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Sas data set options binary

Node 4 of 6. Node 5 of 6. Syntax Tree level 4. Node 11 of Node 12 of Node 13 of Node 14 of Node 15 of Node 16 of Node 17 of Node 18 of Node 19 of Node 20 of Node 21 of Node 22 of Node 23 of Node 24 of Node 6 of 6.

SAS Analytics Global Statements Tree level 1. System Options Tree level 1. DS2 Programming Tree level 1. Macro Language Reference Tree level 1. Output and Graphics Tree level 1. Operating Environments Tree level 1. In-Database Technology Tree level 1.

Metadata Tree level 1. Security Tree level 1. SAS Servers Tree level 1. Accessibility for Base Tree level 1. Node 25 of Node 26 of SAS Studio Tree level 1. Node 27 of Node 28 of SAS 9. Node 29 of Node 30 of These character variables have the potential to contain many repeated blanks in their values.

The following program will create a compressed data set named Company. Compressed is pages; un-compressed would require pages. In general, you use a compressed data set in your programs in the same way that you would use an uncompressed data set. However, there are two options that relate specifically to compressed data sets. Allowing direct access to observations in a compressed data set increases the CPU time that is required for creating or updating the data set.

You can set an option that does not allow direct access for compressed data sets. If it is not important for you to be able to point directly to an observation by number within a compressed data set, it is a good idea to disallow direct access in order to improve the efficiency of creating and updating the data set. Let's look at how to disallow direct access to observations in a compressed data set.

This option is available beginning in SAS 8. YES is the default setting, which allows random access to the data set. NO does not allow random access to the data set. That is, allowing random access reduces the efficiency of writing to a compressed data set but does not affect the efficiency of reading from a compressed data set. Example The following program creates a data set named Company.

Customer data set and ensures that random access to the compressed data set is not allowed. If you delete an observation within the data set, empty disk space remains in its place. However, it is possible to track and reuse free space within the data set when you delete or update observations. By reusing space within a data set, you can conserve data storage space. YES specifies that SAS tracks free space and reuses it whenever observations are added to an existing compressed data set.

NO is the default setting, which specifies that SAS does not track unused space in the compressed data set. The insertion of a new observation into unused space rather than at the end of the data set and the use of direct access are not compatible.

Example The following program creates a compressed data set named Company. Customer data set. A fictional data set named Roster is described in the table below. Variable Type Length LastName Character 20 FirstName Character 15 In uncompressed form, each observation in Roster uses a total of 35 bytes to store these two variables: 20 bytes for the first variable, LastName , and 15 bytes for the second variable, FirstName. The image below illustrates the storage of the first observation in the uncompressed version of Roster.

In compressed form, the repeated blanks are removed from each value. The first observation from Roster uses a total of only 13 bytes: 7 for the first variable, LastName , and 6 for the second variable, FirstName. The image below illustrates the storage of the first observation in the compressed version of Roster.

The indicates the number of uncompressed characters that follow. The indicates the number of blanks repeated at this point in the observation. Only a SAS engine can access these bytes. You cannot print or manipulate them. Remember that in a compressed data set, observations might not all have the same length, because the length of an observation depends on the length of each value in the observation. The raw data file that is referenced by the fileref flat1 contains numeric data about customer orders for a retail company; you want to create a SAS data set named Retail.

Orders from this raw data file. The raw data file that is referenced by the fileref flat2 contains character data about customers for a retail company; you want to create a SAS data set named Retail. Customers from this raw data file. In both cases, you can use the DATA step to create either an uncompressed data file or a compressed data file. Furthermore, you can use either binary or character compression in either case. The following sample programs show each of these techniques. You can use these samples as models for creating benchmark programs in your own environment.

Your results might vary depending on the structure of your data, your operating environment, and the resources that are available at your site. You can also view general recommendations for creating compressed data files. The following program creates the SAS data set Retail. Orders , which contains numeric data and is uncompressed.

The second DATA step reads the uncompressed data file. Quantity 4. The second DATA step reads the compressed data file.

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Node 15 of Node 16 of Node 17 of Node 18 of Node 19 of Node 20 of Node 21 of Node 22 of Node 23 of Node 24 of Node 6 of 6. SAS Analytics Global Statements Tree level 1. System Options Tree level 1. DS2 Programming Tree level 1. Macro Language Reference Tree level 1.

Output and Graphics Tree level 1. Operating Environments Tree level 1. In-Database Technology Tree level 1. Metadata Tree level 1. Security Tree level 1. SAS Servers Tree level 1. Accessibility for Base Tree level 1. Node 25 of Node 26 of SAS Studio Tree level 1. Node 27 of Node 28 of SAS 9. Node 29 of Node 30 of Other Resources Tree level 1.

Node 31 of SPD Engine. Required Arguments NO performs no data set compression. Alias YES. Note: This method is highly effective for compressing medium to large several hundred bytes or larger blocks of binary data character and numeric variables. Compressed is 45 pages; un-compressed would require pages.

