It's 2013! Things Break, Services Falter. Move Forward.

It's a New Year, I have the cloud, but I still have many of the same old Single Points of Failure.

It's known that a single point of failure (SPOF) is a risk. It's an Achilees heel so to speak. That goes for people, companies, planets, AMI's, AZ's, Regions, Countries, or beers in the fridge. Whatever processes you have to do your general day to day work should be able to deal with known SPOF's and be flexible enough to assimilate and adjust to newly found failure modes. But, and this is important, there is a substantial cost associated with eliminating certain SPOF's. Let's say you decided that you no longer are accepting of having Earth be an SPOF for your awesome blog. Well, in that case, you need a space program, and an interplanetary network that puts this desire out of reach unless you are NASA, Elon Musk, or Richard Branson. Admittedly, that is an extreme example but my point is that your tolerance for risk and downtime must be considered carefully for any technology for which you have implicit or assumed service level agreements with your users. Let's think about Netflix for a moment.

Netflix's service was severely impacted this last Christmas Eve by an outage affecting AWS ELB's in their US East region. Based on my arms length information about Netflix operations through what I've read that is public, in my opinion and far more than most organizations, Netflix understands this cost/benefit of utilizing AWS. They say themselves in a recent post:

"Our strategy so far has been to isolate regions, so that outages in the US or Europe do not impact each other."

"Netflix is designed to handle failure of all or part of a single availability zone in a region as we run across three zones and operate with no loss of functionality on two." Source: http://techblog.netflix.com/2012/12/a-closer-look-at-christmas-eve-outage.html

Netflix clearly understands the risk and still they have chosen to take it despite the known risks. They were completely at the mercy of AWS in this last outage since the failure was regional in nature and their systems do not allow for multi-regional failover within a country for a single user account or group of accounts YET; but they are working on it.

As an AWS client, they do have a reasonable expectation as a customer that the underlying primitives they use from AWS to compose their services will work reliably. In this case, that primitive was Elastic Load Balancers. Like an AMI is a virtual server, an ELB is something of a virtual load balancers. In VPC's ELB's can span AZ's but then, the ELB is an SPOF unless your service is capable of re-initializing an ELB dynamically when it ceases to serve its purpose and can then re-route traffic accordingly. This is non-trivial but can also likely be dealt with if you understand the various intricacies of geo aware anycast backed DNS services.

Someone asked me if the AWS outages of 2012 would make me re-think my plans for cloud computing in 2013. This does not change my cloud plans for 2013 in any way. But, to be clear, even though I really like AWS, AWS is not the cloud and the cloud is not AWS. AWS is a big and deeply important part of the cloud ecosystem. I'm quite thankful for all they've done to further the understanding of cloud around the world. They are likely to stay on top, from my point of view, for a long while. I and my teams deployed large amounts of AWS in 2012 supporting the services of numerous clients.

I don't think these outages will cause any meaningful pause in most cloud plans for 2013 for anyone who takes the time to understand these sorts of situations and doesn't just fall prey to FUD (Fear, Uncertainty, Doubt) and really is serious about moving to a cloud computing model will keep marching forward. It's not perfect but the benefits to business and technical agility far outweigh the risks and knowledge ramp up investment that is necessary to make full use of cloud computing.

Things break and outages happen. There are very few systems where this is not true and those systems have been designed specially to deal with an extreme need for continuous availability. Especially complex systems and systems deployed at a large scale like AWS can break in interesting ways. It's not so much that things break that is so bad. It is what is done next that matters to keep the same things from breaking again and again. AWS does a pretty good job on this front in my opinion. It performs, communicates, and adjusts far better than most hosting providers I have historical experience working with over the last 15 years or so. They have raise the bar substantially.

Regarding AWS's IaaS services. It is AWS's job to provide a reasonable SLA and maintain it. It is up to users of the services to provide their users with services that have a reasonable SLA and maintain. Decoupling the service from the server is at the heart of the accelerating innovation in hosting of internet connected services that began quite some time ago and now marches under the banner of cloud computing. Now, if you use their PaaS services, it's a bit of a different situation but that's the subject of a whole different discussion I suspect.

Supporting Information Blast from ProductionScale's blog past is contained within the following older posts of mine (in no particular order):

On Clouds and SPOF's, 4/2011

http://productionscale.com/home/2011/4/22/on-clouds-and-spofs-or-the-great-aws-outage-of-april-2011.html

Cloud Computing Impacts Technical Agility, 8/2010

http://productionscale.com/home/2010/10/8/cloud-computing-impacts-technical-agility.html

The Traits of a Modern IT Organization, 8/2008

http://productionscale.com/home/2008/8/25/the-traits-of-a-modern-it-organization.html

Thoughts on the Business Case for Cloud Computing, 4/2009

http://productionscale.com/home/2009/2/4/thoughts-on-the-business-case-for-cloud-computing.html

Get Your Head in the Clouds, 4/2008

http://productionscale.com/home/2008/4/24/cloud-computing-get-your-head-in-the-clouds.html

Why Should Businesses Bother with Cloud Computing, 3/2009

http://productionscale.com/home/2009/3/13/why-should-business-bother-with-cloud-computing.html

Moving at the Speed of Cloud

The majority of my work in the last three years or so has been all about receiving, getting, pushing, pulling, and generally wrangling streams of data (mostly social data) for the purposes of analytics, comparison, or saving across a broad range of products and services for startups (one of my own) and fortune 500 companies. It's been keeping me busy. All of this for the ultimate reason of helping businesses make better and more well informed decisions about products, services, and more.

During this time I and my colleagues have developed the relationships, partnerships, technology stacks, and processes necessary to deliver these types of applications very quickly and at a high quality level. This has been fun all in all and something for which demand seems to be growing quickly.

To give a sense of the technology "stack" I've mostly settled on for solving these types of problems we are using:

Languages: Scala, Java, Node.js, PHP, Ruby

Frameworks: Symfony2, Play2.0, express.js, twitter bootstrap

Data Store: MySQL, MongoDB, Riak, Redis

Infrastructure: Amazon Web Services

Orchestration: Chef, Custom Scripting, AWS Cloud Formation

That's just a high level snapshot of course, there are a lot of details down inside each of those items from favored libraries to DB clients, and configuration management frameworks.

The best part for me is that it seems like for the first time in a long time many buisinesses seem to understand and believe in the value of the application of technology to solving business problems as a first order task.

The drive for big data aggregation and analytics is a natural evolution of the the maturation of cloud computing as both a technology and a service/process. The continued evolution of programming languages, application frameworks, and even the general understanding of distributed service oriented architectures and how to program REST API's is all improving as such an incredible rate that it's just an awesome time to be creating software.

So much of what we are doing now has been "around" in one form or another for a long time. The science in computer science laid the foundations quite some time ago. It's only now that so much is becomming so  accessible and the information on how to use all these tools is readily available.

I read a recent article/survey posted to Forbes.com that said the cloud is still three years away from it's full impact. The first cloud camp, where I did a session on developing for the cloud, was in 2008. That's only four years ago and look how much has changed! Awesome. 

From where I sit, this is an exciting time with nearly unlimited possibilties. Ideas are critical. Exececution is just as important. If you want to talk about any of these things I'm usually found either in San Francisco or San Rafael so let's chat! Good times!!

Building an Application upon Riak - Part 1

For the past few months some of my colleagues and I have been developing an application with Riak as the primary persistent data store.  This has been a very interesting journey from beginning to now.  I wanted to take a few minute and write a quick "off the top of my head" post about some of the things we learned along the way.  In writing this I realized that our journey breaks down into a handful of categories:

• Making the Decision
• Learning
• Operating
• Scaling
• Mistakes

We made the decision to use Riak around January of 2011 for our application.  We looked at HBase, Cassandra, Riak, MySQL, Postgres, MongoDB, Oracle, and a few others.  There were a lot of things we didn’t know about our application back then.  This is a very important point.

In any event, I’ll not bore you with all the details but we chose Riak.  We originally chose it because we felt it would be easy to manage as our data volume grew as well as because published benchmarks looked very promising, we wanted something based on the dynamo model, adjustable CAP properties per “bucket”, speed, our “schema”, data volume capacity plan, data model, and a few other things.

