signalfx 1.0.8

SignalFx Python Library

This is a programmatic interface in Python for SignalFx’s metadata and ingest APIs. It is meant to provide a base for communicating with SignalFx APIs that can be easily leveraged by scripts and applications to interact with SignalFx or report metric and event data to SignalFx. It is also the base for metric reporters that integrate with common Python-based metric collections tools or libraries.

Installation

To install with pip:

$ pip install signalfx

To install from source:

$ git clone https://github.com/signalfx/signalfx-python.git
$ cd signalfx-python/
$ pip install -e .

Usage

This client library provides programmatic access to SignalFx’s APIs:

• the data ingest API;

• the metadata REST API;

• the SignalFlow API.

You start by instantiating a signalfx.SignalFx() object, which then gives you access to the API client that you want:

import signalfx

sfx = signalfx.SignalFx()

# For the ingest API
ingest = sfx.ingest('ORG_ACCESS_TOKEN')

# For the REST API
rest = sfx.rest('USER_ACCESS_TOKEN')

# For the SignalFlow API
flow = sfx.signalflow('USER_ACCESS_TOKEN')

As you may have noticed, you need to specify an access token when requesting one of those clients. For the ingest client, you need to specify your organization or team’s API access token (which can be obtained from the SignalFx organization you want to report data into). For the REST API and SignalFlow API clients, you must use your user access token. For more information on access tokens, see the API’s Authentication Overview documentation.

Reporting data

Basic usage of the library for reporting data goes as follows:

import signalfx

sfx = signalfx.SignalFx().ingest('MY_TOKEN')
try:
    sfx.send(
        gauges=[
          {'metric': 'myfunc.time',
           'value': 532,
           'timestamp': 1442960607000},
          ...
        ],
        counters=[
          {'metric': 'myfunc.calls',
           'value': 42,
           'timestamp': 1442960607000},
          ...
        ],
        cumulative_counters=[
          {'metric': 'myfunc.calls_cumulative',
           'value': 10,
           'timestamp': 1442960607000},
          ...
        ])
finally:
    # After all datapoints have been sent, flush any remaining messages
    # in the send queue and terminate all connections.
    sfx.stop()

The timestamp must be a millisecond precision timestamp; the number of milliseconds elapsed since Epoch. The timestamp field is optional, but strongly recommended. If not specified, it will be set by SignalFx’s ingest servers automatically; in this situation, the timestamp of your datapoints will not accurately represent the time of their measurement (network latency, batching, etc. will all impact when those datapoints actually make it to SignalFx).

When sending datapoints with multiple calls to send(), it is recommended to re-use the same SignalFx client object for each send() call.

If you must use multiple client objects for the same token, which is not recommended, it is important to call stop() after making all send() calls. Each SignalFx client object uses a background thread to send datapoints without blocking the caller. Calling stop() will gracefully flush the thread’s send queue and close its TCP connections.

Sending multi-dimensional data

Reporting dimensions for the data is also optional, and can be accomplished by specifying a dimensions parameter on each datapoint containing a dictionary of string to string key/value pairs representing the dimensions:

import signalfx

sfx = signalfx.SignalFx().ingest('MY_TOKEN')
try:
    sfx.send(
        gauges=[
          {
            'metric': 'myfunc.time',
            'value': 532,
            'timestamp': 1442960607000,
            'dimensions': {'host': 'server1', 'host_ip': '1.2.3.4'}
          },
          ...
        ], ...)
finally:
    sfx.stop()

See examples/generic_usecase.py for a complete code sample showing how to send data to SignalFx.

