Why do people still use static addressing in configuration files? Fixed hostnames or worse IP addresses?

These things make one’s life a nightmare when moving from one environment to another e.g. desktop to QA, QA to staging or staging to production.

With each transition, one must wade through all the relevant configuration files, find all these addresses and edit them. This creates many an opportunity for error such as missing one configuration variable or mistyping an address. It’s also a nightmare to maintain accurate documentation for all these scattered settings.

And yet this is so unnecessary if one exploits the abilities of DNS (and maybe Bonjour) properly. Just look at some of the cool stuff one can do. Better still most (all?) of it is supported in BIND.

Technorati Tags: architecture, distributed systems, systems

Mapreduce has taken some criticism from Stonebraker and DeWitt. I found this particular quote interesting:

we are amazed at the hype that the MapReduce proponents have spread about how it represents a paradigm shift in the development of scalable, data-intensive applications

Personally I’ve not heard any such claim from the MapReduce community (and who is that anyway?).

I’ve always seen MapReduce as nothing more than a useful tool for processing massive amounts of file-based data in ad-hoc fashions. This ad-hoc requirement is significant to me because DBMS’en have a tendency toward organizing data into static structures:

The DBMS community learned the importance of schemas, whereby the fields and their data types are recorded in storage. More importantly, the run-time system of the DBMS can ensure that input records obey this schema. This is the best way to keep an application from adding “garbage” to a data set. MapReduce has no such functionality, and there are no controls to keep garbage out of its data sets. A corrupted MapReduce dataset can actually silently break all the MapReduce applications that use that dataset.

These static structures can make it difficult to change things in support of new modes of usage. For example, one doesn’t lightly change index terms within a DBMS especially for large amounts of data. Perhaps the breadth of MapReduce queries run at Google would make regular index changes essential hence they chose to avoid a DBMS approach.

I wonder just how much the authors assumed about the way things are done at Google and what kind of “queries” they run. Consider that GFS which apparently underpins MapReduce is focused on append-only, not the sort of thing one sees in the DBMS world which accounts for updates and inserts amongst other things.

Many eyes in the industry have indulged in the bad habit of seeing the DBMS as the data storage equivalent of a Swiss Army Knife. It is the universal hammer for all data storage and analysis nails regardless of the actual requirements. Could Stonebraker and DeWitt have gotten caught up in this “classic mistake”? Surely not given what they’ve said in the past?

Recently I’ve had reason to go back over some data-structure theory, average times for insertion, deletion and search etc. I was reminded of a period around 1999 when I was implementing a Hyperchromatic Tree, a subtype of the basic red-black tree.

Hyperchromatic trees are lazily balanced i.e. balanced in the background rather than as part of the operation that changes the structure of the tree and they have some challenging locking disciplines to implement as well. They are designed to perform better under concurrent access than their ancestors.

Looking at my notes I was running a single test performing concurrent operations continuously for periods of 5 days against an instance containing 1 million entries on JDK 1.4. I suspect this period of my developer life taught me more about deadlocks and race conditions than any other.

Technorati Tags: concurrency, development, software

This just shouldn’t be a surprise. Frankly it’s closing the door long after the horse has bolted - check out Martin Odersky’s comments in this article from way back when. Who wants to bet that in spite of all this we still get closures in Java?

We make this mistake all the time with languages, frameworks, applications and operating systems. When will we learn the lesson I wonder?

Technorati Tags: philosophy, development, software

Bill ponders:

“Sometimes I wonder how a deployed n-tiered scale up monolith can be gradually refactored to a scale out model or run on scale out infrastructure. It’s well documented that companies like Amazon and Ebay have done just that, only how they did it tends to get left out of the slide decks. I suspect it involves thinking quite differently about what ‘good’ application code is….”

I think one reason for the absence of this information in slide decks is that it’s a competitive advantage, a barrier to entry. Mastering scale-out allows one company to handle more load and more customers whilst offering more function than another. Why teach other companies how to compete? For me though the real issue is that the transition is a major effort that defies easy explanation via slideware.

Makeup of a Monolith

The rough form of a monolithic system is a logical (maybe physical) n-tier with simple scale out in the places where it’s easy e.g. a stateless web tier. The remainder is typically a single database containing all data which is fronted by some kind of cache (some systems will also talk to legacy systems via various mechanisms and some will have an extra database or two). Some may view their database replication strategy/cluster to be horizontal scaling but to be the real deal would require explicit partitioning or sharding. All code has access to all of the data in the database and it’s typically not well controlled which leads to what I term the “integration via database” anti-pattern.

In essence to move to a scale out architecture requires us to partition up all the state we have held in the database (admitting there’s incentive to break up the middle tier to avoid it getting overly large and unwieldy). Sounds easy but of course is anything but because there’s just so much beyond “thinking quite differently about what ‘good’ application code is” to learn and change. One might expect that we build things up in horizontal slices, create some new infrastructure, port to the new infrastructure, the usual “upgrade the middleware” type approach but this doesn’t work due to lack of hindsight and the sheer size of the challenge. The antidote to this is to take one little step at a time.

