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Exadata Hybrid Columnar Compression allows you to choose between two different levels of compression, and each of these levels allows a further specification of high or low compression Query compression can reduce storage requirements to a tenth of standard storage Queries can sometimes run faster with this type of compression, as the decompression (described later in this chapter) does not impose a very large CPU impact and the number of I/O operations is reduced due to the compression Archive compression can provide even greater storage compression, with improvements from 15 times to 70 times, depending on the composition of the data Query performance for tables or partitions with archive compression is frequently a bit slower, but archive compression can still accommodate schema changes, so you can add and drop columns to a table with the archive compression option for Exadata Hybrid Columnar Compression in use The potentially dramatic difference in storage requirements for archive compression leads to the possibility of keeping historical data available online, which makes any query overhead insignificant when compared with the option of having to bring historical data back online
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Compression saves storage and reduces I/O operations, but the data still needs to be decompressed before it can be used in result sets Although all compression is performed by the CPUs in the database nodes, EHCC data can be decompressed by the CPUs in the Exadata Storage
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Server or those in the database nodes The choice is made based on the type of query processing performed A Smart Scan query returns result sets directly to the PGA, so the CPUs on the Exadata Storage Server perform the decompression Queries that cannot use Smart Scan return standard data blocks to the database node if these blocks are compressed with EHCC, the database server CPUs must decompress them, which requires cycles and memory space to store the decompressed data NOTE This decompression only has to happen on the columns required by the query Remember that the block headers in an EHCC block detail where the different columns are stored, so if a block does not contain requested columns, there is no need for decompression These different locations for decompression have some impact on resource utilization on the database nodes, which you should be aware of as you plan your compression approach
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Of course, Exadata Hybrid Columnar Compression gives you the benefit of reduced storage, saving disk costs and allowing for the use of larger amounts of data in your Exadata-based databases But the size reduction provided by Exadata Hybrid Columnar Compression also has implications for the use of other resources For instance, a reduced storage footprint leads to a corresponding reduction in memory requirements for active use of the compressed data Reduced memory requirements not only provide for greater efficiency in the use of memory, but can also alter the way that queries are satisfied A tenfold reduction in the size of data could mean that an entire table can now be cached in the Exadata Smart Flash Cache, which will be discussed later Think of the benefits produced here the complete elimination of traditional disk I/O for the table Keep in mind that Exadata Hybrid Columnar Compression will have the greatest impact on queries that are disk-bound, as opposed to queries where the limitation is the CPU The source of benefits comes from reduced storage, which has an effect if the performance of a query is bounded by I/O speed
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ILM and Oracle Compression
As you have already read, Exadata Hybrid Columnar Compression is not the only compression option available for the Oracle database Although Exadata Hybrid Columnar Compression will usually produce the greatest benefits with the highest levels of compression, you may not want to use it in all scenarios For instance, if your database usage patterns call for a lot of INSERT and UPDATE activity, you may find that the ability of Advanced Compression to compress these types of data produces more benefits overall for those portions of data that experience this type of activity The practice of Information Lifecycle Management, or ILM, is designed to achieve maximum benefits for your data in terms of both performance and storage savings throughout its useful life This lifecycle involves periods of a lot of write activity, when the data is relatively current, and a gradual reduction in changes as the data ages Oracle s multiple compression options, coupled with the ability to apply the options to partitions as well as complete tables, are ideally suited to implementing an ILM strategy You can choose to use Advanced Compression for partitions that receive the bulk of write activity, getting a good level of compression savings while still having data actively compressed as it is added or modified, while possibly leaving the most active partitions uncompressed As the data in a partition ages, you can convert the compression to query compression with Exadata Hybrid Columnar Compression, increasing the compression benefits while still allowing rapid access of the data in the partition As the partition s data moves towards almost complete quiescence, you can modify the Exadata Hybrid Columnar Compression level to archive compression, providing even more benefits while still having the data available online
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