NO does not allow random access to the data set. That is, allowing random access reduces the efficiency of writing to a compressed data set but does not affect the efficiency of reading from a compressed data set. Example The following program creates a data set named Company. Customer data set and ensures that random access to the compressed data set is not allowed. If you delete an observation within the data set, empty disk space remains in its place. However, it is possible to track and reuse free space within the data set when you delete or update observations.

By reusing space within a data set, you can conserve data storage space. YES specifies that SAS tracks free space and reuses it whenever observations are added to an existing compressed data set. NO is the default setting, which specifies that SAS does not track unused space in the compressed data set. The insertion of a new observation into unused space rather than at the end of the data set and the use of direct access are not compatible. Example The following program creates a compressed data set named Company.

Customer data set. A fictional data set named Roster is described in the table below. Variable Type Length LastName Character 20 FirstName Character 15 In uncompressed form, each observation in Roster uses a total of 35 bytes to store these two variables: 20 bytes for the first variable, LastName , and 15 bytes for the second variable, FirstName. The image below illustrates the storage of the first observation in the uncompressed version of Roster. In compressed form, the repeated blanks are removed from each value.

The first observation from Roster uses a total of only 13 bytes: 7 for the first variable, LastName , and 6 for the second variable, FirstName. The image below illustrates the storage of the first observation in the compressed version of Roster. The indicates the number of uncompressed characters that follow. The indicates the number of blanks repeated at this point in the observation.

Only a SAS engine can access these bytes. You cannot print or manipulate them. Remember that in a compressed data set, observations might not all have the same length, because the length of an observation depends on the length of each value in the observation. The raw data file that is referenced by the fileref flat1 contains numeric data about customer orders for a retail company; you want to create a SAS data set named Retail.

Orders from this raw data file. The raw data file that is referenced by the fileref flat2 contains character data about customers for a retail company; you want to create a SAS data set named Retail. Customers from this raw data file. In both cases, you can use the DATA step to create either an uncompressed data file or a compressed data file.

Furthermore, you can use either binary or character compression in either case. The following sample programs show each of these techniques. You can use these samples as models for creating benchmark programs in your own environment. Your results might vary depending on the structure of your data, your operating environment, and the resources that are available at your site.

You can also view general recommendations for creating compressed data files. The following program creates the SAS data set Retail. Orders , which contains numeric data and is uncompressed. The second DATA step reads the uncompressed data file. Quantity 4. The second DATA step reads the compressed data file. Customers , which contains character data and is uncompressed.

Compressing your data files is another method that you can use to conserve data storage space. Compressing a data file is a process that reduces the number of bytes that are required in order to represent each observation in a data file. Compressed Data File Structure Compressed data files treat an observation as a single string of bytes by ignoring variable types and boundaries.

This space is used for deletion status, compressed length, pointers, and flags. The image below depicts the structure of a compressed data file. Now that you have seen how to create a compressed data set, let's look at working with compressed data sets. Suppose you want to create two SAS data sets from data that is stored in two raw data files.

Save data storage space by compressing data, but remember that compressed data causes an increase in CPU usage because the data must be uncompressed for processing. Compressing data always uses more CPU resources than not compressing data. Use binary compression only if the observation length is several hundred bytes or more.

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This situation is perfect for compression. However, when you shorten the length to 4 bytes, the layout of the value is no longer suitable for compression. You'll use extra CPU resources to uncompress the data set as well as to expand variables back to 8 bytes. Binary compression uses two techniques at the same time. This option searches for the following:. With that in mind, you can see that the bytes in a numeric variable are just as likely to be compressed as those in a character variable because the compression process does not consider those bytes to be numeric or character.

They are just viewed as bytes. Consider a missing value that is represented in hexadecimal notation as FFFF In the middle of that value is a string of five zero bytes 0x00 that can be replaced by two compression code-bytes. So, what starts as a sequence of 8 bytes ends up as a sequence of 5 bytes. Some data sets are not going to compress well and the data set will grow larger, so know your data. She has written several papers and presented them at various SAS conferences and user events.

Excellent article, good to see quality content we can all share between all users. Earth By Drones Com Alan. Save my name, email, and website in this browser for the next time I comment. This site uses Akismet to reduce spam. Learn how your comment data is processed. Alias: ON Tip: Use this compression algorithm for character data. Tip: This method is highly effective for compressing medium to large several hundred bytes or larger blocks of binary data numeric variables.

Because the compression function operates on a single record at a time, the record length needs to be several hundred bytes or larger for effective compression. Details Compressing a file is a process that reduces the number of bytes required to represent each observation.

Use this compression algorithm for character data. This method is highly effective for compressing medium to large several hundred bytes or larger blocks of binary data numeric variables. Data Set Options:. System Options:.