Exadata performance features

Recently I have reviewed an Exadata implementation (about 66TB data warehouse with multiple marts running on different services on a single database of full-rack Exadata V2) for performance improvements. This post tries to summarize the key points application developers / designers / DBAs should be aware of while deploying the applications on  to Oracle Exadata V2.

1. “Smart Scan” is a Cell Offloading feature that the selection / projection of an SQL is offloaded to the storage cell instead of doing that operation on the compute node after reading all the required blocks to the buffer cache. This works with Full Table Scans (FTS) and Full Index Scans when using the direct path reads. This can dramatically improve the performance of a FTS but that does not mean all the processing need to happen over FTS and all the indexes to be removed! When the single row look up need to happen or very small amount of records are read from a large table, still the index based look up is much faster than the FTS even with smart scan.

Smart Scan is more a run-time decision rather than an optimizer time decision. Smart Scan depends on the number of sessions requesting for the data, number of dirty blocks, size of the table (_small_table_threshold – by default Oracle considers 2% of buffer cache as small table threshold; this may not be good in some times! This parameter may be tweaked at session level as needed.)

On the explain plan one can see the “STORAGE” keyword on action like “TABLE ACCESS STORAGE FULL” and the statistic value in V$ views is “cell physical IO bytes eligible for predicate offload”.

To force the direct read on serial operations at a session level “_serial_direct_read” can be set to TRUE.

2. “Storage Index” is another feature that each cell builds a dynamic negative index of what data is surely not there on the cell for each column value by making a min and max value ranges that are stored on the cell for a given column. This structure is a in-memory index dynamically built after seeing multiple queries that are offloaded to the storage. This feature gives performance improvement similar to “partition pruning” on partitioned tables. To take best advantage of this feature, an ordered load of data into the table based on the most used where clause predicate columns is recommended.  The ETL processes should use a serial loading of data using “APPEND” hint into the table such that the best advantage of storage index can be achieved on SELECT statements.

3. In a data warehouse type environment, when the most of the times all the rows are accessed but every time only a subset of columns are accessed from the table, Hybrid Columnar Compression improves the performance. Using a COMPRESS FOR QUERY HIGH mode of HCC all the queries that use few columns of the table would only read the required column blocks and perform better.

It is important to consider these features during design of an application and building the application to take advantage of these features will tremendously reduce the resource consumption on the platform at the same time giving best throughput.


It is important to have correct indexing strategy, correct partitioning strategy in place even with these features to absolutely make sure the performance is predictable.  Just leaving the tables to grow large with all the history data without the right partitioning strategy will leave the performance degrade over time even with smart scan, storage indexes and HCC!


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