Helper library implementing best practices for strongly typed models and reporting metrics
Project description
dt-extensions-models
Helper library implementing best practices for strongly typed models and reporting metrics.
Installation
pip install dt-extensions-models
Usage
Use just like you would use Pydantic models in general. However, there are a couple of new additions.
Example
Raw data
Imagine a response from an JSON REST API coming in like this:
{
"name": "my_tunnel",
"outgoing_bytes": 5,
"proxyid": [
{
"p2name": "my_proxy",
"status": "UP",
"incoming_bytes": 3,
"outgoing_bytes": 2
}
]
}
Parsing requirements
and we want to parse it and report 5 metrics and 2 events:
Metrics:
- Counter
fortigate.tunnel.bytes.in.count
for incoming traffic to the tunnel, if any. - Counter
fortigate.tunnel.bytes.out.count
for outgoing traffic from the tunnel, if any. - Counter
fortigate.tunnel.proxy.bytes.in.count
for incoming traffic for each of the proxies within the tunnel. - Counter
fortigate.tunnel.proxy.bytes.out.count
for outgoing traffic for each of the proxies within the tunnel. - Gauge
fortigate.tunnel.proxy.status
to reflect the overall status of each proxy.
Additionally, we want each proxy metric to have 3 dimensions:
status
- status of this proxy. "UP" if it's "up" or "UP" and "DOWN" for everything else.proxy
- value of thep2name
field.tunnel
- name of the tunnel this proxy belongs to.
And for each tunnel we also want a tunnel
dimension with the tunnel's name
.
Events:
- Info event
Small outgoing traffic!
if outgoing traffic on tunnel is less than 80 bytes. - Custom alert event
Proxy my_proxy is slow!
if the proxy is up but its outgoing traffic is less than 10 bytes.
AND we want our code to complain or throw exceptions if the incoming data is invalid!
Imagine the sheer amount of code and validation required to implement all of the requirements above!
Implementation
Here is how you could define a nested model that can be evaluated both for metrics and events in just about 80 lines. The example intentionally uses as much variation as possible to demonstrate the flexibility of the library.
from dynatrace_extension_models import Field, MetricInfo, EventInfo, IngestBase, MetricType, DtEventType
class TunnelProxy(IngestBase):
p2name: str = Field(...)
status: str = Field("unknown")
incoming_bytes: int | None = Field(None, title="incoming_bytes")
outgoing_bytes: int | None = Field(None, title="outgoing_bytes")
def properties(self) -> dict:
return {
"proxy": self.p2name,
"status": self.status.upper(),
"tunnel": getattr(self._parent, "name"),
}
def status_to_metric(self) -> float:
return 1 if self.status.upper() == "UP" else 0
def proxy_is_slow_title(self) -> str:
return f"Proxy {self.p2name} is slow!"
_metrics = [
MetricInfo(
key="fortigate.tunnel.proxy.status",
properties=properties,
value=status_to_metric,
),
MetricInfo(
key="fortigate.tunnel.proxy.bytes.in.count",
type=MetricType.COUNT,
properties=properties,
value=incoming_bytes,
),
MetricInfo(
key="fortigate.tunnel.proxy.bytes.out.count",
type=MetricType.COUNT,
properties=properties,
value=outgoing_bytes,
)
]
_events = [
EventInfo(
title=proxy_is_slow_title,
properties=properties,
type=DtEventType.CUSTOM_ALERT,
when=lambda v: v.status.upper() == "UP" and v.outgoing_bytes < 10,
)
]
class Tunnel(IngestBase):
name: str = Field(...)
incoming_bytes: int | None = Field(None, title="incoming_bytes")
outgoing_bytes: int | None = Field(None, title="outgoing_bytes")
proxyid: list[TunnelProxy] | None = Field(None)
def not_enough_traffic(self) -> bool:
return self.outgoing_bytes < 80
def current_incoming_bytes(self) -> int | None:
return self.incoming_bytes
_metrics = [
MetricInfo(
key="fortigate.tunnel.bytes.in.count",
type=MetricType.COUNT,
properties={"tunnel": "{name}"},
value=current_incoming_bytes,
),
MetricInfo(
key="fortigate.tunnel.bytes.out.count",
type=MetricType.COUNT,
properties={"tunnel": "{name}"},
value=outgoing_bytes,
)
]
_events = [
EventInfo(
title="Small outgoing traffic!",
type=DtEventType.CUSTOM_INFO,
when=not_enough_traffic,
)
]
Results
If you initialize the Tunnel
model instance with the JSON above and convert it to metrics and events, here is what you would get.
import requests
data = requests.get("https://some-api.com").json()
tunnel = Tunnel(**data)
print(tunnel.mint_lines)
print(tunnel.event_dicts)
output:
fortigate.tunnel.bytes.out.count,tunnel="my_tunnel" count,5 1719449431472
fortigate.tunnel.proxy.bytes.in.count,proxy="my_proxy",status="UP",tunnel="my_tunnel" count,3 1719449431472
fortigate.tunnel.proxy.bytes.out.count,proxy="my_proxy",status="UP",tunnel="my_tunnel" count,2 1719449431472
fortigate.tunnel.proxy.status,proxy="my_proxy",status="UP",tunnel="my_tunnel" gauge,1 1719449431472
{'title': 'Proxy my_proxy is slow!', 'event_type': <DtEventType.CUSTOM_ALERT: 'CUSTOM_ALERT'>, 'timeout': 15, 'properties': {'proxy': 'my_proxy', 'status': 'UP', 'tunnel': 'my_tunnel'}}
{'title': 'Small outgoing traffic!', 'event_type': <DtEventType.CUSTOM_INFO: 'CUSTOM_INFO'>, 'timeout': 15}
Ingesting metrics and sending events in Python EF2 framework
Continuing the code above, inside your Python EF2 Dynatrace extension you would do the following:
import requests
from dynatrace_extension import Extension, Status, StatusValue
class ExtensionImpl(Extension):
def query(self) -> None:
data = requests.get("https://some-api.com").json()
tunnel = Tunnel(**data)
# Ingest metrics
self.report_mint_lines(tunnel.mint_lines)
# Ingest events
for event in tunnel.event_dicts:
self.report_dt_event_dict(event)
🤩 Beautiful!
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