LOBREAD SQL Trace entry in Oracle 11.2 (and tracing OPI calls with event 10051)

March 20, 2011 by 10 Comments

A few days ago I looked into a SQL Tracefile of some LOB access code and saw a LOBREAD entry there. This is a really welcome improvement (or should I say, bugfix of a lacking feature) for understanding resource consumption by LOB access OPI calls. Check the bottom of the output below:

*** 2011-03-17 14:34:37.242
WAIT #47112801352808: nam='SQL*Net message from client' ela= 189021 driver id=1413697536 #bytes=1 p3=0 obj#=99584 tim=1300390477242725
WAIT #0: nam='gc cr multi block request' ela= 309 file#=10 block#=20447903 class#=1 obj#=99585 tim=1300390477243368
WAIT #0: nam='cell multiblock physical read' ela= 283 cellhash#=379339958 diskhash#=787888372 bytes=32768 obj#=99585 tim=1300390477243790
WAIT #0: nam='SQL*Net message to client' ela= 2 driver id=1413697536 #bytes=1 p3=0 obj#=99585 tim=1300390477243865
[...snipped...]
WAIT #0: nam='SQL*Net more data to client' ela= 2 driver id=1413697536 #bytes=2048 p3=0 obj#=99585 tim=1300390477244205
WAIT #0: nam='SQL*Net more data to client' ela= 4 driver id=1413697536 #bytes=2048 p3=0 obj#=99585 tim=1300390477244221
WAIT #0: nam='gc cr multi block request' ela= 232 file#=10 block#=20447911 class#=1 obj#=99585 tim=1300390477244560
WAIT #0: nam='cell multiblock physical read' ela= 882 cellhash#=379339958 diskhash#=787888372 bytes=32768 obj#=99585 tim=1300390477245579
WAIT #0: nam='SQL*Net more data to client' ela= 16 driver id=1413697536 #bytes=2020 p3=0 obj#=99585 tim=1300390477245685
WAIT #0: nam='SQL*Net more data to client' ela= 6 driver id=1413697536 #bytes=2048 p3=0 obj#=99585 tim=1300390477245706
WAIT #0: nam='SQL*Net more data to client' ela= 5 driver id=1413697536 #bytes=1792 p3=0 obj#=99585 tim=1300390477245720
LOBREAD: c=1000,e=2915,p=8,cr=5,cu=0,tim=1300390477245735

In past versions of Oracle the CPU (c=) usage figures and other stats like number of physical/logical reads of the LOB chunk read OPI call were just lost – they were never reported in the tracefile. In past only the most common OPI calls, like PARSE, EXEC, BIND, FETCH (and recently CLOSE cursor) were instrumented with SQL Tracing. But since 11.2(.0.2?) the LOBREAD’s are printed out too. This is good, as it reduces the amount of guesswork needed to figure out what are those WAITs for cursor #0 – which is really a pseudocursor.

Why cursor#0? It’s because normally, with PARSE/EXEC/BIND/FETCH, you always had to specify a cursor slot number you operated on (if you fetch from cursor #5, it means that Oracle process went to slot #5 in the open cursor array in your session’s UGA and followed the pointers to shared cursor’s executable parts in library cache from there). But LOB interface works differently – if you select a LOB column using your query (cursor), then all your application gets is a LOB LOCATOR (sort of a pointer with LOB item ID and consistent read/version SCN). Then it’s your application which must issue another OPI call (LOBREAD) to read the chunks of that LOB out from the database. And the LOB locator is independent from any cursors, it doesn’t follow the same cursor API as regular SQL statements (as it requires way different functionality compared to a regular select or update statement).

So, whenever a wait happened in your session due to an access using a LOB locator, then there’s no specific cursor responsible for it (as far as Oracle sees internally) and that’s why a fake, pseudocursor #0 is used.

Note that on versions earlier than 11.2(.0.2?) when the LOBREAD wasn’t printed out to trace – you can use OPI call tracing (OPI stands for Oracle Program Interface and is the server-side counterpart to OCI API in the client side) using event 10051. First enable SQL Trace and then the event 10051 (or the other way around if you like):

SQL> @oerr 10051

ORA-10051: trace OPI calls

SQL> alter session set events '10051 trace name context forever, level 1';

Session altered.

Now run some LOB access code and check the tracefile:

*** 2011-03-17 14:37:07.178
WAIT #47112806168696: nam='SQL*Net message from client' ela= 6491763 driver id=1413697536 #bytes=1 p3=0 obj#=99585 tim=1300390627178602
OPI CALL: type=105 argc= 2 cursor=  0 name=Cursor close all
CLOSE #47112806168696:c=0,e=45,dep=0,type=1,tim=1300390627178731
OPI CALL: type=94 argc=28 cursor=  0 name=V8 Bundled Exec
=====================
PARSING IN CURSOR #47112802701552 len=19 dep=0 uid=93 oct=3 lid=93 tim=1300390627179807 hv=1918872834 ad='271cc1480' sqlid='3wg0udjt5zb82'
select * from t_lob
END OF STMT
PARSE #47112802701552:c=1000,e=1027,p=0,cr=0,cu=0,mis=1,r=0,dep=0,og=1,plh=3547887701,tim=1300390627179805
EXEC #47112802701552:c=0,e=29,p=0,cr=0,cu=0,mis=0,r=0,dep=0,og=1,plh=3547887701,tim=1300390627179884
WAIT #47112802701552: nam='SQL*Net message to client' ela= 2 driver id=1413697536 #bytes=1 p3=0 obj#=99585 tim=1300390627179939
WAIT #47112802701552: nam='SQL*Net message from client' ela= 238812 driver id=1413697536 #bytes=1 p3=0 obj#=99585 tim=1300390627418785
OPI CALL: type= 5 argc= 2 cursor= 26 name=FETCH
WAIT #47112802701552: nam='SQL*Net message to client' ela= 1 driver id=1413697536 #bytes=1 p3=0 obj#=99585 tim=1300390627418945
FETCH #47112802701552:c=0,e=93,p=0,cr=5,cu=0,mis=0,r=1,dep=0,og=1,plh=3547887701,tim=1300390627418963
WAIT #47112802701552: nam='SQL*Net message from client' ela= 257633 driver id=1413697536 #bytes=1 p3=0 obj#=99585 tim=1300390627676629
OPI CALL: type=96 argc=21 cursor=  0 name=LOB/FILE operations
WAIT #0: nam='SQL*Net message to client' ela= 2 driver id=1413697536 #bytes=1 p3=0 obj#=99585 tim=1300390627676788
[...snip...]
WAIT #0: nam='SQL*Net more data to client' ela= 2 driver id=1413697536 #bytes=1792 p3=0 obj#=99585 tim=1300390627677054
LOBREAD: c=0,e=321,p=0,cr=5,cu=0,tim=1300390627677064

Check the bold and especially the red string above.  Tracing OPI calls gives you some extra details of what kind of tasks are executed in the session. The “LOB/FILE operations” call indicates that whatever lines come after it (unlike SQL trace call lines where all the activity happens before a call line is printed (with some exceptions of course)) are done for this OPI call (until a next OPI call is printed out). OPI call tracing should work even on ancient database versions…

By the way, if you are wondering, what’s the cursor number 47112801352808 in the “WAIT #47112801352808″ above? Shouldn’t the cursor numbers be small numbers?

Well, in 11.2.0.2 this was also changed. Before that, the X in CURSOR #X (and PARSE #X, BIND #X, EXEC #X, FETCH #X) represented the slot number in your open cursor array (controlled by open_cursors) in your session’s UGA. Now, the tracefile dumps out the actual address of that cursor. 47112801352808 in HEX is 2AD94DC9FC68 and it happens to reside in the UGA of my session.

Naturally I asked Cary Millsap about whether he had spotted this LOBREAD already and yes, Cary’s way ahead of me – he said that Method-R’s mrskew tool v2.0, which will be out soon, will support it too.

It’s hard to not end up talking about Cary’s work when talking about performance profiling and especially Oracle SQL trace, so here are a few very useful bits which you should know about:

If you want to understand the SQL trace & profiling stuff more, then the absolute must document is Cary’s paper on the subject – Mastering Performance with Extended SQL Trace:

Also, if you like to optimize your work like me (in other words: you’re proactively lazy ;-) and you want to avoid some boring “where-the-heck-is-this-tracefile-now” and “scp-copy-it-over-to-my-pc-for-analysis” work then check out Cary’s MrTrace plugin (costs ~50 bucks and has a 30-day trial) for SQL Developer. I’ve ended up using it myself regularly although I still tend to avoid GUIs:

Filed Under: Oracle Tagged With: , , , , ,

ORA-4031 errors, contention, cursor management issues and shared pool fragmentation – free secret seminar!

March 16, 2011 by 18 Comments

Free stuff! Free stuff! Free stuff! :-)

The awesome dudes at E2SN have done it again! (and yes, Tom, this time the “we at E2SN Ltd” doesn’t mean only me alone ;-)

On Tuesday 22nd March I’ll hold two (yes two) Secret Oracle Hacking Sessions – about ORA-04031: unable to allocate x bytes of shared memory errors, cursor management issues and other shared pool related problems (like fragmentation). This event is free for all! You’ll just need to be fast enough to register, both events have 100 attendee limit (due to my GotoWebinar accont limitations).

I am going to run this online event twice, so total 200 people can attend (don’t register for both events, please). One event is in the morning (my time) to cater for APAC/EMEA region and the other session is for EMEA/US/Americas audience.

The content will be the same in both sessions. There will be no slides (you cant fix your shared pool problems with slides!) but there will be demos, scripts, live examples and fun (for the geeks among us anyway – others go and read some slides instead ;-)!

Filed Under: Cool stuff, Oracle Tagged With: , , , , , , ,

Exadata CAN do smart scans on bitmap indexes

March 15, 2011 by 20 Comments

As I’m finishing up a performance chapter for the Exadata book (a lot of work!), I thought to take a quick break and write a blog entry.

This is not really worth putting into my Oracle Exadata Performance series (which so far has only 1 article in it anyway) .. so this is a little stand-alone article …

Everybody knows that the Exadata smart scan can be used when scanning tables (and table partitions). You should also know that smart scan can be used with fast full scan on Oracle B-tree indexes (a fast full scan on an index segment is just like a full table scan, only on the index segment (and ignoring branch blocks)).

For some reason there’s a (little) myth circulating that smart scans aren’t used for scanning bitmap indexes.

So, here’s evidence, that smart scan can be used when scanning bitmap indexes:

SQL> select /*+ tanel3 */ count(*) from t1 where owner like '%XYZXYZ%';

...

Plan hash value: 39555139

-----------------------------------------------------------------------------------
| Id  | Operation                             | Name        | E-Rows | Cost (%CPU)|
-----------------------------------------------------------------------------------
|   0 | SELECT STATEMENT                      |             |        |   505 (100)|
|   1 |  SORT AGGREGATE                       |             |      1 |            |
|   2 |   BITMAP CONVERSION COUNT             |             |    400K|   505   (0)|
|*  3 |    BITMAP INDEX STORAGE FAST FULL SCAN| BI_T1_OWNER |        |            |
-----------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

   3 - storage(("OWNER" LIKE '%XYZXYZ%' AND "OWNER" IS NOT NULL))
       filter(("OWNER" LIKE '%XYZXYZ%' AND "OWNER" IS NOT NULL))

So, as you see the execution plan sure shows a FAST FULL SCAN on a BITMAP INDEX segment, which happens to be on Exadata STORAGE.

Also, you see a storage() predicate applied on the line 3 of the execution plan, which means that Oracle will attempt to use a smart scan predicate offload – but this can’t always be done!

So, you can’t really determine whether a smart scan happened during execution just by looking into the execution plan, you should really check some V$SESSION statistics too. That’s where my Snapper script becomes handy.

I started Snapper on my session just before running the above query. The “smart table scan” and “smart index scan” performance counters are updated right after Oracle has opened the segment header and determines, from the number of blocks in the segment, whether to call the smart scan codepath or not. In other words, the smart scan counters are inremented in the beginning of the segment scan.

The output is following (some irrelevant counters are stripped for brevity):


@snapper all 5 1 "301"
Sampling SID 301 with interval 5 seconds, taking 1 snapshots...
setting stats to all due to option = all

-- Session Snapper v3.52 by Tanel Poder @ E2SN ( http://tech.e2sn.com )

