Here’s a funny little problem I came across some time ago when setting up some materialized views. I have two tables, orders and order_lines, and I’ve set up materialized view logs for them that allow a join materialized view (called orders_join) to be fast refreshable. Watch what happens if I refresh this view just before gathering stats on the order_lines table.
August 15, 2013
August 9, 2013
Just glancing through the 12c manuals (Server Reference 12.1 June 2013 – E17615-16) to check a particular database limit, I came across the following: “Services – maximum per instance – 115″. That’s a bit of a problem, given that you can have 254 pluggable (tenant) databases in a single container database, and each plugged database gets its own service – but I’m guessing that that bit of the manual is wrong, after all it didn’t say anything about pluggable databases at all. It’s hard to keep documentation up to date as things change.
Here’s a random thought, though, loosely linked to database limits. If you’re looking ahead to a time when you have lots of tenants in a container database, you might want to start by migrating your existing databases from smallfile tablespaces to bigfile tablespaces (which may make it a good idea to run with change tracking enabled) so that the final container database doesn’t have a totally unmanageable number of database files.
Update 13th Aug 2013
Read the comments for a limit on the total number of services a container database can run.
July 30, 2013
A couple of thoughts.
The intent of ASSM is to minimise contention when multiple small transactions are busy inserting data concurrently into the same table. As a consequence, you may be able to find a number of odd behaviour patterns if you do experiments with a single session running one transaction at a time; or when executing a single large transaction, or when experimenting with small tables.
July 4, 2013
Weighing in at a massive 54 characters – the longest parameter name in 12c is:
Followed very closely by (you guessed it)
June 25, 2013
The news is out that 12c is now available for download (Code, Docs and Tutorials). There are plenty of nice little bits in it, and several important additions or enhancements to the optimizer, but there’s one feature that might prove to be very popular:
SQL> alter table p1 move partition solo ONLINE; Table altered.
June 19, 2013
Here’s a little quiz: If I take the average row length of the rows in a table, multiply by the number of rows, and convert the result to the equivalent number of blocks, how can the total volume of data in the table be greater than the total number of blocks below the table high water mark ? I’ve got three tables in a schema, and they’re all in the same (8KB block, 1M uniform extent, locally managed) tablespace, but here’s a query, with results, showing their space utilisation – notice that I gather schema stats immediately before running my query:
June 9, 2013
I wrote a note a few years ago about SQL*Net compression (this will open in a new window so that you can read the posts concurrently), showing how the order of the data returned by a query could affect the amount of network traffic. An example in the note demonstrated, using autotrace statistics that the number of bytes transferred could change dramatically if you sorted your return data set. At the time I asked, and postponed answering, the question: “but how come the number of SQL*Net round trips has not changed ?”
May 10, 2013
Here’s a quick and dirty script to create a procedure (in the SYS schema – so be careful) to check the Hakan Factor for an object. If you’re not familiar with the Hakan Factor, it’s the value that gets set when you use the command “alter table minimize records_per_block;”.
April 29, 2013
Materialized views open up all sorts of possibilities for making reporting more efficient – but at the same time they can introduce some “interesting” side effects when you start seeing refreshes taking place. (Possibly one of the most dramatic surprises appeared in the upgrade that switched many refreshes into “atomic” mode, changing a “truncate / append” cycle into a massively expensive “delete / insert” cycle).
If you want to have some ideas of the type of work that is involved in the materialized view “fast refresh”, you could look at some recent articles by Alberto Dell’Era on (very specifically) outer join materialized views (which a link back to a much older article on inner join materialized view refresh):
March 27, 2013
Here’s a little detail that appeared in 11gR2 that may help you answer questions about open cursors. Oracle has added a “cursor type” column to the view v$open_cursor, so you can now see which cursors have been held open because of the pl/sql cursor cache, which have been held by the session cursor cache, and various other reasons why Oracle may take a short-cut when you fire a piece of SQL at it.