Basic Approach

Instead of the “big scary re-architecture” we take vertical slices through the monolith, separating them out so that we can evolve each with a limited amount to consider. We push these pieces all the way through into deployment so as to expose all our processes and tools to this new type of entity. This is not easy but:

1. At least it’s being done against a small vertical slice which makes it a more manageable problem.
2. The transition cannot be managed in one big step, it will be necessary to manage old and new alongside each other so we need to get used to that.

Another option for learning how to work outside of the monolith is to implement some new set of features in this new style and drive that through the delivery chain. Obviously whilst this develops knowledge it’s not breaking down the existing monolith, so let’s move on to consider the basic steps (order is not fixed) for extracting a vertical element:

1. Identify some reasonable sized function or chunk of data to partition.
2. Define an interface which will wrap this function and/or data.
3. Move code and data behind the interface.
4. Modify other code to access code and data via the interface rather than e.g. direct access to the database.

Step (1) often requires us to examine both data and function. Ideally the interface we introduce in step (2) would look like a remote service. It may for some period of time still be part of the monolith and therefore local. In some cases we can’t make the interface remote initially because the refactor would be too complex so we must later come back and recast the interface as a remote service. What form does this new interface take? Ignoring religious issues it could be WS-*, REST, some other form of remote invocation (sometimes custom but not fine-grained method calls) or messaging.

The arrival of this new interface allows us to introduce behaviours such as asynchronous operation and eventual consistency into a controlled area of our codebase. It’s possible that the data we’ve wrapped behind the interface is still being drawn from the original database so we can also consider moving this data to it’s own separate storage mechanism (which may or may not be another database) and introduce sharding and partitioning.

Challenges

Some developers will fall back on “remoteness dogma” stating that remoteness is a performance inhibitor. Indeed they are right but scale is at least (if not more) important and becomes a key focus when we can’t buy a bigger box to make the monolith perform. For me the real issue at hand is the corruption of the mind that comes from thinking transactionally. In this world we focus on minimising the amount of time a transaction takes for fear of lock contention and blocking threads too long. We become obsessed with consistency, feeling compelled to do all work ahead of the actual transaction. This thinking naturally leads to optimising the execution paths heavily and eschewing remoteness. Key to tackling these issues is socialising use of asynchronous techniques and the fact that consistency is in the eye of the beholder (and determined by how often they can observe the relevant data).

During the recasting of a vertical slice into a standalone element it’s usually necessary to ensure it can cope with existing load (and a bit more). Establishing what the current load looks like can be challenging as monitoring and statistics are often centred around information available from the database, application servers, load-balancers and operating systems i.e. the infrastructure. This data is certainly useful but says little about what’s actually happening in the application code. A good way to address this problem and improve the lot of the operational teams is to introduce a programme of application instrumentation that will deliver appropriate statistics and high-level diagnostics.

Inevitably we will be adopting and/or building some new infrastructure but we must be careful how much we try to acquire and/or custom build in advance of real-world experience (see the hindsight link above). Fortunately there’s little that’s actually required up front other than some mechanisms for service location and statistics gathering so the impact of mistakes is usefully limited. Follow-on candidates might include messaging, deployment and security. Remember also that many vendors are still producing infrastructure suited to big-iron monolithic development and single data-centre environments (which can make resilience in the face of certain kinds of failure difficult).

Static configuration (per-machine or in tools) can be extremely troublesome containing a lot of URL’s, server addresses, machine names and database references. All of these items need changing at each stage from development, through testing, QA, staging and production. The move toward a more distributed approach will only make this worse as it creates a need to copy and tweak more configuration on more machines. It’s important from the early stages to focus on eliminating as much of this as possible. In an ideal world a machine would be configured with at most its designated duty and environment (testing, development, production) obtaining everything else it needs from services in the environment.

Wrap Up

To go horizontal is extremely challenging and cannot be addressed by the typical re-architecting initiatives many companies indulge in. There’s too much to learn and change such that the only option is a slow, step by step, learn as you go transition that gradually chips away at the monolith. For Amazon it seems this transition has taken at least five years and over at eBay it looks like they started the transition sometime around 1999/2000.

Technorati Tags: architecture, distributed systems

In general, writing software is made hard by our inability to predict the future. We’re always caught between the two stools of building what we need now and what we might need in the future. Writing infrastructure is even harder because it’s an expression of common patterns and challenges of implementation in a specific context such as one company’s systems or services. The broader the context (e.g. all enterprises), the harder it gets to account for all possible permutations of usage.

So to a core trouble with building infrastructure: common patterns and challenges of implementation can only be established by reviewing history. Basically until some systems have been built, we can’t tell with certainty what the infrastructure should look like.

It’s all too easy to believe that some problem we’re currently faced with is generic and therefore best tackled by:

How do we know something is generic? Experience. To be confident something is generic we must have seen it across our systems universe (that is the collection of systems in our domain of concern). The danger is that multiple teams adopt their own solution to the same problem but this isn’t necessarily a bad thing. Each team is doing valuable investigation work that will help identify the most promising options for a solution. The trick of course is to have sufficient cross-team discussion about architecture so as to avoid excessive proliferation of independent solutions.

I have a little mantra that I use to remind myself and others of this thorny issue: Experience leads infrastructure.

Technorati Tags: architecture, philosophy, systems