Sql killedserver
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This document discusses SQL skills and how queries can negatively impact server performance if not written efficiently. It covers topics like query plans, execution contexts, using parameters, indexing, handling large datasets, and external influences on SQL performance. Specific "bad" SQL examples are also provided and analyzed. The presenter aims to help developers optimize their SQL and prevent poorly written queries from bringing servers to their knees.
Sql killedserver
- 1. MY SQL SKILLS KILLED THE SERVER Dave Ferguson @dfgrumpy dev.Objective() 2015
- 2. WHO AM I? I am an Adobe Community Professional I started building web applications a long time ago Contributor to Learn CF in a week I have a ColdFusion podcast called CFHour w/ Scott Stroz (@boyzoid) (please listen) 3x California State Taekwondo Weapons Champion
- 3. WHAT WILL WE COVER? Running Queries When good SQL goes bad Bulk processing Large volume datasets Indexes Outside influences
- 5. (I KNOW SQL) “WHY AM I HERE?”
- 6. Because you have probably written something like this…
- 7. select * from myTable
- 8. “I can write SQL in my sleep” select * from myTable where id = 2
- 9. “I can write joins and other complex SQL” Select mt.* from myTable mt join myOtherTable mot on mt.id = mot.id where mot.id = 2
- 10. “I might even create a table” CREATE TABLE `myFakeTable` ( `id` int(11) unsigned NOT NULL AUTO_INCREMENT, `someName` varchar(150) NOT NULL DEFAULT '', `someDescription` text, `type` varchar(50) DEFAULT NULL, `status` int(11) NOT NULL, PRIMARY KEY (`id`) );
- 11. But, how do you know if what you did was the best / most efficient way to do it?
- 12. Did the internet tell you it was right?
- 13. Did you get some advice from a someone?
- 14. “My app works fine. It has thousands of queries and we only see slowness every once in a while. ”
- 15. Have you ever truly looked at what your queries are doing?
- 16. Most developers don't bother. They leave all that technical database stuff up to the DBA. But what if you are the developer AND the DBA?
- 17. Query Plan Uses Execution Contexts Created for each degree of parallelism for a query Execution Context Specific to the query being executed. Created for each query QUERY EXECUTION
- 18. Execution Context & Query Plan
- 19. Have you ever looked at a query plan? Do you know what a query plan is?
- 20. Query Plan, In the event you were curious…
- 21. WHAT A QUERY PLAN WILL TELL YOU Path taken to get data Almost like a Java stack trace Indexes usage How the indexes are being used Cost of each section of plan Possible suggestions for performance improvement Whole bunch of other stuff
- 22. How long are plans / contexts kept? 1 Hour 1 Day ‘Til SQL server restarts Discards it immediately The day after forever Till the server runs out of cache space
- 23. What can cause plans to be flushed from cache? Forced via code Memory pressure Alter statements Statistics update auto_update statistics on
- 24. HOW CAN WE KEEP THE DATABASE FROM THROWING AWAY THE PLANS?
- 25. MORE IMPORTANTLY, HOW CAN WE GET THE DATABASE TO USE THE CACHED PLANS?
- 26. • Force it • Use params • Use stored procedures • Get more ram • Use less queries SIMPLE ANSWER
- 27. HOW DOES SQL DETERMINE IF THERE IS A QUERY PLAN?
- 29. THIS QUERY WILL CREATE A EXECUTION CONTEXT.. select id, name from myTable where id = 2 THAT…
- 30. WILL NOT BE USED BY THIS QUERY. select id, name from myTable where id = 5
- 31. WHY IS THAT? Well, the queries are not the same.
- 32. According to the SQL optimizer, select id, name from myTable where id = 2 select id, name from myTable where id = 5 this query… and this query… are not the same. So, they each get their own execution plan.
- 33. PLANS CAN BECOME DATA HOGS select id, name from myTable where id = 2 If the query above ran 5,000 times over the course of an hour (with different ids), you could have that many plans cached. That could equal around 120mb of cache space!
- 34. TO RECAP… EXECUTION CONTEXTS ARE GOOD TOO MANY ARE BAD
- 35. USING QUERY PARAMS… The secret sauce to plan reuse
- 36. <cfquery name="testQuery"> select a.ARTID, a.ARTNAME from ART a where a.ARTID = <cfqueryparam value="5” cfsqltype="cf_sql_integer"> </cfquery> Using a simple query… let’s add a param for the id.
