Python bindings to Datadog's API and a user-facing command line tool.
Project description
A Python client for the DataDog API.
Library Documentation: http://pydoc.datadoghq.com
HTTP API Documetation: http://api.datadoghq.com/
DataDogHQ: http://datadoghq.com
Change Log
- 1.2.2
Release date: 2013.04.24
fabric: capture arguments passed to fabric events
- 1.2.1
Release date: 2013.03.06
Fixed backwards compatability issues in the tagging api introduced in 1.2.0
- 1.2.0
Release date: 2013.02.19
Context manager for capturing histogram of code execution time (thanks @cpennington)
Support for setting source type when submitting host tags
- 1.1.2
Release date: 2012.12.19
fabric: Allow custom task event naming
- 1.1.1
Release date: 2012.11.27
Better handling of API errors
- 1.1.0
Release date: 2012.11.06
Support for the metric alert API.
- 1.0.14
Release date: 2012.10.16
Ensure the timed decorator preserves function attributes.
- 1.0.13
Release date: 2012.09.21
Added missing dashboards endpoint, which fetches all of an org’s dashes
- 1.0.11
Release date: 2012.08.13
Fixes Fabric support when mixing old- and new-style tasks.
- 1.0.10
Release date: 2012.08.03
Extra command-line arguments: –api-key and –application-key
- 1.0.9
Release date: 2012.06.22
Fabric support
- 1.0.7, 1.0.8
never published
- 1.0.6
Release date: 2012.06.05
Bug fix in dogshell on python 3
- 1.0.5
Release date: 2012.05.29
Compatible with python 3.2
- 1.0.4
Release date: 2012.04.16
A rash of performance improvements to DogStatsApi’s stats collection methods.
DogStatsApi’s metrics collection and flushing can now be disabled with a configuration change.
Removed locking from DogStatsApi’s metrics collection, trading some accuracy when many threads are writing to the same instance in exchange for performance.
- 1.0.0
Release date: 2012.03.21
This release is a backwards incompatible rewrite of the library.
The API wrapper was renamed from SimpleClient to DogHttpApi. It’s interface was also updated. Check the docs for changes.
Added DogStatsApi, a tool for collecting metrics with little application overhead.
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