Search results

python-signalfx-0.3.9-1.lbn19.noarch

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. Usage API access token To use this library, you need a SignalFx API access token, which can be obtained from the SignalFx organization you want to report data into. Reporting data Basic usage of the library for reporting data goes as follows: import signalfx sfx = signalfx.SignalFx('MY_TOKEN') 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}, ... ]) 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('MY_TOKEN') sfx.send( gauges=[ { 'metric': 'myfunc.time', 'value': 532, 'timestamp': 1442960607000, 'dimensions': {'host': 'server1', 'host_ip': '1.2.3.4'} }, ... ], ...) 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('MY_TOKEN') sfx.send_event( event_type='deployments', dimensions={ 'host': 'myhost', 'service': 'myservice', 'instance': 'myinstance'}, properties={ 'version': '2015.04.29-01'}) See examples/generic_usecase.py for a complete code example. 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('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('your_api_token') sfx.add_dimensions({AWS_ID_DIMENSION: get_aws_unique_id()}) sfx.send( gauges=[ { 'metric': 'myfunc.time', 'value': 532, 'timestamp': 1442960607000 'dimensions': {'host': 'server1', 'host_ip': '1.2.3.4'} }, ]) 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(): pass sfx = signalfx.pyformance.SignalFxReporter(api_token='MY_TOKEN') sfx.start() callme() callme() gauge('test').set_value(42) ... See examples/pyformance_usecase.py for a complete code example using Pyformance.

python-SilverCity-0.9.7-2.lbn13.x86_64

SilverCity is a lexing package, based on Scintilla, that can provide lexical analysis for over 20 programming and markup langauges. Scripting language bindings currently exist for Python.

python-SilverCity-0.9.7-3.lbn19.x86_64

SilverCity is a lexing package, based on Scintilla, that can provide lexical analysis for over 20 programming and markup langauges. Scripting language bindings currently exist for Python.

python-simpleflake-0.1.5-1.lbn19.noarch

Distributed ID generation in python for the lazy.

python-simplegeneric-0.6-2.lbn13.noarch

The simplegeneric module lets you define simple single-dispatch generic functions, akin to Python's built-in generic functions like len(), iter() and so on. However, instead of using specially-named methods, these generic functions use simple lookup tables, akin to those used by e.g. pickle.dump() and other generic functions found in the Python standard library.

python-simplegeneric-0.6-2.lbn13.noarch

The simplegeneric module lets you define simple single-dispatch generic functions, akin to Python's built-in generic functions like len(), iter() and so on. However, instead of using specially-named methods, these generic functions use simple lookup tables, akin to those used by e.g. pickle.dump() and other generic functions found in the Python standard library.

python-simplegeneric-0.8-6.fc19.noarch

The simplegeneric module lets you define simple single-dispatch generic functions, akin to Python's built-in generic functions like len(), iter() and so on. However, instead of using specially-named methods, these generic functions use simple lookup tables, akin to those used by e.g. pickle.dump() and other generic functions found in the Python standard library.

python-simplejson-2.5.2-1.lbn13.x86_64

simplejson is a simple, fast, complete, correct and extensible JSON <http://json.org> encoder and decoder for Python 2.5+. It is pure Python code with no dependencies, but includes an optional C extension for a serious speed boost. The encoder may be subclassed to provide serialization in any kind of situation, without any special support by the objects to be serialized (somewhat like pickle). The decoder can handle incoming JSON strings of any specified encoding (UTF-8 by default). simplejson is the externally maintained development version of the json library included with Python 2.6 and Python 3.0, but maintains backwards compatibility with Python 2.5. It gets updated more regularly than the json module in the python stdlib.

python-simplejson-2.5.2-1.lbn13.x86_64

simplejson is a simple, fast, complete, correct and extensible JSON <http://json.org> encoder and decoder for Python 2.5+. It is pure Python code with no dependencies, but includes an optional C extension for a serious speed boost. The encoder may be subclassed to provide serialization in any kind of situation, without any special support by the objects to be serialized (somewhat like pickle). The decoder can handle incoming JSON strings of any specified encoding (UTF-8 by default). simplejson is the externally maintained development version of the json library included with Python 2.6 and Python 3.0, but maintains backwards compatibility with Python 2.5. It gets updated more regularly than the json module in the python stdlib.

python-simplejson-2.6.0-2.fc18.armv6hl

simplejson is a simple, fast, complete, correct and extensible JSON <http://json.org> encoder and decoder for Python 2.5+. It is pure Python code with no dependencies, but includes an optional C extension for a serious speed boost. The encoder may be subclassed to provide serialization in any kind of situation, without any special support by the objects to be serialized (somewhat like pickle). The decoder can handle incoming JSON strings of any specified encoding (UTF-8 by default). simplejson is the externally maintained development version of the json library included with Python 2.6 and Python 3.0, but maintains backwards compatibility with Python 2.5. It gets updated more regularly than the json module in the python stdlib.