Posts Tagged ‘scalability’

Performance v Scalability – For Employers

Wednesday, December 4th, 2013

In a recent discussion with a fellow peer reviewing a job description he was applying for, we got into a discussion on the specifics of a Performance Engineer verses a Scalability Engineer.

Performance and Scalability are two very different goals. While it is true that improving performance can lead to increased scalability capacity with the same physical resources, increasing the scalability of your application does not necessarily lead to improved performance.

Performance is all about perception. In layman’s terms, how quickly can you provide a response to a request from your customer. As volume increases, performance generally degrades after a certain point, and then as volume continues, often the outcome is complete failure. Having a suitable scalable architecture can enable you to provide consistent performance for a given and growing workload.

A Scalability Engineer needs to have architectural skills, management skills, deployment skills and automation skills. A Performance Engineer needs to have more specific technology skills, development skills and some architectural skills.

A great example of a performance problem is when a client contacts me to help with a slow performing website. When the home page takes 5 seconds to load, but only 500ms of that is the actual page generation, and ultimately the maximum possible amount of time spent in the database, in isolation as a database expert I could only improve on 10% of the actual problem. As a performance engineer, your knowledge of the full stack including the web container, the data store accesses (persistent and non-persistent), optimizing the network payload size with compression, various techniques of caching and parallelism capacities are all essential skills needed.

A scalability problem is when your site supports 5,000 concurrent users, but it needs to support 25,000. Applying the primary skills just listed will not solve your scalability need. Simply adding 5x of servers is a simple way to provide support for more concurrent users, but where is the bottleneck or limitation of your application as you scale. Does adding 5x web servers place too much load on your caching tier or your database tier? While most applications utilize load balancing for web traffic, and so a new webserver is generally straightforward (to a point), can your application even support adding more database servers? Or does your architecture lead to read scalability, but not write scalability? Not being able to scale writes is a clear single point of failure for scalability. Most scalability needs require (re)architecture of your stack and the management of how this can be achieved while maintaining an operational site. After a point when you have 500+ servers, adding 50 more servers is generally the role of great automated deployment processes. The problem is usually greater when moving from 5 servers to 25 servers.

For employers that are writing a job description and using a specific job title, consider if the objectives in the description matches the title.

This leads to the question, what about a Reliability Engineer? That is another detailed discussion that relates to performance and scalability, but also have very different goals. Clearly defining your uptime needs is just one question a reliability engineer needs to ask.

Basic scalability principles to avert downtime

Saturday, April 23rd, 2011

In the press in the last two days has been the reported outage of Amazon Web Services Elastic Compute Cloud (EC2) in just one North Virginia data center. This has affected many large website includes FourSquare, Hootsuite, Reddit and Quora. A detailed list can be found at ec2disabled.com.

For these popular websites was this avoidable? Absolutely.

Basic scalability principles if deployed in these systems architecture would have averted the significant downtime regardless of your development stack. While I work primarily in MySQL these principles are not new, nor are they complicated, however they are fundamental concepts in scalability that apply to any technology including the popular MongoDB that is being used by a number of affected sites.

Scalability 101 involves some simple basic rules. Here are just two that seem to have been ignored by many affected by this recent AWS EC2 outage.

  1. Never put all your eggs in one basket. If you rely on AWS completely, or you rely on just one availability zone that is putting all your eggs in one basket.
  2. Always keep your important data close to home. When it comes to what is most critical to your business you need access and control to your information. At 5am in the morning when the CEO asks how long will our business be unavailabla and what is needed to resolve the problem, the answer “We have no control over this and have no ETA” is not an acceptable answer.

With a successful implementation and appropriate data redundancy you may not have an environment immediately available however you have access to your important information and the ability to create one quickly. Many large hosting companies can provide additional H/W on near demand, especially if you have an initial minimal footprint. Indeed using Amazon Web Services (AWS) as a means to avert a data center disaster is an ideal implementation of Infrastructure As A Service (IAAS). Even with this issue, organizations that had planned for this type of outage could have easily migrated to another AWS availability zone that was unaffected.

