10 Challenges of Microservices and Solutions – Tips & Tricks


I am cloud API developer and architect and currently working on Google’s GCP based microservices for a large retail client of USA.

Transitioning/implementing to microservices creates significant challenges for organizations. I have identified these challenges and solution based on my real exposure of microservices on PROD.

I am writing this white paper in June 2018. At this time, Microservices architecture is not matured enough to address completely all the existing challenges, however open source communities and IT product companies are trying to address all these open issues. All new researches on this topics are based on the finding solutions to the new challenges.

These are the major 10 challenges of microservices architecture and proposed solutions-

1. Data Synchronization – We have event sourcing architecture to address this issue using the async messaging platform. Saga design pattern can address this challenge.
2. Security – API Gateway can solve these challenges. Kong is very popular open source which is being used by many companies on the production system. The custom solution can also be developed for API security using JWT token, Spring Security, and Netflix Zuul/ Zuul2. There are enterprise solutions are also available like Apigee, Okta ( 2 step authentication). Openshift for public cloud security for its top features of RedHat Linux Kernal based security and namespace-based app to app security.
3. Versioning – It will be taken care by API registry and discovery APIs using dynamic Swagger API, which can be updated dynamically and shared with the consumers on the server.
4. Discovery – It will be addressed by API discovery tools like Kubernetes, OpenShift. It can also be done using Netflix Eureka at the code level. However, doing with the orchestration layer will be better and that can be managed by these tools rather doing and maintaining thru code and configuration.

5. Data Staleness – The database should be always updated to give recent data, API will fetch data from the recent and updated database. A timestamp entry can also be added with each record in the database to check and verify the recent data. Caching can be used and customized with an acceptable eviction policy based on the business requirement.
6. Debugging and Logging – There are multiple solutions- Externalised logging can be used by pushing log messages to an async messaging platform like Kafka, Google PubSub etc. A correlation ID can be provided by the client in the header to REST APIs to track the relevant logs across all the pods/docker containers. Also, local debugging can be done individually of each microservice using IDE or checking the logs.
7. Testing– This issue can be addressed using unit testing by mocking REST APIs, Mocking integrated/dependent APIs which are not available for testing using WireMock, BDD Cucumber integration testing, performance testing using Jmeter and any good profiling tool like Jprofiler, DynaTrace, YourToolKit, VisualVM etc.
8. Monitoring – Monitoring can be done using Open source tools Prometheus in the combination of Grafana by creating Gauge and matrices, Kubernetes/OpensShift, Influx DB, Apigee, the combination of Graphana and Graphite.
9. DevOps Support – Microservices deployment and support related challenges can be addressed using state on the art DevOps tools GCP Kubernetes, OpenShift with Jenkin.
10. Fault Tolerance – Netflix Hystrix can be used to break the circuit if there is no response from the API for the given SLA/ETA.