Here’s a scenario that’ll be depressingly familiar to most reading this: after ages of running fine, and no changes to the code, a query suddenly starts running for magnitudes longer than it used to. In this instance it was an ETL step which used to take c.1 hour, and was now at 5 hours and counting. Since it still hadn’t finished, and the gods had conspired to bring down Grid too (unrelated), I generated a SQL Monitor report to see what was happening: [sourcecode language=“sql”] select DBMS_SQLTUNE.

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There is a well-documented problem relating to DBMS_STATS.COPY_TABLE_STATS between partitions where high/low values of the partitioning key column were just copied verbatim from the source partition. This particular problem has now been patched (see 8318020.8). For background, see Doug Burns’ blog and his excellent paper which covers the whole topic of statistics on partitioned tables. This post Maintaining statistics on large partitioned tables on the Oracle Optimizer blog details what the dbms_stats.

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Chucking a stick in the spokes of your carefully-tested ETL/BI … My opinion is that automated stats gathering for non-system objects should be disabled on Oracle Data Warehouses across all environments. All it does it cover up poor design or implementation which has omitted to consider statistics management. Once you get into the realms of millions or billions of rows of data, the automated housekeeping may well not have time to stat all of your tables on each run.

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Just because something produces the correct numbers on the report, it doesn’t mean you can stop there. How you are producing those numbers matters, and matters a lot if you have an interest in the long-term health of your system and its ability to scale. OBIEE is the case in point here, but the principle applies to any architecture with >1 tiers or components. Let me start with a rhetorical question.

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Chris Mellor at The Register posted an interesting article a couple of days ago, entitled HP and Violin build Oracle Exadata killer. The slidedeck has been removed from HP’s FTP site, but a bit of Google magic throws up a couple of mirror copies. It’s an entertaining read (“Do a Proof of Concept! 94% win rate!! We can and do win against Exadata!!”), and a nice illustration of the FUD techniques that companies use in marketing their products against others.

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Whilst perusing the Oracle database documentation, I noticed something which caught my eye: As well as reading the documentation online as HTML or downloading as PDF for viewing on your computer etc, you can also download it in formats (Mobi and ePub) designed for eReaders such as the Kindle and iPad (the latter obviously isn’t “just” an eReader). For information on format support, there’s a handy table on Wikipedia. It looks like the availability of mobi/epub files isn’t universal.

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Oracle XE 11gR2 beta has just been released, some details here and download here. It’s not a great deal of use for sandboxing DWH-specific stuff, given this list of excluded functionality (and this is by no means everything that’s not included): Bitmapped index, bitmapped join index, and bitmap plan conversions Oracle Partitioning Parallel Data Pump Export/Import Parallel query/DML Parallel Statement Queuing (source) However, it’s always interesting to have to hand for trying out other things.

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This post expands on one I made last year here about sampling frequency (of I/O throughput, but it’s a generic concept). The background to this is my analysis of the performance and capacity of our data warehouse on Oracle 11g. Before I get too boring, here’s the fun bit: Pork Pies per Hour (PP/h) Jim wants to enter a championship pork-pie eating competition. He’s timed himself practising and over the course of an hour he eats four pork pies.

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I wrote last year about Graphing I/O data using gnuplot and Oracle V$SYSSTAT, using a script from Kevin Closson in his article How To Produce Raw, Spreadsheet-Ready Physical I/O Data With PL/SQL. Good For Exadata, Good For Traditional Storage. Here I’ve got a simple comparison of the data recorded through this script (in essence, Oracle’s V$SYSSTAT), and directly on the OS through HP’s MeasureWare. It’s graphed out with my new favourite tool, rrdtool:

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Over the last few months I’ve been doing a lot of exploring of OBIEE Systems Management data, covered in a mini-series of blog posts, Collecting OBIEE systems management data. There are a vast number of counters exposed, ranging from the very interesting (Active Sessions, Cache Hits, etc) to the less so (Total Query Piggybacks, although for some seriously hardcore performance tuning even this may be of interest). This short blog post is about a couple of counters which I’ve been monitoring but which looks to not be entirely reliable.

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I’ve added two new toys to my geek arsenal today. First is one with which I’ve dabbled before, but struggled to master. The second is a revelation to me and which I discovered courtesy of twitter. rrdtool rrdtool is a data collection and graphing tool which I’ve been aware of for a while. I wanted to use it when I wrote about Collecting OBIEE systems management data with JMX, but couldn’t get it to work.

