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Talk summary: Realizing the Microservices Vision with Service Mesh by Arijit Mukherji

Some note on the talk “Fully Realizing the Microservices Vision with Service Mesh” by Arijit Mukherji of SignalFx at AWS re:Invent 2018 (DEV312)

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A service mesh is an infrastructure layer for service to service to communication. It makes communication visible (observable), manageable and controllable.

To put it another way, a Service Mesh is a happy marriage between a proxy and a policy

How do microservices talk to each other?

  • Directly: via IP address or domain name; similar to direct dialing someone on the phone
  • Service discovery: when a service comes up, it registers itself; analogy is a telephone directory)
  • Service mesh: Add a Layer7 proxy between the services. Analogy: You have an assistant who is making the call, and looking up the number, for you


  • Data plane: where the requests flow
  • Control plane: A policy between the services that controls how the proxies are behaving; it controls how the communications are happening

Common uses cases of the service mesh:
1. Error handling

  • Automatic retry on errors
  • Circuit breaker

2. Load Balancing

  • Proxy acts as a Layer 7 Load Balancer
  • Every Microservice to Microservice can be load balanced without each service (and development team) needing to worry about it

3. Request routing

  • Route requests in interesting ways (I’m guessing this could be things like┬ádon’t allow HTTP GET on /private”, canary type control or intelligent routing based on customer, or indeed your internal infrastructure, needs)

4. Security

  • e.g. encrypt/decrypt data


How did we get here?

Lessons learned from networking…

1. Standard on a simple architecture

  • The problem with having communication code as part of our app library (even as a library dependency) is that in a polyglot env, you need to rewrite that common in different languages
  • With proxies and Service Meshes, we pull that code into an independent binary/service that can be independenlty pushed and configurable

2. Replace humans with bots

  • Autogenerate and deploy policy driven configs

3. Adapt configuration based on feedback

  • Continually observe the environment and react to it
  • Typically, systems have desired and observed states, which are not always the same
  • Use the discrepancies to report feedback. Feedback is critical.

Service mesh can assist in the following

A) Code deployments

  • Fully automated deployments
  • Many deployments still require manual involvement.
  • Can we automate the entire workflow?
  • Specify in config how the deployment should go
    1. Deploy v2
    2. Route x% to v2 (canary)
    3. Monitor functionality, perofrmance, errors, compring v1 to v2
    4. Rollback or continue

B) Runtime behavior optimization

  1. Handle errors, retries, timeouts (already discussed)
  2. Circuit breaking
    If you have n instances of a serice, and x are not working well (e.g., failing, slow) then since the proxies are sending requests on your behalf, proxies can stop routing to those impaired instances
  3. Cost and performance optimization
    If you are in a service mesh environment,
    – For costs savings: select instances that are closer (from a network point of view e.g. in same region or AZ) may save network costs (???)
    – For performance optimization, you might route to those that are responding fastest.
    In other words, you can change behavior on the fly.

C) Testing – Chaos Engineering

Service mesh can aid choas engineering

  1. Error simulation
    It can assist simulate errors such as errors to a specific host, x % of all requests fail, errors on a particular customer or region
  2. Automate chaos experiments

D) Service mesh and monitoring

Service mesh will
1. increase breadth of coverage

  • Fix spotty adoption through auto-instrumentation of ALL communication
  • Unified vendor-agnostic target for all telemetry (Metrics, Logs, and traces)

2. Establish a high bar on quality
And consolidate monitoring

3. Provide feedback


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