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Even though I got my copy of Prof. Eugenia Cheng’s awesome How to Bake π a couple of weeks back, I started reading it only over this weekend. I am only on page 19 enjoying all the stuff regarding cookies that Prof. Cheng is using to explain abstraction. This is a beautiful piece of explanation and if you are a programmer you may get an extra mile out of the concepts that she explains here. Let’s see if we [...]
Some time back I blogged about the possibilities that probabilistic techniques and randomization bring on to the paradigm of stream computing. Architectures based on big data not only relate to high volume storage, but also on low latency velocities, and this is exactly where stream computing has a role to play. I discussed a few data structures like bloom filters, count min sketch and hyperloglog and algorithms like Locality Sensitive Hashing that use probabilistic techniques to reduce the search [...]
I have been doing quite a bit of domain modeling using functional programming mostly in Scala. And as it happens when you work on something for a long period of time you tend to identify more and more patterns that come up repeatedly within your implementations. You may ignore these as patterns the first time, get a feeling of mere coincidence the next time, but third time really gives you that aha! moment and you feel like documenting it as [...]
I look forward to 2015 as the year when randomized algorithms, probabilistic techniques and data structures become more pervasive and mainstream. The primary driving factors for this will be more and more prevalence of big data and the necessity to process them in near real time using minimal (or constant) memory bandwidth. You are given data streams where possibly you will see every data only once in your lifetime and you need to churn out analytics from them in real [...]
Manning has launched the MEAP of my upcoming book on Domain Modeling.
The first time I was formally introduced to the topic was way back when I played around with Erik Evans’ awesome text on the subject of Domain Driven Design. In the book he discusses various object lifecycle patterns like the Factory, Aggregate or Repository that help separation of concerns when you are implementing the various interactions between the elements of the domain model. Entities are artifacts [...]
I was looking at the presentation that Dean Wampler made recently regarding domain driven design, anemic domain models and how using functional programming principles help ameliorate some of the problems there. There are some statements that he made which, I am sure made many OO practitioners chuckle. They contradict popular beliefs that encourage OOP as the primary way of modeling using DDD principles.
One statement that resonates a lot with my thought is “DDD encourages understanding of the domain, but [...]
In the last post I looked at a pattern that enforces constraints to ensure domain objects honor the domain rules. But what exactly is a domain object ? What should be the granularity of an object that my solution model should expose so that it makes sense to a domain user ? After all, the domain model should speak the language of the domain. We may have a cluster of entities modeling various concepts of the domain. But only [...]
When you model a domain, you model its entities and behaviors. As Eric Evans mentions in his book Domain Driven Design, the focus is on the domain itself. The model that you design and implement must speak the ubiquitous language so that the essence of the domain is not lost in the myriads of incidental complexities that your implementation enforces. While being expressive the model needs to be extensible too. And when we talk about extensibility, one related attribute [...]
In my last post I discussed the count-min sketch data structure that can be used to process data streams using sub-linear space. In this post I will continue with some of my thoughts on how count-min sketches can be used in a typical event sourced application architecture. An event sourcing system typically has a query model which provides a read only view of how all the events are folded to provide a coherent view of the system. I have [...]
In today’s age of Big Data, streaming is one of the techniques for low latency computing. Besides the batch processing infrastructure of map/reduce paradigm, we are seeing a plethora of ways in which streaming data is processed at near real time to cater to some specific kinds of applications. Libraries like Storm, Samza and Spark belong to this genre and are starting to get their share of user base in the industry today.