Conference slides: MySQL Cluster Performance Tuning
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The document focuses on performance tuning for MySQL Cluster, covering key topics such as scaling, partitioning, and query optimization techniques. It emphasizes the importance of using simple access patterns and partitioning strategy to enhance system performance, detailing best practices for insert and query tuning. Additionally, it highlights various configurations and tuning parameters for optimal MySQL Cluster performance.
![Ndb_cluster_connection_pool
Gives atleast 70% better performance and a MySQL Server that
can scale beyond four database connections.
Set Ndb_cluster_connection_pool=2x<CPU cores>
It is a good starting point
One free [mysqld] slot is required in config.ini for each
Ndb_cluster_connection.
4 mysql servers,each with Ndb_cluster_connection_pool=8 requires 32
[mysqld] in config.ini
Copyright Severalnines 2012](https://image.slidesharecdn.com/severalninesmysqlclusterpt2-120523023903-phpapp01/85/Conference-slides-MySQL-Cluster-Performance-Tuning-28-320.jpg)
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Conference slides: MySQL Cluster Performance Tuning
- 1. Performance Tuning of MySQL Cluster Santa Clara, April 2012 Johan Andersson Severalnines AB [email protected] Cell +46 73 073 60 99 Copyright Severalnines 2012
- 2. 2 Agenda Scaling and Partitioning Designing a Scalable System Insert Performance Tuning Query Tuning Random tricks Disk Data Tuning Copyright Severalnines 2012
- 3. 3 Here is ... Access Layer App App Server Server MYSQL MYSQL STORAGE LAYER DATA DATA NODE NODE P0 P1 Node group 0 Copyright Severalnines 2012
- 4. 4 It can scale linearly ... Access Layer App App App App App App App App Server Server Server Server Server Server Server Server MYSQL MYSQL MYSQL MYSQL MYSQL NDBAPI NDBAPI NDBAPI STORAGE LAYER STORAGE LAYER STORAGE LAYER DATA DATA DATA DATA DATA DATA NODE NODE NODE NODE NODE NODE P0 P1 P2 P3 P4 P5 Node group 0 Node group 1 Node group 2 Copyright Severalnines 2012
- 5. if you find the bottlenecks A lot of CPU is used on the data nodes Probably a lot of large index scans and full table scans are used. A lot of CPU is used on the mysql servers Probably a lot of GROUP BY/DISTINCT or aggregate functions. Hardly no CPU is used on either mysql or data nodes Probably low load Time is spent on network (a lot of “ping pong” to satisfy a request). System is running slow in general Disks (io util), queries, swap (should never happen) Copyright Severalnines 2012
- 6. and if you know how Adding mysqlds – trivial – if the mysqld is the bottleneck BUT! Adding data nodes More data nodes does not automatically give better performance ● Latency may increase for a single query ● Total throughout will be improved How to get both? Copyright Severalnines 2012
- 7. 7 Designing a Scalable System Define the most typical Use Cases List all my friends, session management etc etc. Optimize everything for the typical use case Keep it simple Complex access patterns does not scale Simple access patterns do ( Primay key and Partitioned Index Scans ) Note! There is no parameter in config.ini that affects performance – only availability. Everything is about the Schema and the Queries. Tune the mysql servers (sort buffers etc) as you would for innodb. Copyright Severalnines 2012
- 8. 8 Simple Access PRIMARY KEY lookups are HASH lookup O(1) INDEX searches a T-tree and takes O(log n) time. In 7.2 JOINs are ok, but in 7.1 you should try to avoid them. Copyright Severalnines 2012
- 9. 9 Data Access Access Layer App App App App App App App App Server Server Server Server Server Server Server Server MYSQL MYSQL MYSQL MYSQL MYSQL NDBAPI NDBAPI NDBAPI STORAGE LAYER DATA DATA DATA DATA DATA DATA NODE NODE NODE NODE NODE NODE P0 P1 P2 P3 P4 P5 Node group 0 Node group 1 Node group 2 Copyright Severalnines 2012
- 10. 10 Data Access One Request can hit all Partitions Sub-optimal and won't scale Copyright Severalnines 2012
- 11. 11 Data Access Access Layer App App App App App App App App Server Server Server Server Server Server Server Server MYSQL MYSQL MYSQL MYSQL MYSQL NDBAPI NDBAPI NDBAPI STORAGE LAYER DATA DATA DATA DATA DATA DATA NODE NODE NODE NODE NODE NODE P0 P1 P2 P3 P4 P5 Node group 0 Node group 1 Node group 2 Copyright Severalnines 2012
- 12. 12 Data Access One Request hits one partition Scales! The number of Partitions (data nodes) does not matter! Partitioning is important! Copyright Severalnines 2012
