With the help of this training, you can learn about creating structure in the ABAP Directory. This training injects you complete knowledge about ERP. SAP ABAP Training is useful for evolving applications for the SAP R/3 system.
The SAP HANA Acronym stands for ( High-performance ANalytic Appliance). I saw the term SAP’s latest technology within the communication of that blog, no doubt it could be quite good for an ABAPer to keep learning them, and IMHO it could be even better if an ABAPer has some basic awareness of the latest technique trends in open source community.SAP HANA is a platform that serves to replace the classical hard disk based data storage system with an in-memory alternative. What exactly is SAP HANA? How is it able to bring such dramatic improvements to our programs? And finally, how will switching over to SAP HANA impact me as an ABAP developer? SAP HANA Overview on a Technical LevelAs another ABAP veteran who has been programming using ABAP in SAP for ten years, I have some additional points. What we would like to know is not the what, but the how. This all sounds wonderful, but technical folks understand that nothing works by magic. You’ve probably heard that it’s “the future” and a “game changer.” You’ve also probably heard that it’s much faster, and it can take tasks that require hours and finish them in minutes.
A DMBS manages an array of HDDs that store data in a row based format. A) The trainer has more time to explain topics in depth than compared to learning.As a refresher, lets recall how a classical database works. Let’s begin our over overview of SAP HANA by contrasting it to the traditional DBMS.A good way to learn ABAP is by getting trained in a training institute. While HANA can perform any and all features of a traditional DBMS, it also provides many benefits that a classical database server does not.
(see below)Although many advances have been made over the years to improve HDD technology speed and storage capability, the HDD can never match the speed of computer memory. Hard disk drives are mechanical devices, requiring a rotating magnetized platter that stores binary data, and a read/write head that accesses and reads that data. Reading, writing, and searching using HDD are an inherent bottleneck to any classical database. The principle drawback of a classical database is the fact that it uses HDDs.
SAP HANA Architecture SAP HANA and Column StoresA major difference between SAP HANA and traditional databases is that HANA uses column-store for most database tables, while classical DBs use row-store. This allows for even greater speed of access, data retrieval, and data processing. Simply changing the architecture from HDD based tomemory based storage is enough to provide drastic speed increases on its own, but SAP HANA takes it a step further by providing features on top of this in-memory platform. Since retrieving data from computer memory is 100,000 times faster than retrieving data from HDDs on average, the speed increases are exponential and instantaneous. By storing the vast majority of data in memory, HANA sidesteps the costly action of reading data from HDDs to provide data to users.
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It also allows for common aggregate database functions to be performed much faster. Columnar storage often eliminates the necessity for secondary indexes, because the data is stored in an index-like fashion already. You can either create secondary indexes to speed up data retrieval, or risk performing full table scans when retrieving data outside of the primary index.Below is an example of how data would be stored in traditional DBMS as rows…This data would be stored in a traditional database as follows:Column-store addresses some of these shortcomings of row-store by keeping like fields together. In addition, row-storage often provides only two options when searching a non-primary field.
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The SAP HANA database provides a series of compression techniques that can be used for the data in the column store, both in the main memory and in the persistence. Compressing data for rows of data can be complicated since you’re often dealing with many different data types for an individual record. This is because the like values are stored together in the database, requiring fewer reads to retrieve the data.In a column based system, the data would be stored as follows:Tom:001,Tom:002,Matt:003,Susan:004,Susan:005 0123:001,0234:002,0234:003,1203:004,0123:005 Another advantage of storing data as columns, which SAP HANA takes full advantage of, is the ability to easily compress data.
You will notice that the value Tom and Susan repeat within the table. Let’s review an example of this with our previous example database. To understand all the different HANA Compression Techniques, RLE, CLUSTER, SPARSE, PREFIX…etc, please see the SAP HANA HELP.Compressing data from columns is much easier since it is very common for data values to repeat in a table for multiple records. In this context, encoding means “translating” the content of a field into an integer value. The compression technique I will explore is based on dictionary encoding, where the column contents are stored as encoded integers in the attribute vector.
Query the Dictionary Table to determine the value ID of the desired name. An example database query using dictionary-based compression would be done in two steps: 00, 01, 02, 03).Using this information, it’s possible to convert our database table into two distinct tables, while saving space: Dictionary Table:By utilizing of these two tables, we can represent the entire first column in our example database table while taking up less space. As such we can represent all the current values in the table with four distinct values (i.e.
Inserting, updating, or deleting a record would require decompressing the table, performing the action, and then recompressing it again.SAP’s solution for this was to implement an “insert-only” approach to database updates. The “Insert-Only” DatabaseThe downside of this compression technique is that updating the database directly becomes more costly. This level of memory savings would simply not be possible in a row-based database. Order numbers, personal identifiers, etc). Instead of having to store a 320 bit value for each of these records, we could effectively replace each one of these values with a 2 bit field! This process can be repeated for all columns, although the benefits would decrease for field values that are less repetitive (e.g.
ABAP for HANANow that we have a basic understanding of SAP HANA and the benefits it provides, we can start to evaluate what it means for us ABAPers and the coding approach on the system. Then, when it’s time to SELECT a record, the original table and delta tables are joined and only the most recent record will be returned.Once the table is no longer in use, the HANA system will consolidate the changes leaving only the most recent records in the compressed version of the table. The original table will have all of the original compressed records, while the delta will have the uncompressed records that represent additions and changes.SAP is able to keep track of the latest (and therefore most up-to-date) versions of a specific record by attaching a transactional ID.
Once we have the data within our program (whether it be in variables, internal tables, or your preferred data structure type), we perform operations to produce a file, report, or output. Although retrieving large amounts of data from the database is time consuming, this approach was superior to hitting the database multiple times. CODE-TO-DATA The Old Approach – Data-to-Code:In traditional ABAP coding, it is standard practice to limit hits to the database by bringing in as much data as possible initially, then performing operations on this data. In fact, since the SAP HANA system has been optimized to work with the data in the system, it can perform these operations much faster than the calling ABAP program.As a result of this fact, a paradigm shift was needed to the programming approach to fully take advantage of all the benefits SAP HANA offers. Beyond just storing and retrieving data, the SAP HANA can perform many complex operations on the data being retrieved itself.
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