percona.com www.mysqlperformanceblog.com 2 This webinar Performance Percona Cloud
Tools and Techniques for Index Design. Bill Karwin, Percona Inc.
Tools and Techniques for Index Design Bill Karwin, Percona Inc. It s About Performance What s the most frequent recommendation in database performance audits? What s the easiest way to speed up SQL queries,
MySQL Performance and Scalability. Peter Zaitsev CEO, Percona Inc Percona Live, Washington,DC 11 January 2012
MySQL Performance and Scalability Peter Zaitsev CEO, Percona Inc Percona Live, Washington,DC 11 January 2012 MySQL Performance and Scalability Basic Definitions Ways to Get good Performance and Scalability
OKC MySQL Users Group
OKC MySQL Users Group OKC MySQL Discuss topics about MySQL and related open source RDBMS Discuss complementary topics (big data, NoSQL, etc) Help to grow the local ecosystem through meetups and events
MySQL and OpenStack Deep Dive
MySQL and OpenStack Deep Dive Peter Boros, Percona Jay Pipes, Mirantis www.mirantis.com November, 2014 Diving into MySQL and OpenStack Our setup Analyzing MySQL query loads Takeaways Objectives Examine
Oracle 1Z MySQL 5.6 Database Administrator. Download Full Version :
Oracle 1Z0-883 MySQL 5.6 Database Administrator Download Full Version : http://killexams.com/pass4sure/exam-detail/1z0-883 D. The mysqld binary was not compiled with SSL support. E. The server s SSL certificate
Writing High Performance SQL Statements. Tim Sharp July 14, 2014
Writing High Performance SQL Statements Tim Sharp July 14, 2014 Introduction Tim Sharp Technical Account Manager Percona since 2013 16 years working with Databases Optimum SQL Performance Schema Indices
DATABASE PERFORMANCE AND INDEXES. CS121: Relational Databases Fall 2017 Lecture 11
DATABASE PERFORMANCE AND INDEXES CS121: Relational Databases Fall 2017 Lecture 11 Database Performance 2 Many situations where query performance needs to be improved e.g. as data size grows, query performance
PostgreSQL to MySQL A DBA's Perspective. Patrick
PostgreSQL to MySQL A DBA's Perspective Patrick King @mr_mustash Yelp s Mission Connecting people with great local businesses. My Database Experience Started using Postgres 7 years ago Postgres 8.4 (released
MySQL Query Tuning 101. Sveta Smirnova, Alexander Rubin April, 16, 2015
MySQL Query Tuning 101 Sveta Smirnova, Alexander Rubin April, 16, 2015 Agenda 2 Introduction: where to find slow queries Indexes: why and how do they work All about EXPLAIN More tools Where to find more
Covering indexes. Stéphane Combaudon - SQLI
Covering indexes Stéphane Combaudon - SQLI Indexing basics Data structure intended to speed up SELECTs Similar to an index in a book Overhead for every write Usually negligeable / speed up for SELECT Possibility
percona-agent Documentation
percona-agent Documentation Release 1.0.10 Percona June 30, 2015 Contents 1 Getting Started with Percona Cloud Tools 3 1.1 How PCT Works............................................. 3 1.2 Tools...................................................
