⚡️ Quickstart

As a quick example, we demonstrate how to set up and configure a StreamPipes client. In addition, we will get the available data lake measures out of StreamPipes.

from streampipes.client import StreamPipesClient
from streampipes.client.config import StreamPipesClientConfig
from streampipes.client.credential_provider import StreamPipesApiKeyCredentials

config = StreamPipesClientConfig(
    credential_provider = StreamPipesApiKeyCredentials(
        username = "test@streampipes.apache.org",
        api_key = "DEMO-KEY",
        ),
    host_address = "localhost",
    https_disabled = True,
    port = 80
)

client = StreamPipesClient(client_config=config)

# get all available datat lake measures
measures = client.dataLakeMeasureApi.all()

# get amount of retrieved measures
len(measures)

Output:

1

# inspect the data lake measures as pandas dataframe
measures.to_pandas()

Output:

measure_name timestamp_field ... pipeline_is_running num_event_properties
0 test s0::timestamp ... False 2
[1 rows x 6 columns]

Alternatively, you can provide your credentials via environment variables. Simply define your credential provider as follows:

>>> from streampipes.client.credential_provider import StreamPipesApiKeyCredentials

StreamPipesApiKeyCredentials.from_env(username_env="USER", api_key_env="API-KEY")

username is always the username that is used to log in into StreamPipes.
The api_key can be generated within the UI as demonstrated below: