Are you getting the most out of your Exadata performance? Part 1

August 8, 2011 by 5 Comments

In almost all of the Exadata migration projects I’ve been part of, the client sees immediate speedup & performance increase when testing their workload on Exadata (of course, we’ve made sure that we do plan & execute the tasks right). However, my performance geek’s nature usually doesn’t allow to stop there and leave the client with just 2x or 3x performance increase. For data warehousing and reporting workloads, Exadata can do much better than just 2-3x performance increase! 

This is why I will write this article series about Getting the Most out of your Exadata Performance. I will write a bunch of random articles, based on my experience and lessons learned – and some day I may consolidate it all into a more formal paper.

So, here’s the first article (PDF format).

Enjoy! :-)

(Leave your comments & feedback here …)

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For Exadata geeks

June 20, 2011 by 6 Comments

Just letting you know that we are all (almost) done with the Expert Oracle Exadata book work and it will be published in less than a month!!!

Expect (many) new blog entries soon! :-)

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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!

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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.
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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…

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