The fourth link in my google search for ‘feature toggle’ is a link to this Building Real Software post. It’s about not about feature toggles described by Martin Folwer. It’s about feature toggles got wrong.
If you consider toggling features with flags and apply it literally, what you get is a lot of branching. That’s all. Some tests should be written twice to handle a positive and a negative scenario for the branch. The reason for this is a design not prepared to handle toggling properly. In the majority of cases, it’s a design which is not feature-based on its own.
The featured based design is created on the basis of closed components, which handle the given domain aspect. Some of them may be big like ‘basket’, some may be much smaller, like ‘notifications’ reacting to various changes and displaying needed information. The important thing is to design the features as closed components. Once you have it done this way, it’s easier to think about the page without notifications or ads. Again, disabling the feature is not a mere flag thrown in different pieces of code. It’s disabling or replacing the whole feature.
One of my favorite architecture styles, event driven architecture helps in a great manner to build this kind of toggles. It’s quite easy to simply… not handle the event at all. If you consider the notifications, if they are disabled, they simply do not react to various events like ‘order-processed’, etc. The separate story is to not create cycles of dependencies, but still, if you consider the reactive nature of connections between features, that’s a great enabler for introducing toggling with all of advantages one can derive from it with A/B tests, canary releases in mind.
I’m not a fan boy of feature toggling, I consider it as an important tool in architects arsenal though.
In the recent post Rinat Abdullin provides a retrospective for Lokad.CQRS framework which was/is a starting point for many CQRS journeys. It’s worth to mention that Rinat is the author of this library. The whole article may sound a bit harsh, it provides a great retrospection from the author’s and user’s point of view though.
I agree with the majority points of this post. The library provided abstractions allowing to change the storage engine, but the directions taken were very limiting. The tooling for messages, ddd console, was the thing at the beginning, but after spending a few days with it, I didn’t use it anyway. The library encouraged to use one-way messaging all the way down, to separate every piece. Today, when CQRS mailing lists are filled with messages like ‘you don’t have to use queues all the time’ and CQRS people are much more aware of the ability to handle the requests synchronously it’d be easier to give some directions.
The author finishes with
So, Lokad.CQRS was a big mistake of mine. I’m really sorry if you were affected by it in a bad way.
Hopefully, this recollection of my mistakes either provided you with some insights or simply entertained.
which I totally disagree with! Lokad.CQRS was the tool that shaped thinking of many people, when nothing like that was available on the market. Personally, it helped me to build a event-driven project (you can see the presentation about this here) based on somehow on Lokad.CQRS but with other abstractions and targeted at very good performance, not to mention living documentation built with Mono.Cecil.
Lokad.CQRS was a ground breaking library providing a bit too much tooling and abstracting too many things. I’m really glad if it helped you to learn about CQRS as it helped me. Without this, I wouldn’t ask all the questions and wouldn’t learn so much.
The provided retrospective is invaluable and brings a lot of insights. I’m wishing you all to make that kind of ground breaking mistakes someday.
Currently, I’m working with some pieces of a legacy code. There are good old-fashioned DAL, BLL layers which reside in separate projects. Additionally, there is a common
project with all the interfaces one could need elsewhere. The whole solution is deployed as one solid piece, without any of the projects used anywhere else. What is your opinion about this structure?
To my mind, splitting one solid piece into non-functional projects is not the best option you can get. Another approach which fits this scenario is using feature orientation and one project in solution to rule them all. An old, the deeper you get in namespace, the more internal you become, is the way to approach feature cross-referencing. So how would one could design a project:
- Admin.cs (entity)
I see the following advantages:
- If any of the features requires reference to another, it’s an easy thing to add one.
- There’s no need of thinking where to put the interface, if it is going to be used in another project of this solution.
- You don’t onionate all the things. Now, there are top-bottom pillars which one could later on transform into services if needed.
To sum up, you could deal with features oriented toward business or layers oriented toward programming layers. What would you choose?
I thought for a while about presenting a few projects which are in my opinion real pearls. Let’s start with the EventStore and one in one of its aspects: the transaction log.
If you’re not familiar with this project, EventStore is a stream database providing complex event processing. It’s oriented around streams of events, which can be easily aggregated or repartitioned with projections. Based on ever appended streams and projections chasing the streams one can build a truly powerful logic around processing events.
One of the interesting aspects of EventStore is its storage engine. You can find a bit of description in here. ES does not abstract a storage away, the storage is a built-in part of the database itself. Let’s take a look at its parts before discussing its further:
Appending to the log
One the building blocks of ES is SEDA architecture – the communication within db is based on publishing and consuming messages, which one can notice reviewing StorageWriterService. The service subscribes to multiple messages, mentioned in implementations of the IHandle interface. The arising question is how often does the service flushed it’s messages to disk. One can notice, that method EnqueueMessage beside enqueuing incoming messages counts ones marked by interface IFlushableMessage. What is it for?
Each Handle method call Flush at its very end. Additionally, as the EnqueueMessage increases the counter of messages requiring flush, each Handle method decreases the counter when it handles a flushable message. This brings us to the conclusion that the mentioned counter is equal 0 iff there are no more flushable messages in the queue.
