Nested Loops – This is one of the fear factors for all the ABAP developers as this consumes lot of program execution time. If the number of entries in the internal tables is huge, then the situation would be too worse. The solution for this is to use parallel cursor method whenever there is a need for Nested Loop.
Program using Normal Nested Loop:
REPORT ZNORMAL_NESTEDLOOP.
TABLES:
likp,
lips.
Data:
t_likp type table of likp,
t_lips type TABLE OF lips.
data:
W_RUNTIME1 TYPE I,
W_RUNTIME2 TYPE I.
START-OF-SELECTION.
select *
from likp
into table t_likp.
select *
from lips
into table t_lips.
get RUN TIME FIELD w_runtime1.
loop at t_likp into likp.
loop at t_lips into lips where vbeln eq likp-vbeln.
endloop.
endloop.
get RUN TIME FIELD w_runtime2.
w_runtime2 = w_runtime2 - w_runtime1.
write w_runtime2.
Nested
REPORT zparallel_cursor2.
TABLES:
likp,
lips.
DATA:
t_likp TYPE TABLE OF likp,
t_lips TYPE TABLE OF lips.
DATA:
w_runtime1 TYPE i,
w_runtime2 TYPE i,
w_index LIKE sy-index.
START-OF-SELECTION.
SELECT *
FROM likp
INTO TABLE t_likp.
SELECT *
FROM lips
INTO TABLE t_lips.
GET RUN TIME FIELD w_runtime1.
SORT t_likp BY vbeln.
SORT t_lips BY vbeln.
LOOP AT t_likp INTO likp.
LOOP AT t_lips INTO lips FROM w_index.
IF likp-vbeln NE lips-vbeln.
w_index = sy-tabix.
EXIT.
ENDIF.
ENDLOOP.
ENDLOOP.
GET RUN TIME FIELD w_runtime2.
w_runtime2 = w_runtime2 - w_runtime1.
WRITE w_runtime2.
Analysis report: Runtime in microseconds:
Iteration 1:
Normal Nested Loop: 34,796,147
Using Parallel Cursor: 63,829
Iteration 2:
Normal Nested Loop: 38,534,583
Using Parallel Cursor: 56,894
Iteration 3:
Normal Nested Loop: 34,103,426
Using Parallel Cursor: 50,510
Sap - Performance Tuning Using Parallel Cursor
Databases and Sql as a Middle Tier Storage Medium
DOTNUTSHELL Technologies are an experienced database designing company, with Oracle accredicated qualification providing services both in the OpenSource and
Microsoft SQL Server is a relational database management system. Its primary query language is Transact-SQL, an implementation of the ANSI/ISO standard Structured Query Language (SQL) used by both Microsoft and Sybase.
Microsoft SQL Server uses a variant of SQL called T-SQL, or Transact-SQL, an implementation of SQL-92. T-SQL mainly adds additional syntax for use in stored procedures, and affects the syntax of transaction support.
SQL Server includes support for database mirroring and clustering. A SQL server cluster is a collection of identically configured servers, which help distribute the workload among multiple servers. All the servers share an identical virtual server name, and it is resolved into the IP address of any of the identically configured machines by the clustering runtime. Automatic failover clustering is also available, in which the workload of a server is transferred to another system in the event of a system failure.SQL server also supports data partitioning for distributed databases. Database mirroring, introduced in SQL Server 2005, allows creation of mirrors (or replicas) of database contents, along with transaction logs, on another instance of SQL Server, based on certain predefined triggers.
Microsoft and other vendors provide a number of software development tools designed to allow business applications to be developed using the data stored by Microsoft SQL Server. Microsoft SQL Server 2005 now includes the common language runtime (CLR) component for Microsoft .NET. Applications developed with .NET languages such as VB.NET or C# can implement stored procedures and other functions. Older versions of Microsoft development tools typically use APIs to access Microsoft SQL Server functionality.
We can design and engineer databases based on your business model and provide tools and interfaces that can be used to mine your business data. We also have experience in database persistence and create custom persistence frameworks and algorythms which can be used to predict patterns and trends from your data.
Oracle Database is a relational database management system released by Oracle Corporation. Oracle Database has become a major feature of database computing.
An Oracle database system comprises at least one instance of the application, along with data storage. An instance comprises a set of operating-system processes and memory-structures that interact with the storage. Typical processes include PMON (the process monitor) and SMON (the system monitor).
Users of Oracle databases refer to the server-side memory-structure as the SGA (System Global Area). The SGA typically holds cache information such as data-buffers, SQL commands and user information. In addition to storage, the database consists of online redo logs (which hold transactional history). Processes can in turn archive the online redo logs into archive logs (offline redo logs), which provide the basis (if necessary) for data recovery and for some forms of data replication.