- 37. select a.ARTID, a.ARTNAME from ART a where a.ARTID = ? THE QUERY OPTIMIZER SEES THIS…
- 38. testQuery (Datasource=cfartgallery, Time=1ms, Records=1) in /xxx/x.cfm select a.ARTID, a.ARTNAME from ART a where a.ARTID = ? Query Parameter Value(s) - Parameter #1(cf_sql_integer) = 5 THE DEBUG OUTPUT LOOKS LIKE THIS…
- 39. testQuery (Datasource=cfartgallery, Time=8ms, Records=5) in /xxx/x.cfm select a.ARTID, a.ARTNAME from ART a where a.ARTID in (?,?,?,?,?) Query Parameter Value(s) - Parameter #1(CF_SQL_CHAR) = 1 Parameter #2(CF_SQL_CHAR) = 2 Parameter #3(CF_SQL_CHAR) = 3 Parameter #4(CF_SQL_CHAR) = 4 Parameter #5(CF_SQL_CHAR) = 5 IT EVEN WORKS ON LISTS…
- 40. testQuery (Datasource=cfartgallery, Time=3ms, Records=1) in /xxx/x.cfm select a.ARTID, a.ARTNAME, ( select count(*) from ORDERITEMS oi where oi.ARTID = ? ) as ordercount from ART a where a.ARTID in (?) Query Parameter Value(s) - Parameter #1(cf_sql_integer) = 5 Parameter #2(cf_sql_integer) = 5 MORE ACCURATELY, THEY WORK ANYWHERE YOU WOULD HAVE DYNAMIC INPUT...
- 41. When can plans cause more harm then help? ► When your data structure changes ► When data volume grows quickly ► When you have data with a high degree of cardinality.
- 42. How do I deal with all this data?
- 43. What do I mean by large data sets? ► Tables over 1 million rows ► Large databases ► Heavily denormalized data
- 44. Ways to manage large data ► Only return what you need (no “select *”) ► Try and page the data in some fashion ► Optimize indexes to speed up where clauses ► Avoid using triggers on large volume inserts ► Reduce any post query processing as much as possible
- 45. Inserting / Updating large datasets ► Reduce calls to database by combining queries ► Use bulk loading features of your Database ► Use XML/JSON to load data into Database
- 46. Combining Queries: Instead of doing this…
- 47. Do this…
- 48. Gotcha’s in query combining ► Errors could cause whole batch to fail ► Overflowing allowed query string size ► Database locking can be problematic ► Difficult to get any usable result from query
- 49. Upside query combining ► Reduces network calls to database ► Processed as a single batch in database ► Generally processed many times faster than doing the insert one at a time ► I have used this to insert over 50k rows into mysql in under one second.
- 50. Indexes The secret art of a faster select
- 51. Index Types ► Unique ► Primary key or row ID ► Covering ► A collection of columns indexed in an order that matches where clauses ► Clustered ► The way the data is physically stored ► Table can only have one ► NonClustered ► Only contain indexed data with a pointer back to source data
- 52. Seeking and Scanning ► Index SCAN (table scan) ► Touches all rows ► Useful only if the table contains small amount of rows ► Index SEEK ► Only touches rows that qualify ► Useful for large datasets or highly selective queries ► Even with an index, the optimizer may still opt to perform a scan
- 53. To index or not to index… ► DO INDEX ► Large datasets where 10 – 15% of the data is usually returned ► Columns used in where clauses with high cardinality ► User name column where values are unique ► DON’T INDEX ► Small tables ► Columns with low cardinality ► Any column with only a couple values
- 54. Do I really need an index?
- 55. It Depends.
- 58. Other things that can effect performance ► Processor load ► Memory pressure ► Hard drive I/O ► Network
- 59. Processor ► Give SQL Server process CPU priority ► Watch for other processes on the server using excessive CPU cycles ► Have enough cores to handle your database activity ► Try to keep average processor load below 50% so the system can handle spikes gracefully
- 60. Memory (RAM) ► Get a ton (RAM is cheap) ► Make sure you have enough RAM to keep your server from doing excess paging ► Make sure your DB is using the RAM in the server ► Allow the DB to use RAM for cache ► Watch for other processes using excessive RAM
- 61. Drive I/O ► Drive I/O is usually the largest bottle neck on the server ► Drives can only perform one operation at a time ► Make sure you don’t run out of space ► Purge log files ► Don’t store all DB and log files on the same physical drives ► On windows don’t put your DB on the C: drive ► If possible, use SSD drives for tempdb or other highly transactional DBs ► Log drives should be in write priority mode ► Data drives should be in read priority mode
- 62. Network ► Only matters if App server and DB server are on separate machines (they should be) ► Minimize network hops between servers ► Watch for network traffic spikes that slow data retrieval ► Only retrieving data needed will speed up retrieval from DB server to app server ► Split network traffic on SQL server across multiple NIC cards so that general network traffic doesn’t impact DB traffic
- 64. Important stats ► Recompiles ► Recompile of a proc while running shouldn’t occur ► Caused by code in proc or memory issues ► Latch Waits ► Low level lock inside DB; Should be sub 10ms ► Lock Waits ► Data lock wait caused by thread waiting for another lock to clear ► Full Scans ► Select queries not using indexes
- 65. Important stats continued.. ► Cache Hit Ratio ► How often DB is hitting memory cache vs Disk ► Disk Read / Write times ► Access time or write times to drives ► SQL Processor time ► SQL server processor load ► SQL Memory ► Amount of system memory being used by SQL
- 66. Where SQL goes wrong (Good examples of bad SQL)
- 67. Inline queries that well… shouldn’t be
- 69. Transactions – Do you see the issue?
- 71. THAT IS ALL THANK YOU Dave Ferguson @dfgrumpy [email protected] www.cfhour.com dev.Objective() 2015Don’t forget to fill out the survey
Editor's Notes
- #35: There is only so much cache room to go around