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- send_log | ||
- get_log_cursor | ||
- warning | ||
- http | ||
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## Stubs | ||
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# Log Crawlers | ||
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## Overview | ||
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Some systems expose their logs from HTTP endpoints instead of files that the Logs Agent can tail. | ||
In such cases, you can create an Agent integration to crawl the endpoints and submit the logs. | ||
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The following diagram illustrates how crawling logs integrates into the Datadog Agent. | ||
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<div align="center" markdown="1"> | ||
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```mermaid | ||
graph LR | ||
subgraph "Agent Integration (you write this)" | ||
A[Log Stream] -->|Log Records| B(Log Crawler Check) | ||
end | ||
subgraph Agent | ||
B -->|Save Logs| C[(Log File)] | ||
D(Logs Agent) -->|Tail Logs| C | ||
end | ||
D -->|Submit Logs| E(Logs Intake) | ||
``` | ||
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</div> | ||
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## Interface | ||
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::: datadog_checks.base.checks.logs.crawler.base.LogCrawlerCheck | ||
options: | ||
heading_level: 3 | ||
members: | ||
- get_log_streams | ||
- process_streams | ||
- check | ||
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::: datadog_checks.base.checks.logs.crawler.stream.LogStream | ||
options: | ||
heading_level: 3 | ||
members: | ||
- records | ||
- __init__ | ||
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::: datadog_checks.base.checks.logs.crawler.stream.LogRecord | ||
options: | ||
heading_level: 3 | ||
members: [] |
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# Submit Logs from HTTP API | ||
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## Getting Started | ||
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This tutorial assumes you have done the following: | ||
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- [Set up your environment](../../index.md#getting-started). | ||
- Read the [logs crawler documentation](../../base/logs-crawlers.md). | ||
- Read about the [HTTP capabilities](../../base/http.md) of the base class. | ||
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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: | ||
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``` | ||
ddev create ACME | ||
``` | ||
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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 | ||
``` | ||
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In order to spin up the integration in our scaffolding, if we add the following to `tests/conftest.py`: | ||
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```python | ||
@pytest.fixture(scope='session') | ||
def dd_environment(): | ||
yield {'tags': ['tutorial:acme']} | ||
``` | ||
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Then run: | ||
``` | ||
ddev env start acme py3.11 --dev | ||
``` | ||
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## Define an Agent Check | ||
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We start by registering an implementation for our integration. | ||
At first it is empty, we will expand on it step by step. | ||
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Open `datadog_checks/acme/check.py` in our editor and put the following there: | ||
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```python | ||
from datadog_checks.base.checks.logs.crawler.base import LogCrawlerCheck | ||
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class AcmeCheck(LogCrawlerCheck): | ||
__NAMESPACE__ = 'acme' | ||
``` | ||
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Now we'll run something we will refer to as *the check command*: | ||
``` | ||
ddev env agent acme py3.11 check | ||
``` | ||
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We'll see the following error: | ||
``` | ||
Can't instantiate abstract class AcmeCheck with abstract method get_log_streams | ||
``` | ||
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We need to define the `get_log_streams` method. | ||
As [stated in the docs](../../base/logs-crawlers.md#datadog_checks.base.checks.logs.crawler.base.LogCrawlerCheck.get_log_streams), it must return an iterator over `LogStream` subclasses. | ||
The next section describes this further. | ||
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## Define a Stream of Logs | ||
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In the same file, add a `LogStream` subclass and return it (wrapped in a list) from `AcmeCheck.get_log_streams`: | ||
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```python | ||
from datadog_checks.base.checks.logs.crawler.base import LogCrawlerCheck | ||
from datadog_checks.base.checks.logs.crawler.stream import LogStream | ||
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class AcmeCheck(LogCrawlerCheck): | ||
__NAMESPACE__ = 'acme' | ||
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def get_log_streams(self): | ||
return [AcmeLogStream(check=self, name='ACME log stream')] | ||
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class AcmeLogStream(LogStream): | ||
"""Stream of Logs from ACME""" | ||
``` | ||
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Now running *the check command* will show a new error: | ||
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``` | ||
TypeError: Can't instantiate abstract class AcmeLogStream with abstract method records | ||
``` | ||
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Once again we need to define a method, this time [`LogStream.records`](../../base/logs-crawlers.md#datadog_checks.base.checks.logs.crawler.stream.LogStream.records). | ||
This method accepts a `cursor` argument. | ||
We ignore this argument for now and explain it later. | ||
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```python | ||
from datadog_checks.base.checks.logs.crawler.stream import LogRecord, LogStream | ||
from datadog_checks.base.utils.time import get_timestamp | ||
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... # Skip AcmeCheck to focus on LogStream. | ||
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class AcmeLogStream(LogStream): | ||
"""Stream of Logs from ACME""" | ||
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def records(self, cursor=None): | ||
return [ | ||
LogRecord( | ||
data={'message': 'This is a log from ACME.', 'level': 'info'}, | ||
cursor={'timestamp': get_timestamp()}, | ||
) | ||
] | ||
``` | ||
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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. | ||
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### What is a Log Record? | ||
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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`. | ||
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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`. | ||
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Some things to consider when defining cursors: | ||
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- 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. | ||
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### Scraping for Log Records | ||
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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 `LogRecord`s. | ||
We will construct them from data that we collect from the external API, in this case the one from *ACME*. | ||
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Below are some tips and considerations when scraping external APIs: | ||
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1. Use the `cursor` argument to checkpoint your progress. | ||
1. The Agent schedules an integration run approximately every 10-15 seconds. | ||
1. The intake won't accept logs that are older than 18 hours. For better performance skip such logs as you generate `LogRecord` items. |
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