Furthermore, system architecture to support various levels of data availability and scalability ensure you can handle many more various types of unavailability without significant system down time as recently seen. There are many different types of availability and unavailability, know what your definition of downtime is and supporting disasters should be your primary focus of scalability, not an after thought.

As an expert in performance and scalability I can help your organization in the design of a suitable architecture to support successful scalability and disaster. This is not rocket science however many organizations gamble without the expertise of a professional to ensure business viability.

Part 2 – Simple lessons in improving scalability

Thursday, February 24th, 2011

Given the popular response from my first lesson in improving scalability where I detailed simple ways to eliminate unnecessary SQL, let me share another common bottleneck with MySQL scalability that can be instantly overcome.

Analyzing the writes that occur on a system can expose obvious potential bottlenecks. The MySQL Binary Log is a wealth of information that can be mined. Simple DML Counts per table can be achieved by a single line command.

Let’s look at the following example output of a production system:

mysqlbinlog /path/to/mysql-bin.000999 |  \
   grep -i -e "^update" -e "^insert" -e "^delete" -e "^replace" -e "^alter"  | \
   cut -c1-100 | tr '[A-Z]' '[a-z]' |  \
   sed -e "s/\t/ /g;s/\`//g;s/(.*$//;s/ set .*$//;s/ as .*$//" | sed -e "s/ where .*$//" |  \
   sort | uniq -c | sort -nr

Of the approx 100,000 DML statements we get the following breakdown.

  55283 update sessions
  25204 insert into sessions
  12610 update items
  10536 insert into item_categories
   7532 update users
   5168 delete from item_categories
 

More then 50% of the statements that are written to the binary log and therefore replicated are INSERT’s into the sessions table. A further 25% are UPDATE’s to the same table. This represents 75% of DML statements in just the two most frequent statements.

What is disappointing is that these statements do not belong in MySQL. This is an example of when MySQL is being abused for a purpose where other products are more suited. While there is the argument in using MySQL for storing data, the impact in MySQL memory management, backup/recovery and slave replication throughput and lag can significantly impact scalability of your important MySQL data.

What is observed here is session management where a key value store product should be used as an alternative. In most circumstances it is likely this information is not even required to be persisted. The obvious replacement here is using memcached. If you do wish to persist this data there is an ever increasing list of products including Redis, Tokyo Cabinet/Kyoto Cabinent, Membrain, Voldemort etc that are specifically designed as a key-value store. Even the popular noSQL MongoDB can be easily substituted to perform as a key-value session manager with the added benefits of being a more fully functional product for other purposes.

This is often a common mistake when you use a framework such as Ruby on Rails (RoR) or PHP Code Igniter and many others.

Simple lessons in improving scalability

Wednesday, February 16th, 2011

It can be very easy to improve scalability with a MySQL server by a few simple rules. Here is one of them.

“The most efficient way to improve an SQL statement is to eliminate it”

There are numerous ways to eliminate SQL statements, however before I give a classic example that I’ve observed again with a client, let me explain the basic premise of why this improves scalability?

The MySQL kernel can only physically process a certain number of SQL statements for a given time period (e.g. per second). Regardless of the type of machine you have, there is a physical limit. If you eliminate SQL statements that are unwarranted and unnecessary, you automatically enable more important SQL statements to run. There are numerous other downstream affects, however this is the simple math. To run more SQL, reduce the number of SQL you need to run.

Here is the output of a small sample of analyzed TCP/IP packets via mk-query-digest.

# Rank Query ID           Response time Calls R/Call Apdx V/M   Item
# ==== ================== ============= ===== ====== ==== ===== ==========
#    1 0xD631CB919867DB50  0.0436 47.3%    92 0.0005 1.00  0.00 SELECT TTDOD
#    2 0x04FE01C5B31FD305  0.0258 27.9%   329 0.0001 1.00  0.00 ADMIN PING
#    3 0x93321857BCD8E771  0.0229 24.8%    36 0.0006 1.00  0.00 SELECT TTD

There are many problems here including the Row at a Time (RAT) nature of the SQL, the excessive pings however that’s a topic for another time. Let us look at the first statement.

