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== dtab == |
== dtab == |
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* https://linkerd.io/advanced/dtabs/ |
* https://linkerd.io/advanced/dtabs/ |
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| + | |||
| + | === finagle === |
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| + | * https://twitter.github.io/finagle/guide/Names.html |
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| + | |||
| + | == Service Mesh Docs == |
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| + | * https://buoyant.io/2016/05/04/real-world-microservices-when-services-stop-playing-well-and-start-getting-real/ |
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| + | |||
| + | * What is service mesh |
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| + | <blockquote> |
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| + | a service mesh is a layer that manages the communication between apps (or between parts of the same app, e.g. microservices). In traditional apps, this logic is built directly into the application itself: retries and timeouts, monitoring/visibility, tracing, service discovery, etc. are all hard-coded into each application. |
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| + | </blockquote> |
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| + | |||
| + | * A service mesh like linkerd provides critical features to multi-service applications running at scale: |
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| + | # Baseline resilience: retry budgets, deadlines, circuit-breaking |
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| + | # Top-line service metrics: success rates, request volumes, and latencies. |
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| + | # latency and failure tolerance: Failure- and latency-aware load balancing that can route around slow or broken service instances. |
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| + | # Distributed tracing a la Zipkin and OpenTracing |
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| + | # Service discovery: locate destination instances. |
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| + | # Protocol upgrades: wrapping cross-network communication in TLS, or converting HTTP/1.1 to HTTP/2.0. |
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| + | # Routing: route requests between different versions of services, failover between clusters, etc. |
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Latest revision as of 08:58, 19 April 2018
docs[]
linkerd offical doc[]
dtab[]
finagle[]
Service Mesh Docs[]
- What is service mesh
a service mesh is a layer that manages the communication between apps (or between parts of the same app, e.g. microservices). In traditional apps, this logic is built directly into the application itself: retries and timeouts, monitoring/visibility, tracing, service discovery, etc. are all hard-coded into each application.
- A service mesh like linkerd provides critical features to multi-service applications running at scale:
- Baseline resilience: retry budgets, deadlines, circuit-breaking
- Top-line service metrics: success rates, request volumes, and latencies.
- latency and failure tolerance: Failure- and latency-aware load balancing that can route around slow or broken service instances.
- Distributed tracing a la Zipkin and OpenTracing
- Service discovery: locate destination instances.
- Protocol upgrades: wrapping cross-network communication in TLS, or converting HTTP/1.1 to HTTP/2.0.
- Routing: route requests between different versions of services, failover between clusters, etc.