Sending events

Events can be sent to SignalFx via the send_event() function. The event type must be specified, and dimensions and extra event properties can be supplied as well.

import signalfx

sfx = signalfx.SignalFx().ingest('MY_TOKEN')
try:
    sfx.send_event(
        event_type='deployments',
        dimensions={
            'host': 'myhost',
            'service': 'myservice',
            'instance': 'myinstance'},
        properties={
            'version': '2015.04.29-01'})
finally:
    sfx.stop()

Metric metadata and tags

The library includes functions to search, retrieve, and update metric metadata and tags. Deleting tags is also supported.

import signalfx

sfx = signalfx.SignalFx().rest('MY_TOKEN')
sfx.update_tag('tag_name',
               description='An example tag',
               custom_properties={'version': 'some_number'})

AWS integration

Optionally, the client may be configured to append additional dimensions to all metrics and events sent to SignalFx. One use case for this is to append the AWS unique ID of the current host as an extra dimension. For example,

import signalfx
from signalfx.aws import AWS_ID_DIMENSION, get_aws_unique_id

sfx = signalfx.SignalFx().ingest('MY_TOKEN')

# This dimension will be added to all datapoints sent.
sfx.add_dimensions({AWS_ID_DIMENSION: get_aws_unique_id()})

try:
    sfx.send(
        gauges=[
          {
            'metric': 'myfunc.time',
            'value': 532,
            'timestamp': 1442960607000
            'dimensions': {'host': 'server1', 'host_ip': '1.2.3.4'}
          },
        ])
finally:
    sfx.stop()

Pyformance reporter

pyformance is a Python library that provides CodaHale-style metrics in a very Pythonic way. We offer a reporter that can report the pyformance metric registry data directly to SignalFx.

from pyformance import count_calls, gauge
import signalfx.pyformance

@count_calls
def callme():
    # whatever
    pass

sfx = signalfx.pyformance.SignalFxReporter(token='MY_TOKEN')
sfx.start()

callme()
callme()
gauge('test').set_value(42)

See examples/pyformance_usecase.py for a complete code example using Pyformance.

Executing SignalFlow computations

SignalFlow is SignalFx’s real-time analytics computation language. The SignalFlow API allows SignalFx users to execute real-time streaming analytics computations on the SignalFx platform. For more information, head over to our Developers documentation:

• SignalFlow Overview

• Getting started with the SignalFlow API

Executing a SignalFlow program is very simple with this client library:

import signalfx

program = "data('cpu.utilization').mean().publish()"
flow = signalfx.SignalFx().signalflow('MY_TOKEN')
try:
    print('Executing {0} ...'.format(program))
    computation = flow.execute(program)
    for msg in computation.stream():
        if isinstance(msg, signalfx.signalflow.messages.DataMessage):
            print('{0}: {1}'.format(msg.logical_timestamp_ms, msg.data))
        if isinstance(msg, signalfx.signalflow.messages.EventMessage):
            print('{0}: {1}'.format(msg.timestamp_ms, msg.properties))
finally:
    flow.close()

Metadata about the streamed timeseries is received from .stream(), but it is automatically intercepted by the client library and made available through the Computation object returned by execute():

if isinstance(msg, signalfx.signalflow.messages.DataMessage):
    for datapoint in msg.data:
        tsid = datapoint['tsId']
        metadata = computation.get_metadata(tsid)
        value = datapoint['value']
        # Display metadata and datapoint value as desired

For more examples of how to execute SignalFlow computation with this library, interpret and use the returned stream messages, you can look at the simple example in examples/signalflow.py or at the SignalFlow CLI and its implementation which uses this library.

Known Issues

Sending only 1 datapoint and not seeing it in the chart

The reason you are not seeing the metrics in the chart is because the script that is calling the Python client module is exiting right after calling the send method. The Python client library is mainly targeted towards sending a continuous stream of metrics and was implemented to be asynchronous.

To work around this problem (most common in short-lived scripts for example), register an atexit function to cleanly stop the datapoint sending thread when your program exits:

import atexit
import signalfx

sfx = signalfx.SignalFx().ingest('MY_TOKEN')
atexit.register(sfx.stop)

SSLError when working with tags, metrics, dimensions, metrictimeseries, organization

ERROR:root:Posting to SignalFx failed.
SSLError: hostname 'api.signalfx.com' doesn't match either of '*.signalfuse.com', 'signalfuse.com'.

Root Cause: SignalFx’s API endpoints (api.signalfx.com, ingest.signalfx.com and stream.signalfx.com) have SSL SNI enabled and the urllib3 module in Python versions prior to 2.7.8 had a bug that causes the above issue. This was fixed in later versions of Python; we recommend using Python 2.7.9 or newer when using this library.