Archive for the 'oracle' Category

When Storage is REALLY Fast Even Zero-Second Wait Events are Top 5. Disk File Operations I/O: The Mystery Wait Event.

The SLOB code that is generally available here differs significantly from what I often test with in labs. Recently I was contorting SLOB to hammer an EMC XtremIO All-Flash Array in a rather interesting way. Those of you in the ranks of the hundreds of SLOB experts out there will notice two things quickly in the following AWR snippet:

1)   Physical single block reads are being timed by the Oracle wait interface at 601 microseconds (3604/5995141 == .000601) and this is, naturally for SLOB, the top wait event.

2)   Disk file operations I/O is ranking as a top 5 timed event. This is not typical for SLOB.



The 601us latencies for XtremIO are certainly no surprise. After all, this particular EMC storage array is an All-Flash Array so there’s no opportunity for latency to suffer as is the case with alternatives such as flash-cache approaches. So what is this blog post about? It’s about Disk file operations I/O.

I needed to refresh my memory on what the Disk file operations I/O event was all about. So, I naturally went to consult the Statistics Description documentation. Unfortunately there was no mention of the wait even there so I dug further to find it documented in the Description of Wait Events section of the Oracle Database 11g documentation which states:

This event is used to wait for disk file operations (for example, open, close, seek, and resize). It is also used for miscellaneous I/O operations such as block dumps and password file accesses.

Egad. A wait is a blocking system call. Since open(2)/close(2) and seek(2) are non-blocking on normal files I suppose I could have suffered a resize operation–but wait, this tablespace doesn’t allow autoextend.  I suppose I really shouldn’t care that much given the fact that the sum total of wait time was zero seconds. But I wanted to understand more so I sought information from the user community–a search that landed me happily at Kyle Hailey’s post on here. Kyle’s post had some scripts that looked promising for providing more information about these waits but unfortunately in my case the scripts returned no rows found.

So, at this point, I’ll have to say that the sole value of this blog post is to point out the fact that a) the Oracle documentation specifically covering statistics descriptions is not as complete as the Description of Wait Events section and b) the elusive Disk file operations I/O wait event remains, well, elusive and that this is now part I in a multi-part blog series until I learn more. I’ll set up some traces and see what’s going on. Perhaps Kyle will chime in.




EMC XtremIO – The Full-Featured All-Flash Array. Interested In Oracle Performance? See The Whitepaper.

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:

Executive Summary

Physical I/O patterns generated by Oracle Database workloads are well understood. The predictable nature of these I/O characteristics have historically enabled platform vendors to implement widely varying I/O acceleration technologies including prefetching, coalescing transfers, tiering, caching and even I/O elimination. However, the key presumption central to all of these acceleration technologies is that there is an identifiable active data set. While it is true that Oracle Database workloads generally settle on an active data set, the active data set for a workload is seldom static—it tends to move based on easily understood factors such as data aging or business workflow (e.g., “month-end processing”) and even the data source itself. Identifying the current active data set and keeping up with movement of the active data set is complex and time consuming due to variability in workloads, workload types, and number of workloads. Storage administrators constantly chase the performance hotspots caused by the active dataset.

All-Flash Arrays (AFAs) can completely eliminate the need to identify the active dataset because of the ability of flash to service any part of a larger data set equally. But not all AFAs are created equal.

Even though numerous AFAs have come to market, obtaining the best performance required by databases is challenging. The challenge isn’t just limited to performance. Modern storage arrays offer a wide variety of features such as deduplication, snapshots, clones, thin provisioning, and replication. These features are built on top of the underlying disk management engine, and are based on the same rules and limitations favoring sequential I/O. Simply substituting flash for hard drives won’t break these features, but neither will it enhance them.

EMC has developed a new class of enterprise data storage system, XtremIO flash array, which is based entirely on flash media. XtremIO’s approach was not simply to substitute flash in an existing storage controller design or software stack, but rather to engineer an entirely new array from the ground-up to unlock flash’s full performance potential and deliver array-based capabilities that are unprecedented in the context of current storage systems.

This paper will help the reader understand Oracle Database performance bottlenecks and how XtremIO AFAs can help address such bottlenecks with its unique capability to deal with constant variance in the I/O profile and load levels. We demonstrate that it takes a highly flash-optimized architecture to ensure the best Oracle Database user experience. Please read more:  Link to full paper from

Oracle Exadata Database Machine: Proving 160 Xeon E7 Cores Are As “Slow” As 128 Xeon E5 Cores?

Reading Data Sheets
If you are in a position of influence affecting technology adoption in your enterprise you likely spend a lot of time reading data sheets from vendors.  This is just a quick blog entry about something I simply haven’t taken the time to cover even though the topic at hand has always be a “problem.” Well, at least since the release of the Oracle Exadata Database Machine X2-8.

