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My sql performance

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This presentation describes commonly faced MySQL performance issues and share best practices for improving performance.

This presentation describes commonly faced MySQL performance issues and share best practices for improving performance.

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My sql performance My sql performance Presentation Transcript

  • Solving MySQL Performance Issues
  • Problems• Overview – Most of the tables de-normalized / 2nd normal form – Way too many indexes – Missing constraints – No subtype relationships – Repetitive data – Inconsistent naming convention – Wrong choice of datatypes – No partitioning
  • Data Issues• Severe requirement for data standardization?• Oversized datatypes having incorrect data text/bigint)• Nullable columns• Unique constraints not present where required creating duplicates in master tables
  • Performance Issues• Due to oversized columns and row sizes, number of rows fitting in a page is less, causing more IO• Joins requiring more memory• All sorts goes for temporary file creation on disk increasing IO• Lock (shared / Exclusive) time increase due to above reasons, reducing concurrency• Memory inefficiently used due to oversized rows.• Every update, needs to update unnecessary indexes increasing lock time.
  • Fixes1. Reduce the number of rows by creating archive db.2. Remove unwanted indexes and reduce insert/update overheads3. Upgrade to MySQL 5.54. Partitioning ?
  • Short Term fixes at Application tier• Set isolation level to READ_COMMITTED• Set dynamic update and dynamic insert = TRUE. 95% of the updates update all columns even if few columns are modified.• Review transaction boundaries. Few flows dont close (commit/rollback) leaving open transactions• Query tuning – Not all hibernate queries are optimal. Most of them do left outer join even when not required.• Query tuning – Review queries not using indexes, badly formed queries• Most queries bring all columns (Select *) and usually discarded. This is an overhead on cache, n/w and the app. Tier session as well.
  • Long Term Fixes1. Schema changes (Normalized, smaller tables)2. Partitioning3. Sharding (Application aware / Abstract)4. Fan- In topology or Cluster
  • Master – Master Setup• In order scale writes, this is usually a technique that is used. However here are pit falls – – Management of auto increment columns – A violation of a constraint on one of the masters can break replication rendering the failed master as slave – Resyncing of masters is time consuming and error prone (replication limitation)