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Understanding Statistics and its affect on Query Performance

December 21st, 2014 No comments

    Query Processing and its performance is the heart of any RDBMS.  SAP ASE uses cost based optimizer for query processing and optimization means it creates the different access path (query plan) for a query and it picks up best plan which has least cost for a given query.  This query plan cost  is calculated from the data distribution and statistics  information of a table and its columns.

There are two types of statistics –

Object Level Stats :  The object level statistics describe a table and its indexes and include values such as number of rows and pages in the table and/or index (es), the number of empty pages, and the cluster ratios among others.  Some of the object level statistics are updated automatically by ASE, others when ‘update statistics’ is run.

Column Level Stats : The column level statistics describe the distribution of values in the column; they consist of the column’s histogram and density values and are updated when an index is created or an ‘update statistics’ command is run.

System Tables which store these stats:

The systabstats and sysstatistics tables store statistics for all tables, indexes, and any un-indexed columns for which you have explicitly created statistics.

systabstats stores information about the table or index as an object, and is updated by query processing, data definition language, and update statistics commands.  Simply it stores object level of statistics.These statistics include:

  • Number of data pages for a table, or the number of leaf level pages for an index.
  • Number of rows in the table
  • Height of the index
  • Average length of data rows and leaf rows
  • Number of forwarded and deleted rows
  • Number of empty pages
  • Statistics to increase the accuracy of I/O cost estimates, including cluster ratios, the number of pages that share an extent with an allocation page, and the number of OAM and allocation pages used for the object
  • Stopping points for the reorg command so that it can resume processing
  •  systabstats contains one row for each clustered index, one row for each nonclustered index, one row for each table without a clustered index, and one row for each partition.
    •  The storage for clustered index information depends on the locking scheme for the table:
    •  For data-only-locked tables, systabstats stores an additional row for a clustered index.
    • For allpages-locked tables, the data pages are treated as the leaf level of the index, so the systabstats entry for a clustered index is stored in the same row as the table data.The indid column for clustered indexes on allpages-locked tables is always 1.

 sysstatistics table stores one or more rows for each indexed column on a user table; it also stores statistics for unindexed columns.

  •  The first row for each column stores basic statistics about the column, such as the density for joins and search arguments, the selectivity for some operators, and the number of steps stored in the histogram for the column.
  • If the index has multiple columns, or if you specify multiple columns when you generate statistics for unindexed columns, there is a row for each prefix subset of columns.
  • Additional rows store histogram data for the leading column. Histograms do not exist if indexes were created before any data was inserted into a table. To generate a histogram, run update statistics after inserting data.

Stay Tuned ! We will talk about more in coming posting!

Huge Page Support in Linux – To Increase Database Performance.

January 19th, 2014 No comments

Memory Management internally uses TLB cache to map the Virtual address to physical address.
If the TLB cache is small (TLB Miss) (since page size is small), it will need to refer the Page table. Page Table look ups are costly as compare to TLB cache.
That’s reason the applications ( Like Database) which have heavy memory demand can be configured to Huge TLB Pages so that Page Table access can be reduced  and overall application performance can be increased.
Linux has had support for huge pages since around 2003 where it was mainly used for large shared memory segments in database servers.
ASE Database performance can be increased bt 2-7% by using huge page on Linux Platform. You can check Huge Page Support on Linux :

cat /proc/meminfo | grep Huge
HugePages_Total: XXX
HugePages_Free:  XXX
HugePages_Rsvd:   XXX
Hugepagesize:     2048 kB

Source: http://linuxgazette.net/155/krishnakumar.html

From a memory management perspective, the entire physical memory is divided into “frames” and the virtual memory is divided into “pages”. The memory management unit performs a translation of virtual memory address to physical memory address. The information regarding which virtual memory page maps to which physical frame is kept in a data structure called the “Page Table”. Page table lookups are costly. In order to avoid performance hits due to this lookup, a fast lookup cache called Translation Lookaside Buffer(TLB) is maintained by most architectures. This lookup cache contains the virtual memory address to physical memory address mapping. So any virtual memory address which requires translation to the physical memory address is first compared with the translation lookaside buffer for a valid mapping. When a valid address translation is not present in the TLB, it is called a “TLB miss”. If a TLB miss occurs, the memory management unit will have to refer to the page tables to get the translation. This brings additional performance costs, hence it is important that we try to reduce the TLB misses.

