Writing a Riak application is very much not like writing an application that relies on a relational database. The core ideas and vocabulary from database theory still apply, of course, but many of the decisions that inform the application layer are transformed.
Effectively, all of these anti-patterns make some degree of sense when writing an application against an RDBMS (such as MySQL). Unfortunately, none of them lend themselves to great Riak applications.
Riak's tools for finding data (2i, MapReduce, and full-text search) are useful but should be used judiciously. None of these scale nearly as well as key/value operations. Queries that may work well on a few nodes in development may run more slowly in a busy production environment, especially as the cluster grows in size.
Key/value operations seem primitive (and they are) but you'll find that they are flexible, scalable, and very, very fast (and predictably so). One thing to always bear in mind about key/value operations:
Reads and writes in Riak should be as fast with ten billion values in storage as with ten thousand.
Design the main functionality of your application around the straight key/value operations that Riak provides and your software will continue to work at blazing speeds when you have petabytes of data stored across dozens of servers.
Normalizing data is generally a useful approach in a relational database, but it is unlikely to lead to happy results with Riak.
Riak lacks foreign key constraints and join operations, two vital parts of the normalization story, so reconstructing a single record from multiple objects would involve multiple read requests. This is certainly possible and fast enough on a small scale, but it is not ideal for larger requests.
In contrast, imagine the performance of your application if most of your requests involved a single read operation. Much faster and predictably so, even at scale. Preparing and storing the answers to queries you're going to ask for later is a best practice for Riak.
See Denormalization for more discussion.
One of the first hurdles Basho faced when releasing Riak was educating developers on the complexities of eventual consistency and the need to intelligently resolve data conflicts.
Because Riak is optimized for high availability, even when servers are offline or disconnected from the cluster due to network failures, it is not uncommon for two servers to have different versions of a piece of data.
The simplest approach to coping with this is to allow Riak to choose a winner based on timestamps. It can do this more effectively if developers follow Basho's guidance on sending updates with vector clock metadata to help track causal history. But concurrent updates cannot always be automatically resolved via vector clocks, and trusting server clocks to determine which write was the last to arrive is a terrible conflict resolution method.
Even if your server clocks are magically always in sync, are your business needs well served by blindly applying the most recent update? Some databases have no alternative but to handle it that way, but we think you deserve better.
Typed buckets in Riak 2.0 default to retaining conflicts and requiring the application to resolve them, but we're also providing replicated, conflict-free data types (we call them Riak Data Types) to automate conflict resolution on the server side.
If you want to minimize the need for conflict resolution, modeling with as much immutable data as possible is a big win.
Conflict Resolution covers this in much more detail.
For years, functional programmers have been singing the praises of immutable data, which can confer significant advantages when using a distributed datastore like Riak.
Most obviously, conflict resolution is dramatically simplified when objects are never updated (because it is avoided entirely).
Even in the world of single-server database servers, updating records in place carries costs. Most databases lose all sense of history when data is updated, and it's entirely possible for two different clients to overwrite the same field in rapid succession, leading to unexpected results.
Some data is always going to be mutable, but thinking about the alternative can lead to better design.
A perfectly natural response when first encountering a populated database is to see what's in it. In a relational database, you can easily retrieve a list of tables and start browsing their records.
As it turns out, this is a terrible idea in Riak.
Not only is Riak optimized for unstructured, opaque data, it is also not designed to allow for trivial retrieval of lists of buckets (very loosely analogous to tables) and keys.
Doing so can put a great deal of stress on a large cluster and can significantly impact performance.
It's a rather unusual idea for someone coming from a relational mindset, but being able to algorithmically determine the key that you need for the data you want to retrieve is a major part of the Riak application story.
Because Riak sends multiple copies of your data around the network for every request, values that are too large can clog the pipes, so to speak, causing significant latency problems.
Basho generally recommends 1-4MB objects as a soft cap; larger sizes are possible with careful tuning, however.
We'll return to object size when discussing Conflict Resolution; for the moment, suffice it to say that if you're planning on storing mutable objects in the upper ranges of our recommendations, you're particularly at risk of latency problems.
For significantly larger objects, Riak CS offers an Amazon S3-compatible (and also OpenStack Swift-compatible) key/value object store that uses Riak under the hood.
This is more of an operations anti-pattern, but it is a common misunderstanding of Riak's architecture.
It is quite common to install Riak in a development environment using
devrel build target, which creates 5 full Riak stacks (including
Erlang virtual machines) to run on one server to simulate a cluster.
However, running Riak on a single server for benchmarking or production use is counterproductive, regardless of whether you have 1 stack or 5 on the box.
It is possible to argue that Riak is more of a database coordination platform than a database itself. It uses Bitcask or LevelDB to persist data to disk, but more importantly, it commonly uses at least 64 such embedded databases in a cluster.
Needless to say, if you run 64 databases simultaneously on a single filesystem you are risking significant I/O and CPU contention unless the environment is carefully tuned (and has some pretty fast disks).
Perhaps more importantly, Riak's core design goal, its raison d'être, is high availability via data redundancy and related mechanisms. Writing three copies of all your data to a single server is mostly pointless, both contributing to resource contention and throwing away Riak's ability to survive server failure.