Practical MySQL indexing guidelines
Practical MySQL indexing guidelines Percona Live October 24th-25th, 2011 London, UK Stéphane Combaudon stephane.combaudon@dailymotion.com Agenda Introduction Bad indexes & performance drops Guidelines
Percona Server: secret features
Date, time, place: MySQL Conference & Expo 2011 Reporter: Vadim Tkachenko Co-founder, CTO, Percona Inc In this talk: some not high profile, but addictive features. Once you start to use them you can t
MySQL Indexing. Best Practices. Peter Zaitsev, CEO Percona Inc August 15, 2012
MySQL Indexing Best Practices Peter Zaitsev, CEO Percona Inc August 15, 2012 You ve Made a Great Choice! Understanding indexing is crucial both for Developers and DBAs Poor index choices are responsible
<Insert Picture Here> Looking at Performance - What s new in MySQL Workbench 6.2
Looking at Performance - What s new in MySQL Workbench 6.2 Mario Beck MySQL Sales Consulting Manager EMEA The following is intended to outline our general product direction. It is
Oracle Exam 1z0-883 MySQL 5.6 Database Administrator Version: 8.0 [ Total Questions: 100 ]
![Oracle Exam 1z0-883 MySQL 5.6 Database Administrator Version: 8.0 [ Total Questions: 100 ]](/thumbs/82/85840258.jpg "Oracle Exam 1z0-883 MySQL 5.6 Database Administrator Version: 8.0 [ Total Questions: 100 ]")
s@lm@n Oracle Exam 1z0-883 MySQL 5.6 Database Administrator Version: 8.0 [ Total Questions: 100 ] Oracle 1z0-883 : Practice Test Question No : 1 Consider the Mysql Enterprise Audit plugin. You are checking
Operational DBA In a Nutshell - HandsOn Reference Guide
1/12 Operational DBA In a Nutshell - HandsOn Reference Guide Contents 1 Operational DBA In a Nutshell 2 2 Installation of MySQL 2 2.1 Setting Up Our VM........................................ 2 2.2 Installation
Best Practices for Database Administrators
Best Practices for Database Administrators Sheeri K. Cabral Database Administrator The Pythian Group, www.pythian.com cabral@pythian.com 2008 MySQL User Conference & Expo MIRE Make It Really Easy Automate
MySQL Performance Improvements
Taking Advantage of MySQL Performance Improvements Baron Schwartz, Percona Inc. Introduction About Me (Baron Schwartz) Author of High Performance MySQL 2 nd Edition Creator of Maatkit, innotop, and so
Introduction to MySQL NDB Cluster. Yves Trudeau Ph. D. Percona Live DC/January 2012
Introduction to MySQL NDB Cluster Yves Trudeau Ph. D. Percona Live DC/January 2012 Agenda What is NDB Cluster? How MySQL uses NDB Cluster Good use cases Bad use cases Example of tuning What is NDB cluster?
Monitoring MySQL Performance with Percona Monitoring and Management
Monitoring MySQL Performance with Percona Monitoring and Management Santa Clara, California April 23th 25th, 2018 MIchael Coburn, Product Manager Your Presenter Product Manager for PMM (also Percona Toolkit
The Care and Feeding of a MySQL Database for Linux Adminstrators. Dave Stokes MySQL Community Manager
The Care and Feeding of a MySQL Database for Linux Adminstrators Dave Stokes MySQL Community Manager David.Stokes@Oracle.com Simple Introduction This is a general introduction to running a MySQL database
Monitoring MySQL Performance with Percona Monitoring and Management
Monitoring MySQL Performance with Percona Monitoring and Management Your Presenters Michael Coburn - PMM Product Manager Working at Percona for almost 5 years Consultant, Manager, TAM, now Product Manager
Lecture 19 Query Processing Part 1
CMSC 461, Database Management Systems Spring 2018 Lecture 19 Query Processing Part 1 These slides are based on Database System Concepts 6 th edition book (whereas some quotes and figures are used from
MySQL Performance Optimization and Troubleshooting with PMM. Peter Zaitsev, CEO, Percona
MySQL Performance Optimization and Troubleshooting with PMM Peter Zaitsev, CEO, Percona In the Presentation Practical approach to deal with some of the common MySQL Issues 2 Assumptions You re looking
MySQL Indexing. Best Practices for MySQL 5.6. Peter Zaitsev CEO, Percona MySQL Connect Sep 22, 2013 San Francisco,CA
MySQL Indexing Best Practices for MySQL 5.6 Peter Zaitsev CEO, Percona MySQL Connect Sep 22, 2013 San Francisco,CA For those who Does not Know Us Percona Helping Businesses to be Successful with MySQL
Upgrading MySQL Best Practices. Apr 11-14, 2011 MySQL Conference and Expo Santa Clara,CA by Peter Zaitsev, Percona Inc
Upgrading MySQL Best Practices Apr 11-14, 2011 MySQL Conference and Expo Santa Clara,CA by Peter Zaitsev, Percona Inc MySQL Upgrade How many of you have performed MySQL upgrade? Home many of you have done
