Join predicate pushdown

When a query contains a regular or inline view, there are 3 basic strategies for the optimizer to choose from:

1) merge the view (no “VIEW” operation in the plan)
2) instantiate the view as the whole and join it to the rest of the query (the plan shows a VIEW “operation”)
3) push join predicates inside the view (the plan shows “VIEW PUSHED PREDICATE”).

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Join cardinality

In my previous post I showed an example of how a query’s performance can be improved using the waste minimization technique. My focus was primarily on identifying and enforcing the correct plan, but I received some questions regarding the root cause of the problem: why the optimizer came up with a wrong join order? It’s a very interesting question, and it deserves a separate post so that it could be explored in detail.

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Query tuning by waste minimization: a real-life example

Today I’d like to share another tuning example from a recent case at work, which in my opinion is good for illustrating typical steps involved in SQL optimization process.

I was handed a poorly performing query with a relatively verbose text, so I will only give the general structure here (it will also prevent me from accidentally disclosing some sensitive information from that application):

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AWR analysis: another case study

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):


DB Name         DB Id    Instance     Inst Num Release     RAC Host
------------ ----------- ------------ -------- ----------- --- ------------
XXXX           XXXXX     XXXXX               1  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)

Cache Sizes
~~~~~~~~~~~                       Begin        End
                             ---------- ----------
               Buffer Cache:     5,856M     5,856M  Std Block Size:         8K
           Shared Pool Size:     2,048M     2,048M      Log Buffer:    14,340K

Load Profile
~~~~~~~~~~~~                            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
               Transactions:                174.31


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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.

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It’s beenĀ 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:
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Plotting SLOB results in high resolution


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.

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