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markusthoemmes and vvraskin Adjust activation metric schema to reflect reality. (#4006)
* Adjust activation metric schema to reflect reality.

1. The `causedBy` annotation is actually an optional String vs. a boolean flag.
2. The `name` field is actually intended to contain the fully-qualified name of the action vs. a composite of the invocation namespace and an action name (which doesn't really represent anything).

Co-authored-by: Christian Bickel <github@cbickel.de>

* Require certain fields for sending activation metrics.

* Fix import.

* Pull enabled check to the call-site

* Move constructing logic and test it.

* Set loglevel to error.
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README.md

Getting started with OpenWhisk

OpenWhisk is an Apache Incubator Project. It is an open source implementation of a distributed, event-driven compute service. You can run it on your own hardware on-prem, or in the cloud. When running in the cloud you could use a Platform as as Service (PaaS) version of the OpenWhisk provided by IBM Cloud Functions, or you can provision it yourself into Infrastructure as a Service (IaaS) clouds, such as IBM Cloud, Amazon EC2, Microsoft Azure, Google GCP, etc.

OpenWhisk runs application logic in response to events or direct invocations from web or mobile apps over HTTP. Events can be provided from IBM Cloud services like Cloudant and from external sources. Developers can focus on writing application logic, and creating actions that are executed on demand. The benefits of this new paradigm are that you do not explicitly provision servers and worry about auto-scaling, or worry about high availability, updates, maintenance and pay for hours of processor time when your server is running but not serving requests. Your code executes whenever there is an HTTP call, database state change, or other type of event that triggers the execution of your code. You get billed by millisecond of execution time (rounded up to the nearest 100ms in case of OpenWhisk) or on some platforms per request (not supported on OpenWhisk yet), not per hour of JVM regardless whether that VM was doing useful work or not.

This programming model is a perfect match for microservices, mobile, IoT and many other apps – you get inherent auto-scaling and load balancing out of the box without having to manually configure clusters, load balancers, http plugins, etc. All you need to do is to provide the code you want to execute and give it to your cloud vendor. The rest is “magic”. A good introduction into the serverless programming model is available on Martin Fowler's blog.

Overview

Implementation guide

Programming model

Official OpenWhisk project website http://OpenWhisk.org.