Make Your Life Easier with Maatkit. Baron Schwartz MySQL Conference & Expo 2009
Make Your Life Easier with Maatkit Baron Schwartz MySQL Conference & Expo 2009 -2- About the Speaker Director of Consulting at Percona Degree in Computer Science from UVA Lead Author of High Performance
Carnegie Mellon Univ. Dept. of Computer Science /615 - DB Applications. Administrivia. Administrivia. Faloutsos/Pavlo CMU /615
Carnegie Mellon Univ. Dept. of Computer Science 15-415/615 - DB Applications C. Faloutsos A. Pavlo Lecture#14(b): Implementation of Relational Operations Administrivia HW4 is due today. HW5 is out. Faloutsos/Pavlo
MongoDB For MySQL DBAs. Alexander Rubin - Principal Architect, Percona
MongoDB For DBAs Alexander Rubin - Principal Architect, Percona Agenda How to in MongoDB Storage engines in MongoDB Opera4ons in MongoDB Replica4on in MongoDB Sharding in MongoDB Other examples 2 What
Open Source Database Performance Optimization and Monitoring with PMM. Fernando Laudares, Vinicius Grippa, Michael Coburn Percona
Open Source Database Performance Optimization and Monitoring with PMM Fernando Laudares, Vinicius Grippa, Michael Coburn Percona Fernando Laudares 2 Vinicius Grippa 3 Michael Coburn Product Manager for
percona-agent Documentation
percona-agent Documentation Release 1.0.10 Percona July 01, 2015 Contents 1 How PCT Works 3 2 Quick Start 5 3 Contents 7 3.1 PCT Account............................................... 7 3.2 Percona Agent..............................................
Understanding Performance through Measurement, Benchmarking and Profiling
Understanding Performance through Measurement, Benchmarking and Profiling Presented by: René Cannaò @rene_cannao Senior Operational DBA www.palominodb.com WHY RUN BENCHMARK? 2 Looking for bottlenecks Hardware
Advanced MySQL Query Tuning
Advanced MySQL Query Tuning Alexander Rubin July 21, 2013 About Me My name is Alexander Rubin Working with MySQL for over 10 years Started at MySQL AB, then Sun Microsystems, then Oracle (MySQL Consulting)
Effective Testing for Live Applications. March, 29, 2018 Sveta Smirnova
Effective Testing for Live Applications March, 29, 2018 Sveta Smirnova Table of Contents Sometimes You Have to Test on Production Wrong Data SELECT Returns Nonsense Wrong Data in the Database Performance
Indexes - What You Need to Know
Indexes - What You Need to Know http://www.percona.com/training/ 2011-2017 Percona, Inc. 1 / 53 Indexes - Need to Know QUERY PLANNING 2011-2017 Percona, Inc. 2 / 53 About This Chapter The number one goal
InnoDB Scalability Limits. Peter Zaitsev, Vadim Tkachenko Percona Inc MySQL Users Conference 2008 April 14-17, 2008
InnoDB Scalability Limits Peter Zaitsev, Vadim Tkachenko Percona Inc MySQL Users Conference 2008 April 14-17, 2008 -2- Who are the Speakers? Founders of Percona Inc MySQL Performance and Scaling consulting
Extracting Performance and Scalability Metrics from TCP. Baron Schwartz April 2012
Extracting Performance and Scalability Metrics from TCP Baron Schwartz April 2012 Agenda Fundamental Metrics of Performance Capturing TCP Data Part 1: Black-Box Performance Analysis Detecting Stalls and
System Characteristics
System Characteristics Performance is influenced by characteristics of the system hosting the database server, for example: - Disk input/output (I/O) speed. - Amount of memory available. - Processor speed.
Tired of MySQL Making You Wait? Alexander Rubin, Principal Consultant, Percona Janis Griffin, Database Evangelist, SolarWinds
Tired of MySQL Making You Wait? Alexander Rubin, Principal Consultant, Percona Janis Griffin, Database Evangelist, SolarWinds Who Am I? Senior DBA / Performance Evangelist for Solarwinds Janis.Griffin@solarwinds.com
Advanced MySQL Query Tuning
Advanced MySQL Query Tuning Alexander Rubin August 6, 2014 About Me My name is Alexander Rubin Working with MySQL for over 10 years Started at MySQL AB, then Sun Microsystems, then Oracle (MySQL Consulting)
Load Testing Tools. for Troubleshooting MySQL Concurrency Issues. May, 23, 2018 Sveta Smirnova
Load Testing Tools for Troubleshooting MySQL Concurrency Issues May, 23, 2018 Sveta Smirnova Introduction This is very personal webinar No intended use No best practices No QA-specific tools Real life
An Oracle White Paper April MySQL Enterprise Monitor and Query Analyzer Feature
An Oracle White Paper April 2010 MySQL Enterprise Monitor and Query Analyzer Feature Introduction... 2 Application Strategy: Performance and Scalability... 3 Common Challenges to Performance and Scalability...