On normal configurations of x86 based machines, the page size is 4K, but the hardware offers support for pages which are larger in size. For example, on x86 32-bit machines (Pentiums and later) there is support for 2Mb and 4Mb pages. Other architectures such as IA64 support multiple page sizes. In the past Linux did not support large pages, but with the advent of HugeTLB feature in the Linux kernel, applications can now benefit from large pages. By using large pages, the TLB misses are reduced. This is because when the page size is large, a single TLB entry can span a larger memory area. Applications which have heavy memory demands such as database applications, HPC applications, etc. can potentially benefit from this.

Source : https://lwn.net/Articles/374424/

Memory Mgmt uses Translation Look Buffer(TLB) Cache to map Virtual to physical address, The amount of memory that can be translated by this cache is referred to as the “TLB reach” and depends on the size of the page and the number of TLB entries.

If the TLB miss time is a large percentage of overall program execution, then the time should be invested to reduce the miss rate and achieve better performance.
Using more than one page size(Huge Page) was identified in the 1990s as one means of reducing the time spent servicing TLB misses by increasing TLB reach.
Broadly speaking, database workloads will gain about 2-7% performance using huge pages whereas scientific workloads can range between 1% and 45%.
Huge pages are not a universal gain, so transparent support for huge pages is limited in mainstream operating systems
it is possible that huge pages will be slower if the workload reference pattern is very sparse and making a small number of references per-huge-page.
Many modern operating systems, including Linux, support huge pages in a more explicit fashion, although this does not necessarily mandate application change. Linux has had support for huge pages since around 2003 where it was mainly used for large shared memory segments in database servers such as Oracle and DB2

Configuring sqsh – Interactive database shell- replacement for isql

January 7th, 2014 No comments

Sqsh (pronounced skwish) is short for SQshelL (pronounced s-q-shell), it is intended as a replacement for the venerable ‘isql’ program supplied by Sybase.

Sqsh is much more than a nice prompt, it is intended to provide much of the functionality provided by a good shell, such as variables, aliasing, redirection, pipes,back-grounding, job control, history, command substitu-tion, and dynamic configuration. Also, as a by-product of the design, it is remarkably easy to extend and add functionality.

Sqsh was developped by Scott C. Gray, and is currently maintained by Michael Peppler (mpeppler@peppler.org) and also by Martin Wesdorp (mwesdorp@users.sourceforge.net).

You can download sqsh from Source Forge.

Sqsh is held under the GNU General Public License (GPL) and therefore may be freely distributed under the terms of this license.

Basic instructions for Setup :

1. Download the sqsh from here.
sqsh-1

2. Extract  the sqsh tar :
sqsh-2
3. After extract , you will have following directories :
sqsh-3
4.  Move to sqsh directory and create the installation dir with permission to sybase user (in my case installation dir /opt/sqsh). After that execute ./configure –prefix=”/opt/sqsh”sqsh-4
5. Run the make in same dir as below sqsh-5
6. Now finally run the make install sqsh-6
7. Install the manual pages for sqsh as
sqsh-7
8. Move to /opt/sqsh/bin and try to connect data server using sqsh
[sybase@LinuxServer ~]$ cd /opt/sqsh
[sybase@LinuxServer sqsh]$ cd bin
[sybase@LinuxServer bin]$ pwd
/opt/sqsh/bin
[sybase@LinuxServer bin]$ ls -ltr
total 588
-rwxr-xr-x. 1 sybase sybase 599071 Jan  7 15:51 sqsh
[sybase@LinuxServer bin]$ sqsh -v
sqsh-2.4
[sybase@LinuxServer bin]$ sqsh -U sa -S LINUX_PROD
sqsh-2.4 Copyright (C) 1995-2001 Scott C. Gray
Portions Copyright (C) 2004-2013 Michael Peppler and Martin Wesdorp
This is free software with ABSOLUTELY NO WARRANTY
For more information type ‘\warranty’
Password:
[4] LINUX_PROD.master.1> select @@servername,getdate();
 ———————————————————— ——————-
 LINUX_PROD                                                   Jan  7 2014  3:58PM

(1 row affected)
[5] LINUX_PROD.master.1>

You can configure sqsh prompt using /opt/sqsh/etc/sqshrc.  Like in sqshrc file I changed my prompt as ;
#\set prompt='[$histnum] ${DSQUERY}.${database}.${lineno}> ‘
\set prompt=’${DSQUERY}.${database}.${lineno}> ‘

Now see the prompt of sqsh with history , also I suppressed the banner message using -b
[sybase@LinuxServer bin]$ sqsh -b -SLINUX_PROD -Usa
Password:
LINUX_PROD.master.1> select @@servername,getdate()
LINUX_PROD.master.2> go
———————————————————— ——————-
LINUX_PROD                                                   Jan  7 2014  4:06PM
(1 row affected)
LINUX_PROD.master.1>

Link from Rob Tricks with sqsh & Manual Page for sqsh

Happy New Year !!!

