babelchart
Introduction
babelchart simplifies the transformation of annotated time series data.
It is a Python library that acts as glue between data sources (such as Amazon CloudWatch or Graphite) and sinks
(such as Google Charts or Highcharts). It solves the problem of converting time series data from the source's format
into something usable by, say, a charting tool.
By providing a common interface for these transformations, babelchart makes it easy to add your own converters. Out of the box, the following converters are included:
- sources
- Amazon CloudWatch
- Graphite
- sinks
- Google Charts
babelchart is open source under the Apache 2.0 license.
Installation
babelchart was developed using Python 2.7. That said, it should work on Python 2.6+.
You can install it via pip:
pip install babelchartExamples
Graphite -> Google Charts
>>> # Fetch and parse the json response from Graphite
>>> import requests
>>> graphite_url = 'http://mygraphiteserver/render/?target=mymetrics.m1&target=&mymetrics.m2&format=json'
>>> graphite_result = requests.get(graphite_url).json()
>>>
>>> # Transform it into a list of TimeSeries objects
>>> from babelchart.sources.graphite import GraphiteSource
>>> tss = GraphiteSource.to_series(graphite_result)
>>>
>>> # Generate the Google Charts data response
>>> from babelchart.sinks.googlecharts import GoogleChartsSink
>>> GoogleChartsSink.from_series(tss)
'google.visualization.Query.setResponse({"status":"ok","table":{"rows":[{"c":[{"v":"Date(2014,2,4,16,0,0)"},{"v":1.62},null,{"v":0.54},null]},{"c":[{"v":"Date(2014,2,4,17,30,0)"},null,null,null,null]},{"c":[{"v":"Date(2014,2,4,17,0,0)"},{"v":1.51},null,{"v":0.54},null]},{"c":[{"v":"Date(2014,2,4,16,30,0)"},{"v":2.38},null,{"v":0.91},null]},{"c":[{"v":"Date(2014,2,4,15,30,0)"},{"v":1.42},null,{"v":0.63},null]}],"cols":[{"type":"datetime","id":"Date","label":"Date"},{"type":"number","id":"mymetrics.m1","label":"mymetrics.m1"},{"type":"string","id":"mymetrics.m1 note","label":"mymetrics.m1 note"},{"type":"number","id":"mymetrics.m2","label":"mymetrics.m2"},{"type":"string","id":"mymetrics.m2 note","label":"mymetrics.m2 note"}]},"reqId":"0","version":"0.6"});'CloudWatch -> Google Charts
>>> # Fetch the boto-parsed data from CloudWatch
>>> import boto, datetime
>>> c = boto.connect_cloudwatch()
>>> cloudwatch_result = c.get_metric_statistics(
... 900,
... datetime.datetime.utcnow() - datetime.timedelta(hours=1),
... datetime.datetime.utcnow(),
... 'CPUUtilization',
... 'AWS/EC2',
... ['Average', 'Maximum'],
... dimensions={'InstanceId':['i-78daf759']}
... )
>>>
>>> # Transform it into a list of TimeSeries objects
>>> from babelchart.sources.cloudwatch import CloudWatchSource
>>> tss = CloudWatchSource.to_series(cloudwatch_result)
>>>
>>> # Generate the Google Charts data response
>>> from babelchart.sinks.googlecharts import GoogleChartsSink
>>> GoogleChartsSink.from_series(tss)
'google.visualization.Query.setResponse({"status":"ok","table":{"rows":[{"c":[{"v":"Date(2014,2,5,17,38,0)"},{"v":2.543},null,{"v":3.16},null]},{"c":[{"v":"Date(2014,2,5,17,8,0)"},{"v":2.513},null,{"v":3.16},null]},{"c":[{"v":"Date(2014,2,5,16,53,0)"},{"v":2.567},null,{"v":3.03},null]},{"c":[{"v":"Date(2014,2,5,17,23,0)"},{"v":2.527},null,{"v":3.19},null]}],"cols":[{"type":"datetime","id":"Date","label":"Date"},{"type":"number","id":"Average","label":"Average"},{"type":"string","id":"Average note","label":"Average note"},{"type":"number","id":"Maximum","label":"Maximum"},{"type":"string","id":"Maximum note","label":"Maximum note"}]},"reqId":"0","version":"0.6"});'
