Twitter VP of Infrastructure Operations Engineering Mazen Rawashdeh shared news and numbers yesterday on his blog:

"Last night, the world tuned in to Twitter to share the election results as U.S. voters chose a president and settled many other campaigns. Throughout the day, people sent more than 31 million election-related Tweets (which contained certain key terms and relevant hashtags). And as results rolled in, we tracked the surge in election-related Tweets at 327,452 Tweets per minute (TPM). These numbers reflect the largest election-related Twitter conversation during our 6 years of existence, though they don’t capture the total volume of all Tweets yesterday."

"Last night, Twitter averaged about 9,965 TPS from 8:11pm to 9:11pm PT, with a one-second peak of 15,107 TPS at 8:20pm PT and a one-minute peak of 874,560 TPM. …

While we were at MySQL Connect last month, we interviewed long-time MySQL developer JD Duncan about integrating NoSQL and JavaScript with MySQL. His team developed the Node.js API for MySQL. This week, we present the interview we did. In Ear Candy, we discuss what makes a senior DBA, and in At the Movies we present a ScaleBase webinar.

Events
Oracle's "Scale with MySQL" seminars:
Bucharest Romania, Tuesday 13 November
Madrid, Tuesday 27 November

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To fully harness the continued improvements in performance tuning you get with MySQL, take the MySQL Performance Tuning course.

This 4 day class teaches you practical, safe, highly efficient ways to optimize performance for the MySQL Server. You will learn the skills needed to use tools for monitoring, evaluating and tuning. 

You can take this course in the following three ways:

• Training-on-Demand: Follow this course at your own pace and from your own desk with streaming video of instructor delivery and booking time to follow hands-on exercises at your own convenience.
• Live-Virtual: Attend a live instructor-led event from your own desk. Choose from the numerous events on the schedule.
• In-Class:  Travel to an education center to follow this class. A sample of events on the schedule is shown below:

Solid State Drive (SSD) have made it big and have made their way not only in desktop computing but also in mission-critical servers. SSDs have proved to be a break-through in IO performance and leave HDD far far behind in terms of Random IO performance. Random IO is what most of the database administrators would be concerned about as that is 90% of the IO pattern visible on database servers like MySQL. I have found Intel 520-series and Intel 910-series to be quite popular and they do give very good numbers in terms of Random IOPS. However, its not just performance that you should be concerned about, failure predictions and health gauges are also very important, as loss of data is a big NO-NO. There is a great deal of misconception about the endurance level of SSD, as its mostly compared to rotating disks even when measuring endurance levels, however, there is a big difference in how both SSD and HDD work, and that has a direct impact on the endurance …

Today, one of the MySQL database server response time has been increased within the pool of servers and noticed it was returning the query in 0.20 secs (randomly) as opposed[...]

Not much to add really to the bug I’ve filed here: bug#67099.

I personally can think of some very nasty consequences of applying this on the slaves I manage, and the reason I’m posting the bug is that while I guess this is too late to fix in 5.6 as it’s effectively a new feature, I’m sure many sites may bump into this and be somewhat disappointed if they want to use the new GTID feature and have several slaves.  Hence, if the fix/feature has to go into MySQL 5.7 then I hope it goes in sooner rather than later. We will see.

Updated: 2013-09-19

I probably should have updated this earlier but it does seem that Oracle have taken these comments on board. See: WL6559.  It looks like they plan to do this for 5.7 which is good news. I’m really hoping …

While the index merge access types certainly are useful for a number of queries, there has been some frustration expressed both from customers and the community about how it...

1. is not used when it should have been
2. is used when ref access is obviously better
3. merges suboptimal indexes
4. is too restricted in which conditions can be used

I could come up with numerous examples of related bugs and feature requests dating back more than six years. To list a few: 17673, 30151, 23322, 65274, …

When InnoDB compresses a page it needs the result to fit into its predetermined compressed page size (specified with KEY_BLOCK_SIZE). When the result does not fit we call that a compression failure. In this case InnoDB needs to split up the page and try to compress again. That said, compression failures are bad for performance and should be minimized.

Whether the result of the compression will fit largely depends on the data being compressed and some tables and/or indexes may contain more compressible data than others. And so it would be nice if the compression failure rate, along with other compression stats, could be monitored on a per table or even on a per index basis, wouldn't it?

This is where the new INFORMATION_SCHEMA table in MySQL 5.6 kicks in. INFORMATION_SCHEMA.INNODB_CMP_PER_INDEX provides exactly this helpful information. It contains the following fields:

Ever since its first release, we are continuing consolidating and developing InnoDB Full-Text Search feature. There is one recent improvement that worth blogging about. It is an effort with MySQL Optimizer team that simplifies some common queries’ Query Plans and dramatically shorted the query time. I will describe the issue, our solution and the end result by some performance numbers to demonstrate our efforts in continuing enhancement the Full-Text Search capability.

The Issue:

As we had discussed in previous Blogs, InnoDB implements Full-Text index as reversed auxiliary tables. The query once parsed will be reinterpreted into several queries into related auxiliary tables and then results are merged and consolidated to come up with the final result. So at the end of the query, we’ll have all matching records on hand, sorted by their ranking or by their Doc IDs.

Unfortunately, MySQL’s optimizer and …

Read the original article at Anatomy of a Performance Review

A lot of firms come to us with a specific scalability problem. “Our user base is growing rapidly and the website is falling over!” Or they’re selling more widgets, “Our shopping cart is slowing down and we’re seeing users abandon their purchases”. These are real startup growing pains, so what to do?

We like to take a measured approach with these types of challenges, so we thought it would be helpful to run through a hypothetical scenario and see how we work.

Having trouble with scalability? Check out our 5 things toxic to scalability piece. …