- 13. 13 Partitioning MySQL Cluster auto-partitions based on the Primary Key Data is spread randomly If possible better to Partition on a part of the Primary Key CREATE TABLE user_friends( userid, friendid , somedata, PRIMARY KEY (userid, friendid)) PARTITION BY KEY(userid) All records with userid=X will be stored in the same partition! Ultra important for MySQL Cluster 7.2 and Fast JOINs. Copyright Severalnines 2012
- 14. 14 Partitioning EXPLAIN PARTITIONS <query> Tells you what partitions you touch. Also verify with: mysql> show global status like 'ndb_pruned_scan_count’; +-----------------------+-------+ | Variable_name | Value | +-----------------------+-------+ | Ndb_pruned_scan_count | 1 | +-----------------------+-------+ Increases when Partition Pruning could be used. Copyright Severalnines 2012
- 15. 15 Insert Performance Scaling Inserts Option 1) Batch INSERTS if you can ● An insert batch of 10 records will perform 10x faster than 10 single inserts! ● INSERT INTO t1 VALUES (<record1>), (<record2>), …,(<recordN>) Option 2) Many threads (parallelism) Or a combo of both Dumpfiles or LOAD DATA INFILEs Chunk them up and load in parallel on several mysqlds Copyright Severalnines 2012
- 16. 16 Insert Performance INSERTs in a table with AUTO_INCREMENT MySQL Server query Data nodes for an auto_increment – The mysqld can hold a range of autoincs (cache) – Before an INSERT, and autoinc must be fetched from either Data node (slow) or on the cache (fast) ndb_autoincremet_prefetch_sz sets the cache size and it affects insert perf: – ndb_autoincrement_prefetch_sz=1: 1211.91TPS – ndb_autoincrement_prefetch_sz=256: 3471.71TPS – ndb_autoincrement_prefetch_sz=1024: 3659.52TPS Copyright Severalnines 2012
- 17. 17 Insert Performance ndb_batch_size can also be important with LOAD DATA INFILE or dumps SET GLOBAL|SESSION NDB_BATCH_SIZE = 16M You may get LongMessageBuffer Overload ● Increase it in config.ini to 32M or 48M Also REDO logs may get overloaded if your disks are too slow and/or the REDO is too small. Copyright Severalnines 2012
- 18. 18 Query Performance Queries needs to be tuned as “usual”: Slow query / general log From a monitoring system (like ClusterControl) + a methodology Copyright Severalnines 2012
- 19. Query Performance Slow query log set global slow_query_log=1; set global long_query_time=0.01; set global log_queries_not_using_indexes=1; General log (if you don’t get enough info in the Slow Query Log) Activate for a very short period of time (30-60seconds) – intrusive Can fill up disk very fast – make sure you turn it off. set global general_log=1; Use Severalnines ClusterControl Includes a Cluster-wide Query Monitor. Query frequency, EXPLAINs, lock time etc. Performance Monitor and Manager. Copyright Severalnines 2012
- 20. Data Types BLOB/TEXT columns are stored in an external hidden table. First 255B are stored inline in main table Reading a BLOB/TEXT requires two reads One for reading the Main table + reading from hidden table Change to VARBINARY/VARCHAR if: Your BLOB/TEXTs can fit within an 8052B record (record size is currently 8052 Bytes) Reading/writing VARCHAR/VARBINARY is less expensive Note 1: BLOB/TEXT are also more expensive in Innodb as BLOB/TEXT data is not inlined with the table. Thus, two disk seeks are needed to read a BLOB. Note 2: Store images, movies etc outside the database on the filesystem. Copyright Severalnines 2012
- 21. Data Types Example CREATE TABLE `t1_blob` ( `id` int(11) NOT NULL AUTO_INCREMENT, `data1` blob, `data2` blob, PRIMARY KEY (`id`) )ENGINE=ndbcluster Performance (8 threads, one mysqld, two data nodes): data1 and data2 as BLOBs: 5844TPS data1 and data2 as VARBINARY: 19206TPS ~3x Copyright Severalnines 2012
- 22. Denormalize Tables sharing the same PRIMARY KEY can be denormalized. Table T1: <UID, SOME_DATA> Table T2: <UID, SOME_OTHER_DATA SELECT * from T1,T2 WHERE T1.UID=T2.UID and T2.UID=1 requires two roundtrips. Starting with MySQL Cluster 7.2 only one roundtrip is needed,. Denormalize Table T12: <UID,SOME_DATA, SOME_OTHER_DATA> Improvement: 2X in throughput Copyright Severalnines 2012
- 23. Query Tuning < 7.2 Don't trust the OPTIMIZER in MySQL Cluster 7.1 and earlier Statistics gathering is non-existing Optimizer thinks there are only 10 rows to examine in each table! You have to do a lot of FORCE INDEX / STRAIGH_JOIN to get queries run the way you want. Copyright Severalnines 2012
