wavefront-sdk-python
Advanced tools
VMware Aria Operations for Applications (formerly known as Tanzu Observability by Wavefront) Python SDK lets you send raw data from your Python application to Operations for Applications 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 Operations for Applications Python SDKs to send data to Operations for Applications, you can also use this SDK directly. For example, you can send data directly from a data store or CSV file to Operations for Applications.
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.
Python versions 3.8 - 3.11 are supported.
Install wavefront-sdk-python:
pip install wavefront-sdk-python
You can send metrics, histograms, or trace data from your application to the service using a Wavefront Proxy or direct ingestions.
Let's create a WavefrontClient to send data to Operations for Applications either through the Wavefront Proxy or directly over HTTP.
Deprecated implementations:
WavefrontDirectClientandWavefrontProxyClientare deprecated starting with Wavefront proxy version 7.0. We recommend all new applications to use theWavefrontClient.
Use WavefrontClientFactory to create a WavefrontClient instance, which can send data directly to the service or send data using a Wavefront proxy.
The WavefrontClientFactory supports multiple client bindings. If more than one client configuration is specified, you can create a WavefrontMultiClient instance, which can send data to multiple services.
WavefrontClient you must install a Wavefront proxy.http://<domain>.wavefront.com. You can also use HTTP client with Wavefront proxy version 7.0 or later. Example: http://proxy.acme.corp:2878.Example: Use a factory class to create a WavefrontClient and send data to Operations for Applications through a Wavefront proxy or using direct ingestion.
from wavefront_sdk.client_factory import WavefrontClientFactory
# Create a sender with:
# Required Parameter
# URL format to send data via proxy: "proxy://<your.proxy.load.balancer.com>:<somePort>"
# URL format to send data via direct ingestion: "https://TOKEN@DOMAIN.wavefront.com"
# Optional Parameter
# max queue size (in data points). Default: 50000
# batch size (in data points). Default: 10000
# flush interval (in seconds). Default: 1 second
client_factory = WavefrontClientFactory()
client_factory.add_client(
url="<URL for proxy or direct ingestions>",
max_queue_size=50000,
batch_size=10000,
flush_interval_seconds=5)
wavefront_sender = client_factory.get_client()
http://<domain>.wavefront.com.http://console.cloud.vmware.com.Example: Use a factory class to create a WavefrontClient and send data to Operations for Applications via VMware Cloud Services API Token.
from wavefront_sdk.client_factory import WavefrontClientFactory
# Create a sender with:
# Required Parameter
# URL format to send data via direct ingestion: "https://<DOMAIN>.wavefront.com"
# URL format to get tokens via VMware Cloud Services authentication: "https://<CSP_ENDPOINT>.cloud.vmware.com"
# VMware Cloud Services API Token for VMware Cloud Services authentication: "<CSP_API_TOKEN>"
# Optional Parameter
# max queue size (in data points). Default: 50000
# batch size (in data points). Default: 10000
# flush interval (in seconds). Default: 1 second
client_factory = WavefrontClientFactory()
client_factory.add_client(
url="<URL for direct ingestions>",
csp_base_url='<URL for csp authentication>',
csp_api_token="<Token for csp api>",
max_queue_size=50000,
batch_size=10000,
flush_interval_seconds=5)
wavefront_sender = client_factory.get_client()
http://<domain>.wavefront.com.http://console.cloud.vmware.com.Example: Use a factory class to create a WavefrontClient and send data to Operations for Applications by using the server to server OAuth app.
from wavefront_sdk.client_factory import WavefrontClientFactory
# Create a sender with:
# Required Parameter
# URL format to send data via direct ingestion: "https://<DOMAIN>.wavefront.com"
# URL format to get tokens via csp authentication: "https://<CSP_ENDPOINT>.cloud.vmware.com"
# VMware Cloud services OAuth App ID for csp authentication: "<CSP_APP_ID>"
# VMware Cloud services OAuth App secret for csp authentication: "<CSP_APP_SECRET>"
# Optional Parameter
# VMware Cloud services Organization ID for csp authentication. Default: None
# max queue size (in data points). Default: 50000
# batch size (in data points). Default: 10000
# flush interval (in seconds). Default: 1 second
client_factory = WavefrontClientFactory()
client_factory.add_client(
url="<URL for direct ingestions>",
csp_base_url='<URL for csp authentication>',
csp_app_id="<ID for csp oauth app>",
csp_app_secret="<Secret for csp oauth app>",
csp_org_id="<ID for csp organization>",
max_queue_size=50000,
batch_size=10000,
flush_interval_seconds=5)
wavefront_sender = client_factory.get_client()
Example: Creating a WavefrontMultiClient to send data to multiple Operations for Applications services.
from wavefront_sdk.client_factory import WavefrontClientFactory
client_factory = WavefrontClientFactory()
client_factory.add_client("proxy://our.proxy.lb.com:2878")
client_factory.add_client("https://someToken@DOMAIN.wavefront.com")
# Send traces and spans to the tracing port. 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. Assume you have installed and started the proxy on <proxy_hostname>.
client_factory.add_client("http://<proxy_hostname>:30000")
wavefront_sender = client_factory.get_client()
Operations for Applications supports different metric types, such as gauges, counters, delta counters, histograms, traces, and spans. See Metrics for details. To send data to Operations for Applications using the wavefront_sender you need to instantiate the following:
The following examples show how to send a single data point to the service. You use the Wavefront Sender you created above.
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 -.
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"})
If you are directly using the Sender SDK to send data to the service, 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 the 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"),
("service", "istio"),
("http.method", "GET")],
span_logs=None)
# 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"})
The following examples show how to generate data points manually and send them as a batch to Operations for Applications.
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 -.
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)
If you are directly using the Sender SDK to send data to the service, 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)
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)
Remember to flush the buffer and close the sender before shutting down the application.
# To shut down a sender from a WavefrontClientFactory
wavefront_sender = client_factory.get_client()
# If the application failed to send metrics/histograms/tracing-spans,
# you can get the total failure count as follows:
total_failures = wavefront_sender.get_failure_count()
# On-demand buffer flush
wavefront_sender.flush_now()
# Close the sender connection
wavefront_sender.close()
To get support with our project and contribute, follow the instructions in CONTRIBUTING.md.
To create a new release, follow the instructions in RELEASING.md
FAQs
VMware Aria Operations for Applications Python SDK
We found that wavefront-sdk-python demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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