January 2nd, 2014 No comments

Happy New Year 2014!!

In 2014, we’ll talk a lot about Sybase ASE on version 15.7.x.

We will also talk about data replication with SAP Sybase Replication Server, SAP Sybase IQ and SAP HANA.

Stay Tuned in new year !!

Team,
sybaseblog.com

Isolation Levels by example

December 28th, 2013 No comments

For Earlier post on Isolation Level please refer here.

Understanding Isolation Level “1” : Avoids Dirty Reads (Default isolation level for ASE)

Transaction T1 (Session 1)modifies a data item. Another transaction T2 (Session 2)then reads that data item before T1 performs a COMMIT or ROLLBACK. If T1 then performs a ROLLBACK, T2 has read a data item that was never committed and so never really existed.

 Session1  Session 2   Remarks
1> select @@isolation “Isolation Level”
2>go
 Isolation Level
 —————
               1
(1 row affected)
1> insert into pmtmaster values(1,100)
2> go
1> begin tran
2> update pmtmaster set id2=200 where id1=1
3> go
1>
   

In Session1, updating the row with id2=1
 
 1> print “Session 2″
2> go               
Session 2           
1> select @@isolation “Isolation Level”
2> go
 Isolation Level
 —————
               1
(1 row affected)
1> select * from pmtmaster where id1=1
2> go
^C^C
[CanCan]
 In Isolation Level 1, that is default mode, we can not read dirty data as it is still not committed by other tran.
     So Isolation Level 1, avoids dirty reads.

Understanding Isolation Level “0”

Session1 Session 2 Remarks
1> select @@isolation “Isolation Level”
2>go
Isolation Level
—————
1
(1 row affected)
1> insert into pmtmaster values(1,100)
2> go
1> begin tran
2> update pmtmaster set id2=200 where id1=1
3> go
1>
   Same as Above , In Session1, updating the row with id2=1
 
1> print “Session 2″2> goSession 2
1> set transaction isolation level 0
2> go
1> select @@isolation “Isolation Level”
2> go
Isolation Level
—————
0
(1 row affected)
1> begin tran
2> select * from pmtmaster where id1=1
2> go
id1         id2
———– ———–
1         200
(1 row affected)
 
 Now with isolation level 0 , I am trying to read data and it is allowing dirty reads.
 rollback    If Session 1  rollbacks, session will have inconsistent.

Understanding Isolation Level “2” : Avoid Repeatable Reads

What is Repeatable Reads?

Transaction T1 (session 1) reads a data item. Another transaction T2 (session 2) then modifies or
deletes that data item and commits. If T1 then attempts to reread the data item, it receives a modified                                      value or discovers  that the data item has been deleted.

Session1 Session 2 Remarks
 

1> select @@isolation “Isolation Level”
2> go
Isolation Level
—————
1
(1 row affected)
1> begin tran
2> select * from pmtmaster where id2=200
3> go
id1         id2
———– ———–
1         200
2         200
(2 rows affected)
 
 Transaction T1 (session 1) reads a data item.
 Session1 Continues..  

 1> begin tran
2> update pmtmaster set id2=300 where id1=2
3> go
1> commit
2> go
 Another transaction T2 (session 2) then modifies.
 1> select * from pmtmaster where id2=200
2> go
 id1         id2
 ———– ———–
           1         200
(1 row affected)
 
 If T1 then attempts to reread the data item, it receives a modified value/different result set in same transaction. This is issue in repeatable reads. Lets review how can we avoid it.

How to avoid Repeatable Reads?

