dictionary techniques in data compression

To make sense of the data you need the dictionary and the storage needed for it has to be taken into account. This is where "sliding window" dictionary compression comes in. Let’s quickly start by introducing the concept. Dictionary compression is a standard compression … It is assumed that the dictionary portion has already been processed by the compression algorithm. x��TMo�0��W�h�*�[׭À��C�C��ɀ$E�vC��e;UQ��8�I��|�>�����r�\^GZ��CcL&����ЮhaTi۸�V�}�ͦ�qA��I���U$給�1W��E�U�:&������~�|}�'�R�4�s�;�@��) iπ�P�ul_����RAi78m��I"���d|t���D�gnIY�� ���чK�Q�{�a���7����D�a�ܴW]�N��=����c7���C�kjp���o+�(2܃��s�sX4��dg wH�W�_�8�*�n����RDžJ�� �b�}+��jz�*��I�#��Y����q[�Xn�)�Js���*��s*��1Nֆ}R1`ylV>���t�����eMJ��Ln�rF�������X��ra�4�xI�|��~����p�ٓ�%�s�Y�*:��֝��y栽 �I.�$=ݎ ��@6�uR�a�5�K��f�L�t��Ai��ӀSr�� !S��&�!X⤍�rW���x&���f1���ϸ@p��eʝ�2��p~���"9> For applying any type of compression on a data table, we must calculate a compression value first using appropriate factors. The breakthrough came in 1977 with a theoretical paper by Jacob Ziv and Abraham Lempel. The Dictionary Encoding is a compression technique that decreases the number of bits used to represent data and therefore, reduce the number of operation to get the data from main memory to CPU, enhancing system speed performance. %PDF-1.4 If you already know something about coding theory you might have heard of Huffman coding and statistical modeling of data etc. Data compression techniques compress the data in column stores in the HANA database. Suppose, in a data value array; the value 4 is repeating 8 times consecutively, then, we will add a prefix value 8 before the numeric value 4. This is the basis of dictionary compression. stream First it is important to distinguish between lossless and lossy compression. All Rights Reserved. If an empty column is stored in the HANA memory, compression is not applied at that time. Consider the text of this article as a subject for compression. Data compression techniques primarily store data and avoid data redundancy. Further, on the second level, there are advanced compression methods that we apply to the data compressed by dictionary compression. Instead of giving you each word spelled out letter by letter I could give you the page number and line number of the word in a standard dictionary. Instead of using fixedlength phrases from a window into … Run the SQL command given below to check the compression properties and apply compression on a loaded table. It generates a bit vector value of the cluster encoded value. You might also think that the data compression methods that we actually use, e.g. It is a default compression method which compulsorily applies on all columns of a data table in HANA database. These techniques—both static and adaptive (or dynamic)—build a list of commonly occurring patterns and encode these patterns by transmitting their index in the list. As already mentioned the theoretical solution to lossless compression is the Huffman code which finds the most efficient coding and stores the data in the smallest number of bits. Alternatively, we can build a list of commonly occurring patterns and encode these patterns by transmitting their index in the list → dictionary techniques 2/31. The compression factor is the ratio of the size of uncompressed data to the size of compressed data in SAP HANA. stream Why not use the document as its own dictionary? Suppose, a data value with value ID 5 appears 3 times consecutively starting from position 0, then, the compression will store only at one instance off value ID 5 and the start position as 0. ndThe Data Compression Book,2 Ed., Mark Nelson and Jean-Loup Gailly. when zipping a file, are sophisticated variation on these methods. 5 0 obj the decompression doesn't have to produce an identical copy of the original. In a piece of an array, if all the value IDs are the same and repeating, then we take only one instance of that value ID. Clearly dictionary coding is tantalising but how to construct the dictionary without incurring large storage overheads is the real problem. model prediction). It turns up in lots of compression utilities - ZIP, Compress, Deflate and in GIF and PNG format files. %�쏢 Dictionary Compression. It is a necessary step before storing data in HANA database so that SAP HANA’s performance optimizes. When the encoder finds such a match, it substitutes a reference to the string's position in the data structure. Thus, the compression value is calculated using a compression factor. Static dictionary techniques are quite straightforward to explain. Thus we have succeeded in reducing a phrase of 15 characters to a three element token and this must take less space to store. <> One of the most important lossless forms of compression is the LZW dictionary based method. �3#�� ��l�d��v�Y����~�b�J1�}��. Further, on the second level, there are advanced compression methods that we apply to the data compressed by dictionary compression. You can easily imagine, and a back of the envelope calculation will quickly convince you, that you reduce the amount of data needed to store the text using dictionary references. Only repeating values remain in the compressed array. The problem is what dictionary should you use? Thus, in such cases, there are few distinct values and more repeating values which is further compressed into a cluster-specific dictionary unit representing the value IDs in even fewer bits. In this section, we’ll learn about different compression techniques for compressing data in a column store. What happens next is that you try to find a match for the string in the look-ahead buffer in the dictionary section. The chunk to the left is used as the dictionary and the chunk to the right is used as a look ahead buffer that holds the section of the file that we are trying to compress. Most of the adaptive techniques are based on these two papers by Ziv and Lempel, the 1977 paper, referred to as the LZ77 dictionary technique, and the 1978 paper referred to as the LZ78 technique. Data Compression The Dictionary Way - ZIP. LZW Compression Article from Dr. Dobbs Journal: Implementing LZW compression using Java, by Laurence VanhelsuwØ Dictionary-Based Compression The compression algorithms we studied so far use a statistical model to encode single symbols Compression: Encode symbols into bit strings that use fewer bits. Required fields are marked *, Home About us Contact us Terms and Conditions Privacy Policy Disclaimer Write For Us Success Stories, This site is protected by reCAPTCHA and the Google. Dictionary-based compression algorithms Two dictionary- based compression techniques called LZ77 and LZ78 have been developed. We determine the size of uncompressed data as a product of nominal record size and number of records in a table. This optimizes SAP HANA performance as processing numbers are more efficient than character values. The first level of compression is a basic type which is Dictionary compression. Typically, data in column stores can undergo a two-fold compression. We use indirect encoding even after cluster encoding. Keeping you updated with latest technology trends, Join DataFlair on Telegram. Your email address will not be published. One of the most important lossless forms of compression is the LZW dictionary based method. If you have any queries, drop them in the comment section below. Dictionary compression is a standard compression method to reduce data volume in the main memory. Here, you will find information about the basics of data compression in SAP HANA plus the different compression techniques used ranging from basic to advanced level techniques. In sparse encoding compression, we remove the value which repeats most often in a value ID array from the main array. Copyright © 2009-2020 i-programmer.info. They are most useful with sources that generate a relatively small number of patterns quite frequently, such as … We can apply advanced compression technique on data compressed bye dictionary compression method. The methods under advanced compression type are prefix encoding, run-length encoding, cluster encoding, indirect encoding, and sparse encoding. In lossy compression you can throw away data and the reconstruction, i.e. Previously, we learned replication modes in SAP HANA, now, let’s move on to the data compression techniques used in column store in SAP HANA. Then bit vector value indicates the position of this value. x��WKs�6���_�[�̊KB|��v���N{h�v{pd�q;ew�?��X������i;��O�ĤP�$��E��^;��� – Data Warehousing Methods in SAP HANA. 11. SAP HANA – Crystal Reports (Enterprise 4.1), Applied on: Single predominant column value, Applied on: Several frequent column values. Data compression enables performance optimization in terms of decreasing operational costs by keeping data efficiently in the main memory, speeding up searches and calculations. Start off with a buffer that acts as a window onto the file being compresses. While it is true that these traditional methods are capable of excellent compression performance they are very slow to implement and not really suitable for real time use. It is also an important idea in programming and you really do need to know something about how it works - if only to avoid reinventing it from scratch. Lossless data compression doesn't throw away any data - it simply finds the most efficient coding for the data by eliminating redundancies. Applied on: Single predominant column value and not appropriately clustered value ID array. Good in theory but not so good in practice. Once you find a match the string in the look-ahead is coded by generating a three-element token: So for example, the token 32,14,d means that the phrase in the look-ahead buffer matches the dictionary at position 32 and the match continues for 14 characters. The methods under advanced compression type are prefix encoding, run-length encoding, cluster encoding, indirect encoding, and sparse encoding. They're suitable for … LZ77 is a "sliding window" technique in which the dictionary consists of a set of fixed- length phrases found in a "window" into the previously seen text. In this chapter we will look at techniques that incorporate the structure in the data in order to increase the amount of compression. It is even possible that someone might have implemented the idea in a practical form before the great breakthrough - it is so simple. Whereas, the size of compressed data is the total size of a table residing in the main memory of SAP HANA. Most data sources are correlated, thus, the coding step is generally preceded by a de-correlation step (i.e. To activate the automatic compression, the value of parameter “active” in the optimize_compression section must be YES. In the run-length encoding compression method, only one of the repeating value IDs is stored along with its start position. In this case it is enough that the decompressed version looks or sounds as good. It is also an important idea in programming and you really do need to know something about how it works - if only to avoid reinventing it from scratch. The cluster encoding compression method cuts a long value ID array into small chunks of 1024 elements. This token is output as the next token in the compressed file which is simply a stream of such tokens generated in compressing the entire file. Do you know? You should be able to see that this token can be decoded back to the phrase that it represents simply by getting the 14 characters starting at position 32 from the dictionary and adding a letter "d" onto the end. It turns up in lots of compression utilities - ZIP, Compress, Deflate and in GIF and PNG format files. The first level of compression is a basic type which is Dictionary compression. This avoids data redundancy and saves a lot of in-memory space. Basics & Types of Dictionary Techniques | Data Compression

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