Apptuit Python Client
Project description
Python client for Apptuit
Installation -
pip install apptuit
Usage -
Querying for data
In [1]: from apptuit import Apptuit
In [2]: import time
In [3]: token = 'my_token'
In [4]: apptuit = Apptuit(token=token)
In [5]: start_time = int(time.time()) - 3600
In [6]: query_res = apptuit.query("fetch('proc.cpu.percent').downsample('1m', 'avg')", start=start_time)
In [7]: df = query_res[0].to_df()
In [8]: type(df)
Out[8]: pandas.core.frame.DataFrame
In [9]: df.shape
Out[9]: (116, 89)
# Another way of creating the DF is accessing by the metric name in the query
In [7]: another_df = query_res['proc.cpu.percent'].to_df()
Sending data
In [1]: from apptuit import Apptuit, DataPoint
In [2]: import time
In [3]: import random
In [4]: token = "mytoken"
In [5]: client = Apptuit(token=token, host="http://localhost", port=4242)
In [6]: metric = "proc.cpu.percent"
In [7]: tags = {"host": "localhost", "ip": "127.0.0.1"}
In [8]: curtime = int(time.time())
In [9]: dps = []
In [10]: for i in range(10000):
...: dps.append(DataPoint(metric, tags, curtime + i * 60, random.random()))
...:
In [11]: dps[0]
Out[11]: proc.cpu.percent{ip:127.0.0.1, host:localhost}
In [12]: client.send(dps)
In [13]: dps = []
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