I’ve been looking for an excuse to muck about with scala for a while now. So I thought i’d do a post similar to those I’ve done the past for .NET, python, perl and R. Best practices for Java were included in my book Oracle Performance Survival Guide (but I’d be more than happy to post them if anyone asks).
One of the great things about scala is that it runs (more...)
I seem to be getting a lot of surprising performance results lately on our X-2 quarter rack Exadata system, which is good – the result you don’t expect is the one that teaches you something new.
This time, I was looking at using a temporary tablespace based on flash disks (more...)
I’ve been doing some work on the Exadata Smart Flash Cache recently and came across a situation in which setting CELL_FLASH_CACHE to KEEP will significantly slow down smart scans on a table.
If we create a table with default settings, then the Exadata Smart Flash Cache (ESFC) will not be (more...)
Of all the claims I make about SSD for Oracle databases, the one that generates the most debate is that placing redo logs on SSD is not likely to be effective. I’ve published data to that effect in particular see Using SSD for redo on Exadata - pt 2 (more...)
Prior to storage server software version 220.127.116.11.0 (associated with Exadata X3), Exadata Smart Flash Cache was a “write-through” cache, meaning that write operations are applied both to the cache and to the underlying disk devices, but are not signalled as complete until the IO to the (more...)
It’s been tough to find time to do actual performance research of late, but I have managed to get a test system prepared that will allow me to determine if Solid State disks offer some performance advantage over spinning disks when the redo entries are very large. This is (more...)
When Steven and I wrote MySQL Stored Procedure programming our biggest reservation about the new stored procedure language was the lack of support for proper error handling. The lack of the SIGNAL and RESIGNAL clauses prevented a programmer from raising an error that could be propagated throughout a call (more...)
In my last post, I looked at the effect of the Exadata smart flash logging. Overall, there seemed to be a slight negative effect on median redo log sync times. This chart (slightly different from the last post because of different load and configuration of the system), shows how there’s a “hump” of redo log syncs that take slightly longer when the flash logging is enabled:
But of course, the flash logging feature was designed to improve performance not of the “average” redo log sync, but of the “outliers”.
In my tests, I had 40 concurrent (more...)
Exadata storage software 18.104.22.168 introduced the Smart flash logging feature. The intent of this is to reduce overall redo log sync times - especially outliers - by allowing the exadata flash storage to serve as a secondary destination for redo log writes. During a redo log sync, Oracle will write to the disk and flash simultaneously and allow the redo log sync operation to complete when the first device completes.
Jason Arneil reports some initial observations here, and Luis Moreno Campos summarized it here.
I’ve reported in the past on using SSD for (more...)
If, like me, you want to play around with data in a Hadoop cluster without having to write hundreds or thousands of lines of Java MapReduce code, you most likely will use either Hive (using the Hive Query Language HQL) or Pig.
Hive is a SQL-like language which compiles to Java map-reduce code, while Pig is a data flow language which allows you to specify your map-reduce data pipelines using high level abstractions.
The way I like to think of it is that writing Java MapReduce is like programming in assembler: you need to manually construct every low level (more...)