Friday, May 30, 2014

Oracle Optimizer Investigated with Flame Graphs

Topic: This post is about investigating the Oracle Optimizer using stack sampling and flame graphs. We will see how the complexity of the Optimizer has increased over the years and in particular with the introduction of cost-based optimization and more recently with 12c adaptive optimization.

Context and goal: Stack sampling and visualization with flame graphs are powerful techniques to probe the activity of OS processes and in particular for advanced investigations and troubleshooting of Oracle execution (see additional information and references in the article Flame Graphs for Oracle). In the following we will briefly illustrate the use of these techniques with three examples drilling down into the Oracle Optimizer hard parse activity.

Rule based optimizer

In Figure 1 here below we can see the flame graph for the hard parsing of "select count(*) from dba_source" using rule base optimization. For consistency with the following tests the database engine used is 12c and rule-based optimization is forced by running alter session set optimizer_mode=rule prior to parsing.
The captured workload shows the almost all the (CPU) time was spent under hard parsing (kksParseCursor) and in particular hard parsing (kksLoadChild), as expected.
The actual parsing of the child cursor can be seen under kksParcheChildCursor (and opi parse) and occupies a large fraction of the samples.

Figure 1: flame graph of select count(*) from dba_source in rule mode. See more details in the SVG version of the graph by  hovering over the graph lines.

Cost based optimizer 11g

In Figure 2 we can see the flame graph for parsing select count(*) from dba_source using the 11g cost based optimizer. This has been obtained running under 12c binaries (for consistency with the rest of the examples) by runnig alter session set optimizer_features_enable='' prior to parsing.
The first important difference that we see between this CBO flame graph and the previous one for rule-based optimization is the additional number of data points that have been collected (about 10 times more). This shows that considerably more code has to be executed for cost based optimization compared to rule, at least in this exampl. A result that was expected. The activity of parsing the child cursor under kksParcheChildCursor that we highlighted in Figure 1, is still there in Figure 2, with about the same absolute wait in terms of samples but relatively much less prominent due to the additional cost-based activities. Most of the captured samples, i.e. CPU time, is now spent under the function 'opitca'. We see the appearance of many more function related to query optimizations, for example with prefixes 'apa' (Access Path Analysis?), 'kko' (Kernel Kompile Optimizer?), 'kkq' (Kernel Kompile Query?), or simply with the keyword "Cost" in their name. In particular 'kko*' functions appear to be directly associated with CBO from the documentation.

Figure 2: flame graph of select count(*) from dba_source using 11g CBO.See more details in the SVG version of the graph by hovering over the graph lines.

Cost based optimizer and adaptive optimization in 12c

Figure 3 shows the flame graph for parsing select count(*) from dba_source parsed in 12c, with optimizer_features_enable=''. Additional relevant parameters are the default values for  optimizer_adaptive_features=TRUE and optimizer_adaptive_reporting_only=FALSE. What we notice in Figure 3 is that the amount of sampled points has increased compared to the 11g CBO, this shows that  additional work was done. Next we notice that queries are executed during parsing. From the explain plan details of the statement we see that 2 Sql Plan Directives were used when parsing this statement. The directive that caused additional dynamic sampling (or dynamic statistics as they are called now in 12c in this context) are related to SINGLE TABLE CARDINALITY MISESTIMATE for SYS.OBJ$. By performing a 10046 trace (not reported for brevity) we can confirm that the additional recursive SQL is indeed related to sampling SYS.OBJ$.
Another method to confirm that the additional work highlighted in Figure 3 comes from adaptive optimization is to turn the feature off by setting optimizer_adaptive_features=FALSE and then measuring the flame graph again (the results confirm the conclusions reported above, however the graph is omitted from this article for brevity).

Figure 3: flame graph of select count(*) from dba_source using 12c optimizer. See more details by downloading the SVG version of the graph and by hovering over the lines with the function names.

How the graphs have been generated

  • Stack trace data has been sampled at high frequency using perf (on RHEL6): /usr/bin/perf record -a -g -F 100000 -p <pid_of_oacle_session> sleep 5
  • trace data is then dumped into a txt file with: /usr/bin/perf script >perf_parse_12c.txt
  • Finally the trace file is processed using the Flamegraph stack trace visualizer: grep -v 'cycles:' perf_parse_12c.txt| sed -f os_explain.sed|FlameGraph-master/ |FlameGraph-master/ --minwidth 4 --title "Flame Graph: parsing of select count(*) from dba_source, CBO with adaptive optimzations" > perf_parse_12c.svg
On the Oracle side the parsing workload was generated with the script here below:

   v_mycursor number;
   v_mysql varchar2(1000);
    v_mysql := 'select /*random'||dbms_random.string(null,6)||'*/ count(*) from dba_source';
    v_mycursor := dbms_sql.open_cursor;
    -- now manually start perf stack trace collection at OS level

Note a simpler version of this could be to just to run an 'explain plan' of the statement under study (select count(*) from dba_source in this example), however that would include also the work done to insert into the plan_table:

Some hints on how to read the graphs and interpret the Oracle function names can be found in the article "Flame Graphs for Oracle"
An additional link with pointer on how to understand stack traces for Oracle parsing operations can be found in Tanel's blog


The author has demonstrated the use of stack sampling with perf and flame graph visualization for investigating the Oracle Optimizer and how its operations have evolved across major Oracle versions. Notably the 12c Optimizer shows in the example under study a longer and more complex code path for parsing than 11g Optimizer. This has been traced and explained by the new 12c feature of adaptive optimization working together with SQL Plan Directives to provide dynamic statistics at parse time. 

Credits to Brendan Gregg for Flame Graphs and to Tanel Poder for publishing extensive work and examples on using stack trace analysis for Oracle troubleshooting.

Friday, May 9, 2014

Flame Graphs for Oracle

Topic: This post is a hands-on introduction to using on-CPU Flame Graphs for investigating Oracle workloads. This technique is about collecting and visualizing sampled stack trace data to analyze and troubleshoot Oracle processes at the OS level (in particular applied to Linux).

Motivations: The techniques and tools described here can be used for performance investigations to complement wait-event based information gathered from the Oracle engine, such as information available with ASH and sql monitoring. They can also be used for complex troubleshooting when the wait interface is failing to instrument a particular problem under study. Last but not least they can be used as a learning aid to investigating Oracle internals.

Context and credits: The use of stack sampling has previously been covered for Oracle investigations by Tanel Poder in several articles (see Tanel's Advanced Oracle Troubleshooting Guide and for example this article).
Brendan Gregg has done original work on the collection and visualization of stack sampled data and by introducing the Flame Graphs visualization technique.
The techniques discussed in this post rely on perf for data collection. Perf is a relatively new addition to the performance tools available for Linux (requires kernel 2.6.31 or higher, for example RHEL6 or OL6). A few additional references on using perf for investigating Oracle are: Frits Hoogland, Kevin Closson, Craig Shallahamer.

This article: In this post we will approach the use of stack sampling with perf and visualization with Flame Graphs by introducing examples of growing complexities and by detailing the steps needed to reproduce the results. The main idea is to give a few hints of what this type of approach can do to help us analyze process execution and performance rather than producing a comprehensive guide.

Example 1: Flame Graph of query execution

In Figure 1 here below we can see a Flame Graph representation of the stack traces collected during the execution of "select count(*) from dba_source" (the details of how the graph was generated are described later in this article).

Figure 1: Flame Graph of "select count(*) from dba_source". See more details for each stack layer by hovering over the graph lines in the SVG version

The stack traces expose the name of many Oracle's internal functions. There is no public documentation of those internal functions, however much can be guessed by their name and from the documentation in the Oracle Support document "ORA-600 Lookup Error Categories" (formerly MOS note 175982.1, more recently found as an attachment to Note 1321720.1 and also in other links on the internet).
In addition to the support note, Tanel Poder has already done a great job of "decoding" an important class of internal functions, the rowsource functions with prefix qer (query execution rowsource) in his os_explain script. Rowsource functions expose execution plan details, similarly to what is exposed in ASH data (v$active_session_history) at the columns SQL_PLAN_OPERATION and SQL_PLAN_OPTIONS.
Note also that in Flame Graphs the horizontal axis is not time, stack traces are simply pasted by ordering alphabetically (follow this link to Brendan Gregg's article on Flame Graphs).

If we extract only the rowsource entries from the stack trace data represented in Figure 1, we obtain a simplified Flame Graph, which highlights the parts related to query's execution plan: Figure 2. We can recognize there the execution plan operations and how they stack and nest on top of each other. Although SQL is mainly a declarative language here we have a good view of how this is translated at run time into procedural execution: after all the Oracle query engine is just another (complex) C program.

Figure 2: Flame Graph of "select count(*) from dba_source" where the stack data has been filtered to show only rowsources. See more details for each stack layer by hovering over the graph lines in the SVG version

The interpretation of the stack samples of a relatively complex execution plan like this one can be challenging. In particular we want to match the measured stack traces with the execution plan operations. This becomes easier if we cross check the stack traces with the execution plan details, as show in Figure 3 for this query.
One subtle, but important point worth repeating is that horizontal axis in the Flame Graph is not time (Brendan Gregg's article on Flame Graphs). However the length of each line in the graph is proportional to the number of captured stack frames, therefore to its execution time. This last sentence may not be valid for a general  case but it holds for this example, as the whole query execution time was 'on-CPU' (see more on this later on when we discuss Flame Graphs for I/O-bound workload)

Figure 3: Execution plan and details of the rows processed by each step (Actual Rows, "A-Rows") and logical reads ("Buffers") for the query "select count(*) from dba_source" (Oracle version

How the Flame Graphs of Figure 1 and Figure 2 have been generated
  • Stack samples have been collected with perf running on RHEL6. This example will not work on old kernel, notably it will not work on RHEL5. Perf has been run as the root user on the DB server.
  • Flame Graphs have been generated using You can download the latest version from that link for example as a zip (or clone the project locally using "git clone").
  • Download os_explain and edit out the string replacement part in a file called os_explain.sed. Here below an example of these steps:

Steps for data collection:
  • Session1: run sqlplus and find the os pid of its server process. For example use this SQL: select spid from v$process where addr in (select  paddr from v$session where audsid =sys_context('USERENV','SESSIONID'));
  • Session1: run the test SQL to load data into the cache, for this example run: select count(*) from dba_source; 
  • Session 2: on a different window as root start recording the stack traces. As the execution is very short in duration we can afford high frequency sampling (100KHz). Example: perf record -a -g -F100000 -p <pid of sqlplus process>
  • Session 1: run the sql under study, in this example: select count(*) from dba_source; 
  • Session 2: CTRL-C to stop recording

Steps for data processing and visualization:

  • Generate the stack trace in a txt file: perf script >myperf_script.txt
  • Create the flame graph: grep -v 'cycles:' myperf_script.txt|sed -f os_explain.sed|../FlameGraph-master/ |../FlameGraph-master/ --title "Flame Graph: select count(*) from dba_source" >Figure1.svg
  • Create the flame graph with only rowsources: grep -i -e qer -e opifch -e ^$ test_perf_script23.txt|sed -f os_explain.sed|../FlameGraph-master/|../FlameGraph-master/ --title "Flame Graph Rowsource: select count(*) from dba_source" >Figure2.svg

Example 2: profiling multiple processes - logical IO

In this example we trace CPU workload running from multiple sessions and generate on-CPU Flame Graphs. We use SLOB for logical IO testing to generate the CPU-bound workload, that is SLOB queries run with their entire data set in the buffer cache in this example.

Figure 4: Flame Graph of SLOB logical IO (8 concurrent sessions). See more details in the SVG version

Similarly to what we discussed in the Example 1 above, we can now filter the stack traces to highlight the rowsource data. In addition in this example we also leave the details of the functions that start with the letter 'k', notably this includes the Oracle kernel function kcbgtcr (Kernel Cache Buffer Get Consistent Read).

Figure 5: Flame Graph of SLOB logical IO (8 concurrent sessions) where the stack data has been filtered to show only rowsource functions and functions stating with 'k'. See more details in the SVG version

We can correlate the rowsource execution steps as we see them in in Figure 5 with the execution plan of the SLOB test query reported here below (Figure 6).

Figure 6: Execution plan of the SLOB query used for the tests described in this article.

How the Flame Graphs 4 and 5 have been generated

Test setup:
  • See example 1 above on the details about downloading the FlameGraph scripts and about the requirements for perf and os_explain.sed
  • Download SLOB and configure the test environment. 8 schemas have been used for the tests reported here. See also this post for additional details on testing with SLOB.
  • Make sure that SLOB runs with all data in buffer cache. 8 schemas of 1M blocks each have been used for this tests and a buffer cache of 70 GB.
  • To facilitate loading data into the buffer cache this SQL was run prior to testing:
    • alter session set "_serial_direct_read"=never;
    • alter table user1.cf1 cache;
    • select count(*) from user1.cf1;
    • repeat for the rest of the test users.
    • while a SLOB test is running we can check that the workload is not doing physical IO for example using Tanel's snapper. For example: snapper all 10 1 "select inst_id, sid from gv$session where username like 'USER%'"

Test execution:
  • Session 1: run the SLOB test. In the test case described here below I used 8 sessions (that is I started the test with ./ 8
  • Session1: identify the PIDs of the server sessions running SLOB workload. For example use: select spid from v$process where addr in (select  paddr from v$session where username like 'USER%');
  • Session 2: gather perf data for 20 seconds at default sampling rate (1 KHz):  perf record -g -a -p 51929,51930,51931,51932,51933,51934,51962,51963 sleep 20