The following is the output showing the state of a particular session just after it has started up in SQL*Plus and called a PL/SQL procedure to run a simple count:
March 22, 2013
This note is about a feature of LOBs that I first desribed in “Practial Oracle 8i” but have yet to see used in real life. It’s a description of how efficient Oracle can be, which I’ll start with a description of, and selection from, a table:
February 25, 2013
Question – How can you have a single file in a single tablespace showing multiple free extents when there are no objects using any space in that file ? For example, from an 220.127.116.11 database:
February 11, 2013
I was at a client site recently where one of the end-users seemed to have discovered a cunning strategy for optimising a critical SQL statement. His problem was that his query screen times out after 2 minutes, so any query he runs has to complete in less than two minutes or he doesn’t see the results. Unfortunately he had a particular query which took nearly 32 minutes from cold to complete – partly because it’s a seven-table join using ANSI OUTER joins, against tables ranging through the 10s of millions of rows and gigabytes of data – the (necessary) tablescan of the table that had to be first in the join order took 70 seconds alone.
But our intrepid user seems to have made an important discovery and engineered a solution to his performance problem. I think he’s noticed that when you run a query twice in a row the second execution is often faster than the first. I can’t think of any other reason why the same person would run the same query roughly every four minutes between 8:00 and 9:00 am every morning (and then do the same again around 5:00 in the afternoon).
Looking at the SQL Monitoring screen around 10:00 the first day I was on-site I noticed this query with a very pretty graphic effect of gradually shrinking blue bars as 32 minutes of I/O turned into 2 minutes of CPU over the course of 8 consecutive executions which reported run times something like: 32 minutes, 25 minutes, 18 minutes, 12 minutes, 6 minutes, 4 minutes, 2.1 minutes, 2 minutes.
It’s lucky (for that user) that the db_cache_size is 60GB. On the other hand this machine is one of those Solaris boxes that likes to pretend that it’s got 128 CPUs when really it’s only 16 cores with 8 lightweight threads per core – you don’t want anyone running a query that uses 2 solid CPU minute on one of those boxes because it’s taking out 1/16th of your CPU availability, while reporting a load of 1/128 of your CPUs.
Footnote: the query can be optimised (properly) – it accessed roughly 100M rows of data to return roughly 300 rows (with no aggregation), so we just need to do a little bit of work on precise access paths.
January 24, 2013
Red Gate have asked me to write a few articles for their Oracle site, so I’ve sent them a short series on “traditional” compression in Oracle – which means I won’t be mentioning Exadata hybrid columnar compression (HCC a.k.a. EHCC). There will be five articles, published at the rate of one per week starting Tuesday (15th Jan). I’ll be supplying links for them as they are published.
December 28, 2012
Here’s a little puzzle if you have nothing better to do between now and the new year. The following extract came from 18.104.22.168, but could have come from 10.2.0.5 or 22.214.171.124 (and many others). I’ve got a simple heap table where the last thing I (or anyone) did was “alter table t1 move” before dumping the first (data) block of the table. Looking at the resulting trace file, I see the following:
fsbo=0x56e fseo=0xf4d avsp=0x5f tosp=0x5f
If you need to have the acronyms decoded they are (according to my best guess):
- fsbo – free space, begin offset
- fseo – free space, end offset
- avsp – available space
- tosp – total space
Doing the arithmetic, the free space starts at offset 0x56e and ends at 0xf4d, which means the free space gap is 2,527 bytes; but the total space available for use is only 0x5f bytes, i.e. 95 bytes. So what has happened to the other 2,432 ?
Remember – I dumped the block immediately after issuing “alter table t1 move”, so there are no issues of delayed block cleanout, uncommitted transactions etc. to worry about.
Footnote: the reason why you have “available space” and “total space” is to keep track of the space made available by deleted rows. The “avsp” (usually) reports the size of the gap between the row directory and the row heap; the “tosp” includes the space in the holes left in the row heap after rows have been deleted (or updated in a way that moves them up to the top of the heap, leaving a gap behind them, or updated in situ in a way that reduces the row length leaving a little hole).