SELECT `Date` FROM TTDOD WHERE ID = 9999;

That seems a simple enough query however let’s look at the table.

mysql> select count(*) from TTDOD;
+----------+
| count(*) |
+----------+
|        0 |
+----------+

In this case, the query will NEVER return any rows because the table is currently empty. Sure this may change in the future, however as this is more an exception processing situation the simple act of managing the knowledge this table rarely has any rows, and building a solution to inform the application of this can completely eliminate the need for this query to ever be executed.

FYI, the above sample is from less then 2 seconds of sampling. Removing the first query reduces the number of queries executed in this time slice by 20%. Regardless of whether this is typical load or load during a batch job the principle stands. We have not even started to look at what we can do with the next query.

OTN MySQL conference slides

Wednesday, November 3rd, 2010

2010 has been the first year I have re-presented any of my developed MySQL presentations. Historically I have always created new presentations, however Paul Vallee gave me some valuable advice at UC 2010. In the past two weeks I’ve traveled to seven countries in South America on the OTN LA tour where I have been speaking about and promoting MySQL.

My three current presentations have been improved and even simplified, more future improvements are planned. There is definitely a benefit in repeating a good presentation multiple times.

Common MySQL Scalability Mistakes

Saturday, October 2nd, 2010

This week I was one of the presenters at the first Surge Scalability Conference in Baltimore. An event that focused not just on one technology but on what essential tools, technologies and practices system architects need to know about for successfully scaling web applications.

While MySQL is an important product in many environments, it is only one component for a successful technology stack and for many organizations is one of several products that manage your data.

My presentation was on the common MySQL scalability mistakes and how to avoid them. This is a problem/solution approach and is a companion talk with Successful MySQL Scalability which describes design for successfully scalability from the ground up.

Successful MySQL Scalability Presentation

Friday, September 17th, 2010

Last night I was the invited guest at the SF MySQL Meetup. In my presentation “Successful MySQL Scalability” I talked about a set of principles to ensure appropriate system architecture, data availability and best practices to build an ideal solution for your business. The presentation was also live streamed and is available online.

Speaking at Surge Scalability 2010 – Baltimore, MD

Wednesday, July 28th, 2010

I will be joining a great list of quality speakers including John Allspaw, Theo Schlossnagle, Rasmus Lerdorf and Tom Cook at Surge 2010 in Baltimore, Maryland on Thu 30 Sep, and Fri Oct 1st 2010. Surge 2010 Speaker - Baltimore, MD

My presentation on “The most common MySQL scalability mistakes, and how to avoid them.” will include discussing various experiences observed in the field as a MySQL Consultant and MySQL Performance Tuning expert.

Abstract:

The most common mistakes are easy to avoid however many startups continue to fall prey, with the impact including large re-design costs, delays in new feature releases, lower staff productivity and less then ideal ROI. All growing and successful sites need to achieve higher Availability, seamless Scalability and proven Resilience. Know the right MySQL environments to provide a suitable architecture and application design to support these essential needs.

Overview:

Some details of the presentation would include:

  • The different types of accessible data  (e.g. R/W, R, none)
  • What limits MySQL availability (e.g software upgrades, blocking statements, locking etc)
  • The three components of scalability – Read Scalability/Write Scalability/Caching
  • Design practices for increasing scalability and not physical resources
  • Disaster is inevitable. Having a tested and functional failover strategy
  • When other products are better (e.g. Static files, Session management via Key/Value store)
  • What a lack of accurate monitoring causes
  • What a lack of breakability testing causes
  • What does “No Downtime” mean to your organization.
  • Implementing a successful “failed whale” approach with preemptive analysis
  • Identifying when MySQL is not your bottleneck