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Quickstart

Go from zero to confident with Axiom. Learn datasets, queries, virtual fields, monitors, and dashboards in progressive steps.

This guide takes you from foundational knowledge to advanced proficiency with Axiom. The sections are structured to build upon one another but can also serve as standalone references. By the end, you can independently query data, diagnose performance, derive insights, and set up proactive alerting.

Axiom fundamentals

Axiom is the modern machine data platform. Machine data is any record a system produces: logs, distributed traces, metrics, and events like product analytics, marketing attribution, or security audit trails. Axiom stores and queries all of it through one engine.

Datasets store related machine data, similar to a table in a traditional database. For example, the Axiom Playground includes datasets like sample-http-logs for HTTP request logs and github-push-event for GitHub activity.

Fields are named pieces of data on each record, like columns in a spreadsheet. Fields have a name (for example, status, resp_body_size_bytes, geo.city) and a value. Axiom supports various data types including strings, numbers, booleans, and complex JSON objects.

Send data to Axiom

You can send your first event with a single HTTP request:

curl -X 'POST' 'https://AXIOM_DOMAIN/v1/ingest/DATASET_NAME' \
    -H 'Authorization: Bearer API_TOKEN' \
    -H 'Content-Type: application/x-ndjson' \
    -d '{ "http": { "request": { "method": "GET", "duration_ms": 231 }, "response": { "body": { "size": 3012 } } }, "url": { "path": "/download" } }'

To send events continuously, Axiom supports a wide range of tools and libraries:

For the full list, see Methods for sending data.

Explore your data

Start by identifying the correct dataset in the Datasets tab. You can explore the fields within a dataset to find relevant information. For example, in sample-http-logs you might look at resp_header_size_bytes (response size), status (HTTP status code), and geo.country (geographic origin).

Build your first queries

You typically interact with this data by creating queries using one of the following interfaces:

Set up monitors

Monitors run queries on a schedule and trigger notifications when conditions are met. This moves you from reactive investigation to proactive awareness.

  • Threshold monitors trigger when an aggregated value crosses a threshold (for example, error counts above 100 in 5 minutes). This is the most common type.
  • Match monitors trigger for each individual event that matches a specific pattern (for example, a critical error message). Use these sparingly for high-volume events.
  • Anomaly monitors use machine learning to detect unexpected deviations from a historical baseline, without a static threshold.

Build dashboards

Dashboards are collections of saved queries visualized as charts, tables, and other elements. They provide a single view for monitoring a service, tracking an experiment, or sharing key metrics.

What's next