The Oracle RDBMS stores data logically in the form of table spaces and physically in the form of data files. Table spaces can contain various types of memory segments; for example, Data Segments, Index Segments etc. Segments in turn comprise one or more extents. Extents comprise groups of contiguous data blocks. Data blocks form the basic units of data storage. At the physical level, data-files comprise one or more data blocks, where the block size can vary between data-files.
Oracle databases can be used to create distributed database running over contiguous or disparate logistical locations. It can be used to store data and information and provides high availably and performance and is used extensively as a practial and reliable database.
We can design and engineer databases based on your business model and provide tools and interfaces that can be used to mine your business data. We also have experience in database persistence and create custom persistence frameworks and algorithms which can be used to predict patterns and trends from your data.
MySQL is a multithreaded, multi-user SQL database management system which has, according to
Libraries for accessing MySQL databases are available in all major programming languages with language-specific APIs. In addition, an ODBC interface called MyODBC allows additional programming languages that support the ODBC interface to communicate with a MySQL database, such as ASP or ColdFusion. The MySQL server and official libraries are mostly implemented in ANSI C/ANSI C++.
MySQL is popular for web applications and acts as the database component of the
LAMP (Linux-Apache-MySQL-PHP/Perl/Python)
MAMP (Mac-Apache-MySQL-PHP/Perl/Python)
WAMP (Windows-Apache-MySQL-PHP/Perl/Python)
Its popularity for use with web application is closely tied to the popularity of PHP, which is often combined with MySQL. PHP and MySQL are essential components for running popular content management systems such as WordPress and Drupal blogging platforms. Wikipedia runs on MediaWiki software, which can also use PHP and a MySQL database.
MySQL is the most widely used OpenSource SQL database in the world. It provides features and services which make it ideal for small to medium enterprises to effectively manage small to reasonably large business data. It is also fulyl compliant with the SQL standard and provides support for stored procedures and object abstraction when coupled with object persistence frameworks.
We can design and engineer databases based on your business model and provide tools and interfaces that can be used to mine your business data. We also have experience in database persistence and create custom persistence frameworks and algorythms which can be used to predict patterns and trends from your data.
Mysql Backup Ideas
MySQL Backup of mixed type tables have been a night mare for me always. When it comes to the Win32 port serving a client application, which was a recent client requirement we at Saturn had to grease our brains a little too much. Finally all the requirements were met.
The Requirements were that all triggers, proceedures and functions along with table defenitions, permissions and data should be backed up. The backup and restore should be with maximum efficiency. Triggers should not be triggered when restoration is happening.
Considering all the above points, it was finally decided, to go with a suggested solution as follows. The structure of tables would be taken using show create table, parsed and properly formatted to a particular markup with a create table, a load data in file, and a set of alter tables.
Considering tables with mixed types, ie some tables are InnoDB, and others MyISAM, all InnoDB tables will be converted to InnoDB after the data has been populated, if there are any indexes ( there should be), and even auto_increment primary keys, are built after the data is populated. The data is taken as a csv using select into outfile. The triggers are taken from the data folder as files, and copied to a temporary folder.
The point where we had to break our heads were the functions and procedures, which was due to the mysql connector/v5 which we were using did not support delimiter, and finally what we did was to dump the proc table from mysql database with the same method. All the resulting files are zipped together with some meta information.
When the Restore was developed, we faced another problem, that simply dropping the TRG files did not activate the triggers, but we needed to restart the mysql server.
Finally all is well and the client is satisfied. The backups, taken will only be a full backup. But for the incremental one, it would be better to enable the replication service, and use mysqlbinlog.exe.
Getitnext Releases Version 1.1 for Ebay Searches
Searching an innovative new
search solution that dramatically improves the buying experience of the
eBay marketplace, today announced it has released version 1.1 of its
online search application. The latest release comes 5 months after the
company launched its service to the public and features a significant
number of upgrades based on feedback received from its online user
community.
While GetItNext’s innovative and proprietary approach still allows users
to quickly and easily eliminate any unwanted eBay listings, the site now
features a wide array of new tools that vastly improve user experience.
The most significant new features incorporated into this release are:
1) Homepage Redesign - A new, award winning look and feel. 2) Find a
deal - Search for items with 0 bids and less than 4 hours remaining.
Zero competition = bargains for users. 3) Bulk Deal - Easily search for
lots, multiples and wholesale items. 4) Refine your search - Add
keywords and categories to make your search results even better. 5)
Email it - Found a great deal on an item and want to share it? Email it
to your friends so they can start saving too!
“We are extremely pleased and excited about this release”, said Ron
Stewart, President and CEO at GetItNext. “Our users now have access to
new and upgraded tools, further enhancing their experience searching for
items on eBay. The main goal for GetItNext continues to be enabling our
users to make informed decisions with all details at hand when making a
purchase. The features included in this release accomplish just that –
plus we’ve built in a number of recommendations we have received from
our users since we launched GetItNext.”
In only 5 months, GetItNext has accomplished a number of significant
milestones including being awarded the 2007 IMA award for the design of
its website as well as steady growth in the number of unique site
visits, registered newsletter recipients and overall site usage. “We’re
thrilled with the response our service has received thus far”, Stewart
continues. “It shows that our community values what we provide, and in
return we want to make sure we listen to what they have to say so we can
incorporate the feedback into future releases”.
About getitnext:
Getitnext is an easy to use search solution that dramatically improves
the buying experience of the eBay marketplace. In contrast to current
first generation eBay tools and techniques, GetItNext’s Web 2.0 design
gives users an experience closer to a desktop application than a
traditional Web page. As a result, GetItNext’s proprietary search
techniques and industry leading tools make eBay easier to use and saves
users time and money.
What is Data Management?
Data Management is the comprehensive series of procedures to be followed and have developed and maintained the quality data, using the technology and available resources. It can also be defined that it is the execution of architectures under certain predefined policies and procedures to manage the full data lifecycle of a company or organization. It is comprised of all the disciplines related to data management resources.
Following are the key stages or procedures or disciplines of data management:
1. Database Management system
2. Database Administration
3. Data warehousing
4. Data modeling
5. Data quality assurance
6. Data Security
7. Data movement
8. Data Architectures
9. Data analysis
10. Data Mining
1. Database Management system:
It is one of the computer software from various types and brands available these days. These software are designed for specifically for the purpose of data management. These are few of these; Ms Access, Ms SQL, Oracle, MySql, etc. The selection of any one of these depends upon the company policy, expertise and administration.
2. Database Administration:
Data administration is group of experts who are responsible for all aspects of data management. The roles and responsibilities of this team depends upon the company’s over all policy towards the database management. They implement the systems using protocols of software and procedures, to maintain following properties:
a. Development and testing database,
b. Security of database,
c. Backups of database,
d. Integrity of database, and its software,
e. Performance of database,
f. Ensuring maximum availability of database
3. Data warehousing
Data warehousing, in other words is the system of organization of historical data, its storage capability etc. Actually this system contains the raw material for the management of query support systems. That raw material is such that the analysts can retrieve any type of historical data in any form, like trends, time stamped data, complex queries and analysis. These reports are essential for any company to review their investments, or business trends which in turn will be used for future planning.
The data warehousing are based on following terms:
a. The databases are organized so that all the data elements relating to the same events are linked together,
b. All changes to the databases are recorded, for future reports,
c. Any data in databases is not deleted or over written, the data is static, readable only,
d. The data is consistent and contains all organizational information.
4. Data modeling
Data modeling is the process of creating a data model by applying and model theory to create data model instance. The data modeling is actually, defining, structuring and organizing the data using predefined protocol. Then the theses structures are implemented in data management system. In addition, it also will impose certain limitation on the database with in the structure.
5. Data quality assurance
Data quality assurance is the procedure to be implemented in data management systems, to remove anomalies and inconsistencies in the databases. This also performs cleansing of databases to improve the quality of databases.
6. Data Security
It is also called as data protection, this is system or protocol which is implemented with in the system to ensuring that the databases are kept fully safe and no one can corrupt by access controlling. The data security, on other hand, also provides the privacy and protection to the personal data. Many companies and governments of the world have created law to protect the personal data.
7. Data movement
It is one term broadly related to the data warehousing that is ETL (Extract, Transform and Load). ETL is process involved in data warehousing and is very important as it is the way data is loaded into the warehouse.
8. Data Architectures
This is most important part of the data management system; it is the procedure of planning and defining the target states of the data. It is, realizing the target state, describing that how the data is processed, stored and utilized in any given system. It created criterion to processes the operation to make it possible to design data flows and controls the flow of data in any given system.
Basically, data architecture is responsible for defining the target states and alignment during the initial development and then maintained by implementations of minor follow-ups. During the defining of the states, data architecture breaks into minor sub levels and parts and then brought up to the desired form. Those levels can be created under the three traditional data architectural processes:
a. Conceptual, which represents all business entities
b. Logical means the how these business entities are related.
c. Physical, is the realization of the data mechanism for specific function of database.
From above statements, we can define that the data architecture includes complete analysis of the relationship between functions, data types and the technology.