MySQL Performance Optimization and Troubleshooting with PMM. Peter Zaitsev, CEO, Percona Percona Technical Webinars 9 May 2018
MySQL Performance Optimization and Troubleshooting with PMM Peter Zaitsev, CEO, Percona Percona Technical Webinars 9 May 2018 Few words about Percona Monitoring and Management (PMM) 100% Free, Open Source
Chapter 8: Working With Databases & Tables
Chapter 8: Working With Databases & Tables o Working with Databases & Tables DDL Component of SQL Databases CREATE DATABASE class; o Represented as directories in MySQL s data storage area o Can t have
Microsoft Developing Microsoft SQL Server 2012 Databases. Download Full Version :
Microsoft 70-464 Developing Microsoft SQL Server 2012 Databases Download Full Version : https://killexams.com/pass4sure/exam-detail/70-464 QUESTION: 172 DRAG DROP You administer a SQL Server 2014 instance.
Optimizer Standof. MySQL 5.6 vs MariaDB 5.5. Peter Zaitsev, Ovais Tariq Percona Inc April 18, 2012
Optimizer Standof MySQL 5.6 vs MariaDB 5.5 Peter Zaitsev, Ovais Tariq Percona Inc April 18, 2012 Thank you Ovais Tariq Ovais Did a lot of heavy lifing for this presentation He could not come to talk together
Migrating to Aurora MySQL and Monitoring with PMM. Percona Technical Webinars August 1, 2018
Migrating to Aurora MySQL and Monitoring with PMM Percona Technical Webinars August 1, 2018 Introductions Introduction Vineet Khanna (Autodesk) Senior Database Engineer vineet.khanna@autodesk.com Tate
Query Optimization With MySQL 8.0 and MariaDB 10.3: The Basics
Query Optimization With MySQL 8.0 and MariaDB 10.3: The Basics Jaime Crespo Percona Live Europe 2018 -Frankfurt, 5 Nov 2018- dbahire.com/pleu18 Agenda 1. Introduction 6. Joins 2. Query Profiling 7. Subqueries
Introduction to troubleshooting Basic techniques. Sveta Smirnova Principal Support Engineer March, 10, 2016
Introduction to troubleshooting Basic techniques Sveta Smirnova Principal Support Engineer March, 10, 2016 Table of Contents Introduction How to find problematic query Solving issues Syntax errors Logic
Optimizing BOINC project databases
Optimizing BOINC project databases Oliver Bock Max Planck Institute for Gravitational Physics Hannover, Germany 5th Pan-Galactic BOINC Workshop Catalan Academy of Letters, Sciences and Humanities Barcelona,
Query Optimization, part 2: query plans in practice
Query Optimization, part 2: query plans in practice CS634 Lecture 13 Slides by E. O Neil based on Database Management Systems 3 rd ed, Ramakrishnan and Gehrke Working with the Oracle query optimizer First
Session 6: Relational Databases
INFM 603: Information Technology and Organizational Context Session 6: Relational Databases Jimmy Lin The ischool University of Maryland Thursday, October 16, 2014 Databases Yesterday Databases Today What
MySQL Database Administrator Training NIIT, Gurgaon India 31 August-10 September 2015
MySQL Database Administrator Training Day 1: AGENDA Introduction to MySQL MySQL Overview MySQL Database Server Editions MySQL Products MySQL Services and Support MySQL Resources Example Databases MySQL
1Z MySQL 5 Database Administrator Certified Professional Exam, Part II Exam.
