Monolithic to Microservices

Review of ‘AWS re:Invent 2015 | (ARC309) Microservices: Evolving Architecture Patterns in the Cloud’ presentation.

From monolithic to microservices, on AWS.

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Ruby on rails -> Java based functional services.

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Java based functional services resulted in requirement for everything to be loaded into memory – 20 minutes to start services. Still very large services –  with many engineers working on it. This means that commits to those repos take a long time to QA. So a commit is made then start working on something else then a week or two later have to fix what you hardly remember. Then to get specific information out of those big java apps it was required to parse the entire homepage. So…

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Big sql database is still there – This will mean changes with schema change are difficult to do without outages. Included in this stage of micro services was:

  • Team autonomy – give teams a problem and they  can build whatever services they need
  • Voluntary adoption – tools/techniques/processes
  • Goal driven initiatives
  • Failing fast and openly

What are the services? – Email, shipping cost, recommendations, admin, search

Anatomy of a service:

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A service has its own datastore and it completely owns its own datastore. This is where dependency on one big schema is avoided. Services at average 2000 lines of code and 32 source files.

Service discovery – enormously simple? – Discovery is a client needs to get to a services, how is it going to get there. ‘It has the name of the service – look up that URL’.

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Use ZooKeeper as a highly available store.

Moving all this to the cloud via ‘lift and shift’ with AWS Direct Connect. In AWS all services where their own ec2 instance and dockerized.

Being a good citizen in a microservices organisation:

  • Service Consumer
    • Design for failure
    • Expect to be throttled
    • Retry with exponential backoff
    • Degrade gracefully
    • Cache when appropriate
  • Service Provider
    • Publish your metrics
      • Throughput, error rate, latency
    • Protect yourself (throttling)
    • Implementation details private
    • Maintain backwards compatability
    • See Amazon API gateway

Again service discovery – simple with naming conventions, DNS and load balancers. Avoid DNS issues with dynamic service registry (ZooKeeper, Eureka, Consul, SmartStack).

Data management – moving away from the schema change problems. And the other problems (custom stored procedures, being stuck with one supplier, single point of failure). Microservices must include decentralisation of datastores, services own their datastores and do not share them. This has a number of benefit from being able to chose whatever data store technology best meets the service needs, make changes without affecting other services, scale the data stores independently. So how do we ensure transactional integrity? Distributed locking sounds horrible. Do all services really need strict transactional integrity? Use queues to retry later.

Aggregation of data – I need to do analytics on my data? AWS Kenesis firehose, Amazon SQS, custom feed.

Continuous Delivery/Deployment  – Introduced some AWS services Code Deploy — or just use Jenkins.

How many services per container/instance – Problems with monitoring granularity, scaling is less granular, ownership is less atomic, continuos deployment is tougher with immutable containers.

I/O Explosion –  Mapping dependencies between services is tough. Some will be popular/hotspots. Service consumers need to cache were they can. Dependency injection is also an option – You can only make a request from services A if you have the required data from service B and C in your request.

Monitoring –  Logs are key, also tracing request through fanned dependency can be much easier with a requirement for a header that is passed on.

Unique failures – Watched a day in the life of a Netflix engineers… good points on failures. We accept and increased failure rate to maintain velocity. We just want to ensure failures are unique. For this to happen we need to have open and safe feedback.


Scala tail recursion vs non-tail receursion performance testing with dynaTrace

The scala compiler has partial tail call optimization (

Running some like methods with and without allowing the scala compiler to optimize demonstrated the performance improvements gained by this optimization.

First up, simple factoral functions (source:

For explanation of these functions and why/not they are tail recursive see the source above.


Non-Tail recursive average response time: approx 7 ms

Tail recursive average response time: approx 0.7ms

The number of test conducted was limited to about 10 each and the test environment was an Ubuntu VM. The results will not be highly accurate but with a difference this significant it is clear that tail optimized recursion is very different from normal recursion (On a JVM). Looking at the purepath for each method reveals tail-recursion optimization working as expected and saving alot of execution time:

Comparing the two purepaths, the 10x increase in response time is clearly caused by the lack of tail recursion on the left.