Data processing and visualization:
  • Generate the stack trace in a txt file: perf script >myperf_scriptSLOB.txt
  • Create the flame graph: grep -v 'cycles:' myperf_scriptSLOB.txt|sed -f os_explain.sed|../FlameGraph-master/|../FlameGraph-master/ --title "Flame Graph: SLOB physical IO" >Figure1_SLOB.svg
  • Create the flame graph with only rowsource steps and k* function: grep -i -e qer -e opifch -e ' k' -e ^$ myperf_scriptSLOB.txt|sed -f os_explain.sed|../FlameGraph-master/|../FlameGraph-master/ --title "Flame Graph: SLOB physical IO - filtered" >Figure2_SLOB.svg

Example 3: profiling multiple processes - physical IO

We will use the same SLOB workload as example 2 but this time with a very small buffer cache therefore forcing the DB to do physical I/O to read SLOB data from the storage. Random I/O reads will be instrumented by Oracle with the db file sequential read wait event (see also this post for additional details and examples on testing random I/O with SLOB).

Figure 7: Flame Graph of SLOB physical IO (8 concurrent sessions). See more details in the SVG version

By comparing Figure 7 (SLOB physical I/O) with Figure 4 (SLOB logical I/O) we notice that

  • we have many more function calls in the case of physical I/O than in the corresponding graph with only logical I/O. Moreover in Figure 7 we can see several stack traces that contain only OS kernel calls and that cannot be easily associated with a particular step in Oracle execution. Note the system under tests used NAS storage so physical IO appears as network traffic.
  • Moreover from the SVG graph versions we can count the number of samples stack traces and notice that in the case of physical IO we have considerably less values. This is because perf will only collect stack traces when the process is on-CPU.
  • Another important fact that we notice is the appearing of Oracle kernel kcbzib function. This seems to be associated with reading blocks into the cache with physical reads.

Figure 8: Flame Graph of SLOB physical IO (8 concurrent sessions) where the stack data has been filtered to show only rowsources and functions stating with 'k'. See more details by hovering over the graph lines in the SVG version

Example 4: profiling the server workload

This example is about measuring and visualizing stack traces collected for on entire server. The interpretation becomes more complex as multiple workloads are measured together. Moreover system calls for I/O and other activities are measured as well.
Another important fact to take into consideration is the footprint of data collection. For this reason a reduced frequency of 99 HZ was used for sampling.

Data gathering at 99HZ for 20 seconds for the whole instance: perf record -g -a -F99 sleep 20

The steps for data visualization are the same as was used in Examples 2 and 3. Here below in Figure 9 the Flame Graph of the measured data and a 'filtered version' in Figure 10.
  • Generate the stack trace in a txt file: perf script >perf_data.txt
  • The commands used to generate the 'filtered' Figure 10 are: grep -i -e qer -e opifch -e ' k' -e exe -e ^$ perf_data.txt|sed -f os_explain.sed|../FlameGraph-master/|../FlameGraph-master/ --title "Flame Graph - filtered" >perf_Figure10.svg

From the row source names reported in Figure 10 below we can guess what the workload on the server was, which types of operations were executed, with details on the queries, such as the access path used and on the type of DML. We can also measure which Oracle functions were executed for most of the CPU time. It's worth mentioning again that only on-CPU workload was captured.

Figure 9: Flame Graph of the workload of an entire server for 20 seconds, stack traces sampled at 99 Hz. See more details in the SVG version

Figure 10: Flame Graph of the workload of an entire server for 20 seconds, stack traces sampled at 99 Hz. where the stack data has been filtered to show only rowsources + functions stating with 'k' or containing 'exe' in their name. See more details in the SVG version


High-frequency sampling stack traces is a powerful technique to investigate process execution at the OS level, in particular for CPU-bound processes. This technique can be successfully applied to the study of Oracle performance and complex troubleshooting. It can complement and in some cases supplement the information exposed by Oracle's V$ views and wait event interface in general. Flame graphs are a visualization technique introduced by Brendan Gregg to facilitate the analysis of sampled stack data. The analysis of stack traces for Oracle processes currently relies on the little information available on the Oracle Support document "ORA-600 Lookup Error Categories" (formerly MOS note 175982.1, more recently seen with note 1321720.1) and on the published work of experts who have approached this field and published their findings, notably Tanel Poder. 
Much of the potential of these techniques is still untapped, we have covered in this article a few basic examples.

Additional resources on interpreting stack traces and on profiling Oracle rowsources and SQL execution can be found in Tanel's blog and Enkitec TV video and in Alexander Anokhin's blog.
More info on the use of Flame Graphs can be found in Brendan Gregg's blog.
See also on this blog a post about using Flame Graphs for investigating Oracle optimizer.