Working with metrics and logs as a developer

First see our monitoring doc for an overview what Baserow offers to monitor itself.

This doc explains how to:

  1. Setup your dev environment so you can monitor it, find performance issues etc
  2. Add new logs to the codebase and when to do so
  3. What tracing is and how to add new spans tracing your functions
  4. Add new metrics to the codebase and when to do so

Setting up honeycomb to view Baserow telemetry in your local dev env

  1. Sign up at
  2. Create your own environment inside of honeycomb, you will configure your local dev setup to send events here.
  3. Click on your new environment in the sidebar, click the config icon.
  4. Switch to API keys and copy your API key.
  5. Edit your local .env and set:
  1. ./ restart
  2. Go to your honeycomb environment and you should start seeing new datasets being created!

Debugging telemetry

Look at the logs of your otel-collector for a starting place:

docker logs baserow-otel-collector-1

Under the hood

  • also launches an Open Telemetry Collector service configured by the file in deploy/otel/otel-collector-config.yaml.
  • When you enable telemetry using BASEROW_ENABLE_OTEL=true the dev containers are configured by the OTEL_EXPORTER_OTLP_ENDPOINT=http://otel-collector:4318 in to send telemetry to that local collector.
  • Then this local collector will send telemetry to honeycomb using your HONEYCOMB_API_KEY where you can finally inspect everything.

How to log

To log something just:

from loguru import logger

def application_code():'something')

See Loguru’s docs for more information, it has a ton of awesome features.

When and what to log

As of Feb 2023 Baserow doesn’t log that much. Now we have a nicer logging framework loguru and a way of shipping and storing logs using OTEL we should log much more.

  1. Log for humans, so they can diagnose what happened in Baserow.
  2. Use the different logging levels available to you error/warning/info/debug/trace.
  3. Don’t be afraid of putting into too many logs.

How add spans to trace requests and method performance

Read this first to understand what a trace and span is and why we want them.

Tracing a function

You can use the helper decorator baserow_trace to wrap a function in a span to track it’s execution time and other attributes:

from opentelemetry import trace

tracer = trace.get_tracer(__name__)

class SomeClass:
    def my_func(
        # do expensive operation we want to track how long it takes

@baserow_trace will:

  1. Wrap the function in a span
  2. Set the span name to the functions module name + the functions qualified name automatically
  3. Catch errors and mark the span as failed and register the exception against the span so it gets sent to the collector.

Tracing every method in a class

Instead of having to annotate every method in a class with @baserow_trace you can use the baserow_trace_methods function which generates a metaclass that does it for you. By default, it will trace all methods in the class not starting with _.

baserow_trace_methods also supports the only and exclude parameters which let you restrict which exact methods to trace.

from opentelemetry import trace

tracer = trace.get_tracer(__name__)

class SomeClass(metaclass=baserow_trace_methods(tracer)):
    def a(self):

    def b(self):

    def c(self):

    def d(self):

This comes in very useful when working with a class that has abstract methods that will be implemented by many sub classes (which we do allot in Baserow).

See below for an example of how to trace every single subclasses implementation of .do for an abstract base class!

import abc
from opentelemetry import trace

tracer = trace.get_tracer(__name__)

class ActionType(metaclass=baserow_trace_methods(tracer, abc=True, only='do')):
    def do(self):
        # Every sub class of ActionType will have their `do` method traced!

    def b(self):

    def c(self):

    def d(self):

Adding attributes to the current span

Its often very useful to add attributes to the current span so we can filter and query by those later when inspecting the telemetry. We have a simple helper function that lets you do this:


Or you can just use the default OTEL methods:

    span = get_current_span()
span.set_attribute(f"baserow.my_span_attr", value)

Using the OTEL API directly

Remember you can also just manually use the OTEL Python API. The helper functions shown above are just to help you.

How to track metrics

You can also keep track of various numerical and statistical metrics using open telemetry. We don’t provide any helper methods as the otel functions are straight-forward. Read this for all of the available types of metrics you can use, but a simple example is shown below:

Important: Any attributes you add to metric will result in a brand-new event being send per periodic metric send for that specific combination of metric and attributes. You must make sure that any attributes added will have only a constant possible number of values and a small number of them. This is to prevent an ever-increasing number of metric events being sent to the server.

For example, below if we called counter.add(1, {"table_id"}) OTEL will send a metric data point for every single table it has seen every single sync resulting in an ever-increasing number of metric events being sent. However, if instead the attribute we added was something like “bulk_created”: True or False this is fine as there are only two possible values.

from opentelemetry import metrics

meter = metrics.get_meter(__name__)
rows_created_counter = meter.create_counter(
    description="The number of rows created in user tables.",

def create_row(table):
    # create some row
    # keep track of how many have been made!