promql-http-api 0.3.3

Query a Prometheus server and get a Pandas DataFrame

PromQL HTTP API

This python package provides a Prometheus HTTP API client library. It encapsulates and simplifies the collection of data from a Prometheus server. One major feature of this library is that responses to queries are returned as Pandas DataFrames.

Prometheus is an open-source system monitoring and alerting toolkit. It collects metrics from configured targets at given intervals, evaluates rule expressions, displays the results, and can trigger alerts if some condition is observed to be true. The Prometheus server exposes an HTTP API for querying the collected data, and a query language called PromQL.

This library is intended to help data scientists who would like to harvest data from a Prometheus server for analysis and visualization. The library is design to be simple to use, and to provide a convenient interface to the Prometheus HTTP API. It is also designed to be performant and scalable, by using the requests library and caching HTTP connections to the Prometheus server between API accesses.

For unstable connections, the library supports retrying failed requests. The user may specify the number of retries, the time-out between retries, and the back-off factor for the retry interval.

Version 0.2.0 improves timezone awareness. Starting in this version, the HTTP API query is issued with a timestamp value in the 'time', 'start', and 'end' fields. In addition, the query schema now supports a 'timezone' element (provided as a timezone object), which controls the timestamp column formatting in the returned dataframe.

Installation

To install as a root user:

python3 -m pip install promql-http-api

To install as a non-root user:

python3 -m pip install --user promql-http-api

To uninstall:

python3 -m pip uninstall promql-http-api

Usage Examples

Here is a basic usage example:

from promql_http_api import PromqlHttpApi

api = PromqlHttpApi('http://localhost:9090')
tz = pytz.timezone('UTC')
q_time = datetime.now()
q = api.query('up', tz.localize(q_time))
df = q.to_dataframe()
print(df)

On the first line we create a PromqlHttpApi object named api. This example assumes that a Prometheus server is running on the local host, and it is listening to port 9090. Replace this URL as needed with the appropriate URL for your server.

Next, we use the api object to create a Query object named q. The query() function takes two parameters: a query string and a date-time string.

To execute the query explicitly, without converting the result to a DataFrame, you can use:

# Execute the query explicitly
promql_response_data = q()

# Convert the cached result to a DataFrame
df = q.to_dataframe()

Alternately, by calling the to_dataframe() method alone, we will implicitly execute the query.

# Execute the query implicitly
df = q.to_dataframe()

Adding retries and time-out to the query work only with explicit execution:

# Execute the query explicitly
# with 5 retries and retry intervals of 5, 10, 20, and 40 seconds
promql_response_data = q(retries=5, timeout=5, backoff=2)

# Convert the cached result to a DataFrame
df = q.to_dataframe()

HTTP Authentication (and other headers)

The PromqlHttpApi object takes an optional headers parameter. This parameter is a dictionary of HTTP headers to be included in the request. The headers parameter is useful for including authentication information in the request. Here is an example of how to use the headers parameter:

api = PromqlHttpApi('http://localhost:9090', headers={'Authorization': 'token 0123456789ABCDEF'})

Working with schemas

The to_dataframe() method takes an optional schema parameter. The schema is a dictionary that controls several elements of the query. A schema may include the following element keys: columns, dtype, and timezone.

The columns element controls the PromQL response elements to be included as columns in the returned DataFrame. The returned DataFrame will always include a timestamp column (in seconds since the epoch), and a value column. If columns is not provided, all the fields returned in a PromQL response will be included in the returned DataFrame. If columns is provided, only the fields listed in columns will be included in the returned DataFrame.

The dtype element controls the data type of the value column in the returned DataFrame. The default is str. The dtype element may be set to any valid Pandas data type.

The timezone element allows the user to request an additional datetime column which is formatted in the specified timezone. The timezone element must be a timezone object from the pytz library. If the timezone element is not provided, the returned DataFrame will not include a datetime column.

Here is an example of how to use a schema:

schema = {
    'columns': ['node', 'sensor'],
    'dtype': float,
    'timezone': pytz.timezone('US/Eastern')
}

df = q.to_dataframe(schema)

Debugging

If something goes wrong, you can look at the HTTP response and the PromQL response information. Here are some examples:

from promql_http_api import PromqlHttpApi
api = PromqlHttpApi('http://localhost:9090')
tz = pytz.timezone('UTC')
q_time = datetime.now()
q = api.query('up', tz.localize(q_time))
q()
promql_response = q.response
http_response = promql_response.response
print(f'HTTP response status code  = {http_response.status_code}')
print(f'HTTP response encoding     = {http_response.encoding}')
print(f'PromQL response status     = {promql_response.status()}')
print(f'PromQL response data       = {promql_response.data()}')
print(f'PromQL response error type = {promql_response.error_type()}')
print(f'PromQL response error      = {promql_response.error()}')

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List of Supported APIs

API	Method
/api/v1/query	query(query, time)
/api/v1/query_range	query_range(query, start, end, step)
/api/v1/format_query	format_query(query)
/api/v1/series	series(match)
/api/v1/labels	labels()
/api/v1/label/<label_name>/values	label_values(label)
/api/v1/targets	targets(state)
/api/v1/rules	rules(type)
/api/v1/alerts	alerts()
/api/v1/alertmanagers	alertmanagers()
/api/v1/status/config	config()
/api/v1/status/flags	flags()
/api/v1/status/runtimeinfo	runtimeinfo()
/api/v1/status/buildinfo	buildinfo()

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Testing

The package contains limited unit testing. Run the tests from the package top folder using:

pytest

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Future work

Implement a CI/CD pipeline with a Prometheus instance in a Docker container to test API accesses.

If you use this library and would like to help - please contact the author.

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References

Prometheus / HTTP API