Oracle 1Z0-874 MySQL 5 Database Administrator Certified Professional Exam, Part II Exam TYPE: DEMO http://www.examskey.com/1z0-874.html Examskey Oracle 1Z0-874 exam demo product is here for you to test
MySQL vs MongoDB. Choosing right technology for your application. Peter Zaitsev CEO, Percona All Things Open, Raleigh,NC October 23 rd, 2017
MySQL vs MongoDB Choosing right technology for your application Peter Zaitsev CEO, Percona All Things Open, Raleigh,NC October 23 rd, 2017 1 MySQL vs MongoDB VS 2 Bigger Question What Open Source Database
Query Optimization Percona, Inc. 1 / 74
Query Optimization http://www.percona.com/training/ 2011-2017 Percona, Inc. 1 / 74 Table of Contents 1. Query Planning 3. Composite Indexes 2. Explaining the EXPLAIN 4. Kitchen Sink 2011-2017 Percona,
Scaling Without Sharding. Baron Schwartz Percona Inc Surge 2010
Scaling Without Sharding Baron Schwartz Percona Inc Surge 2010 Web Scale!!!! http://www.xtranormal.com/watch/6995033/ A Sharding Thought Experiment 64 shards per proxy [1] 1 TB of data storage per node
Sheeri Cabral. Are You Getting the Best Out of Your MySQL Indexes? Slides
Are You Getting the Best Out of Your MySQL Indexes? Slides http://bit.ly/mysqlindex2 Sheeri Cabral Senior DB Admin/Architect, Mozilla @sheeri www.sheeri.com What is an index? KEY vs. INDEX KEY = KEY CONSTRAINT
IT Best Practices Audit TCS offers a wide range of IT Best Practices Audit content covering 15 subjects and over 2200 topics, including:
IT Best Practices Audit TCS offers a wide range of IT Best Practices Audit content covering 15 subjects and over 2200 topics, including: 1. IT Cost Containment 84 topics 2. Cloud Computing Readiness 225
SQL QUERY EVALUATION. CS121: Relational Databases Fall 2017 Lecture 12
SQL QUERY EVALUATION CS121: Relational Databases Fall 2017 Lecture 12 Query Evaluation 2 Last time: Began looking at database implementation details How data is stored and accessed by the database Using
Delegates must have a working knowledge of MariaDB or MySQL Database Administration.
MariaDB Performance & Tuning SA-MARDBAPT MariaDB Performance & Tuning Course Overview This MariaDB Performance & Tuning course is designed for Database Administrators who wish to monitor and tune the performance
Mastering the art of indexing
Mastering the art of indexing Yoshinori Matsunobu Lead of MySQL Professional Services APAC Sun Microsystems Yoshinori.Matsunobu@sun.com 1 Table of contents Speeding up Selects B+TREE index structure Index
Administrivia Carnegie Mellon Univ. Dept. of Computer Science /615 - DB Applications
Administrivia Carnegie Mellon Univ. Dept. of Computer Science 15-415/615 - DB Applications HW6 is due right now. HW7 is out today Phase 1: Wed Nov 9 th Phase 2: Mon Nov 28 th C. Faloutsos A. Pavlo Lecture#18:
Preventing and Resolving MySQL Downtime. Jervin Real, Michael Coburn Percona
Preventing and Resolving MySQL Downtime Jervin Real, Michael Coburn Percona About Us Jervin Real, Technical Services Manager Engineer Engineering Engineers APAC Michael Coburn, Principal Technical Account
MySQL 101. Designing effective schema for InnoDB. Yves Trudeau April 2015
MySQL 101 Designing effective schema for InnoDB Yves Trudeau April 2015 About myself : Yves Trudeau Principal architect at Percona since 2009 With MySQL then Sun, 2007 to 2009 Focus on MySQL HA and distributed
Using Percona Monitoring and Management to Troubleshoot MySQL Performance Issues
Using Percona Monitoring and Management to Troubleshoot MySQL Performance Issues Michael Coburn, Product Manager PMM September 7th, 2017 1 Your presenter Michael Coburn - Product Manager PMM Working at
Resolving and Preventing MySQL Downtime
Resolving and Preventing MySQL Downtime Common MySQL service impacting challenges, resolutions and prevention. Jervin Real Jervin Real Technical Services Manager APAC Engineer Engineering Engineers 2 What
<Insert Picture Here> DBA s New Best Friend: Advanced SQL Tuning Features of Oracle Database 11g
DBA s New Best Friend: Advanced SQL Tuning Features of Oracle Database 11g Peter Belknap, Sergey Koltakov, Jack Raitto The following is intended to outline our general product direction.
