On one of the databases I’m looking after (22.214.171.124, Solaris, non-RAC), several different INSERT statements (all into tablespaces with manually managed segments) suffer from occasional hiccups. The symptoms are always the same: in one of the sessions, the INSERT gets stuck doing lots of single-block I/O against one of the indexes on the inserted table, and if other sessions are running similar INSERTs, they hang on enq: TX – index contention. The situation can last just a few seconds, but sometimes it’s much longer than this (several minutes), in which case the impact on the application is quite serious.
Category: case study
Method R and parallelism (another real-life example)
In my previous post I mentioned method R as probably the most efficient approach to SQL optimization. However, it is important to focus on correct metrics for it to work correctly.
Consider this example (once again, the query is still running, so the only reliable diagnostic tool at our disposal is SQL real-time monitor):
Continue reading “Method R and parallelism (another real-life example)”
DB time vs duration
Performance tuning is all about time. You measure the time it takes for a certain process to complete, and then you search for ways to reduce this time to improve end-users experience and/or increase the application productivity. But minimizing time is not enough — it’s important to minimize the correct time metric. A typical mistake in database performance optimization to optimize DB time instead of the duration experienced by the end user. If this happens, this can easily result in a situation when DB time is reduced significantly, but the process is still taking almost as long as before, the SLA is still breached, bosses and users are increasingly frustrated.
In this post, I will give two real-life examples of cases when the difference between DB time and elapsed time was the key to understanding the problem.
Continue reading “DB time vs duration”
Read consistency overhead
SQL performance can degrade for many reasons, some of most common are:
– plan changes
– data skewness
– low caching efficiency
– data growth
All these factors are relatively well known. A somewhat less common, although not exceptionally rare scenario, is read consistency overhead due to concurrent DML against queried tables. Because of being less common, this scenario is often overlooked, which leads to false diagnoses (and eventually to “fixes” that can do more harm than good).
Occasionally I encounter a situation when I need to affect a part of the plan that corresponds to a view, e.g.:
select * from ( select v.x, x.y from v ) q where q.x = 1
Such situations are resolved using global hints. Oracle offers two ways to specify a global hint: via a query block identifier (system generated or user defined) or via view aliases. System-generated query block identifiers can be obtained via dbms_xplan.display with ALL or ALIAS option (they have the form SEL$n, where n appears to be same as the depth, e.g. in our case 1 corresponds to the main query, 2 to the inline view, 3 to the view V inside that inline view) or defined by the user via qb_name hint.
Tuning Analytic Functions
In general, tuning analytic functions (and more generally, all sort operations) is rather difficult. While for most poorly performing queries it’s relatively straightforward to gain some improvements by applying “eliminate early” principle one way or another, for slow sort operations it’s rarely applicable. Usually options are limiting to rewriting a query without analytics (e.g. using self-joins or correlated subqueries to achieve the same goal) or manually resizing the workarea to reduce/eliminate the use of disk. Recently, however, I had a case where I managed to obtain an excellent performance gain using a different technique that I would like to share in this post.
The original query was selecting about 100 columns using the LAG function on one of the columns in the WHERE clause, but in my test case I’ll both simplify and generalize the situation. Let’s create a table with a sequential id, three filtering columns x, y and z, and 20 sufficiently lengthy columns.
CPU starvation disguised as an I/O issue (yet another AWR case study)
In AWR analysis, what appears to be the root cause of the issue, can easily turn out to be just a symptom. Last week, Rajat sent me an AWR report which is a perfect illustration of this (thanks Rajat), I posted the key sections from this report below (sorry for less than perfect formatting — I had to manually re-format the HTML version of the report into text).
WORKLOAD REPOSITORY report for DB Name DB Id Instance Inst num Release RAC Host DSS 37220993 dss 1 10.2.0.4.0 NO dssdbnz Snap Id Snap Time Sessions Cursors/Session Begin Snap: 18471 12-Oct-12 08:30:28 131 1.5 End Snap: 18477 12-Oct-12 14:30:24 108 1.8 Elapsed: 359.93 (mins) DB Time: 25,730.14 (mins) Load Profile Per Second Per Transaction Redo size: 325,282.85 103,923.02 Logical reads: 33,390.52 10,667.77 Block changes: 1,307.95 417.87 Physical reads: 1,927.33 615.75 Physical writes: 244.65 78.16 User calls: 391.34 125.03 Parses: 68.14 21.77 Hard parses: 3.33 1.06 Sorts: 47.86 15.29 Logons: 3.15 1.01 Executes: 234.32 74.86 Transactions: 3.13 % Blocks changed per Read: 3.92 Recursive Call %: 61.11 Rollback per transaction %: 24.71 Rows per Sort: 3325.52 Top 5 Timed Events Event Waits Time(s) Avg Wait(ms) % Total Call Time Wait Class free buffer waits 10,726,838 344,377 32 22.3 Configuration db file sequential read 6,122,262 335,366 55 21.7 User I/O db file scattered read 3,597,607 305,576 85 19.8 User I/O CPU time 161,491 10.5 read by other session 2,572,875 156,821 61 10.2 User I/O Operating System Statistics Statistic Total AVG_BUSY_TIME 2,093,109 AVG_IDLE_TIME 63,212 AVG_IOWAIT_TIME 18,463 AVG_SYS_TIME 87,749 AVG_USER_TIME 2,004,722 BUSY_TIME 16,749,988 IDLE_TIME 510,692 IOWAIT_TIME 152,594 SYS_TIME 707,137 USER_TIME 16,042,851 LOAD 4 OS_CPU_WAIT_TIME ############### RSRC_MGR_CPU_WAIT_TIME 0 VM_IN_BYTES 5,503,492,096 VM_OUT_BYTES 2,054,414,336 PHYSICAL_MEMORY_BYTES 34,288,209,920 NUM_CPUS 8 NUM_CPU_SOCKETS 8
Continue reading “CPU starvation disguised as an I/O issue (yet another AWR case study)”
AWR report case study: stating the obvious
It’s been a while since I came across an interesting and complex case. However, yesterday on an OTN forum thread I saw a case which was interesting by its simplicity. Even though it’s almost trivial on the technical level, it’s very useful to highlight typical tuning “rookie mistakes” (I can remember quite a few cases from not so long ago, when I did similar mistakes, too).
The author posts a question about “library cache: mutex X” events that are ruining performance of his 2-node RAC cluster. The original post doesn’t contain any specifics except for CPU utilization percentage on both nodes.
Within a few hours, a few replies appear, most of them either trying to shed light on this particular wait event or sharing similar experiences. I asked the original poster to provide key sections of an AWR report (workload profile, top events, database/host CPU, top SQL), which he soon did:
Continue reading “AWR report case study: stating the obvious”
Troubleshooting log file sync waits — a simple case study
Yesterday I was asked to give my opinion on the AWR below:
DB Name DB Id Instance Inst num Release RAC Host ***** ***** ***** 1 10.2.0.4.0 NO ****** Snap Id Snap Time Sessions Cursors/Session Begin Snap: 66340 6/20/2012 14:30 117 9.3 End Snap: 66341 6/20/2012 15:00 115 9.1 Elapsed: 30.16 (mins) DB Time: 35.57 (mins) Report Summary Cache Sizes Begin End Buffer Cache: 1,888M 1,888M Std Block Size: 8K Shared Pool Size: 1,024M 1,024M Log Buffer: 3,168K Load Profile Per Second Per Transaction Redo size: 504,437.43 3,150.59 Logical reads: 23,547.86 147.07 Block changes: 1,900.01 11.87 Physical reads: 1,931.69 12.06 Physical writes: 50.85 0.32 User calls: 478.35 2.99 Parses: 20.39 0.13 Hard parses: 0.05 0 Sorts: 8.67 0.05 Logons: 0.1 0 Executes: 250.29 1.56 Transactions: 160.11 % Blocks changed per Read: 8.07 Recursive Call %: 18.31 Rollback per transaction %: 0 Rows per Sort: 178.25 ... Top 5 Timed Events Event Waits Time(s) Avg Wait(ms) % Total Call Time Wait Class CPU time 1,882 88.2 log file sync 293,178 1,217 4 57 Commit log file parallel write 290,903 961 3 45 System I/O db file scattered read 301,788 45 0 2.1 User I/O db file parallel write 11,839 31 3 1.5 System I/O
Continue reading “Troubleshooting log file sync waits — a simple case study”
Library cache locks: a case study
Recently I was asked to help with a case involving library cache locks, and even though it was really simple, I think it’s good for illustrating a few points in performance analysis.
As it often happens, it all started with customers complaining about “the database being slow”. I asked for more specific details, and I found that it wasn’t the entire database, but rather a reporting subsystem. Since I didn’t have specific session sid or sql_id at hand, I started with analyzing a 30-min AWR report, and since there wasn’t much time to look at everything, I just focused on top timed events:
Event Waits Time (s) Avg Wait(ms) %Total Wait Class CPU time 16,188 46.2 db file sequential read 18,230,956 11,787 1 33.6 User I/O db file parallel write 278,480 4,509 16 12.9 System I/O library cache lock 1,131 3,297 2,915 9.4 Concurrency log file sync 120,356 1,686 14 4.8 Commit