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Exadata Offload – The Secret Sauce

The “Secret Sauce” for Exadata is its ability to offload processing to the storage tier. Offloading and Smart Scan are two terms that are used somewhat interchangeably. Offloading is a more generic term that means doing work at the storage tier that would otherwise have to be done on the database tier (this can include work that is not related to executing queries such as optimization of incremental backups). Smart Scans on the other hand are the access mechanism used to offload query processing tasks. For example, storage servers can apply predicate filters at the storage layer, instead of shipping every possible block back to the database server(s). Another thing that happens with Smart Scans is that the volume of data returned can be further reduced by column projection (i.e. if you only select 1 column from a 100 column table, there is no need to return the other 99 columns). Offloading is geared to long running queries that access a large amount of data. Offloading only works if Oracle decides to use its direct path read mechanism. Direct path reads have traditionally been done by parallel query slaves, but can also be done by serial queries. In fact, as of 11g, Oracle has changed the decision making process resulting in more aggressive use of serial direct path reads. I’ve seen this feature described both as “serial direct path reads” and “adaptive direct path reads”.

I’ll digress here a bit to discuss this feature since direct path reads are critical to Exadata Offloading. Direct path reads do not load blocks into Oracle’s buffer cache. Instead, the data is returned directly to the PGA of the process requesting the data. This means that the data does not have to be in Oracle block format. That means no 8K block that is only partially filled, that may only have a record or two that you’re interested in, containing every column including ones you don’t want, and with additional header information – needs to be shipped back up from the storage layer. Instead, a much more compact result set containing only the columns requested and hopefully only the rows you need are returned. As I said, direct path reads are traditionally used by parallel query slaves. They are also used in a few other instances such as LOB access and sorts that spill over into TEMP. So the ability to use direct path reads is very important to the Exadata platform and thus the changes to the make them more attractive in 11g. Here are a few links to info on the subject of serial direct path reads:

  1. Doug Burns has a good post on 11g serial direct path reads.
  2. Alex Fatkulin has a very good post on some of the factors controlling adaptive direct path reads.
  3. There is a note on MOS (793845.1) on changes in 11g in the heuristics to choose between direct path reads or reads through the buffer cache.
  4. You may also find MOS note (50415.1) on misleading nature of “direct path read” wait events of interest.

Also be aware that direct path reads are only available for full scans (tables or indexes). So any statement that uses an index range scan to get to a row in a table via a rowid will not use this mechanism. Also keep in mind that direct path requires extra processing to ensure that all blocks on disk are current – (i.e. an object level check point), so frequently modified tables will suffer some overhead before direct path reads can be initiated.

I must say that I think the changes to the heuristics in 11g may be a little on the aggressive side for non-Exadata platforms (the changes may well be driven by Exadata). And by the way, serial direct path reads are not always faster than the normal reads that go through the buffer cache. Dion Cho has a good post on a performance problem due to serial direct path reads kicking in on one node of an 11g RAC environment (not Exadata). The node doing the direct path reads was running the query much slower than the node using the normal buffer cache reads. He also has a post on turning off serial direct path reads.

But enough about the direct path reads stuff, on to the Offloading. One of the first things I wanted to know when I got my first look at a system running on Exadata was whether a particular query was eligible for offloading and if so, how much of the expected i/o was saved by the feature. So of course I wrote a little script to show me that. Turns out there is plenty of info in V$SQL to see what’s going on. I called the script fsx.sql (short for Find_Sql_eXadata). Here’s a little demo:

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