Functional Programming And Microservices

Bobby Calderwood might win me over on microservices with talk like this:

This view of microservices shares much in common with object-oriented programming: encapsulated data access and mutable state change are both achieved via synchronous calls, the web of such calls among services forming a graph of dependencies. Programmers can and should enjoy a lively debate about OO’s merits and drawbacks for organizing code within a single memory and process space. However, when the object-oriented analogy is extended to distributed systems, many problems arise: latency which grows with the depth of the dependency graph, temporal liveness coupling, cascading failures, complex and inconsistent read-time orchestration, data storage proliferation and fragmentation, and extreme difficulty in reasoning about the state of the system at any point in time.

Luckily, another programming style analogy better fits the distributed case: functional programming. Functional programming describes behavior not in terms of in-place mutation of objects, but in terms of the immutable input and output values of pure functions. Such functions may be organized to create a dataflow graph such that when the computation pipeline receives a new input value, all downstream intermediate and final values are reactively computed. The introduction of such input values into this reactive dataflow pipeline forms a logical clock that we can use to reason consistently about the state of the system as of a particular input event, especially if the sequence of input, intermediate, and output values is stored on a durable, immutable log.

It’s an interesting analogy.

Related Posts

Tips For Using PolyBase With Cloudera QuickStart VM

I have a post on using Cloudera’s QuickStart VM with PolyBase: Here’s something which tripped me up a little bit while connecting to Cloudera using SQL Server. The data node name, instead of being quickstart.cloudera like the host name, is actually localhost. You can change this in /etc/cloudera-scm-agent/config.ini. Because PolyBase needs to have direct access to the data nodes, […]

Read More

Bayesian Modeling Of Hardware Failure Rates

Sean Owen shows how you can use Bayesian statistical approaches with Spark Streaming, using the example of hard drive failure rates: This data doesn’t arrive all at once, in reality. It arrives in a stream, and so it’s natural to run these kind of queries continuously. This is simple with Apache Spark’s Structured Streaming, and proceeds […]

Read More

Categories

December 2017
MTWTFSS
« Nov Jan »
 123
45678910
11121314151617
18192021222324
25262728293031