Start\_date date encode delta32k) - this date column uses deta32k encoding Gender varchar(7) encode text255, - this text column uses text255 encoding By default Redshift will select 100,000 rows as its sample for analyzing the data for compression.Ĭustname varchar(30) encode raw, - this text column uses raw encoding Load the table with a single COPY command, set the COMPUPDATE parameter to ON to overwrite previous compression settings in the table. Any other Column is assigned the LZO compression, which provides a tradeoff between performance and compression ratio.Įnsure the table is empty, and run the following command:.Columns of a numerical type, like REAL and DOUBLE PRECISION, as well as BOOLEAN, are also assigned a RAW compression.Analysts should consider this when selecting a column as a sort key. Automatic Compression with the COPY CommandĬolumns defined as sort keys are assigned a RAW compression, which means that they are not compressed. It is possible to let Redshift automatically select encoding for column compression, or select it manually when creating a table. SMALLINT, INT, BIGINT, DATE, TIMESTAMP, DECIMALĪll except BOOLEAN, REAL, and DOUBLE PRECISION Redshift supports seven column encoding formats: Encoding type There are several ways to encode columnar data when compressing it choosing the right type of encoding for each data type is key to achieving efficient compression. Each column within a table can use a different type of compression. Column Compression in Redshiftīy default, Redshift stores data in a raw, uncompressed format, and you can choose whether to compress data. In contrast, a row-wise database would read the blocks that contain the 95 unneeded columns as well. This saving is repeated for possibly billions or even trillions of records in large datasets. A query that uses five columns will only need to read about 5% of the data. Many database operations only need to access or operate on a small number of columns at a time, and so you can save memory space by only retrieving the columns you actually need for your query.įor example, consider a table that contains 100 columns. ![]() The savings in storage space also carry over to retrieving and storing the data in memory.This creates an opportunity for much more efficient compression compared to a traditional database structure. In a database table, each column contains the same data type, typically with similar data. ![]() ![]()
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