The next example looks at what the compiler does if there is extra code within a tail optimized funtion:

The output of the non-tail recursive function was as expected, printing all of the output expected. The scala compiler optimized over the expected (perhaps incorrectly expected) behaviour of the optimized function. The output was:

As the function was optimized not to run through all of the loops – the println’s simply did not occur. The purepath comparision:

Results are similar to the first test. The right (tail-optimized) purepath call hierarchy shows why there were only 2 screen outputs..


Scala vs Java performance – dynaTrace Analysis – Test 1 Fibonacci Sequence cont’

After some initial JVM monitoring yesterday, I looked at response times today. First thing I had to do was modify the respective classes slightly to get a non-constructor method doing all the work. This made it much easier to gather and chart the results within dynaTrace (though I did later find that there was a check box in dynaTrace to monitor constructors).

The tests today were again on 3 implementations of the Fibonacci sequence:

Java – see source

Scala Simple – see source

Scala Advanced – see source

Test environment info

I spent most of the day playing with dynaTraces monitoring and reporting tools so did not actually do that much testing. The tests I did run where quite short. The first being execution of the fib sequence returning the 10,000 value, the second stretching to 100,000. The charts below show the time results for each test, if more than one test was executed for an implementation in a 30 second cycle, the chart shows the average.

*Update 16-JUL: Retested on host machine (not running in VM) results where much more consistent despite having a number of other services operating at the same time. There are some clear results this time:

Java vs Scala vs Scala Adv - Fibonacci
Running on a host machine, the Java implementation has a slight performance edge over the Scala simple. The Scala advanced implementation is slower. These tests were to the 100,000th Fibonacci value


Stretching the test the 1,000,000th Fibonacci value meant that Scala Adv overran the heap but the difference between Scala simple and Java was negligible

From the results it appears that the variations in response times are most likely caused by external factors (ie: OS or other services taking resouces, Garbage collections etc). I will really need to increase the sample size to get a better understanding of what is influencing response times.

Fibonacci sequence to 10,000th value
Fibonacci sequence to 100,000th value

Scala vs Java performance – dynaTrace Analysis – Test 1 Fibonacci Sequence

Trying to make some comparisons between Scala and Java. Using dynaTrace I can get very useful and granular insight into the behaviours of each.  To keep things as comparable as possible the tests will be on simple, short algorithms. Large iterations will be conducted so garbage collection and space complexity can be analysed.

Thanks to Julio Faerman for his post and the implementations I have used from that post – see:

With dynaTrace I can get information about the JVM behaviour but also about the behaviour of specific classes and methods. This will not only allow me to make some comparison between Java and Scala implementations but also understand more about what happens when Scala is compiled to byte code.

To start with, nice and simple test – Fibonacci sequence

Three implementations will be tested:

Java Fibonacci  – see code

Scala Simple Fibonacci  – see code

Scala Advanced Fibonacci  – see code

I am running the tests from a controller class with a main method. From there I can instantiate the classes above. Read more about dynaTrace to see how it uses sensors in the JVM to grab a great deal of useful data about the JVM and about classes and methods.

Just to get things rolling, checked out how running the Fib sequence to the 10 million number in the sequence would affect the JVM.  This tests would have taken a long time to complete so after I saw stable behaviour (ie: two sequences) I kill the JVM and moved on to the next sequence. 

The charts below have been modified for human readability. For example, Garbage Collection in the charts was factored up. The charts are also stacked for readability. Within the dynaTrace client this is much easier to read with mouse over pop-ups.

What can be taken from this fairly simple example is that code may work and appear to be performing well. In reality it may be wreaking havoc on the JVM. Next post will look at the response times of these implementations.

JVM Heap usage and Garbage Collection utilization
Removing the Scala Adv Sequence allows for better comparison of Java and Scala Simple
Garbage Collection utilization and Garbage collection caused suspensions (time)
GC utilization, CPU Utilization and Suspension time cause by GC – without Scala Advanced


Test details:



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