 

1> set transaction isolation level 2
2> go
1> select @@isolation “Isolation Level”
2> go
 Isolation Level
 —————
               2
(1 row affected)
1> begin tran
2> select * from pmtmaster where id2=200
3> go
 id1         id2
 ———– ———–
           1         200
           2         200
(2 rows affected)
To avoid Repeatable read problem, enable the isolation level 2
 Session1 Continues…
 1> begin tran
2> update pmtmaster set id2=300 where id1=2
3> go
^C^C
[CanCan]
 Now Transaction T2 will not allow to modify the restult set which was read earlier tran 1(Session 1)
 1> select * from pmtmaster where id2=200
2> go
id1         id2
———– ———–
1         200
2         200
(2 rows affected)
 
 Still T1 will get same number of rows.

but still it has problem of Phantom Read?

Transaction T1 reads a set of data items satisfying some . Transaction T2 then creates data items that satisfy T1’s and commits. If T1 then repeats its read with the same ,  it gets a set of data items different from the first read.

1> begin tran
2> insert into pmtmaster values (3,200)
3> go
1> commit
2> go  
You can not modifies the result set but still you can insert new values and affect the result set.
1> select @@isolation “Isolation Level”
2> go
Isolation Level
—————
2(1 row affected)
1> select * from pmtmaster where id2=200
2> go
id1         id2
———– ———–
1         200
2         200
3         200
(3 rows affected) 
 Now you can see the same session 1 is still returning different number of result set. This is called as Phantom Reads.
   
 To Avoid Phantom Reads enable isolation level 3 as below.

Understanding Isolation Level “3″ : Avoid Phantom Reads

 1> set transaction isolation level 3
2> go
1> select @@isolation “Isolation Level”
2> go
Isolation Level
—————
3
(1 row affected)
1> begin tran
2> select * from pmtmaster where id2=200
3> go
id1         id2
———– ———–
1         200
2         200(2 rows affected)
To Avoid Phantom Reads , Lets enable isolation level 3
 
1> begin tran
2> update pmtmaster set id2=300 where id1=2
3> go
^C^C
[CanCan] 
 Now you can not modify the result set.
  1> begin tran
2> insert into pmtmaster values (3,200)
3> go
^C^C
[CanCan]
 You can not create data items to affect the result set
 1> select * from pmtmaster where id2=200
2> go
id1         id2
———– ———–
1         200
2         200
(2 rows affected)
 
 All the time you will get same result set.

SAP Recognized as a Market Leader by Gartner, Inc. in Operational Database Management Systems Magic Quadrant

November 17th, 2013 No comments

As per Gartner:

SAP

Located in Walldorf, Germany, SAP (www.sap.com) has several DBMS products that are used for transaction systems: SAP Sybase Adaptive Server Enterprise (ASE), SAP Sybase iAnywhere and SAP Hana. Both ASE and iAnywhere are available as software only, while SAP Hana is marketed as an appliance.

Strengths
  • Vision leadership — Moving into DBMS technology, SAP has introduced SAP Hana as an in-memory platform for hybrid transaction/analytical processing (HTAP) and acquired Sybase to add to the DBMS product line.

  • Strong DBMS offerings — In addition to SAP Hana, SAP Sybase ASE continues to support global-scale applications and was first to introduce an in-memory DBMS (IMDBMS) version.

  • Performance — References cited performance (scalability and reliability) as a major strength (one of the highest scores), mostly for SAP Sybase ASE.

Source : http://global.sap.com/corporate-en/news.epx?category=ALL&articleID=21912&searchmode=C&page=1&pageSize=10

& http://www.gartner.com/technology/reprints.do?id=1-1MNA5V2&ct=131105&st=sb

 

 

ASE 15.7 SP100: Sybase ASE’s version numbering goes the SAP way

June 25th, 2013 No comments

Need of In-memory Technology : SAP HANA

May 6th, 2013 No comments

Challenge 1: Massive Data Growth

Massive amounts of data is being created every year and as per he IDC EMC report data growth would be 40K Exabytes by 2020 :

http://germany.emc.com/collateral/about/news/idc-emc-digital-universe-2011-infographic.pdf

http://www.emc.com/collateral/analyst-reports/idc-the-digital-universe-in-2020.pdf

Capture 2

Challenge 2: Fast access to business decision making information.

Business & People want fast exact and correct answer of all questions from this massive amount of data.

Challenge 3: Current Technologies Can not deliver with this massive data growth.

