SQL tips

1. Use EXPLAIN to profile the query execution plan
2. Use Slow Query Log (always have it on!)
3. Don't use DISTINCT when you have or could use GROUP BY
4. Insert performance
   1. Batch INSERT and REPLACE
   2. Use LOAD DATA instead of INSERT
5. LIMIT m,n may not be as fast as it sounds. Learn how to improve it and read more about Efficient Pagination Using MySQL
6. Don't use ORDER BY RAND() if you have > ~2K records
7. Use SQL_NO_CACHE when you are SELECTing frequently updated data or large sets of data
8. Avoid wildcards at the start of LIKE queries
9. Avoid correlated subqueries and in select and where clause (try to avoid in)
10. No calculated comparisons -- isolate indexed columns
11. ORDER BY and LIMIT work best with equalities and covered indexes
12. Separate text/blobs from metadata, don't put text/blobs in results if you don't need them
13. Derived tables (subqueries in the FROM clause) can be useful for retrieving BLOBs without sorting them. (Self-join can speed up a query if 1st part finds the IDs and uses then to fetch the rest)
14. ALTER TABLE...ORDER BY can take data sorted chronologically and re-order it by a different field -- this can make queries on that field run faster (maybe this goes in indexing?)
15. Know when to split a complex query and join smaller ones
16. Delete small amounts at a time if you can
17. Make similar queries consistent so cache is used
18. Have good SQL query standards
19. Don't use deprecated features
20. Turning OR on multiple index fields (<5.0) into UNION may speed things up (with LIMIT), after 5.0 the index_merge should pick stuff up.
21. Don't use COUNT * on Innodb tables for every search, do it a few times and/or summary tables, or if you need it for the total # of rows, use SQL_CALC_FOUND_ROWS and SELECT FOUND_ROWS()
22. Use INSERT ... ON DUPLICATE KEY update (INSERT IGNORE) to avoid having to SELECT
23. use groupwise maximum instead of subqueries
24. Avoid using IN(...) when selecting on indexed fields, It will kill the performance of SELECT query.
25. Prefer using UNION ALL if you don't need to merge the result

Scaling Performance Tips:

1. Use benchmarking
2. isolate workloads don't let administrative work interfere with customer performance. (ie backups)
3. Debugging sucks, testing rocks!
4. As your data grows, indexing may change (cardinality and selectivity change). Structuring may want to change. Make your schema as modular as your code. Make your code able to scale. Plan and embrace change, and get developers to do the same.

Network Performance Tips:

1. Minimize traffic by fetching only what you need.
   1. Paging/chunked data retrieval to limit
   2. Don't use SELECT *
   3. Be wary of lots of small quick queries if a longer query can be more efficient
2. Use multi_query if appropriate to reduce round-trips
3. Use stored procedures to avoid bandwidth wastage

OS Performance Tips:

1. Use proper data partitions
   1. For Cluster. Start thinking about Cluster *before* you need them
2. Keep the database host as clean as possible. Do you really need a windowing system on that server?
3. Utilize the strengths of the OS
4. pare down cron scripts
5. create a test environment
6. source control schema and config files
7. for LVM innodb backups, restore to a different instance of MySQL so Innodb can roll forward
8. partition appropriately
9. partition your database when you have real data -- do not assume you know your dataset until you have real data
10. Reduce swappiness of your OS

MySQL Server Overall Tips:

1. innodb_flush_commit=0 can help slave lag
2. Optimize for data types, use consistent data types. Use PROCEDURE ANALYSE() to help determine the smallest data type for your needs.
3. use optimistic locking, not pessimistic locking. try to use shared lock, not exclusive lock. share mode vs. FOR UPDATE
4. if you can, compress text/blobs
5. compress static data
6. don't back up static data as often
7. enable and increase the query and buffer caches if appropriate
8. config params -- http://docs.cellblue.nl/2007/03/17/easy-mysql-performance-tweaks/ is a good reference
9. Config variables & tips:
   1. use one of the supplied config files
   2. key_buffer, unix cache (leave some RAM free), per-connection variables, innodb memory variables
   3. be aware of global vs. per-connection variables
   4. check SHOW STATUS and SHOW VARIABLES (GLOBAL|SESSION in 5.0 and up)
   5. be aware of swapping esp. with Linux, "swappiness" (bypass OS filecache for innodb data files, innodb_flush_method=O_DIRECT if possible (this is also OS specific))
   6. defragment tables, rebuild indexes, do table maintenance
   7. If you use innodb_flush_txn_commit=1, use a battery-backed hardware cache write controller
   8. more RAM is good so faster disk speed
   9. use 64-bit architectures
10. --skip-name-resolve
11. increase myisam_sort_buffer_size to optimize large inserts (this is a per-connection variable)
12. look up memory tuning parameter for on-insert caching
13. increase temp table size in a data warehousing environment (default is 32Mb) so it doesn't write to disk (also constrained by max_heap_table_size, default 16Mb)
14. Run in SQL_MODE=STRICT to help identify warnings
15. /tmp dir on battery-backed write cache
16. consider battery-backed RAM for innodb logfiles
17. use --safe-updates for client
18. Redundant data is redundant
19. Keep an eye on buffer pool and keybuffer hit rate

Storage Engine Performance Tips:

1. InnoDB ALWAYS keeps the primary key as part of each index, so do not make the primary key very large
2. Utilize different storage engines on master/slave ie, if you need fulltext indexing on a table.
3. BLACKHOLE engine and replication is much faster than FEDERATED tables for things like logs.
4. Know your storage engines and what performs best for your needs, know that different ones exist.
   1. ie, use MERGE tables ARCHIVE tables for logs
   2. Archive old data -- don't be a pack-rat! 2 common engines for this are ARCHIVE tables and MERGE tables
5. use row-level instead of table-level locking for OLTP workloads
6. try out a few schemas and storage engines in your test environment before picking one.

Database Design Performance Tips:

1. Design sane query schemas. don't be afraid of table joins, often they are faster than denormalization
2. Don't use boolean flags
3. Use Indexes
4. Don't Index Everything
5. Do not duplicate indexes
6. Do not use large columns in indexes if the ratio of SELECTs:INSERTs is low.
7. Split out large blob elements in InnoDB
8. be careful of redundant columns in an index or across indexes
9. Use a clever key and ORDER BY instead of MAX
10. Normalize first, and denormalize where appropriate.
11. Databases are not spreadsheets, even though Access really really looks like one. Then again, Access isn't a real database
12. use INET_ATON and INET_NTOA for IP addresses, not char or varchar
13. make it a habit to REVERSE() email addresses, so you can easily search domains (this will help avoid wildcards at the start of LIKE queries if you want to find everyone whose e-mail is in a certain domain)
14. A NULL data type can take more room to store than NOT NULL
15. Avoid NULL in index attributes. Use 0 instead
16. Storing flags in a database can slow down execution due to a bad cardinality. Try using bit flags
17. Don't store flags in a NULL and NOT NULL manner. Update from NULL -> 1 is slower than 0 -> 1
18. Choose appropriate character sets & collations -- UTF16 will store each character in 2 bytes, whether it needs it or not, latin1 is faster than UTF8.
19. Use Triggers wisely
20. Use delayed key wrote
21. use min_rows and max_rows to specify approximate data size so space can be pre-allocated and reference points can be calculated.
22. Use HASH indexing for indexing across columns with similar data prefixes
23. Use myisam_pack_keys for int data
24. be able to change your schema without ruining functionality of your code
25. segregate tables/databases that benefit from different configuration variables
26. Don't access the last key part in a where clause with =
27. Abuse the system for optimiization you're using with system dependant features like RTREE's for optimized range queries