Advanced query optimization techniques on large queries. Peter Boros Percona Webinar
Advanced query optimization techniques on large queries Peter Boros Percona Webinar Agenda Showing the title slide Going through this agenda Prerequisites Case study #1 Case study #2 Case study #3 Case
CSED421 Database Systems Lab. Index
CSED421 Database Systems Lab Index Index of Index What is an index? When to Create an Index or Not? Index Syntax UNIQUE Index / Indexing Prefixes / Multiple-column index Confirming indexes Index types
MySQL Replication : advanced features in all flavours. Giuseppe Maxia Quality Assurance Architect at
MySQL Replication : advanced features in all flavours Giuseppe Maxia Quality Assurance Architect at VMware @datacharmer 1 About me Who s this guy? Giuseppe Maxia, a.k.a. "The Data Charmer" QA Architect
Administrivia Carnegie Mellon Univ. Dept. of Computer Science /615 - DB Applications
Administrivia Carnegie Mellon Univ. Dept. of Computer Science 15-415/615 - DB Applications C. Faloutsos A. Pavlo Lecture#18: Physical Database Design HW6 is due right now. HW7 is out today Phase 1: Wed
Advanced SQL GROUP BY Clause and Aggregate Functions Pg 1
Advanced SQL Clause and Functions Pg 1 Clause and Functions Ray Lockwood Points: s (such as COUNT( ) work on groups of Instead of returning every row read from a table, we can aggregate rows together using
Advanced Database Systems
Lecture IV Query Processing Kyumars Sheykh Esmaili Basic Steps in Query Processing 2 Query Optimization Many equivalent execution plans Choosing the best one Based on Heuristics, Cost Will be discussed
Query Evaluation! References:! q [RG-3ed] Chapter 12, 13, 14, 15! q [SKS-6ed] Chapter 12, 13!
![Query Evaluation! References:! q [RG-3ed] Chapter 12, 13, 14, 15! q [SKS-6ed] Chapter 12, 13!](/thumbs/78/78385651.jpg "Query Evaluation! References:! q [RG-3ed] Chapter 12, 13, 14, 15! q [SKS-6ed] Chapter 12, 13!")
Query Evaluation! References:! q [RG-3ed] Chapter 12, 13, 14, 15! q [SKS-6ed] Chapter 12, 13! q Overview! q Optimization! q Measures of Query Cost! Query Evaluation! q Sorting! q Join Operation! q Other
MySQL 5.6: Advantages in a Nutshell. Peter Zaitsev, CEO, Percona Percona Technical Webinars March 6, 2013
MySQL 5.6: Advantages in a Nutshell Peter Zaitsev, CEO, Percona Percona Technical Webinars March 6, 2013 About Presentation Brief Overview Birds eye view of features coming in 5.6 Mainly documentation
CMU SCS CMU SCS Who: What: When: Where: Why: CMU SCS
Carnegie Mellon Univ. Dept. of Computer Science 15-415/615 - DB s C. Faloutsos A. Pavlo Lecture#23: Distributed Database Systems (R&G ch. 22) Administrivia Final Exam Who: You What: R&G Chapters 15-22
Optimizing Queries with EXPLAIN
Optimizing Queries with EXPLAIN Sheeri Cabral Senior Database Administrator Twitter: @sheeri What is EXPLAIN? SQL Extension Just put it at the beginning of your statement Can also use DESC or DESCRIBE
Index Tuning. Index. An index is a data structure that supports efficient access to data. Matching records. Condition on attribute value
Index Tuning AOBD07/08 Index An index is a data structure that supports efficient access to data Condition on attribute value index Set of Records Matching records (search key) 1 Performance Issues Type
DBMS Performance Tuning
DBMS Performance Tuning DBMS Architecture GCF SCF PSF OPF QEF RDF QSF ADF SXF GWF DMF Shared Memory locks log buffers Recovery Server Work Areas Databases log file DBMS Servers Manages client access to
Switching to Innodb from MyISAM. Matt Yonkovit Percona
Switching to Innodb from MyISAM Matt Yonkovit Percona -2- DIAMOND SPONSORSHIPS THANK YOU TO OUR DIAMOND SPONSORS www.percona.com -3- Who We Are Who I am Matt Yonkovit Principal Architect Veteran of MySQL/SUN/Percona
Chapter 18: Parallel Databases
Chapter 18: Parallel Databases Database System Concepts, 6 th Ed. See www.db-book.com for conditions on re-use Chapter 18: Parallel Databases Introduction I/O Parallelism Interquery Parallelism Intraquery
