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Difference Between Exadata Smart Scans and Buffered Reads

Oracle Exadatas have been around for a long time — but really understanding how they work can put you and your databases in a much better position. 

For instance, what is essentially different in Smart Scan compared to Buffered Read, and why it is considered superior? Let’s get into it.

An Exadata Smart Scan is a Full Segment Scan that is executed by Storage Cells. The reason that Smart Scans can be so much better than regular, Buffered Reads, is apparent when you compare the two processes:

Serial, Buffered Read (Regular Database Read / Not a Smart Scan)

The Oracle database goes through a basic process to retrieve data that, at a high level, includes the following:

This essentially says that the database really only looks for data in the Buffer Cache.  This is true most of the time.  In the above description, “Serial” just means non-parallel. In this process, one CPU on the Compute Node will be busy scanning blocks of data in the Buffer Cache and subsequently waiting for more blocks to be copied into memory (the Read / Wait Cycle).

The negatives of this process?

CPU Count Used = 1 (because only the Compute Node is involved)

Exadata Smart Scan

The same, Serial query on the Exadata platform will look like this:

The difference between the Buffered Read and the Exadata Smart Scan is that the Storage Cells are Scanning and Filtering the data without pushing blocks of data over the network.  Since the data is on the Storage Cell and cellsrv is capable of scanning and filtering data, the efficiency of the IO process is improved significantly. 

Once the data is filtered by the Storage Cell, only the rows and / or columns of data that are required to satisfy the query are extracted from the data blocks and sent back to the Compute Node.  Hence, the IO is much faster (happening locally on each Storage Cell) and the amount of data passed back from the Storage Cell to the Compute Node can be dramatically reduced.

Remember that the Exadata Compute Node is still running a Serial Query.  The inclusion of the Storage Cells provides an inherent level of parallelism equal to the number of Storage Cells multiplied by the Core Count. 

For example, on an Exadata X9-2 Quarter Rack:

CPU Count Used = Compute Node CPU + (Storage Cells * CPU per Storage Cell) CPU Count Used = 1 + (3 * 32) = 97

This should be enough to convince you of the superiority of Exadata Smart Scans. Because, sometimes, seeing is believing and understanding. 

If you don’t have the tools and Exadata machines on which to test all these theories, contact INFOLOB. Our Technology Lab in Irving, TX, is populated with 2 Exadata X6-2 Quarter Racks along with an X5-2 ZDLRA. Please feel free to visit.