I’ve previously discussed the new Zone Map database feature and how they work in a similar manner to Exadata Storage indexes. Just like Storage Indexes (and conventional indexes for that manner), they work best when the data is well clustered in relation to the Zone Map or index. By having the data in the table […]
Just a quick note that I posted slides for the 2 talks I did at ECO in Raleigh this week:
Great crowd. I really enjoyed myself.
Note: You can also find other presentations on my Whitepapers/Presentations page via the link at the top of the screen.
As shared by a well known Oracle and Big Data performance geek!
SQL> ALTER SYSTEM SET inmemory_size = 5T SCOPE=spfile; ALTER SYSTEM SET inmemory_size = 5T SCOPE=spfile * ERROR at line 1: ORA-32005: error while parsing size specification [5T] SQL> ALTER SYSTEM SET inmemory_size = 5120G SCOPE=spfile; System altered.
A good question from Robert Thorneycroft I thought warranted its own post. He asked: “I have a question regarding bitmapped indexes verses index compression. In your previous blog titled ‘So What Is A Good Cardinality Estimate For A Bitmap Index Column ? (Song 2)’ you came to the conclusion that ‘500,000 distinct values in a 1 […]
In Part I, I discussed how Zone Maps are new index like structures, similar to Exadata Storage Indexes, that enables the “pruning” of disk blocks during accesses of the table by storing the min and max values of selected columns for each “zone” of a table. A Zone being a range of contiguous (8M) blocks. I […]
I’ve spent the last couple of evenings playing with the new SQL pattern matching feature in Oracle 12c.
I’m doing some sessions on analytic functions in some upcoming conferences and I thought I should look at this stuff. I’m not really going to include much, if anything, about it as my sessions are focussed on beginners and I don’t really want to scare people off. The idea is to ease people in gently, then let them scare themselves once they are hooked on analytics. I’m thinking about Hooked on Monkey Fonics now…
A recent posting on OTN reminded me that I haven’t been poking Oracle 12c very hard to see which defects in reporting execution plans have been fixed. The last time I wrote something about the problem was about 20 months ago referencing 22.214.171.124; but there are still oddities and irritations that make the nice easy “first child first” algorithm fail because the depth calculated by Oracle doesn’t match the level that you would get from a connect-by query on the underlying plan table. Here’s a simple fail in 12c:
Sometimes, a few pictures (or in this case index block dumps) is better than a whole bunch of words :) In my previous post, I introduced the new Advanced Index Compression feature, whereby Oracle automatically determines how to best compress an index. I showed a simple example of an indexed column that had sections of index entries that were […]
I spent a bit of time at OpenWorld looking at the JSON support in Oracle Database 12c. I started to write some stuff about it on the plane home and I spent the last two mornings finishing it off. You can see the results here.
I’ve tried to keep it light, since the documentation does a pretty good job at explaining all the variations of the syntax. I’ve also avoided trying to teach people about JSON itself. There is loads of stuff about that on the net already.
For the most part I think the JSON support looks pretty cool. During the process of writing the articles I did notice a few of things that I thought might confuse.
While presenting at Oaktable World 2014 in San Fransisco, I discussed the in-memory pre-population speed and indicated that it takes about 30 minutes to 1 hour to load ~300GB of tables. Someone asked me “Why?” and that was a fair question. So, I profiled the in-memory pre-population at startup.
I profiled all in-memory worker sessions using Tanel’s snapper script and also profiled the processes in OS using Linux perf tool with 99Hz sample rate. As there is no other activity in the database server, it is okay to sample everything in the server. Snapper output will indicate where the time is spent; if the time is spent executing in CPU, then the perf report output will tell us the function call stack executing at that CPU cycle. Data from these two profiling methods will help us to understand the root cause of slowness.