1. 1. MY SQL SKILLS KILLED THE SERVER Dave Ferguson @dfgrumpy dev.Objective() 2015
2. 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. 3. WHAT WILL WE COVER? Running Queries When good SQL goes bad Bulk processing Large volume datasets Indexes Outside influences
4. 4. (I KNOW SQL) WHY AM I HERE?
5. 5. Because you have probably written something like this
6. 6. select * from myTable
7. 7. I can write SQL in my sleep select * from myTable where id = 2
8. 8. 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
9. 9. 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`) );
10. 10. But, how do you know if what you did was the best / most efficient way to do it?
11. 11. Did the internet tell you it was right?
12. 12. Did you get some advice from a someone?
13. 13. My app works fine. It has thousands of queries and we only see slowness every once in a while.
14. 14. Have you ever truly looked at what your queries are doing?
15. 15. 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?
16. 16. 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
17. 17. Execution Context & Query Plan
18. 18. Have you ever looked at a query plan? Do you know what a query plan is?
19. 19. Query Plan, In the event you were curious
20. 20. 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
21. 21. 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
22. 22. What can cause plans to be flushed from cache? Forced via code Memory pressure Alter statements Statistics update auto_update statistics on
23. 23. HOW CAN WE KEEP THE DATABASE FROM THROWING AWAY THE PLANS?
24. 24. MORE IMPORTANTLY, HOW CAN WE GET THE DATABASE TO USE THE CACHED PLANS?
25. 25. Force it Use params Use stored procedures Get more ram Use less queries SIMPLE ANSWER
26. 26. HOW DOES SQL DETERMINE IF THERE IS A QUERY PLAN?
27. 27. Something Like this
28. 28. THIS QUERY WILL CREATE A EXECUTION CONTEXT.. select id, name from myTable where id = 2 THAT
29. 29. WILL NOT BE USED BY THIS QUERY. select id, name from myTable where id = 5
30. 30. WHY IS THAT? Well, the queries are not the same.
31. 31. 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.
32. 32. 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!
33. 33. TO RECAP EXECUTION CONTEXTS ARE GOOD TOO MANY ARE BAD
34. 34. USING QUERY PARAMS The secret sauce to plan reuse
35. 35. select a.ARTID, a.ARTNAME from ART a where a.ARTID = Using a simple query lets add a param for the id.
36. 36. select a.ARTID, a.ARTNAME from ART a where a.ARTID = ? THE QUERY OPTIMIZER SEES THIS
37. 37. 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
38. 38. 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
39. 39. 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...
40. 40. 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.
41. 41. How do I deal with all this data?
42. 42. What do I mean by large data sets? Tables over 1 million rows Large databases Heavily denormalized data
43. 43. 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
44. 44. 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
45. 45. Combining Queries: Instead of doing this
46. 46. Do this
47. 47. Gotchas 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
48. 48. 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.
49. 49. Indexes The secret art of a faster select
50. 50. 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
51. 51. 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
52. 52. 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 DONT INDEX Small tables Columns with low cardinality Any column with only a couple values
53. 53. Do I really need an index?
54. 54. It Depends.
55. 55. Really it Depends!
56. 56. Outside influences
57. 57. Other things that can effect performance Processor load Memory pressure Hard drive I/O Network
58. 58. 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
59. 59. 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
60. 60. 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 dont run out of space Purge log files Dont store all DB and log files on the same physical drives On windows dont 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
61. 61. 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 doesnt impact DB traffic
62. 62. Some Important Database Statistics
63. 63. Important stats Recompiles Recompile of a proc while running shouldnt 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
64. 64. 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
65. 65. Where SQL goes wrong (Good examples of bad SQL)
66. 66. Inline queries that well shouldnt be
67. 67. Over joining data
68. 68. Transactions Do you see the issue?
69. 69. THAT IS ALL THANK YOU Dave Ferguson @dfgrumpy dave@dkferguson.com www.cfhour.com dev.Objective() 2015Dont forget to fill out the survey