Historical DBMS :

Historically database systems were designed to perform well on computer systems with limited RAM, this had the effect that slow disk I/O was the main bottleneck in data throughput. Consequently the architecture of these systems was designed with a focus on optimizing disk access, e. g. by minimizing the number of disk blocks (or pages) to be read into main memory when processing a query.

New Hardware Architecture ( up to or more 128 Cores of CPU and 2TB of RAM)

Computer architecture has changed in recent years. Now multi-core CPUs (multiple CPUs on one chip or in one package) are standard, with fast communication between processor cores enabling parallel processing. Main memory is no-longer a limited resource, modern servers can have 1 TB of system memory and this allows complete databases to be held in RAM. Currently server processors have up to 80 cores, and 128 cores will soon be available. With the increasing number of cores, CPUs are able to process increased data per time interval. This shifts the performance bottleneck from disk I/O to the data transfer between CPU cache and main memory

Hana1

Need of In-memory Technology SAP HANA :

From the discussion above it is clear that traditional databases might not use current hardware most efficiently and not able to fulfill current and future business need.

The SAP HANA database is a relational database that has been optimized to leverage state of the art hardware. It provides all of the SQL features of a standard relational database along with a feature rich set of analytical capabilities.

Using groundbreaking in-memory hardware and software, HANA can manage data at massive scale, analyze it at amazing speed, and give the business not only instant access to real time transactional information and analysis but also more flexibility. Flexibility to analyze new types of data in different ways, without creating custom data warehouses and data marts. Even the flexibility to build new applications which were not possible before.

HANA Database Features

Important database features of HANA include OLTP & OLAP capabilities, Extreme Performance, In-Memory , Massively Parallel Processing, Hybrid Database, Column Store, Row Store, Complex Event Processing, Calculation Engine, Compression, Virtual Views, Partitioning and No aggregates. HANA In-Memory Architecture includes the In-Memory Computing Engine and In-Memory Computing Studio for modeling and administration. All the properties need a detailed explanation followed by the SAP HANA Architecture.

Source : www,sap.com and emc and idc reports.

 

Migrating SAP Sybase ASE from AIX to Linux

May 1st, 2013 No comments

Always consider to migrate the Development environment first , then UAT. Before moving to production Perform Regression testing on UAT enviornment.

Please consider to create the script to perform update stats,xp_postload(drop and re create index) for each and every database.

Steps for an ASE Database( You can repeat same steps for other databases) :

Step 1: Run the consistency checks in ASE database in Source (AIX) environment, to make sure that everything is fine.

Step 2: Put the database in single user mode.

Step3: Make sure there is no user activity on the Source Database .

Step 4: Run the sp_flushstats in the database.

Step 5: Take the backup of the database in Source (AIX) environment.

Step 6: Ftp the Files to Target environment. (AIX to Linux)

Step 7: Create and build the dataserver and databases in target Linux environment with exactly same configuration.
You might require to change some of the config param in Linux environment for performance point of view. ( Lets not discuss it here, as it is out of context).

Step 8: Also migrate the Login, roles from source server to target server

Step 9: Load the database in Linux environment.
(If there were user activity during dump process, load will be fail.)

Step 10: Online the database. If the target ASE version is new with source, It will also perform upgrade in this step.

Step 11:  Fix the corrupt indexes using the xp_postload. If the Database size is more than 20G, try drop and re-create index , in this case xp_postload would not be effective.

Step 12: Update the stats on all tables.

Step 13:  If there is replication setup in your environment, please setup replication after that.

Issue Faced:

1. If there is any user online during backup process, your load will fail( in the step for cross platform conversion).

2. After online database, we seen the -ve values in sp_helpdb output for few databases. There are two ways to fix this :

i) Try to run dbcc

dbcc usedextents(<DB name or DB ID>, 0, 1, 1)

ii) Use the Traceflag  7408 and 7409 in Run Server file and reboot the instance. It will not take much time as compare first option.

Traceflag 7408 : Force the server to scan *log segment* allocation pages; to recalculate free log space rather than use saved counts at boot time.
Traceflag 7409 : Force the server to scan *data* segment allocation pages; to recalculate free data page space rather than use saved counts at boot time.

Please let me know if you are planning for migration and need any assistance.

SAP Sybase ASE Q&A Bank

April 29th, 2013 1 comment

Wait is over Now !! 

Please download the Complete ebook for SAP Sybase ASE Q&A Bank Version 1.0  as below:

Pic