Submit Logs from HTTP API¶

Getting Started¶

This tutorial assumes you have done the following:

• Set up your environment.
• Read the logs crawler documentation.
• Read about the HTTP capabilities of the base class.

Let's say we are building an integration for an API provided by ACME Inc. Run the following command to create the scaffolding for our integration:

ddev create ACME

This adds a folder called acme in our integrations-core folder. The rest of the tutorial we will spend in the acme folder.

cd acme

In order to spin up the integration in our scaffolding, if we add the following to tests/conftest.py:

@pytest.fixture(scope='session')
def dd_environment():
    yield {'tags': ['tutorial:acme']}

Then run:

ddev env start acme py3.11 --dev

Define an Agent Check¶

We start by registering an implementation for our integration. At first it is empty, we will expand on it step by step.

Open datadog_checks/acme/check.py in our editor and put the following there:

from datadog_checks.base.checks.logs.crawler.base import LogCrawlerCheck


class AcmeCheck(LogCrawlerCheck):
    __NAMESPACE__ = 'acme'

Now we'll run something we will refer to as the check command:

ddev env agent acme py3.11 check

We'll see the following error:

Can't instantiate abstract class AcmeCheck with abstract method get_log_streams

We need to define the get_log_streams method. As stated in the docs, it must return an iterator over LogStream subclasses. The next section describes this further.

Define a Stream of Logs¶

In the same file, add a LogStream subclass and return it (wrapped in a list) from AcmeCheck.get_log_streams:

from datadog_checks.base.checks.logs.crawler.base import LogCrawlerCheck
from datadog_checks.base.checks.logs.crawler.stream import LogStream

class AcmeCheck(LogCrawlerCheck):
    __NAMESPACE__ = 'acme'

    def get_log_streams(self):
        return [AcmeLogStream(check=self, name='ACME log stream')]

class AcmeLogStream(LogStream):
    """Stream of Logs from ACME"""

Now running the check command will show a new error:

TypeError: Can't instantiate abstract class AcmeLogStream with abstract method records

Once again we need to define a method, this time LogStream.records. This method accepts a cursor argument. We ignore this argument for now and explain it later.

from datadog_checks.base.checks.logs.crawler.stream import LogRecord, LogStream
from datadog_checks.base.utils.time import get_timestamp

... # Skip AcmeCheck to focus on LogStream.


class AcmeLogStream(LogStream):
    """Stream of Logs from ACME"""

    def records(self, cursor=None):
        return [
            LogRecord(
                data={'message': 'This is a log from ACME.', 'level': 'info'},
                cursor={'timestamp': get_timestamp()},
            )
        ]

There are several things going on here. AcmeLogStream.records returns an iterator over LogRecord objects. For simplicity here we return a list with just one record. After we understand what each LogRecord looks like we can discuss how to generate multiple records.

What is a Log Record?¶

The LogRecord class has 2 fields. In data we put any data in here that we want to submit as a log to Datadog. In cursor we store a unique identifier for this specific LogRecord.

We use the cursor field to checkpoint our progress as we scrape the external API. In other words, every time our integration completes its run we save the last cursor we submitted. We can then resume scraping from this cursor. That's what the cursor argument to the records method is for. The very first time the integration runs this cursor is None because we have no checkpoints. For every subsequent integration run, the cursor will be set to the LogRecord.cursor of the last LogRecord yielded or returned from records.

Some things to consider when defining cursors:

• Use UTC time stamps!
• Only using the timestamp as a unique identifier may not be enough. We can have different records with the same timestamp.
• One popular identifier is the order of the log record in the stream. Whether this works or not depends on the API we are crawling.

Scraping for Log Records¶

In our toy example we returned a list with just one record. In practice we will need to create a list or lazy iterator over LogRecords. We will construct them from data that we collect from the external API, in this case the one from ACME.

Below are some tips and considerations when scraping external APIs:

1. Use the cursor argument to checkpoint your progress.
2. The Agent schedules an integration run approximately every 10-15 seconds.
3. The intake won't accept logs that are older than 18 hours. For better performance skip such logs as you generate LogRecord items.