-------------------------------------------------------------------------------------------------------------------------------------
    SID, USERNAME  , TYPE, STATISTIC                                                 ,     HDELTA, HDELTA/SEC,    %TIME, GRAPH
-------------------------------------------------------------------------------------------------------------------------------------
    301, TANEL     , STAT, physical read total IO requests                           ,         13,        2.6,
    301, TANEL     , STAT, physical read total multi block requests                  ,          4,         .8,
    301, TANEL     , STAT, physical read requests optimized                          ,          1,         .2,
    301, TANEL     , STAT, physical read total bytes optimized                       ,      8.19k,      1.64k,
    301, TANEL     , STAT, physical read total bytes                                 ,      4.63M,     925.7k,
    301, TANEL     , STAT, cell physical IO interconnect bytes                       ,     10.02k,         2k,
    301, TANEL     , STAT, physical reads                                            ,        565,        113,
    301, TANEL     , STAT, physical reads cache                                      ,          1,         .2,
    301, TANEL     , STAT, physical reads direct                                     ,        564,      112.8,
    301, TANEL     , STAT, physical read IO requests                                 ,         13,        2.6,
    301, TANEL     , STAT, physical read bytes                                       ,      4.63M,     925.7k,
    301, TANEL     , STAT, db block changes                                          ,          1,         .2,
    301, TANEL     , STAT, cell physical IO bytes eligible for predicate offload     ,      4.62M,    924.06k,
    301, TANEL     , STAT, cell physical IO interconnect bytes returned by smart scan,      1.82k,      364.8,
    301, TANEL     , STAT, cell blocks processed by cache layer                      ,        564,      112.8,
    301, TANEL     , STAT, cell blocks processed by txn layer                        ,        564,      112.8,
    301, TANEL     , STAT, cell blocks processed by index layer                      ,        564,      112.8,
    301, TANEL     , STAT, cell blocks helped by minscn optimization                 ,        564,      112.8,
    301, TANEL     , STAT, cell index scans                                          ,          1,         .2,
    301, TANEL     , STAT, index fast full scans (full)                              ,          1,         .2,
    301, TANEL     , STAT, index fast full scans (direct read)                       ,          1,         .2,
    301, TANEL     , STAT, bytes sent via SQL*Net to client                          ,        334,       66.8,
    301, TANEL     , STAT, bytes received via SQL*Net from client                    ,        298,       59.6,
    301, TANEL     , STAT, SQL*Net roundtrips to/from client                         ,          2,         .4,
    301, TANEL     , STAT, cell flash cache read hits                                ,          1,         .2,
    301, TANEL     , TIME, hard parse elapsed time                                   ,     1.17ms,    233.8us,      .0%, |          |
    301, TANEL     , TIME, parse time elapsed                                        ,      1.5ms,    300.2us,      .0%, |          |
    301, TANEL     , TIME, DB CPU                                                    ,       11ms,      2.2ms,      .2%, |          |
    301, TANEL     , TIME, sql execute elapsed time                                  ,     82.2ms,    16.44ms,     1.6%, |@         |
    301, TANEL     , TIME, DB time                                                   ,    84.36ms,    16.87ms,     1.7%, |@         |
    301, TANEL     , WAIT, enq: KO - fast object checkpoint                          ,    16.18ms,     3.24ms,      .3%, |          |
    301, TANEL     , WAIT, gc cr grant 2-way                                         ,      223us,     44.6us,      .0%, |          |
    301, TANEL     , WAIT, gc current grant 2-way                                    ,      136us,     27.2us,      .0%, |          |
    301, TANEL     , WAIT, cell smart index scan                                     ,    56.04ms,    11.21ms,     1.1%, |@         |
    301, TANEL     , WAIT, SQL*Net message to client                                 ,        7us,      1.4us,      .0%, |          |
    301, TANEL     , WAIT, SQL*Net message from client                               ,      4.42s,   884.47ms,    88.4%, |@@@@@@@@@ |
    301, TANEL     , WAIT, cell single block physical read                           ,      541us,    108.2us,      .0%, |          |
    301, TANEL     , WAIT, events in waitclass Other                                 ,     2.22ms,    443.2us,      .0%, |          |
--  End of Stats snap 1, end=2011-03-13 19:36:31, seconds=5

As you see from the above “cell index scans” statistic – indeed one index segment was scanned using the cell smart scan method.

So, I would rather call this feature “smart segment scan” to reflect that smart scan can scan more than just tables…

I guess one of the reasons why few people have seen smart bitmap index scans in action is that (single-column) bitmap indexes tend to be small. Smaller than corresponding table segments and B-tree index segments. On partitioned tables they’re much more likely going to be under the “_small_table_threshold” calculation which is used for determining whether to do a direct path full segment scan or not (yes, the _small_table_threshold applies to fast full index scan and fast full bitmap index scan too, not just table scans). So, it’s likely that Oracle chooses to do a regular, buffered full bitmap segment scan and thus won’t even consider using smart scan (as smart scans require direct path reads).

By the way – the direct path read (or not) decision is done per segment – not per object (like a table or index). So if you have 10 partitions in a table (or index), half of them are large, half are smaller, then Oracle may end up using direct path reads (and smart scan) on 5 of them and buffered (dumb) scan on the other 5. If you run something like Snapper on the session, then you’d see the smart scan counters go up by 5 only. As written above, Oracle decides whether to do direct path reads (and smart scan) right after opening the header block of a segment (partition) and reading out how many blocks this partition’s segment has below HWM.

The above applied to serial direct path reads – the Parallel Execution slaves should always read using direct path mode, right? …. Wrong :)

Well, partially wrong… In 11.2.0.2, if the parallel_degree_policy = manual, then yes, PX slaves behave like usual and always force a direct path read (and try to use a smart scan). However, with parallel_degree_policy = AUTO, which is the future of PX auto-management, Oracle can decide to do a buffered parallel scan instead, again disabling the use of smart scan…

One more note – I didn’t say anything about whether you should or should not use (bitmap) indexes on Exadata, it’s an entirely different discussion. I just brought out that the smart scan is used for scanning table segments, B-tree index segments and bitmap index segments if conditions are right.

And in the end I have to say…. that even with this evidence you can’t be fully sure that a smart scan was used throughout the entire segment, but more about this in the book and perhaps in a later blog article. We have interesting times ahead ;-)

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Oracle Exadata Performance series – Part 1: Should I use Hugepages on Linux Database Nodes?

March 13, 2011 by 15 Comments

There was a question in LinkedIn forum about whether Linux Hugepages should be used in Oracle Exadata Database layer, as they aren’t enabled by default during ACS install. I’m putting my answer into this blog entry – apparently LinkedIn forums have a limit of 4000 characters per reply… (interestingly familiar number, by the way…:)

So, I thought that it’s time to start writing my Oracle Exadata Performance series articles what I’ve planned for a while… with some war stories from the field, some stuff what I’ve overcome when researching for writing the Expert Oracle Exadata book etc.

I’ve previously published an article about Troubleshooting Exadata Smart Scan performance and some slides from my experience with VLDB Data Warehouse migrations to Exadata.

Here’s the first article (initially planned as a short response in LinkedIn, but it turned out much longer though):

As far as I’ve heard, the initial decision to not enable hugepages by default was that the hugepages aren’t flexible & dynamic enough – you’ll have to always configure the hugepages at OS level to match your desired SGA size (to avoid wastage). So, different shops may want radically different SGA sizes (larger SGA for single-block read oriented databases like transactional/OLTP or OLAP cubes), but smaller SGA for smart scan/parallel scan oriented DWs. If you configure 40GB of hugepages on a node, but only use 1GB of SGA, then 39GB memory is just reserved, not used, wasted – as hugepages are pre-allocated. AMM, using regular pages, will only use the pages what it touches, so there’s no memory wastage due to any pre-allocation issues…

So, Oracle chose to use an approach which is more universal and doesn’t require extra OS level configuration (which isn’t hard at all though if you pay attention, but not all people do). So, less people will end up in trouble with their first deployments although they might not be getting the most out of their hardware.

However, before enabling hugepages “because it makes things faster” you should ask yourself what exact benefit would they bring you?

There are 3 main reasons why hugepages may be useful in Linux:

1) Smaller kernel memory usage thanks to less PTEs thanks to larger pagesizes

This means less pagetable entries (PTEs) and less kernel memory usage. The bigger your SGA and the more processes you have logged on, the bigger the memory usage.

You can measure this in your case – just “grep Page /proc/meminfo” and see how big portion of your RAM has been used by “PageTables”. Many people have blogged about this, but Kevin Closson’s blog is probably the best source to read about this:

2) Lower CPU usage due to less TLB misses in CPU and soft page-fault processing when accessing SGA.

It’s harder to measure this on Linux with standard tools, although it is sure possible (on Solaris you can just run prstat -m to get microstate accounting and look into TFL,DFL,TRP stats).

Anyway, the catch here is that if you are running parallel scans and smart scans, then you don’t access that much of buffer cache in SGA at all, all IOs or smart scan result-sets are read directly to PGAs of server processes – which don’t use large pages at all, regardless of whether hugepages for SGA have been configured or not. There are some special cases, like when a block clone has to be rolled back for read consistency, you’ll have to access some undo blocks via buffer cache… but again this should be a small part of total workload.

So, in a DW, which using mostly smarts scans or direct path reads, there won’t be much CPU efficiency win from large pages as you bypass buffer cache anyway and use small pages of private process memory. All the sorting, hashing etc all happens using small pages anyway. Again I have to mention that on (my favorite OS) Solaris it is possible to configure even PGAs to use large pages (via _realfree_heap_pagesize_hint parameter) … so it’ll be interesting to see how this would help DW workloads on the Exadata X2-8 monsters which can run Solaris 11.

3) Lock SGA pages into RAM so they won’t be paged out when memory shortage happens (for whatever reason).

Hugepages are pre-allocated and never paged out. So, when you have extreme memory shortage, your SGAs won’t be paged out “by accident”. Of course it’s better to ensure that such memory shortages won’t happen – configure the SGA/PGA_AGGREGATE_TARGET sizes properly and don’t allow third party programs consume crazy amounts of memory etc. Of course there’s the lock_sga parameter in Oracle which should allow to do this on Linux with small pages too, but first I have never used it on Linux so I don’t know whether it works ok at all and also in 11g AMM perhaps the mlock() calls aren’t supported on the /dev/shm files at all (haven’t checked and don’t care – it’s better to stay away from extreme memory shortages). Read more about how the AMM MEMORY_TARGET (/dev/shm) works from my article written back in 2007 when 11g came out ( Oracle 11g internals – Automatic Memory Management ).

So, the only realistic win (for DW workload) would be the reduction of kernel pagetables structure size – and you can measure this using PageTables statistic in /proc/meminfo. Kevin demonistrated in his article that 500 connections to an instance with ~8 GB SGA consisting of small pages resulted in 7 GB of kernel pagetables usage, while the usage with large pages (still 500 connections, 8 GB SGA) was about 265 MB. So you could win over 6 GB of RAM, which you can then give to PGA_AGGREGATE_TARGET or to further inrease SGA. The more processes you have connected to Oracle, the more pagetable space is used… Similarly, the bigger the SGA is, the more pagetable space is used…

This is great, but the tradeoff here is manageability and some extra effort you have to put in to always check whether the large pages actually got used or not. After starting up your instance, you should really check whether the HugePages_Free in /proc/meminfo shrank and HugePages_Rsvd increased (when instance has just started up and Oracle hasn’t touched all the SGA pages yet, some pages will show up as Rsvd – reserved).

With a single instance per node this is trivial – you know how much SGA you want and pre-allocate the amount of hugepages for that. If you want to increase the SGA, you’ll have to shut down the instance and increase the Linux hugepages setting too. This can be done dynamically by issuing a command like echo N > /proc/sys/vm/nr_hugepages (where N is the number of huge pages), BUT in real life this may not work out well as if Linux kernel can’t free enough small pages from right physical RAM locations to consolidate 2 or 4 MB contiguous pages, the above command may fail to create the requested amount of new hugepages.

And this means you should restart the whole node to do the change. Note that if you increase your SGA larger to the number of hugepages (or you forget to increase the memlock setting in /etc/security/limits.conf accordingly) then your instance will silently just use the small pages, while all the memory pre-allocated for hugepages stays reserved for hugepages and is not usable for anything else!).

So, this may become more of a problem when you have multiple database instances per cluster node or you expect to start up and shut down instances on different nodes based on demand (or when some cluster nodes fail).

Long story short – I do configure hugepages in “static” production environments, to save kernel memory (and some CPU time for OLTP type environments using buffer cache heavily), also on Exadata. However for various test and development environments with lots of instances per server and constant action, I don’t bother myself (and the client) with hugepages and make everyone’s life easier… Small instances with small number of connections won’t use that many PTEs anyway…

For production environments with multiple database instances per node (and where failovers are expected) I would take the extra effort to ensure that whatever hugepages I have preallocated, won’t get silently wasted because an instance wants more SGA than the available hugepages can accommodate. You can do this by monitoring /proc/meminfo’s HugePage entries as explained above. And remember, the ASM instance (which is started before DB instances) will also grab itself some hugepages when it starts!

Filed Under: Oracle Tagged With: , , , ,

New cursor_bind_capture_destination parameter in Oracle 11.2.0.2

January 19, 2011 by 6 Comments

I just noticed that there’s a new cursor_bind_capture_destination parameter in Oracle 11.2.0.2 (which is really more like Oracle 11gR3 version because of the large amount of new features in it, as opposed to just bugfixes).

This parameter allows you to save some SYSAUX tablespace disk space – if the occasionally captured bind variable values (from V$SQL_BIND_DATA) take too much space. Normally these bind values (in a packed RAW form) are visible in DBA_HIST_SQLSTAT.BIND_DATA column, which can take up to 2kB per statement in a snapshot – it’s stored as RAW(2000). Of course a more convenient way to query the actual bind values is to use DBA_HIST_SQLBIND (you can also use DBMS_SQLTUNE.EXTRACT_BIND function for translating the raw payload to meaningful values).

So, if you choose to capture a lot of SQL statements per AWR snapshot (it’s configurable) and don’t really care about the sampled bind variable values and want to save the disk space, then you can set cursor_bind_capture_destination = MEMORY or OFF (if you don’t want to capture bind variable values at all).

I’m using my pvalid.sql script for checking its valid values (it’s based on the X$ table underlying V$PARAMETER_VALID_VALUES view, so I could see undocumented parameter valid values too):

SQL> @pvalid cursor_bind_capture_destination
Display valid values for multioption parameters matching “cursor_bind_capture_destination”…

  PAR# PARAMETER                                                 ORD VALUE         
—— ————————————————– ———- —————
  2062 cursor_bind_capture_destination                             2 MEMORY
       cursor_bind_capture_destination                             3 MEMORY+DISK
       cursor_bind_capture_destination                             1 OFF

The default is MEMORY+DISK (this is essentially what you get before 11.2.0.2 and you can’t turn it off unless you turn off the AWR flushing of the whole SQLSTATS metrics).

Filed Under: Oracle Tagged With: , ,

Performance Stories from Exadata Migrations

January 11, 2011 by 19 Comments
Here are my UKOUG 2010 slides about Exadata migration performance, this is real life stuff, not repeating the marketing material:
View more presentations from tanelp.
Filed Under: Oracle Tagged With: , ,

Asynch descriptor resize wait event in Oracle

November 23, 2010 by 23 Comments

A lot of people have started seeing “asynch descriptor resize” wait event in Oracle 11gR2. Here’s my understanding of what it is. Note that I didn’t spend too much time researching it, so some details may be not completely accurate, but my explanation will at least give you an idea of why the heck you suddenly see this event in your database.

FYI, there’s a short, but incomplete explanation of this wait event also documented in MOS Note 1081977.1

Update: There’s a bug and a patch related to this wait event too.

The “direct path loader” (KCBL) module is used for performing direct path IO in Oracle, such as direct path segment scans and reading/writing spilled over workareas in temporary tablespace. Direct path IO is used whenever you see “direct path read/write*” wait events reported in your session. This means that IOs aren’t done from/to buffer cache, but from/to PGA directly, bypassing the buffer cache.

 

This KCBL module tries to dynamically scale up the number of asynch IO descriptors (AIO descriptors are the OS kernel structures, which keep track of asynch IO requests) to match the number of direct path IO slots a process uses. In other words, if the PGA workarea and/or spilled-over hash area in temp tablespace gets larger, Oracle also scales up the number of direct IO slots. Direct IO slots are PGA memory structures helping to do direct IO between files and PGA.

 

In order to be able to perform this direct IO asynchronously, Oracle also dynamically scales up the number of OS asynch IO descriptors, one for each slot (up to 4096 descriptors per process). When Oracle doesn’t need the direct IO slots anymore (when the direct path table scan has ended or a workarea/tempseg gets cancelled) then it scales down the number of direct IO slots and asynch IO descriptors. Scaling asynch IO descriptors up/down requires issuing syscalls to OS (as the AIO descriptors are OS kernel structures).

 

I guess this is supposed to be an optimization, to avoid running out of OS AIO descriptors, by releasing them when not they’re not needed, but as that Metalink note mentioned, the resize apparently sucks on Linux. Perhaps that’s why other ports also suffer and have seen the same wait event.

 

The “asynch descriptor resize” event itself is really an IO wait event (recorded in the wait class Other though), waiting for reaping outstanding IOs. Once this wait is over, then the OS call to change the amount of asynch IO descriptors (allocated to that process) is made. There’s no wait event recorded for the actual “resize” OS call as it shouldn’t block.

 

So, the more direct IO you do, especially when sorting/hashing to temp with frequent workarea closing/opening, the more of this event you’ll see (and it’s probably the same for regular tablespace direct path IO too).

 

This problem wouldn’t be noticeable if Oracle kept async io descriptors cached and wouldn’t constantly allocated/free them. Of course then you may end up running out of aio descriptors in the whole server easier. Also I don’t know whether there would be some OS issues with reusing cached aio descriptors, perhaps there is a good reason why such caching isn’t done.

 

Nevertheless, what’s causing this wait event is too frequent aio descriptor resize due to changes in direct IO slot count (due to changes in PGA workarea/temp segment and perhaps when doing frequent direct path scans through lots of tables/partitions too).

 

So, the obvious question here is what to do about this wait event? Well, first you should check how big part of your total response time this event takes at all?

