A few weeks ago, I received a request to review an AWR report for a database suffering from instance-level performance issues. Here are the the key parts of that report (with some masking):
WORKLOAD REPOSITORY report for
DB Name DB Id Instance Inst Num Release RAC Host
------------ ----------- ------------ -------- ----------- --- ------------
XXXX XXXXX XXXXX 1 10.2.0.5.0 NO XXXX
Snap Id Snap Time Sessions Curs/Sess
--------- ------------------- -------- ---------
Begin Snap: 65115 03-May-16 11:00:09 152 17.8
End Snap: 65116 03-May-16 12:00:18 152 17.7
Elapsed: 60.16 (mins)
DB Time: 2,712.41 (mins)
~~~~~~~~~~~ Begin End
Buffer Cache: 5,856M 5,856M Std Block Size: 8K
Shared Pool Size: 2,048M 2,048M Log Buffer: 14,340K
~~~~~~~~~~~~ Per Second Per Transaction
Redo size: 2,455,599.10 14,087.84
Logical reads: 613,415.60 3,519.18
Block changes: 12,238.64 70.21
Physical reads: 12,233.70 70.19
Physical writes: 1,517.54 8.71
User calls: 1,159.19 6.65
Parses: 39,080.15 224.20
Hard parses: 32.45 0.19
Sorts: 708.22 4.06
Logons: 0.31 0.00
Executes: 39,393.06 226.00
Continue reading “AWR analysis: another case study”
Oracle cost-based optimizer (CBO) is great, but sometimes it’s making wrong choices even when correct inputs are fed to it. In such cases, you need a tool to override CBOs choices, and one of the most popular tools is optimizer hints. The main reason they’re so popular is that they allow “quick-and-dirty” kind of fixes for performance issues (provided that query text can be altered). Other ways may be more reliable, but generally require more work, and who wants to do work that can be avoided? Unfortunately, there’s a well known downside to the hints — it’s very easy to run into problems if you only fix a part of the plan.
When this happens, hints can lead to terrible execution plans. For a long time, I’ve been looking for a good example to illustrate this problem, and finally this week I encountered a case which appears to be suitable for this purpose.
Continue reading “Hints”
It’s been forever since I last shared any of my performance troubleshooting experiences at work. This week, I got a case that I think is worth publishing, and I decided to write about it in my blog. So, here we go…
A few days ago, I received a complaint about unstable performance of one of frequently running SQL reports on a 11gR2 database. Most of the time it completed within a couple of minutes, however, on certain occasions it took much longer than that, and once it even took over 20 minutes.
I have a special stored report in SQL developer for conveniently displaying key statistics from DBA_HIST_SQLSTAT which is very helpful as a first step when analyzing unstable SQL performance:
Continue reading “Workarounds”
If you work with I/O benchmarking of Oracle databases, you are almost certainly familiar with SLOB. SLOB is more than just an I/O benchmark — it’s become a de-facto industry standard. It’s simple, powerful and efficient, and it captures a plethora of metrics, both from the OS (output of iostat, mpstat etc.) and the database itself (in the form of an AWR report).
One thing that is missing though is visualization. It’s fairly easy to fix using an external plotting tool (like gnuplot or R), but what data would you plot? AWR only gives you average event times and histograms with ridiculously poor resolution. And if you want to see a high-resolution picture of your I/O (and you do — I’ll discuss the importance of that later on), it’s not enough.
Continue reading “Plotting SLOB results in high resolution”
In this article, I’ll summarize my observations regarding nested loop join mechanisms as well as previously known facts, so that everything would be in one place.
Continue reading “Nested loop internals — summary”
In the previous parts (here and here) of the series we looked at some aspects of nested loop I/O optimizations, but we have left out the most important question (from the practical point of view): how these methods are doing time-wise? Which one(s) is(are) faster, and how much savings are they offering compared to the non-optimized plan? We will turn to these questions now.
Continue reading “Nested loop internals. Part 3: comparative efficiency”
In the previous part of this mini-series we looked at differences in multiblock read behavior for different nested loop optimization mechanisms depending on degree of ordering of the data. In this post I’ll continue to explore the subject, but this time we’ll focus on decision-making process: what factors (other than the obvious ones — like optimizer hints and/or parameters) affect the specific choice of a mechanism?
Continue reading “Nested loop internals. Part 2: decision making”