- 24. Query Tuning < 7.2 Classic example: if you have two similar indexes: index(a) index(a,ts) on the following table CREATE TABLE `t1` ( `id` int(11) NOT NULL AUTO_INCREMENT, `a` bigint(20) DEFAULT NULL, `ts` timestamp NOT NULL DEFAULT CURRENT_TIMESTAMP, PRIMARY KEY (`id`), KEY `idx_t1_a` (`a`), KEY `idx_t1_a_ts` (`a`,`ts`)) ENGINE=ndbcluster DEFAULT CHARSET=latin1 Copyright Severalnines 2012
- 25. Query Tuning < 7.2 mysql> explain select * from t1 where a=2 and ts='2011-10-05 15:32:11'; +----+-------------+-------+------+----------------------+----------+---------+-------+------+-------------+ | id | select_type | table | type | possible_keys | key | key_len | ref | rows | Extra | +----+-------------+-------+------+----------------------+----------+---------+-------+------+-------------+ | 1 | SIMPLE | t1 | ref | idx_t1_a,idx_t1_a_ts | idx_t1_a | 9 | const | 10 | Using where | +----+-------------+-------+------+----------------------+----------+---------+-------+------+-------------+ Use FORCE INDEX(..) ... mysql> explain select * from t1 FORCE INDEX (idx_t1_a_ts) where a=2 and ts='2011-10-05 15:32:11; +| 1 | SIMPLE | t1 | ref | idx_t1_a_ts | idx_t1_a_ts | 13 | const,const | 10 | Using where | 1 row in set (0.00 sec) ..to ensure the correct index is picked! The difference can be 1 record read instead of any number of records! Copyright Severalnines 2012
- 26. 26 Query Tuning in 7.2 ANALYZE TABLE Must be performed periodically to rebuild index stats EXPLAIN EXTENDED/PARTITIONS Make sure the explain show “Child of JOIN pushed down” This means that the Fast JOIN of NDB could be used SHOW WARNINGS; ● Shows why a Query was not pushed down. Copyright Severalnines 2012
- 27. Ndb_cluster_connection_pool Problem: A Sendbuffer on the connection between mysqld and the data nodes is protected by a Mutex. Connection threads in MySQL must acquire Mutex and the put data in SendBuffer. Many threads gives more contention on the mutex Must scale out with many MySQL Servers. Workaround: Ndb_cluster_connection_pool (in my.cnf) creates more connections from one mysqld to the data nodes Threads load balance on the connections gives less contention on mutex which in turn gives increased scalabilty Less MySQL Servers needed to drive load! www.severalnines.com/cluster-configurator allows you to specify the connection pool. >70 % improvement. Copyright Severalnines 2012
- 28. Ndb_cluster_connection_pool Gives atleast 70% better performance and a MySQL Server that can scale beyond four database connections. Set Ndb_cluster_connection_pool=2x<CPU cores> It is a good starting point One free [mysqld] slot is required in config.ini for each Ndb_cluster_connection. 4 mysql servers,each with Ndb_cluster_connection_pool=8 requires 32 [mysqld] in config.ini Copyright Severalnines 2012
- 29. Disk Data Tuning Disk Data Tables Un-indexed columns → tablespace on disk Indexed columns → DataMemory DiskPageBufferMemory (DPBM) – LRU page cache Like innodb_buffer_pool Should be big as possible If data not in DPBM DiskPage Go to TS and fetch (Slow) IndexMemory DataMemory Buffer Memory If data is DPBM REDO LOG Return page (faster) UNDO LOG Tablespace Copyright Severalnines 2012
- 30. Disk Data Tuning DiskPageBufferMemory – Hit ratio (derived from ndbinfo.diskpagebuffer): – 1000*page_requests_direct_return/ (page_requests_direct_return + page_requests_wait_io+ page_requests_wait_queue) – 998 is good (like in innodb). – DiskPageBufferMemory=2048M is a good start DiskPage IndexMemory DataMemory Buffer Memory REDO LOG UNDO LOG Tablespace Copyright Severalnines 2012
- 31. Disk Data Tuning UNDO LOG – Always overseen but can be extended overtime – Set it to 50% of the REDO log size : ● 0.5 x NoOfFragmentLogFiles x FragmentLogFileSize Undo buffer (specd in CREATE LOGFILE GROUP) – 32M to 64M (like the RedoBuffer) – SharedGlobalMemory=512M DiskPage IndexMemory DataMemory Buffer Memory REDO LOG UNDO LOG Tablespace Copyright Severalnines 2012
- 32. More on Cluster Severalnines Forum – http://support.severalnines.com/forums/20323398-mysql-cluster Johan Andersson @ blogspot – http://johanandersson.blogspot.com Configuration and Deployment – http://www.severalnines.com/cluster-configurator – ~20 min to deploy a 4 node cluster (288 seconds is the World Record) Self-training – http://severalnines.com/mysql-cluster-training Copyright Severalnines 2012
- 33. 33 Q&A Copyright Severalnines 2012
- 34. 34 Thank you for your time! [email protected] Twitter: @severalnines Copyright Severalnines 2012