Some of the Stack Details

The primary programming language for our project is Scala.  There is no reasonable scala client at the moment that is kept up to date for Riak so we use the Java client.

We are running our application (a rather interesting business analytics platform if I do say so myself) on AWS using Ubuntu images.

We do all of our configuration management, cloud instance management, monitoring harnesses, maintenance, EC2 instance management, and much more with Opscode Chef.  But, that’s a whole other story.

We are currently running Riak 1.0.1 and will get to 1.0.2 soon.  We started on 0.12.0 I think it was... maybe 0.13.0.  I’ll have to go back and check.

On to some of the learning (and mistakes)

Up and Running - Getting started with Riak is very easy, very affordable, and covered well in the documentation.  Honestly, it couldn't be much easier.  But then... things get a bit more interesting.

REST ye not - Riak allows you to use a REST API over HTTP to interact with the data store.  This is really nice for getting started.  It’s really slow for actually building your applications.  This was one of the first easy buttons we de-commissioned.  We had to move to the protocol buffers interface for everything.  In hind sight this makes sense but we really did originally expect to get more out of the REST interface.  It was completely not usable in our case.

Balancing the Load - Riak doesn’t do much for you when it comes to load balancing your various types of requests.  We settled, courtesy of our crafty operations team on an on application node haproxy to shuttle requests to and from the various nodes.  Let me warn you.  This has worked for us but there be demons here!  The configuration details of running HA proxy to Riak are about as clear as mud and there isn’t much help to be found at the moment.  This was one of those moments over time that I really wished for the client to be a bit smarter.

Now, when nodes start dying, getting to busy, or whatever might come up you’ll be relying on your proxy (haproxy or otherwise) to handle this for you.  We don’t consider ourselves done at all on this point but we’ll get there.

Link Walking (err.. Ambling) - We modeled much of our early data relationships using link walking.  The learning?  S-L-O-W.  Had to remove it completely.  Play with it but don’t plan on using this in production out of the gate.  I think there is much potential here and we’ll be returning to this feature for some less latency sensitive work I perhaps.  Time will tell...

Watchoo Lookin’ for?! Riak Search - When we stared search was a separate project.  But, we knew we would have a use for search in our application.  So, we did everything we could to plan ahead for that fact.  But, by the time we were really getting all hot and heavy (post 1.0.0 deployment) we were finding our a few very interesting things about search.  It's VERY slow when you have a large result set.  It's just the nature of the way it's implemented.  If you think your search result set will return > 2000 items then think long and hard about using Riak's search functions for your primary search. This is, again, one of those things we’ve pulled back on quite a bit. But, the most important bits of learning were to:

• Keep Results Sets small
• Use Inline fields (this helped us a lot)
• Realize that searches run on ONE physical node and one vnode and WILL block (we didn’t really feel this until data really started growing from 100’s of 1000’s of “facets” to millions.

At this point, we are doing everything that we can to minimize the use of search in our application and where we do use it we’re limiting the result sets in various ways and using inline fields pretty successfully.  In any event, just remember Riak Search (stand alone or bundled post 1.0.0 is NOT a high performance search engine).  Again, this seems obvious now but we did design around a bit and had higher hopes.

 
OMG It’s broken what’s wrong - The error codes in the early version of Riak we used were useless to us and because we did not start w/ an enterprise support contract it was difficult sometimes to get help.  Thankfully, this has improved a lot over time.

Mailing List / IRC dosey-do - Dust off your IRC client and sub to the mailing list.  They are great and the Basho Team takes responding there very seriously.  We got help countless times this way.  Thanks team Basho!

I/O - It’s not easy to run Riak on AWS.  It loves I/O.  To be fair, they say this loud and clear so that’s my problem.   We originally tried fancy EBS setup to speed it up and make it persistent.  In the end we ditched all that and went ephemeral.  It was dramatically more stable for us overall.

Search Indexes (aka Pain) - Want to re-index?  Dump your data and reload.  Ouch.  Enough said.  We are working around this in a variety of ways but I have to believe this will change.