In the following references and screenshots you’ll see that Oracle cites 1.5 million flash read IOPS as an expected limit for both the full-rack Oracle Exadata Database Machine X3-2 and the Oracle Exadata Database Machine X3-8. All machines have limits and Exadata is no exception. Notice how I draw attention to the footnote that accompanies the flash read IOPS claim. Footnote number 3 says that both of these Exadata models are limited in flash read IOPS by the database host CPU. Let me repeat that last bit for anyone scrutinizing my words for reasons other than education: The Oracle Exadata Database Machine data sheets explicitly state flash read IOPS are limited by host CPU.

Oracle’s numbers in this case are SQL-driven from Oracle instances. I have no doubt these systems are both capable of achieving 1.5 million read IOPS from flash because, truth be told, that isn’t really all that many IOPS–especially when the IOPS throughput numbers are not accompanied by service times. In the 1990s it was all about “how much” but in modern times it’s about “how fast.” Bandwidth is an old, tired topic. Modern platforms are all about latency. Intel QPI put the problem of bandwidth to rest.

So, again, I don’t doubt the 1.5 million flash read IOPS citation. Exadata has a lot of flash cards and a lot of host processors to drive concurrent I/O. Indeed, with the concurrent processing capabilities of both of these Exadata models, Oracle would be able to achieve 1.5 million IOPS even if the service times were more in line with what one would expect with mechanical storage. Again, we never see service time citations so in actuality the 1.5 million number is just a representation of how much in-flight I/O the platform can handle.

Here is the new truth: IOPS is a storage bandwidth metric.

Host CPU Limited! How Many CPUs?
Here’s the stinger: Oracle blames host CPU for the 1.5 million flash read IOPS number. The problem with that is the X3-2 has 128 Xeon E5-2690 processor cores and the X3-8 has 160 Xeon E7-8870 processor cores. So what is Oracle’s real message here? Is it that the cores in the X3-8 are 20% slower than those in the X3-2 model? I don’t know. I can’t put words in Oracle’s mouth. However, if the data sheet is telling the truth then one of two things is true, either a) the E5-2690 processors are indeed 20% faster on a per-core basis than the E7-8870 or b) there is a processing asymmetry problem.

Not All CPU Bottlenecks Are Created Equal
Oracle would likely not be willing to dive into technical detail to the same level I do. Life is a series of choices–including who you chose to buy storage and platforms from. However, Oracle’s literature is clear about the number of active 40Gb QDR Infiniband ports there are in each configuration and this is where the asymmetry comes in. There are 8 active ports in both of these models. That means there are 8 streams of interrupt handling in both cases–regardless of how many cores there are in total.

As is the case with any networked storage, I recommend you monitor mpstat -P ALL output on database hosts to see whether there are cores nailed to the wall with interrupt processing at levels below total CPU-saturation.  Never settle for high-level aggregate CPU utilization monitoring. Instead, drill down to the per-core level to watch out for asymmetry. Doing so is just good platform scientist work.

Between now and the time you should find yourself in a proof of concept test situation with Exadata, don’t hesitate to ask Oracle why–by their own words–both 128 cores and 160 cores are equally saturated when delivering maximum read IOPS in the database grid. After all, they charge the same per core (list price) to license Oracle Database on either of those processors.

Nice and Concise?
By the way, is there anyone who actually believes that both of these platforms top out at precisely 1.5 million flash read IOPS?

Oracle Exadata Database Machine X3-2 Datasheet


Oracle Exadata Database Machine X3-8 Datasheet


DISCLAIMER: This post tackles citations straight from Oracle published data sheets and published literature.

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The opinions and interests expressed on EMC employee blogs are the employees' own and do not necessarily represent EMC's positions, strategies or views. EMC makes no representation or warranties about employee blogs or the accuracy or reliability of such blogs. When you access employee blogs, even though they may contain the EMC logo and content regarding EMC products and services, employee blogs are independent of EMC and EMC does not control their content or operation. In addition, a link to a blog does not mean that EMC endorses that blog or has responsibility for its content or use.

This disclaimer was put into place on March 23, 2011.

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All content is © Kevin Closson and "Kevin Closson's Blog: Platforms, Databases, and Storage", 2006-2013. Unauthorized use and/or duplication of this material without express and written permission from this blog’s author and/or owner is strictly prohibited. Excerpts and links may be used, provided that full and clear credit is given to Kevin Closson and Kevin Closson's Blog: Platforms, Databases, and Storage with appropriate and specific direction to the original content.


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