Thank you all those who came to attend my session on demystifying latches at New York Oracle Users Group in Manhattan. I hope you found the session useful and enjoyable.
This is a just a quick blog post to direct readers to the best Oracle-related paper detailing the value EMC XtremIO brings to Oracle Database use cases. I’ve been looking forward to the availability of this paper for quite some time as I supported (minimally, really) the EMC Global Solutions Engineering group in this effort. They really did a great job with this testing! I highly recommend this paper for readers who are interested in:
I am an ardent believer of “show me how it works” principle and usually, I have demos in my presentation. So, I was presenting “Tools for advanced debugging in Solaris and Linux” with demos in IOUG Collaborate 2015 in Las Vegas on April 13 and my souped-up laptop (with 32G of memory, SSD drives, and an high end video processor etc ) was not responding when I tried to access folder to open my presentation files.
Sometimes, demos do fail. At least, I managed to complete the demos with zero slides :-) Apologies to the audience for my R-rated rants about laptop issues.
You can download presentations files from the links below.
I recently read a blog post by Kyle Hailey regarding some lack of randomness he detected in the Orion I/O generator tool. Feel free to read Kyle’s post but in short he used dtrace to detect Orion was obliterating a very dense subset of the 96GB file Orion was accessing.
In my earlier post, I talked about, how tableau can be used to visualize the data. In some cases, I find it useful to query AWR base tables directly using Python and graph it using matplotlib package quickly. Since python is preinstalled in almost all computers, I think, this method will be useful for almost everyone. Of course, you may not have all necessary packages installed in your computer, you can install the packages using install python packages . Of course, if you improve the script, please send it to me, I will share it in this blog entry.
Script is available as a zip file: plotdb.py
I blogged earlier about heap dump shared pool heap duration and was curious to see how the inmemory – 18.104.22.168 new feature – is implemented. This is a short blog entry to discuss the inmemory area heap.
I have set the initialization parameters sga_target=32G and inmemory_size=16G, meaning, out of 32GB SGA, 16GB will be allocated to inmemory area and the remaining 16GB will be allocated to the traditional areas such as buffer_cache, shared_pool etc. I was expecting v$sgastat view to show the memory allocated for inmemory area, unfortunately, there are no rows marked for inmemory area (Command “show sga” shows the inmemory area though). However, dumping heapdump at level 2 shows that the inmemory area is defined as a sub-heap of the top level SGA heap. Following are the commands to take an heap dump.
NOTE: There’s a link to the full article at the end of this post.
I recently submitted a manuscript to the EMC XtremIO Business Unit covering some compelling lab results from testing I concluded earlier this year. I hope you’ll find the paper interesting.
There is a link to the full paper at the bottom of this block post. I’ve pasted the executive summary here:
Data visualization is a useful method to identify performance patterns. In most cases, I pull custom performance metrics from AWR repository and use tableau to visualize the data. Of course, you can do the visualization using excel spreadsheet too.
We had huge amount of PX qref waits in a database: