# wavefront-pyformance
[](https://github.com/wavefrontHQ/wavefront-pyformance/actions)
[](https://pypi.org/project/wavefront-pyformance)
[](https://pypi.org/project/wavefront-pyformance)
[](https://pypi.org/project/wavefront-pyformance)
[](https://pypi.org/project/wavefront-pyformance)
This is a plugin for [pyformance](https://github.com/omergertel/pyformance) which adds VMware Aria Operations™ for Applications (formerly known as Wavefront) reporters (via proxy or direct ingestion) and an abstraction that supports tagging at the host level. It also includes support for Wavefront delta counters.
Note: We're in the process of updating the product name to Operations for Applications, but in many places we still refer to it as Wavefront.
## Requirements
Python 3.x are supported.
```
pip install wavefront-pyformance
```
## Usage
### Wavefront Reporter
The Wavefront Reporters support tagging at the host level. If you pass a tag through a reporter, the reporter tags the metrics before sending the metrics to our service.
#### Create a Reporter
You can create a `WavefrontProxyReporter` or `WavefrontDirectReporter` as follows:
```Python
import pyformance
from wavefront_pyformance import wavefront_reporter
reg = pyformance.MetricsRegistry()
# report metrics to a Wavefront proxy every 60s
wf_proxy_reporter = wavefront_reporter.WavefrontProxyReporter(
host=host, # required
port=2878, # default: 2878
source='wavefront-pyformance-example', # default: 'wavefront-pyformance'
registry=reg, # default: None
reporting_interval=60, # default: 60
prefix='python.proxy.', # default: 'proxy.'
tags={'key1': 'val1',
'key2': 'val2'},
enable_runtime_metrics: False, # default: False
enable_internal_metrics: True) # default: True
wf_proxy_reporter.start()
# report metrics directly to a Wavefront server every 60s
wf_direct_reporter = wavefront_reporter.WavefrontDirectReporter(
server=server, # required
token=token, # required
source='wavefront-pyformance-example', # default: 'wavefront-pyformance'
registry=reg, # default: None
reporting_interval=60, # default: 60
clock=None, # default: None
prefix='python.direct.', # default: 'direct.'
tags={'key1': 'val1',
'key2': 'val2'}, # default: None
enable_runtime_metrics=False, # default: False
enable_internal_metrics=False) # default: False
wf_direct_reporter.start()
```
#### Flush and stop Wavefront Reporter
After create Wavefront Reporter, `start()` will make the reporter automatically reporting every `reporting_interval` seconds.
Besides that, you can also call `report_now()` to perform reporting immediately.
```Python
# Report immediately
wf_reporter.report_now()
# Stop Wavefront Reporter
wf_reporter.stop()
```
### Delta Counter
To create a Wavefront delta counter:
```Python
import pyformance
from wavefront_pyformance import delta
reg = pyformance.MetricsRegistry()
d_0 = delta.delta_counter(reg, 'requests_delta')
d_0.inc(10)
```
Note: Having the same metric name for any two types of metrics will result in only one time series at the server and thus cause collisions.
In general, all metric names should be different. In case you have metrics that you want to track as both a Counter and Delta Counter, consider adding a relevant suffix to one of the metrics to differentiate one metric name from another.