 

  1. If it’s someting like 1% of your response time, then this is not your problem anyway and troubleshooting this further would be not practical – it’s just how Oracle works :)
  2. If it’s something like 20% or more of your response time, then it’s clearly a problem and you’d need to talk to Oracle Support to sort out the bug
  3. If it’s anything in between, make sure you don’t have an IO problem first, before telling that this is a bug. In one recent example I saw direct path reads take over a second on average when this problem popped up. The asynch descriptor resize wait event may well disappear from the radar once you fix the root cause – slow IO (or SQL doing too much IO). Remember, the asynch descriptor resize wait event, at least on Linux, is actually an IO wait event, the process is waiting for outstanding IO completion before the descriptor count increase/decrease can take place.
Filed Under: Oracle Tagged With: , , , ,

A: The most fundamental difference between hash and nested loop joins

October 6, 2010 by 14 Comments

Ok guys, thanks for waiting!

I ended up expanding the article quite a lot compared to what I had originally planned. In fact I only wrote 50% of what I plan to write, I’ll update the rest… um… later… Instead of just stating the difference between the joins I took a step back and elaborated something what I often see people doing (and talking about in newsgroups and lists too).

Basically the most fundamental (or biggest or most important) difference between nested loop and hash joins is that:

  • Hash joins can not look up rows from the inner (probed) row source based on values retrieved from the outer (driving) row source, nested loops can.

In other words, when joining table A and B (A is driving table, B is the probed table), then a nested loop join can take 1st row from A and perform a lookup to B using that value (of the column(s) you join by). Then nested loop takes the next row from A and performs another lookup to table B using the new value. And so on and so on and so on.

This opens up additional access paths to the table B, for example when joining ORDERS and ORDER_ITEMS by ORDER_ID (and ORDER_ID is leading column of PK in ORDER_ITEMS table), then for whatever orders are taken from ORDERS table, we can perform a focused, narrow index range scan on ORDER_ITEMS for every ORDER_ID retrieved from the driving ORDERS table. A hash join can’t do that.

Of course this doesn’t mean that hash joins can’t use any indexes for tables they read – index range scans and unique lookups can still be used under a hash join, but only if there are constant values in the query text (in form of literal or bind variables). If there are no such constant (filter) conditions under a hash join, then the other options to use that index would be to do an INDEX FULL SCAN (which is a range scan from end to end of the index) or INDEX FAST FULL SCAN (which is like a full table scan through the entire index segment). However none of these opportunities give the same benefits as nested loops looking up rows from row source B dynamically based on what was retrieved from A during runtime.

Note that this nested loops benefit isn’t limited to indexes only on table B, the difference is more fundamental than just a specific access path. For example, if table B happens to be a single table hash cluster or indexed X$ table, then the nested loop is also able to do “optimized” lookups from these row-sources, based on the values retrieved from table A.

So, my article with a lot of (loosely) related details is here:

In the comments section of my question, Tom, Bernard Polarski, Christian Antognini and Marc Musette got the closest to what I had in my mind when I asked the question. However, of course your mileage may vary somewhat depending on what kind of problems you have experienced the most over all the years. Also, Jonathan Lewis had a valid comment regarding that the answer depends on what exactly does one mean by “fundamental” and yeah this was open to interpretation.

Nevertheless, I wanted to emphasize that there’s a more important difference between NL and hash joins, than the usual stuff you see in training material which talk about implementation details like hash tables and memory allocation…

Some day I will complete that article, I plan to add some design advice in there, like denormalization opportunities for getting the best of the both worlds etc. But now I’m gonna get a beer instead.

Thanks for reading and answering my blog, I was quite impressed by the volume of comments & answers to my question. I must do this more often!

Filed Under: Cool stuff, Oracle Tagged With: , , ,

Q: The most fundamental difference between HASH and NESTED LOOP joins?

September 28, 2010 by 48 Comments

So, what do you think is the most fundamental difference between NESTED LOOPS and HASH JOINS?

This is not a trick question. You’re welcome to write your opinion in the comments section – and I’ll follow up with an article about it (my opinion) later today…

Update: The answer article is here:

http://blog.tanelpoder.com/2010/10/06/a-the-most-fundamental-difference-between-hash-and-nested-loop-joins/

Filed Under: Oracle Tagged With: ,

Exadata v2 Smart Scan Performance Troubleshooting article

July 30, 2010 by 9 Comments

I finally finished my first Exadata performance troubleshooting article.

This explains one bug I did hit when stress testing an Exadata v2 box, which caused smart scan to go very slow – and how I troubleshooted it:

Thanks to my secret startup company I’ve been way too busy to write anything serious lately, but apparently staying up until 6am helped this time! :-) Anyway, maybe next weekend I can repeat this and write Part 2 in the Exadata troubleshooting series ;-)

Enjoy! Comments are welcome to this blog entry as I haven’t figured out a good way to enable comments in the google sites page I’m using…

Filed Under: Oracle Tagged With: , , , ,
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