Basho Enterprise Support - Awesome.  These guys know their shit.  Once you become an enterprise customer they work very hard to help you.  For a real world production application you want Enterprise support via the licensing model.  Thanks again Basho!

The learning curve - It is a significant change for people to think in an eventually consistent distributed key value or distributed async application terms.  Having Riak under the hood means you NEED to think this way.  It requires a shifted mindset that, frankly, not a lot of people have today.  Build this fact into your dev cycle time or prepare to spend a lot of late nights.

Epiphany - One of the developers at work recently had an epiphany (or maybe we all had a group epiphany).  Riak is a distributed key value data store.  It is a VERY good one.  It’s not a search engine.  It’s not a relational database.  It’s not a graph database.  Etc.. etc..  Let me repeat.   Riak is an EXCELLENT distributed key value data store.  Use it as such.  Since we all had this revelation and adjusted things to take advantage of the fact life has been increasingly nice day by day.  Performance is up.  Throughput is up.  Things are scaling as expected.

In Summary - Reading back through this I felt it came off a bit negative.  That's not really fair though.  We're talking about nearly a year of learning.  I love Riak overall and I would definitely use it again.  It's not easy and you really need to make sure the context is correct (as with any database).  I think team Basho is just getting started but are off to a very strong start indeed.  I still believe Riak will really show it's stripes as we started to scale the application.  We have an excellent foundation upon which to build and our application is currently humming along and growing nicely.

I could not have even come close to getting where we are right now with the app we are working on without a good team as well.  You need a good devops-like team to build complex distributed web applications.

Lastly and this is the real summary, Riak is a very good key value data store.  The rest it can do is neat but for now, I'd recommend using it as a KV datastore.

I'm pretty open to the fact that even with several months of intense development and near ready product under our belt we also are only scratching the surface.

What I'll talk about next is the stack, the choices we've made for developing a distributed scala based app, and how those choices have played out.

The SaaS Aggregation Benefit Mirage

In this service oriented on-demand world I’ve been running into something again and again lately that I’ve found interesting and a bit annoying.

To start, imagine I’m going to build an application that uses two 3rd party services on-demand.  We’ll just call them service A and service B and say each have two features.  For this example it does not really matter what the services do.

Service A
  Feature A-1
  Feature A-2
Service B
   Feature B-1
   Feature B-2

So, I create my application and it first uses service A do something and it uses Feature A-1 and A-2.  Then, with the output of that it uses service B to do something else using feature B-2.

Now, a few months down the line when things are going great I get a call from my account manager at Service A telling me I can now get all the features of service B directly from them included.  So, what they are telling me is that my service structure now looks like this:

Service A
  Feature A-1
  Feature A-2
  Feature B-1
  Feature B-2
Service B
   Feature B-1
   Feature B-2

On the surface this looks really good.  It’s the same thing with less hassle right?  Maybe not.

This is where my annoyance surfaces.  Dig in and dig in well.  What I find again and again is that it’s simply not true because of what I’ll just call the filter effect.  What you really are getting with this new and improved service A is more like.

Service A
  Feature A-1
   Feature A-2
   Feature B-1

Notice that Feature B-2 is missing and that probably no body mentioned it.  Or, it’s more like:

Service A
   Feature A-1
   Feature A-2
   Feature C-1
   Feature C-2
   Feature C-3
   Feature C-n-OMG
Service B
   Feature B-1
   Feature B-2

And you don’t care because C isn’t B and all you need as A-1, A-2, and B-2.  While they say it’s equal is not and the app use feature B-2 if you’ll recall.  How much time did you just spend?

So, by the time you get through all this and figure out that the new improved Service A + B is pretty useless and all you really want is what you already have you will have wasted a lot of time.  There are less features, more complexity, less control, and likely much worse service and support for the aggregated services since you have no direct relationship to the end point provider.

So, rambling aside the point is that these service provider mashup aggregaters are not what they often seem on the surface and I’m frequently finding that the best deal is going right to the source and that any “savings” on the surface likely gets eaten up later in a variety of ways that are difficult to predict.  In most cases, it’s best to go to the source to get what you want.