wavefront-sdk-python 1.7.4

Wavefront Python SDK

wavefront-sdk-python

Table of Content

• Prerequisites
• Set Up a Wavefront Sender
• Send a Single Data Point to Wavefront
• Send Batch Data to Wavefront
• Get the Failure Count
• Close the Connection
• License
• How to Get Support and Contribute

Welcome to the Wavefront Python SDK

Wavefront by VMware Python SDK lets you send raw data from your Python application to Wavefront using a wavefront_sender interface. The data is then stored as metrics, histograms, and trace data. This SDK is also referred to as the Wavefront Sender SDK for Python.

Although this library is mostly used by the other Wavefront Python SDKs to send data to Wavefront, you can also use this SDK directly. For example, you can send data directly from a data store or CSV file to Wavefront.

Before you start implementing, let us make sure you are using the correct SDK!

Note:

• This is the Wavefront by VMware SDK for Python (Wavefront Sender SDK for Python)! If this SDK is not what you were looking for, see the table below.

Wavefront SDKs

SDK Type	SDK Description	Supported Languages
OpenTracing SDK	Implements the OpenTracing specification. Lets you define, collect, and report custom trace data from any part of your application code. Automatically derives Rate Errors Duration (RED) metrics from the reported spans.	Java: OpenTracing SDK | Tracing Agent Python: OpenTracing SDK Go: OpenTracing SDK .Net/C#: OpenTracing SDK
Metrics SDK	Implements a standard metrics library. Lets you define, collect, and report custom business metrics and histograms from any part of your application code.	Java: Dropwizard | JVM Python: Pyformance SDK Go: Go Metrics SDK .Net/C#: App Metrics SDK
Framework SDK	Reports predefined traces, metrics, and histograms from the APIs of a supported app framework. Lets you get started quickly with minimal code changes.	Java: Dropwizard | gRPC | JAX-RS | Jersey .Net/C#: ASP.Net core
Sender SDK	Lets you send raw data to Wavefront for storage as metrics, histograms, or traces, e.g., to import CSV data into Wavefront.	Java: Sender SDK Python: Sender SDK Go: Sender SDK .Net/C#: Sender SDK C++: Sender SDK

Prerequisites

• Python 2.7+ and Python 3.x are supported.
• Install wavefront-sdk-python

  pip install wavefront-sdk-python
  

Set Up a Wavefront Sender

You can send metrics, histograms, or trace data from your application to the Wavefront service using a Wavefront proxy or direct ingestions.

• Use direct ingestion to send the data directly to the Wavefront service. This is the simplest way to get up and running quickly.
• Use a Wavefront proxy, which then forwards the data to the Wavefront service. This is the recommended choice for a large-scale deployment that needs resilience to internet outages, control over data queuing and filtering, and more.

You instantiate an object that corresponds to your choice:

• Option 1: Create a WavefrontDirectClient to send data directly to a Wavefront service.
• Option 2: Create a WavefrontProxyClient to send data to a Wavefront proxy.

Option 1: Create a WavefrontDirectClient

When sending data via direct ingestion, you need to create a WavefrontDirectClient, and build it with the Wavefront URL and API token to send data directly to Wavefront.

Prerequisites

• Verify that you have the Direct Data Ingestion permission. For details, see Examine Groups, Roles, and Permissions.
• The URL of your Wavefront instance. This is the URL you connect to when you log in to Wavefront, typically something like https://<domain>.wavefront.com.
• Obtain the API token.

Initialize the WavefrontDirectClient

You initialize a WavefrontDirectClient by providing the access information you obtained in the Prerequisites section..

Optionally, you can specify parameters to tune the following ingestion properties:

• Max queue size - Internal buffer capacity of the Wavefront sender. Any data in excess of this size is dropped.
• Flush interval - Interval for flushing data from the Wavefront sender directly to Wavefront.
• Batch size - Amount of data to send to Wavefront in each flush interval.

Together, the batch size and flush interval control the maximum theoretical throughput of the Wavefront sender. You should override the defaults only to set higher values.

from wavefront_sdk import WavefrontDirectClient

# Create a sender with:
   # your Wavefront URL
   # a Wavefront API token that was created with direct ingestion permission
   # max queue size (in data points). Default: 50,000
   # batch size (in data points). Default: 10,000
   # flush interval  (in seconds). Default: 1 second
wavefront_sender = WavefrontDirectClient(
    server="<SERVER_ADDR>",
    token="<TOKEN>",
    max_queue_size=50000,
    batch_size=10000,
    flush_interval_seconds=5
)

Option 2: Create a WavefrontProxyClient

Prerequisite
Before your application can use a WavefrontProxyClient, you must set up and start a Wavefront proxy.

When sending data via the Wavefront proxy, you need to create a WavefrontProxyClient. Include the following information.

• The name of the host that will run the Wavefront proxy.
• One or more proxy listening ports to send data to. The ports you specify depend on the kinds of data you want to send (metrics, histograms, and/or trace data). You must specify at least one listener port.
• Optional settings for tuning communication with the proxy.

Note: See Advanced Proxy Configuration and Installation for details.

from wavefront_sdk import WavefrontProxyClient

# Create a sender with:
   # the proxy hostname or address
   # the default listener port (2878) for sending metrics to
   # the recommended listener port (2878) for sending histograms to
   # the recommended listener port (30000) for sending trace data to
   # a nondefault interval (2 seconds) for flushing data from the sender to the proxy. Default: 5 seconds
   # if you are directly using the sender sdk to send spans without using any other sdk, use the same port as the customTracingListenerPorts configured in the wavefront proxy for the tracing_port
wavefront_sender = WavefrontProxyClient(
   host="<PROXY_HOST>",
   metrics_port=2878,
   distribution_port=2878,
   tracing_port=30000,
   event_port=2878
   internal_flush=2,
)

Note: When you set up a Wavefront proxy on the specified proxy host, you specify the port it will listen to for each type of data to be sent. The WavefrontProxyClient must send data to the same ports that the Wavefront proxy listens to. Consequently, the port-related parameters must specify the same port numbers as the corresponding proxy configuration properties:

WavefrontProxyClient() parameter	Corresponding property in wavefront.conf
metrics_port	pushListenerPorts=
distribution_port	histogramDistListenerPorts=
tracing_port	traceListenerPorts=

Send a Single Data Point to Wavefront

The following examples show how to send a single data point to Wavefront. You use the Wavefront sender you created above.

Single Metric or Delta Counter

from uuid import UUID

# Wavefront metrics data format:
# <metricName> <metricValue> [<timestamp>] source=<source> [pointTags]
wavefront_sender.send_metric(
    name="new york.power.usage", value=42422.0, timestamp=1533529977,
    source="localhost", tags={"datacenter": "dc1"})

# Wavefront delta counter data format:
# <metricName> <metricValue> source=<source> [pointTags]
wavefront_sender.send_delta_counter(
    name="delta.counter", value=1.0,
    source="localhost", tags={"datacenter": "dc1"})

Note: If your metric name has a bad character, that character is replaced with a -.

Single Histogram Distribution

from uuid import UUID
from wavefront_sdk.entities.histogram import histogram_granularity

# Wavefront histogram data format:
# {!M | !H | !D} [<timestamp>] #<count> <mean> [centroids] <histogramName> source=<source> [pointTags]
# Example: You can choose to send to at most 3 bins: Minute, Hour, Day
# "!M 1533529977 #20 30.0 #10 5.1 request.latency source=appServer1 region=us-west"
# "!H 1533529977 #20 30.0 #10 5.1 request.latency source=appServer1 region=us-west"
# "!D 1533529977 #20 30.0 #10 5.1 request.latency source=appServer1 region=us-west"
wavefront_sender.send_distribution(
    name="request.latency", centroids=[(30, 20), (5.1, 10)],
    histogram_granularities={histogram_granularity.DAY,
                             histogram_granularity.HOUR,
                             histogram_granularity.MINUTE},
    timestamp=1533529977, source="appServer1", tags={"region": "us-west"})

Single Span

If you are directly using the Sender SDK to send data to Wavefront, you won’t see span-level RED metrics by default unless you use the Wavefront proxy and define a custom tracing port (tracing_port). See Instrument Your Application with Wavefront Sender SDKs for details.

from uuid import UUID

# Wavefront trace and span data format:
# <tracingSpanName> source=<source> [pointTags] <start_millis> <duration_milliseconds>
# Example: "getAllUsers source=localhost
#           traceId=7b3bf470-9456-11e8-9eb6-529269fb1459
#           spanId=0313bafe-9457-11e8-9eb6-529269fb1459
#           parent=2f64e538-9457-11e8-9eb6-529269fb1459
#           application=Wavefront http.method=GET
#           1533529977 343500"
wavefront_sender.send_span(
    name="getAllUsers", start_millis=1533529977, duration_millis=343500,
    source="localhost", trace_id=UUID("7b3bf470-9456-11e8-9eb6-529269fb1459"),
    span_id=UUID("0313bafe-9457-11e8-9eb6-529269fb1459"),
    parents=[UUID("2f64e538-9457-11e8-9eb6-529269fb1459")],
    follows_from=None, tags=[("application", "Wavefront"),
                             ("http.method", "GET")],
    span_logs=None)

Single Event

# Wavefront event format:
# @Event <StartTime> <EndTime> "<EventName>"  severity="<Severity>"
# type="<Type>" details="<EventDetail>" host="<Source>" tag="<Tags>"
wavefront_sender.send_event('event name', 1592200048, 1592201048, "localhost",
    ["env:", "dev"], {"severity": "info", "type": "backup", "details": "broker backup"})

Send Batch Data to Wavefront

The following examples show how to generate data points manually and send them as a batch to Wavefront.

Batch Metrics

from uuid import UUID
from wavefront_sdk.common import metric_to_line_data

# Generate string data in Wavefront metric format
one_metric_data = metric_to_line_data(
    name="new-york.power.usage", value=42422, timestamp=1493773500,
    source="localhost", tags={"datacenter": "dc1"},
    default_source="defaultSource")

# Result of one_metric_data:
  # '"new-york.power.usage" 42422.0 1493773500 source="localhost" "datacenter"="dc1"\n'

# List of data
batch_metric_data = [one_metric_data, one_metric_data]

# Send list of data immediately
wavefront_sender.send_metric_now(batch_metric_data)

Note: If your metric name has a bad character, that character is replaced with a -.

Batch Histograms

from uuid import UUID
from wavefront_sdk.entities.histogram import histogram_granularity
from wavefront_sdk.common import histogram_to_line_data

# Generate string data in Wavefront histogram format
one_histogram_data = histogram_to_line_data(
    name="request.latency", centroids=[(30.0, 20), (5.1, 10)],
    histogram_granularities={histogram_granularity.MINUTE,
                             histogram_granularity.HOUR,
                             histogram_granularity.DAY},
    timestamp=1493773500, source="appServer1", tags={"region": "us-west"},
    default_source ="defaultSource")

# Result of one_histogram_data:
  # '!D 1493773500 #20 30.0 #10 5.1 "request.latency" source="appServer1" "region"="us-west"\n
  # !H 1493773500 #20 30.0 #10 5.1 "request.latency" source="appServer1" "region"="us-west"\n
  # !M 1493773500 #20 30.0 #10 5.1 "request.latency" source="appServer1" "region"="us-west"\n'

# List of data
batch_histogram_data = [one_histogram_data, one_histogram_data]

# Send list of data immediately
wavefront_sender.send_distribution_now(batch_histogram_data)

Batch Trace Data

If you are directly using the Sender SDK to send data to Wavefront, you won’t see span-level RED metrics by default unless you use the Wavefront proxy and define a custom tracing port (tracing_port). See Instrument Your Application with Wavefront Sender SDKs for details.

from uuid import UUID
from wavefront_sdk.common import tracing_span_to_line_data

# Generate string data in Wavefront tracing span format
one_tracing_span_data = tracing_span_to_line_data(
    name="getAllUsers", start_millis=1552949776000, duration_millis=343,
    source="localhost", trace_id=UUID("7b3bf470-9456-11e8-9eb6-529269fb1459"),
    span_id=UUID("0313bafe-9457-11e8-9eb6-529269fb1459"),
    parents=[UUID("2f64e538-9457-11e8-9eb6-529269fb1459")],
    follows_from=[UUID("5f64e538-9457-11e8-9eb6-529269fb1459")],
    tags=[("application", "Wavefront"), ("http.method", "GET")],
    span_logs=None, default_source="defaultSource")

# Result of one_tracing_span_data:
  # '"getAllUsers" source="localhost" traceId=7b3bf470-9456-11e8-9eb6-529269fb1459 spanId=0313bafe-
  # 9457-11e8-9eb6-529269fb1459 parent=2f64e538-9457-11e8-9eb6-529269fb1459 followsFrom=5f64e538-
  # 9457-11e8-9eb6-529269fb1459 "application"="Wavefront" "http.method"="GET" 1552949776000 343\n'

# List of data
batch_span_data = [one_tracing_span_data, one_tracing_span_data]

# Send list of data immediately
wavefront_sender.send_span_now(batch_span_data)

Batch Events

from wavefront_sdk.common import event_to_line_data

# Generate string data in Wavefront event format
one_event_data = event_to_line_data(name="event name", start_time=1592200048, end_time=1592201048,
 source="localhost", tags=["env", "dev"], annotations={"severity": "info", "type": "backup", "details": "broker backup"})

# Result of one_event_data:
# '@Event 1592200048 1592201048 "event name" severity="info" type="backup" details="broker backup"
# host="localhost" tag="env" tag="dev"\n'

# List of events
batch_event_data = [one_event_data, one_event_data]

# Send list of events immediately
wavefront_sender.send_event_now(batch_event_data)

Get the Failure Count

If the application failed to send metrics, histograms, or trace data via the wavefront_sender, you can get the total failure count.

# Get the total failure count
total_failures = wavefront_sender.get_failure_count()

Close the Connection

• If the Wavefront sender is a WavefrontDirectClient, flush all buffers and then close the connection before shutting down the application.

  # To shut down a WavefrontDirectClient
  # Flush all buffers.
  wavefront_sender.flush_now()
  
  # Close the sender connection
  wavefront_sender.close()
  

• If the Wavefront sender is a WavefrontProxyClient, close the connection before shutting down the application.

  # To shut down a WavefrontProxyClient
  
  # Close the sender connection
  wavefront_sender.close()
  

License

Apache 2.0 License.

How to Get Support and Contribute

• Reach out to us on our public Slack channel.
• If you run into any issues, let us know by creating a GitHub issue.