This make the fake data generation much faster and configurable.
For the cloud example, the phase 2 is now triggered in less than 5
seconds instead of 6 minutes.
Change-Id: I9967c94d28a380fa19afe9275e769f1465f4f922
As of this commit, to change the configuration using Banana, we
need to make an HTTP POST request to `/banana` REST API. This API is
temporary and is likely to be changed later.
The implementation is done entirely in the `banana` module. Under this
module there are:
* `typeck` module contains the type checker,
* `grammar` module contains the parser and the AST and,
* `eval` module contains the interpreter.
Additionally, a test framework has been created to ease the test of
particular conditions of the language.
Within the banana module, there is a README.md file for each associated
sub-module explaining further the details of the language.
Once this commit is merged, there's still a lot that can be improved:
- All components should be tested in Banana.
- The 'deadpathck' pass could be improved (see TODO)
- We don't support generated JSON ingestors yet.
- Imports will be key for reusability (not implemented).
Change-Id: I1305bdfa0606f30619b31404afbe0acf111c029f
This commit introduces 3 examples of LDPs that process
monasca metrics:
* `Aggregate` - compute simple stats per metric name.
* `Combine` - allow mixing of different metrics by providing
a simple expression parser that gets transformed into
CPython bytecode.
* `Derivative` - compute a metric derivative.
While quite simple, those examples, will help us improve the
framework by emphazing some of the problems we will face when
working on more complex solution such as:
* Spark's inability to share data between sliding windows.
* Ordering of data.
* Sampling of metrics / events.
Change-Id: I259022f20e9b288aa2a08c24ad4a5f41a20e6095
2016-07-01 00:20:22 -06:00
Joan Varvenne, Suksant Sae Lor and David Subiros Perez
Witek Bedyk