Flushing the log
Once the Flush is called a condition is checked whether:
- the call was made with force=true (this never happens) or
- there are no more flush messages in the queue or
- the given time from the last time has passed
This provides a very powerful batching behavior. Under stress, the flush-to-be counter will be constantly greater than 0, providing flushing every given period of time. Under less stress, with no more flushables in the queue, ES will flush every message which needs to flush the log file.
Acking the client
The final part of the processing is the acknowledgement part. The client should be informed about persisting a transaction to disk. I spent a bit of time (with help of Greg Young and James Nugent) of chasing the place where the ack is generated. It does not happen in the StorageWriterService. What’s responsible for considering the message written then? Here comes the second part of the solution, the StorageChaser. In a dedicated thread, in an infinite loop, a method ChaserIteration is called. The method tries to read a next record from a chunk of unmanaged memory, that was ensured to be flushed by the StorageWriterService. Once the chaser finds CommitRecord, written when a transaction is commited, it acks the client by publishing the StorageMessage.CommitAck in ProcessCommitRecord method. The message will be translated to a client message, confirming the commit and sent back to the client.
One cannot deny the beauty and simplicity of this solution. One component tries to flush as fast as possible, or batches a few messages if it cannot endure the pressure. Another one waits for the position to which a file is flushed to be increased. Once it changes, it reads the record (from the in-memory chunk matched with the file on disk) processes it and sends acks. Simple and powerful.
From time to time a system is replaced with another system being capable of doing more, or doing the thing better. It’s quite to common to ask whether no data is lost or does the system preserve needed behaviors of the old one. Sometimes it’s human-application comparison, when a procedure followed by people is replaced with an application, sometimes it’s a question of an old system vs a new system
Cassandra integrity check
Let’s presume one migrates some old-fashioned SQL system to a Cassandra based solution given following:
- total payload of daily data is quite high
- data are written to the Cassandra cluster (more than one node) with ConsistencyLevel Local Quorum
- there should be a possibility to check whether all the data stored in the previous systems are written to the new one
After a bit of consideration one can propose that as the data are written with LocalQuorum, they should be queried with the same level and match in the old solution. This would ensure that data which has been written are being read (famous R + W > N). This could cost a lot as querying hits [N+1]/2 nodes of your cluster, streaming a daily payload through network twice: once to the coordinator, second – to the client. Can we do this better?
Possibly faster integrity check
How about using Consistency Level of One? How can this be done to ensure that the given node consists of all the needed data? By running repair in your local data center on each node, one can ensure that each node consist of all the data it’s responsible for. Then, querying with One is ok. What’s important about nodetool repair is that it does not stream data if it’s not needed. The information sent to match if the given node contains all the data is a Merkle tree, a tree made by hash of hashes of hashes of… Sending this structure is cheap and doesn’t your network so much.
If you consider (know that) running repairs daily is a heavy task for your cluster, you’ll be happy to read about Cassandra 2.1 repair improvements, including incremental repairs.
So stop complaining about your good old fashioned RMDB and get yourself a new shiny cluster of Cassandra nodes :)
Have you ever been in a situation when you’ve got not enough tooling in your preferred environment? What did you do back then? Did you use already known tools or search for something better?
Recently I’ve been involved in a project which truly pushed me out of my comfort zone. I like .NET, but there’s not enough tooling to provide a highly scalable, performant, failure resistant environment. We moved to Java, Storm, Cassandra, Zookeeper – JVM all over the place. It wasn’t easy, but it was interesting and eye opening. Having so many libraries focused on resolving specified concerns instead of the .NET framework-oriented paradigm was very refreshing.
Was it worth it? Yes. Was it good for self-development? Yes. Will I reach for every new library/language? For sure no. The most important thing which I’ve learned so far, was that being adaptable and aware of tools is the most important thing. Mistakes were made, that for sure, but the overall solution is growing in a right direction.
After all, it’s survival of the fittest, isn’t it?
I hope you’re aware of the LMAX tool for fast in memory processing called disruptor. If not, it’s a must-see for nowadays architects. It’s nice to see your process eating messages with speeds ~10 millions/s.
One of the problems addressed in the latest release was a fast multi producer allowing one to instantiate multiple tasks publishing data for their consumers. I must admit that the simplicity of this robust part is astonishing. How one could handle claiming and publishing a one or a few items from the buffer ring? Its easy, claim it in a standard way using CAS operation to let other threads know about the claimed value and publish it. But how publish this kind of info? Here’s come the beauty of this solution:
- allocate a int array of the buffer ring length
- when items are published calculate their positions in the ring (sequence % ring.length)
- set the values in the helper int array with numbers of sequences (or values got from them)
This, with overhead of int array allows:
- waiting for producer by simply checking the value in the int array, if it matches the current number of buffer iteration
- publishing in the same order items were claimed
- publishing with no additionals CASes
Simple, powerful and fast.
Come, take a look at it: MultiProducerSequencer