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A chap called Kevin posted a comment on a previous posting of mine asking did you ever come across anything that could be used to change the LDAP server settings from a command line (admintool.exe, UDML, or otherwise)? I did a quick play around with some UDML and it appears that you can. Set up the initial LDAP server definition in the RPD First I added a dummy LDAP server to samplesales.

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Cary Millsap recently published a paper “Mastering Performance with Extended SQL Trace” describing how to use Oracle trace to assist with troubleshooting the performance of database queries. As with all of Cary Millsap’s papers it is superbly written, presenting very detailed information in a clear and understandable way. (and yes I do have a DBA crush ;-)) It discusses how you can automate the tracing of specific sessions on the database, and requiring the application to be appropriately instrumented.

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I’ve been doing some work recently that involved the use of Materialised Views on Oracle 11g (11.1.0.7), particularly around PCT refresh. There are some things that are not clear from the documentation, or are actually bugs so far as I’m concerned, and I’ve detailed these below. In this example I was working on part of a DWH with c.2 millions rows aggregated up daily. One of the things that I spent a long time trying to get to work was Partition Truncation when using PCT refresh.

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Poking around on My Oracle Support today, I found a link to a white paper dated November 2010, titled “Best Practices for a Data Warehouse on Oracle Database 11g”. It’s new to me and I’ve not noticed a blog post announcing it, so I thought I’d share it here. It’s by Maria Colgan, who has posted in the past on both the Inside the Oracle Optimizer blog and The Data Warehouse Insider blog.

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This is a series of posts where I hope to humbly plug some gaps in the information available (or which has escaped my google-fu) regarding statistics management in Oracle 11g specific to Data Warehousing. Incremental Global Statistics is new functionality in Oracle 11g (and 10.2.0.4?) and is explained in depth in several places including: Oracle® Database Performance Tuning Guide - Statistics on Partitioned Objects Greg Rahn - Oracle 11g: Incremental Global Statistics On Partitioned Tables Inside the Oracle Optimiser - Maintaining statistics on large partitioned tables Amit Poddar - One Pass Distinct Sampling (ppt - slides 52 onwards are most relevant) In essence, Oracle maintains information about each partition when statistics is gathered on the partition, and it uses this to work out the global statistics - without having to scan the whole table.

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Introduction This is the third part of three detailed articles making up a mini-series about OBIEE monitoring. It demonstrates how to capture OBIEE performance information, and optionally graph it out and serve it through an auto-updating webpage. This final article describes how to bolt on to OAS a simple web page hosting the graphs that you created in part 2, plotting data from OBIEE collected in part 1. The webpage This is just an old-school basic HTML page, with a meta-refresh tag (which note that Chrome doesn’t work with) and img tags:

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Introduction This is the second part of three detailed articles making up a mini-series about OBIEE monitoring. It demonstrates how to capture OBIEE performance information, and optionally graph it out and serve it through an auto-updating webpage. This article takes data that part one showed you how to collect into a tab-separated file that looks something like this: [sourcecode] 2010-11-29-14:48:18 1 0 11 0 3 2 1 676 340 0 53 1 0 41 0 3 0 2010-11-29-14:49:18 1 0 11 0 3 2 1 676 0 0 0 1 0 0 0 3 0 2010-11-29-14:50:18 2 0 16 1 4 3 1 679 0 0 0 1 0 0 0 4 0 2010-11-29-14:51:18 2 2 19 1 4 3 1 679 32 0 53 1 0 58 0 4 0 2010-11-29-14:52:18 2 1 19 1 4 3 4 682 0 0 0 1 0 0 0 4 0 2010-11-29-14:53:18 2 1 19 1 4 3 4 682 0 0 0 1 0 0 0 4 0 2010-11-29-14:54:18 2 0 19 1 4 3 1 682 0 0 0 1 0 0 0 4 0 [/sourcecode]

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Introduction This is the first part of three detailed articles making up a mini-series about OBIEE monitoring. It demonstrates how to capture OBIEE performance information, and optionally graph it out and serve it through an auto-updating webpage. For some background on OBIEE’s Systems Management component, along with JMX and MBeans, see here and here. The following assumes you know your mbeans from coffee beans and jmx from a bmx. The metric collection is built around the jmxsh tool.

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Those of you who read my blog regularly may have noticed I have a slight obsession with the OBIEE systems management capability which is exposed through JMX. Venkat has blogged this week about JMX in OBI11g, and it’s clearly a technology worth understanding properly. I’ve recently been tinkering with how to make use of it for monitoring purposes, most recently using JConsole and discussed here. What follows is an extension of this idea, cobbled together with a bit of shell scripting, awk, gnuplot, and sticky backed plastic.

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