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Fri Nov 03 09:34:22 HKT 2017

performance



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Fri Nov 03 09:41:42 HKT 2017 From /weblog/database/performance

numa


https://technet.microsoft.com[..]m/en-us/library/ms345403(v=sql.105).aspx

https://code.kx.com/q/cookbook/linux-production/

http://jcole.us[..]swap-insanity-and-the-numa-architecture/

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Fri Sep 22 15:29:28 HKT 2017 From /weblog/database/performance

architecture


IT Hare: Ultimate DB Heresy: Single Modifying DB Connection. Part I. Performanc - http://highscalability.com[..]y-single-modifying-db-connection-pa.html

How yandex.metrica improve DB reading - http://highscalability.com[..]of-data-structures-in-yandexmetrica.html

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Fri Jul 21 17:24:58 HKT 2017 From /weblog/database/performance

Note of SQL performance tuning


Note of this book - http://www.ocelot.ca/tuning.htm

1) Order of "and" and "or" is matter, if no cost-base optimizer

where column1 = 'a' and column2 = 'b' -> where column2 = 'b' and column1 = 'a' if column2 'b' is less likely

2) <> is more expensive than =

3) Can force to use index if SELECT * FROM table WHERE indexed_column > 0

4) UPPER(column) might lose information, use LOWER is better

5) WHERE column = 'WORD' or column = 'word' is always faster than WHERE LOWER(column) = 'word' , even faster version is test if database if case-insensitive: WHERE column = 'WORD' or ('WORD' <> 'word' AND column = 'word')

6) 32bit integer is the fastest datatype for 32bit OS

7) column in (?, ?) is faster than column =? or column = ? for some database, and don't slow in any database

8) For aggregate functions, prevent using having, try to use fewer columns in group by and use subquery if possible

9) In fact, count(1) is not faster than count(*)

10) CAST before aggregate function: SELECT CAST(SUM(column) AS INT) -> SELECT SUM(CAST(column as INT))

11) Use constant as much as possible, e.g.: select * from t1, t2 where t1.c1 = t2.c1 and t1.c1 = 1 -> select * from t1, t2 where t1.c1=1 andn t2.c1=1

12) Information of Join vs Subqueries

13) Information of optimum column type (VARCHAR, TIMESTAMP, WORD size INT and CHAR(1) usually better), shift effect for page

14) prevent NULL for porting issue

15) Low level table physical information

Some related links - http://www.techartifact.com[..]9/12/sql-optimization-tipsquestions.html

16) Prevent database level transaction, prevent joining, prevent locking ( like auto increment key ), pretty good suggestion - http://www.aviransplace.com/2015/08/12/mysql-is-a-great-nosql/

Few good tips, like avoid cursors - http://www.javaworld.com[..]ent/21-rules-for-faster-sql-queries.html

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Tue Jul 28 15:43:43 HKT 2015 From /weblog/database/performance

index


Case study about how to find out suitable index - http://samsaffron.com[..]e+life+of+a+slow+page+at+Stack+Overflow#

A simple example of how to use EXPLAIN to find out the bottleneck of the query and add suitable index to suitable column - http://hackmysql.com/case4

Bitmap Index vs. B-tree Index: Which and When? - http://www.oracle.com/technology/pub/articles/sharma_indexes.html http://publib.boulder.ibm.com[..]/v5r3/index.jsp?topic=/rzajq/perf21c.htm

script to check if index is missing at foreign key

select table_name, constraint_name,
cname1 || nvl2(cname2,','||cname2,null) ||
nvl2(cname3,','||cname3,null) || nvl2(cname4,','||cname4,null) ||
nvl2(cname5,','||cname5,null) || nvl2(cname6,','||cname6,null) ||
nvl2(cname7,','||cname7,null) || nvl2(cname8,','||cname8,null)
columns
from ( select b.table_name,
b.constraint_name,
max(decode( position, 1, column_name, null )) cname1,
max(decode( position, 2, column_name, null )) cname2,
max(decode( position, 3, column_name, null )) cname3,
max(decode( position, 4, column_name, null )) cname4,
max(decode( position, 5, column_name, null )) cname5,
max(decode( position, 6, column_name, null )) cname6,
max(decode( position, 7, column_name, null )) cname7,
max(decode( position, 8, column_name, null )) cname8,
count(*) col_cnt
from (select substr(table_name,1,30) table_name,
substr(constraint_name,1,30) constraint_name,
substr(column_name,1,30) column_name,
position
from sys.user_cons_columns ) a,
sys.user_constraints b
where a.constraint_name = b.constraint_name
and b.constraint_type = 'R'
group by b.table_name, b.constraint_name
) cons
where col_cnt > ALL
( select count(*)
from sys.user_ind_columns i
where i.table_name = cons.table_name
and i.column_name in (cname1, cname2, cname3, cname4,
cname5, cname6, cname7, cname8 )
and i.column_position <= cons.col_cnt
group by i.index_name
)

http://chriswongdevblog.blogspot.com.au[..]01/dont-forget-to-index-your-oracle.html

Explain how postgres indexes work, look like combine index is not that useful - http://dublintech.blogspot.com.au/2015/07/postgres-indexes.html

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Sun Jan 06 19:22:03 HKT 2013 From /weblog/database/performance

embedded


Compare the performance of LevelDB, BDB and BangDB, it sound like BangDB is best... but this article from BangDB - http://highscalability.com[..]ldb-berkley-db-and-bangdb-for-rando.html

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Fri Jul 01 00:34:29 HKT 2011 From /weblog/database/performance

parameter sniffing


When the query plan is compiled on first run of the procedure after a SQL Server restart, the query is optimised for those initial variables in that initial call.

There’s ways to write the procedure to have a more representative query plan chosen on initial compile each time:

http://blogs.msdn.com[..]ryoptteam/archive/2006/03/31/565991.aspx

This is what is known as parameter sniffing.

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Fri Apr 29 09:34:07 HKT 2011 From /weblog/database/performance

count


Tip of having faster 'select count(*) from table' of sql server, probably can use this as reference to check similar feature on other DB, if that hurt something - http://beyondrelational.com[..]are-there-any-alternatives-to-count.aspx

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Fri Mar 27 23:02:05 HKT 2009 From /weblog/database/performance

sharding


An idea of database sharding, basically replicate a unified version for operation required global access - http://itsfrosty.wordpress.com[..]com/2009/03/20/database-sharding-basics/

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Fri Mar 27 11:49:44 HKT 2009 From /weblog/database/performance

MaterializedView


Oracle can help us to denormalize tables according to query via materialized view, should help performance a lot, probably there is similar feature in other DBMS - http://www.oracle.com[..]ology/products/oracle9i/daily/jul05.html

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