Chapter 18: Parallel Databases. Chapter 18: Parallel Databases. Parallelism in Databases. Introduction
Chapter 18: Parallel Databases Chapter 18: Parallel Databases Introduction I/O Parallelism Interquery Parallelism Intraquery Parallelism Intraoperation Parallelism Interoperation Parallelism Design of
PERFORMANCE TUNING SQL SERVER ON CRAPPY HARDWARE 3/1/2019 1
PERFORMANCE TUNING SQL SERVER ON CRAPPY HARDWARE 3/1/2019 1 FEEDBACK FORMS PLEASE FILL OUT AND PASS TO YOUR HELPER BEFORE YOU LEAVE THE SESSION MONICA RATHBUN Consultant Denny Cherry & Associates Consulting
How to pimp high volume PHP websites. 27. September 2008, PHP conference Barcelona. By Jens Bierkandt
How to pimp high volume PHP websites 27. September 2008, PHP conference Barcelona By Jens Bierkandt 1 About me Jens Bierkandt Working with PHP since 2000 From Germany, living in Spain, speaking English
Course Modules for MCSA: SQL Server 2016 Database Development Training & Certification Course:
Course Modules for MCSA: SQL Server 2016 Database Development Training & Certification Course: 20762C Developing SQL 2016 Databases Module 1: An Introduction to Database Development Introduction to the
MySQL Performance Troubleshooting
MySQL Performance Troubleshooting Best Practices Francisco Bordenave - Architect, Percona Agenda Who am I? Introduction Identifying the source of problem We know where the problem is, now what? Best practices
Automatic MySQL Schema Management with Skeema. Evan Elias Percona Live, April 2017
Automatic MySQL Schema Management with Skeema Evan Elias Percona Live, April 2017 What is Schema Management? Organize table schemas in a repo Execution of all DDL, on the correct MySQL instances, with
HP NonStop Structured Query Language (SQL)
HP HP0-780 NonStop Structured Query Language (SQL) http://killexams.com/exam-detail/hp0-780 B. EXEC SQL UPDATE testtab SET salary = 0; C. EXEC SQL UPDATE testtab SET salary = :-1; D. EXEC SQL UPDATE testtab
Administração e Optimização de Bases de Dados 2012/2013 Index Tuning
Administração e Optimização de Bases de Dados 2012/2013 Index Tuning Bruno Martins DEI@Técnico e DMIR@INESC-ID Index An index is a data structure that supports efficient access to data Condition on Index
MySQL Database Scalability
MySQL Database Scalability Nextcloud Conference 2016 TU Berlin Oli Sennhauser Senior MySQL Consultant at FromDual GmbH oli.sennhauser@fromdual.com 1 / 14 About FromDual GmbH Support Consulting remote-dba
Announcement. Reading Material. Overview of Query Evaluation. Overview of Query Evaluation. Overview of Query Evaluation 9/26/17
Announcement CompSci 516 Database Systems Lecture 10 Query Evaluation and Join Algorithms Project proposal pdf due on sakai by 5 pm, tomorrow, Thursday 09/27 One per group by any member Instructor: Sudeepa
Parallel DBs. April 25, 2017
Parallel DBs April 25, 2017 1 Why Scale Up? Scan of 1 PB at 300MB/s (SATA r2 Limit) (x1000) ~1 Hour ~3.5 Seconds 2 Data Parallelism Replication Partitioning A A A A B C 3 Operator Parallelism Pipeline
Database Architectures
Database Architectures CPS352: Database Systems Simon Miner Gordon College Last Revised: 4/15/15 Agenda Check-in Parallelism and Distributed Databases Technology Research Project Introduction to NoSQL
Backup & Restore. Maximiliano Bubenick Sr Remote DBA
Backup & Restore Maximiliano Bubenick Sr Remote DBA Agenda Why backups? Backup Types Raw Backups Logical Backups Binlog mirroring Backups Locks Tips Why Backups? Why Backups? At some point something will
State of MariaDB. Igor Babaev Notice: MySQL is a registered trademark of Sun Microsystems, Inc.
State of MariaDB Igor Babaev igor@askmonty.org New features in MariaDB 5.2 New engines: OQGRAPH, SphinxSE Virtual columns Extended User Statistics Segmented MyISAM key cache Pluggable Authentication Storage-engine-specific