performance

If you are in Singapore and have 24th Feb afternoon available then you can register and join a free Oracle performance troubleshooting seminar I’m doing in Singapore Management University’s (SMU) campus.

The seminar will be about:

• Systematic Oracle Performance Troubleshooting
• Identifying performance troublemakers
• Understanding execution plans

The date is Wednesday, 24th Feb

The seminar time is from 15:30-19:00 (don’t be late)

Registration and more details are here:

• http://tech.e2sn.com/events/singapore-oracle-meetup/

The LatchProf and LatchProfX scripts allow you to be more systematic with latch contention troubleshooting and tuning. No more guesswork is needed as these scripts give you exact session IDs and in this version also SQLIDs of the troublemaking applications.

You can download the new versions here:

• LatchProf (reads V$ views)
• LatchProfX (reads X$ tables, but gives better info, run as SYS)

Example output (with SQLID info) is below:

I’ve posted a new paper at method-r.com called “Thinking Clearly About Performance.” It’s a topic I’ll be presenting this year at:

• RMOUG (Denver) this month
• Miracle OpenWorld (Billund DK) in April
• UTOUG (Salt Lake City) in May
• Kaleidoscope (Washington DC) in June
  
• MOTS (Ann Arbor) in September

The paper is only 13 pages long, and I think you’ll be pleased with its information density. Here is the table of contents:

Kyle Hailey has started putting together a much needed Oracle wait event reference.

You can access it here.

By the way, Oracle documentation also has a wait event reference section, it has more events, but it’s less detailed…

I have plans to go deep into some wait events and cover some less common ones in tech.E2SN too… in the future ;-)

Coskan Gundogar and Karl Arao have written two interesting articles about Oracle performance analysis and visualization, check these out!

Coskan’s article:

• http://coskan.wordpress.com/2010/01/27/working-with-statspack-part-1a-diagnosis/

Karl’s article:

• http://karlarao.wordpress.com/2010/01/31/workload-characterization-using-dba_hist-tables-and-ksar/

Note that in March I will be releasing PerfSheet v3.0, which will have lots of improvements! ;-)

In my Beyond Oracle Wait interface article I troubleshooted a test case where an execution plan somehow went “crazy” and started burning CPU, lots of logical IOs and the query never completed.

I have uploaded the test case I used to my new website, to a section where I will upload some of my demo scripts which I show at my seminars (and people can download & test these themselves too):

http://tech.e2sn.com/oracle-seminar-demo-scripts

Basically what I do is this:

1. I run the query with bind variable values where only a handful of rows match the filter condition. Thus Oracle picks nested loop join (and indexed access path)
2. Then I run the same query with different bind values, where a lot of rows match the filter condition. Oracle reuses existing execution plan (with nested loops!!!). Oracle ends up looping through a lot of blocks again and again (because nested loop visits the “right” side of the join once for every row coming from the “left” side of the join).

Using nested loops over lots of rows is a sure way to kill your performance.

And an interesting thing with my script is that the problem still happens in Oracle 11.1 and 11.2 too!

Oracle 11g has Adaptive Cursor Sharing, right? This should take care of such a problem, right? Well no, adaptive bind variable peeking is a reactive technique – it only kicks in after the problem has happened!

Craig Shallahamer is now blogging… check out his initial post here!

Earlier I had a few mail exchanges with him and got to check his new work.. called the Stress Identifier. Hmm.. so how it will be helpful? Well if you have already characterized your workload, you may want to definitively determine which internal database activity is stressing your CPU, IO, and network subsystems. So this tool will avoid the guess work of selecting that “unit of work”… check out the link for more details…

Been busy these past few days..

Well, this post is about taking advantage of the built in data store that started in 10gR1.. yeah it’s AWR, and it’s very much like a “Statspack on steroids” (got that from Kyle Hailey’s modules). What’s exciting about 10g and above is we could utilize the “Time Model” and the OSSTAT view and use them together with the SYSTAT view to have a far better workload information when going through all the SNAP_IDs.

I have this “little” issue before of getting lazy when generating multiple AWR reports… yeah it’s just so daunting, you’ll get tired just by generating those reports by hand and the catch is… you’ve not yet analyzed anything yet.. :p   (but yeah, you’re lucky if you’ve got access to the OEM performance page)

[back to Introduction] Partitioning is an essential performance feature for an Oracle data warehouse because partition elimination (or partition pruning) generally results in the elimination of a significant amount of table data to be scanned. This results in a need for less system resources and improved query performance. Someone once told me “the fastest I/O is the one that never happens.” This is precisely the reason that partitioning is a must for Oracle data warehouses – it’s a huge I/O eliminator. I frequently refer to partition elimination as the anti-index. An index is used to find a small amount data that is required; partitioning is used to eliminate vasts amounts of data that is not required. Main Uses For Partitioning I would classify the main reasons to use partitioning in your Oracle data warehouse into these four areas: Data Elimination Partition-Wise Joins Manageability (Partition Exchange Load, Local Indexes, etc.) Information Lifecycle Management (ILM) Partitioning Basics The most common partitioning design pattern found in Oracle data warehouses is to partition the fact tables by range (or interval) on the event date/time column. This allows for partition elimination of all the data not in the desired time window in queries. For example: If I have a [...]

[back to Introduction] Editor’s note: This blog post does not cover Exadata Hybrid Columnar Compression. The first thing that comes to most people’s mind when database table compression is mentioned is the savings it yields in terms of disk space. While reducing the footprint of data on disk is relevant, I would argue it is the lesser of the benefits for data warehouses. Disk capacity is very cheap and generally plentiful, however, disk bandwidth (scan speed) is proportional to the number of spindles, no mater what the disk capacity and thus is more expensive. Table compression reduces the footprint on the disk drives that a given data set occupies so the amount of physical data that must be read off the disk platters is reduced when compared to the uncompressed version. For example, if 4000 GB of raw data can compress to 1000 GB, it can be read off the same disk drives 4X as fast because it is reading and transferring 1/4 of the data off the spindles (relative to the uncompressed size). Likewise, table compression allows for the database buffer cache to contain more data without having to increase the memory allocation because more rows can be stored [...]