### Wavefront Histogram
To create a [Wavefront Histogram](https://docs.wavefront.com/proxies_histograms.html):
```Python
import pyformance
from wavefront_pyformance import wavefront_histogram
reg = pyformance.MetricsRegistry()
h_0 = wavefront_histogram.wavefront_histogram(reg, 'requests_duration')
h_0.add(10)
```
### Python Runtime Metrics
To enable Python runtime metrics reporting,
set the `enable_runtime_metrics` flag to `True`:
```Python
wf_proxy_reporter = wavefront_reporter.WavefrontProxyReporter(
host=host,
port=2878,
registry=reg,
source='runtime-metric-test',
tags={'global_tag1': 'val1',
'global_tag2': 'val2'},
prefix='python.proxy.',
enable_runtime_metrics=True).report_minute_distribution()
wf_direct_reporter = wavefront_reporter.WavefrontDirectReporter(
server=server,
token=token,
registry=reg,
source='runtime-metric-test',
tags={'global_tag1': 'val1',
'global_tag2': 'val2'},
prefix='python.direct.',
enable_runtime_metrics=True).report_minute_distribution()
```
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"description": "# wavefront-pyformance\n\n[](https://github.com/wavefrontHQ/wavefront-pyformance/actions)\n[](https://pypi.org/project/wavefront-pyformance)\n[](https://pypi.org/project/wavefront-pyformance)\n[](https://pypi.org/project/wavefront-pyformance)\n[](https://pypi.org/project/wavefront-pyformance)\n\n\nThis is a plugin for [pyformance](https://github.com/omergertel/pyformance) which adds VMware Aria Operations\u2122 for Applications (formerly known as Wavefront) reporters (via proxy or direct ingestion) and an abstraction that supports tagging at the host level. It also includes support for Wavefront delta counters.\n\nNote: We're in the process of updating the product name to Operations for Applications, but in many places we still refer to it as Wavefront.\n\n## Requirements\nPython 3.x are supported.\n\n```\npip install wavefront-pyformance\n```\n\n## Usage\n\n### Wavefront Reporter\n\nThe Wavefront Reporters support tagging at the host level. If you pass a tag through a reporter, the reporter tags the metrics before sending the metrics to our service.\n\n\n\n#### Create a Reporter\nYou can create a `WavefrontProxyReporter` or `WavefrontDirectReporter` as follows:\n\n```Python\nimport pyformance\nfrom wavefront_pyformance import wavefront_reporter\n\nreg = pyformance.MetricsRegistry()\n\n# report metrics to a Wavefront proxy every 60s\nwf_proxy_reporter = wavefront_reporter.WavefrontProxyReporter(\n host=host, # required\n port=2878, # default: 2878\n source='wavefront-pyformance-example', # default: 'wavefront-pyformance'\n registry=reg, # default: None\n reporting_interval=60, # default: 60\n prefix='python.proxy.', # default: 'proxy.'\n tags={'key1': 'val1',\n 'key2': 'val2'},\n enable_runtime_metrics: False, # default: False\n enable_internal_metrics: True) # default: True\nwf_proxy_reporter.start()\n\n# report metrics directly to a Wavefront server every 60s\nwf_direct_reporter = wavefront_reporter.WavefrontDirectReporter(\n server=server, # required\n token=token, # required\n source='wavefront-pyformance-example', # default: 'wavefront-pyformance'\n registry=reg, # default: None\n reporting_interval=60, # default: 60\n clock=None, # default: None\n prefix='python.direct.', # default: 'direct.'\n tags={'key1': 'val1',\n 'key2': 'val2'}, # default: None\n enable_runtime_metrics=False, # default: False\n enable_internal_metrics=False) # default: False\nwf_direct_reporter.start()\n```\n#### Flush and stop Wavefront Reporter\n After create Wavefront Reporter, `start()` will make the reporter automatically reporting every `reporting_interval` seconds.\n Besides that, you can also call `report_now()` to perform reporting immediately.\n ```Python\n# Report immediately\nwf_reporter.report_now()\n\n# Stop Wavefront Reporter\nwf_reporter.stop()\n```\n\n### Delta Counter\n\nTo create a Wavefront delta counter:\n\n```Python\nimport pyformance\nfrom wavefront_pyformance import delta\n\nreg = pyformance.MetricsRegistry()\nd_0 = delta.delta_counter(reg, 'requests_delta')\nd_0.inc(10)\n```\n\nNote: Having the same metric name for any two types of metrics will result in only one time series at the server and thus cause collisions.\nIn general, all metric names should be different. In case you have metrics that you want to track as both a Counter and Delta Counter, consider adding a relevant suffix to one of the metrics to differentiate one metric name from another.\n\n### Wavefront Histogram\n\nTo create a [Wavefront Histogram](https://docs.wavefront.com/proxies_histograms.html):\n\n```Python\nimport pyformance\nfrom wavefront_pyformance import wavefront_histogram\n\nreg = pyformance.MetricsRegistry()\nh_0 = wavefront_histogram.wavefront_histogram(reg, 'requests_duration')\nh_0.add(10)\n```\n\n### Python Runtime Metrics\n\nTo enable Python runtime metrics reporting, \nset the `enable_runtime_metrics` flag to `True`:\n\n```Python\n wf_proxy_reporter = wavefront_reporter.WavefrontProxyReporter(\n host=host,\n port=2878,\n registry=reg,\n source='runtime-metric-test',\n tags={'global_tag1': 'val1',\n 'global_tag2': 'val2'},\n prefix='python.proxy.',\n enable_runtime_metrics=True).report_minute_distribution()\n\n wf_direct_reporter = wavefront_reporter.WavefrontDirectReporter(\n server=server,\n token=token,\n registry=reg,\n source='runtime-metric-test',\n tags={'global_tag1': 'val1',\n 'global_tag2': 'val2'},\n prefix='python.direct.',\n enable_runtime_metrics=True).report_minute_distribution()\n```\n",
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