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Get started with Workers Analytics Engine

There are three steps to get started with Workers Analytics Engine:

​​ 1. Configure your dataset and binding in Wrangler

All data in Workers Analytics Engine is written to a dataset. A dataset is conceptually like a table in SQL: the rows and columns should have consistent meaning.

To access your dataset from the Workers runtime, you need to create a binding using Wrangler. A binding is like an environment variable that you can use in the Workers runtime which enables you to write to a dataset. A dataset is created implicitly after you define your binding and begin writing to it from a Worker.

In this guide, we will show you how to start using a dataset.

To define a binding. For example:

type = "analytics_engine"
name = "<BINDING_NAME>"

By default, the dataset name is the same as the binding name. If you want, you can also specify the dataset name:

type = "analytics_engine"
name = "<BINDING_NAME>"
dataset = "<DATASET_NAME>"

​​ 2. Write data from the Workers Runtime API

Once a binding is declared in Wrangler and your worker is deployed, you get a new environment variable in the Workers runtime that represents your Workers Analytics Engine dataset. This variable has a method, writeDataPoint(). A data point is a structured event which consists of a vector of blobs and a vector of doubles.

A double is just a number type field that can be aggregated in some way – for example, it could be summed, averaged, or quantiled. A blob is a string type field that can be used for grouping or filtering.

For example, suppose you are collecting air quality samples. Each data point would represent a reading from your weather sensor. Doubles might include numbers like the temperature or air pressure reading. The blobs could include the location of the sensor and the hardware identifier of the sensor.

This is how it translates into code:

async fetch(request, env) {
'blobs': ["Seattle", "USA", "pro_sensor_9000"],
'doubles': [25, 0.5]
return new Response("OK!");

In our initial version, developers are responsible for providing fields in a consistent order, so that they have the same semantics when querying. In a future iteration, we plan to let developers name their blobs and doubles in the binding, and then use these names when writing data points in the runtime.

​​ 3. Query data using GraphQL and SQL API

Data can be queried using either GraphQL or SQL API.

The GraphQL API powers our dashboard and is better suited for building interactive dashboards. At this time, the GraphQL API exposes a highly simplified schema, though we plan to support a richer schema over time.

SQL API is better suited for writing ad hoc queries and integrating with external tools like Grafana. At this time, the SQL API only supports the SELECT statement and a limited subset of SQL functionality.

The SQL API is available as an HTTP endpoint at /v4/$accountTag/analytics_engine/sql using the POST and GET method. You need to include an Authorization: Bearer _____ token where the underscores should be replaced with a Cloudflare API Token that has the Account Analytics Read permission.

​​ Example of querying data with the SQL API

In the following example, we use the SQL API to query the top 10 cities that had the highest average humidity readings when the temperature was above zero.

Here’s how we represent that as SQL:

SELECT blob1 as city, avg(double2) as avg_humidity
FROM analytics_engine
WHERE dataset = 'WEATHER'
AND double1 > 0
ORDER BY avg_humidity

You can then perform the query using any HTTP client. Here’s an example of doing it using cURL:

curl -X GET "" -H "Authorization: Bearer YOUR_API_TOKEN" -H "Content-Type:application/json" -X POST -d "SELECT blob1 as city, avg(double2) as avg_humidity FROM analytics_engine WHERE dataset = 'WEATHER' AND double1 > 0 GROUP BY city ORDER BY avg_humidity DESC LIMIT 10"

Note that, for our initial version, blobs and doubles are accessed via names that have 1-based indexing. In the future, when we let developers name blobs and doubles in their binding, these names will also be available via the SQL API.

​​ Working with time series

Workers Analytics Engine is optimized for powering time series analytics that can be visualized using tools like Grafana. Every event written from the runtime is automatically populated with a timestamp field. It is expected that most time series will round, and then GROUP BY the timestamp. For example:

intDiv(toUInt32(timestamp), 300) * 300 as t,
blob1 as city,
avg(double2) as avg_humidity
FROM analytics_engine
dataset = 'WEATHER'
AND timestamp >= now() - INTERVAL '1' DAY
AND double1 > 0
GROUP BY t, city
ORDER BY t, avg_humidity desc

This query first rounds the timestamp field to the nearest five minutes. Then we group by that field and city, and calculate the average humidity in each city for a five minute period.