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Posts Tagged ‘SAP’

SAP ASE 16 – Design for Extreme Transaction Processing

April 7th, 2014 No comments

SAP ASE 16 Launched Recently : Lot of New Features and Extreme Transaction Ready

SAP ASE 16 provides scalability and speed to support higher throughput and lower latency; security to ensure data privacy and system auditability; and, simplicity of database operations to maximize operational efficiency and lower costs.

Scalability, speed, security, simplicity…these were the guiding principles for our engineers. We’ve increased scalability and speed with extensive optimization in its transaction concurrency management, query plan execution, data compression and utilization of computing resources in large SMP servers. Security enforcement and system auditability have been augmented to provide customers more flexibility to adapt to their specific regulatory compliance needs. And SAP Control Center delivers simplified database management helping to reduce overall cost of ownership.

SAP ASE 16 Overview white paper

SAP SCN

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

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

 

 

SAP® Sybase® Adaptive Server® Enterprise Gains Momentum With Rapid Customer Adoption

November 17th, 2013 No comments

 

In less than 18 months since the offering’s release in April 2012, more than 1,000 customers have chosen to run SAP Business Suite on SAP Sybase ASE and there are more than 2,000 customer installations. Both new and existing SAP customers can run a high-performance relational database management system (RDBMS) optimized for SAP Business Suite that helps improve operational efficiency and significantly reduce overall costs. The announcement was made at the SAP Database and Technology Partner Summit in Barcelona.

Source :: http://www.prnewswire.com/news-releases/sap-sybase-adaptive-server-enterprise-gains-momentum-with-rapid-customer-adoption-229819941.html

http://www.hispanicbusiness.com/2013/11/5/sap_sybase_adaptive_server_enterprise_gains.htm

Real Time HANA Replication from ASE Using SAP Sybase Replication Server

July 31st, 2013 No comments

SAP® Sybase® IQ Software Smashes Previous Results and Sets World Record for Fastest Loading of Big Data

July 27th, 2013 No comments

SAP® Sybase® IQ Software Smashes Previous Results and Sets World Record for Fastest Loading of Big Data

http://finance.yahoo.com/news/newsbyte-sap-sybase-iq-software-130000253.html

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:

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Introducing SAP Sybase IQ 16 : Extreme Delivery

March 3rd, 2013 No comments

Newest Features

For those who are familiar with earlier versions of Sybase IQ, here are the new features added to from SAP Sybase IQ 15.

  • Performance enhancements: The column store engine has been enhanced with extreme compression capabilities that improve I/O rates and reduce the amount of data to be stored on disk.
  • High-speed data loading: High-performance data loading ingests large amounts of data faster than ever — from terabytes to petabytes — making big data available to applications and people faster.
  • Improved scalability: Key improvements maintain high performance and efficiency for the growing volume of unpredictable, user-driven analytic workloads.
  • Data protection: Administrators have further options for protecting the security of enterprise systems.
  • Heightened availability: Enhancements help ensure that enterprise data is always available to business-critical analytics and dashboards.

 

IQ16-Engine