9. Data analysis
Data analysis is the series of procedures which is used to extract required information and produce conclusion reports. Depending upon the type of the data and the query, this might include application of statistical methods, trending, selecting or discarding certain subsets of data based on specific criteria. Actually, data analysis is the verification or disproval of an existing data model, or to the extract the necessary parameters to achieve theoretical model over realty.
10. Data Mining
Data mining is the procedure to extract unknown but useful parameters of data. It also can be defined that it is the series of procedures to extract the useful and desired information from large databases. Data mining is the principle of sorting the large through the large amount of data and selected the relevant and required information for any specific purposes.
Backup on Lto Tape
The value of your data more then the hardware and we know that when we loss over data and that’s why the Backing up data has become golden rule in the computer world. We need to backup data once in a day to avoid panics at the time of data loss. Data backups, hard drive backups, email backups prove beneficial when you lose some important data.
In such case you can trust the backed up data! But how can we create backup of over data and what hardware can we use to have backup on that is reliable and easy to use. Capabilities such as dynamic rate matching and dual mode compression enhance tape drive performance and product life.
Released in 2007, LTO-4 tapes have a native capacity of 800 GB which can go up to 1.6 TB worth if compressed (2:1). The data transfer rate has gone up to 120 MB/s and a 256 bit AES-GCM drive level encryption has been added. LTO 4 Ultrium also features backward compatibility with LTO-2 and LTO-3 drives. Another advantage of using LTO tapes is that future versions are in development so there will be a chance to update your system instead of it being phased out of use. Multiple sources for LTO media and drives reduce production bottlenecks and also ensure investment protection for OEMs and end users alike. Related products available at Tape4Backup are: LTO 1, LTO 2, LTO 3, LTO Cleaning Cartridge, LTO Barcode Labels, LTO Empty Cases and LTO Cartridge Memory Reader.
Backup tape users are increasingly sensitive to data security in the wake of high profile data loss incidents, and encryption techniques have appeared to help ensure security. For example, if an unencrypted tape is lost or stolen, its data is at risk. But, if an encrypted tape is lost or stolen, its data is still considered to be secure. Thus, the use of encryption has a profound effect on corporate liability and reporting obligations.
We can find LTO 1, LTO 2, LTO 3, LTO Cleaning Cartridge, LTO Barcode Labels, LTO Empty Cases and LTO Cartridge Memory Reader on the
The Mythology of Data Governance and Data Stewardship
It will sound almost a cliché if you say that companies thrive on information. If you go through the front page of any news paper, watch any weather channel, come across a press conference, or go over any annual report, you will witness how data dominates the organizations today. The importance has grown so large that companies have to scour for data of the previous years to ascertain their business efficiency. From marketing department to the operation department, organizations rely on the data of every segment to make smart predictions, store historical records, and read the consumer behavior.
The volume of data grows with the augmentation of customer information. Moreover, the amount of data captured at a particular point of time multiplies every second year. Packaged applications are the norms of the present world and external data is an indispensably important component of every organization. However, old tactics and processes have given way for new ones when it comes to managing data. While the boardrooms get abuzz with the vociferous discussions on data among the executives, companies wake up to the realities of upgrading their data management processes and systems
This quest for advanced data management has given rise to the concept of data stewardship and data governance. However, the chaos and confusion over the roles between business and Information Technology continues to take place. Customer data integration (CDI) and master data management (MDM) are two important initiatives which promise to relieve business experts from the labor of defining and maintaining customer data.
The Dilemma of Data Governance
If anything has been most misused in business, it is the phrase of data governance. IT organizations have always been trying to deploy data governance to engage the business in legitimate ownership discussions. Ironically, vendors use this phrase to convey data management practices from modeling to quality automations. Worse still, even the term has been used synonymously for knowledge management and CRM. Even the IT executives mistake data stewardship with data governance.
However, data stewardship and data governance are two different concepts. Data governance is different from data stewardship in a way that it implies a level of organizational supervision that encompasses not only business but also information and technology. Moreover, it also comprises executives who desire to be a part of or are engaged with defining their companies’ policies. While they handle the internal and external regulations in one hand, they implement the customer-focused strategies on the other. Data Governance can be best defined as the mechanisms and decision-marking structures for treating data as one asset, implementing formal policies and administering the management of corporate data.
The process of data governance itself is dependant on the executive committee which institutes the policies, sort out conflicts and questions, consider customer commitments and evaluates success. Many companies that lack the processes or skills to manage their data deploy this too early in their systems. However, unless and until there is any legitimate data management and stewardship take hold, data governance will remain just a matter of discussion.
