Rollup aggregations with query parameters

In this guide, you'll learn how to dynamically aggregate time series data by different time intervals (rollups) to optimize front end performance.

These days, it is not uncommon to have datasets with a per-second resolution for a few years' worth of data. This creates some demands at the storage and the query layers, and it also presents some challenges for the data consumers. Aggregating this data dynamically and on-the-fly is key for resolving the specific demands at scale.

In this guide, you'll create an API Endpoint that aggregates the data in different time-frames depending on the amount of data, so only the needed rows are sent to the front end, thereby gaining performance and speed.

When preparing the API Endpoint you'll focus on 3 things:

  1. Keep the API Endpoint interface extremely simple: "I want events data from this start date to this end date".
  2. Make the API Endpoint return enough data with adequate resolution for the selected date range.
  3. Don't add logic to the front end to do the aggregation of the returned data. You simply request the desired date range knowing that you will receive an amount of data that won't swamp your rendering pipeline.

Prerequisites

You'll need to have at least read through the quick start guide to be familiar with the scenario. The following guide uses a Data Source called events with 100M rows, for a ~5-year timespan.

1. Build the Pipe

In this step, you'll learn how to use Tinybird's templating language to add more logic to your API Endpoints.

In addition to the main docs on using the templating language to pass query parameters to Endpoints, you can also learn about variables and the functions that are available within templates in the CLI > Advanced Templates docs.

The Endpoint created in the quick start guide returns sales per day of an ecommerce store, and takes a start and end date. The problem of having a fixed period of one day to aggregate data is that the amount of data transferred will vary a lot, depending on the selected dates, as well as the work that the client will have to do on the front end to render that data. Also, if the start and end dates are close to each other, the grouping window will be too big and the back end won't return data with a high enough level of detail.

Fortunately, you can add conditional logic when defining API Endpoints in Tinybird and, depending on the range of dates selected, the Endpoint will return data grouped by one of these periods:

  • Weekly
  • Daily
  • Every 4 hours
  • Every 1 hour
  • Every 15 minutes

To do this, you can create a new Pipe named ecommerce_events_dynamic. In the first Node, add the following code to 1) keep only the buy events, and 2) cast the price column to Float32 changing its name to buy_events_with_price:

Filtering raw events Data Source to keep only BUY events
SELECT 
    date,
    product_id,
    user_id,
    toFloat32(JSONExtractFloat(extra_data, 'price')) price
FROM events
WHERE event='buy'

And then, add another transformation Node containing the following query. Name this Node buy_events_dynamic_agg:

Using dynamic parameters to aggregate data depending on the time range
%
SELECT
    {% set days_interval = day_diff(Date(start_date, '2018-01-01'), Date(end_date, '2018-01-31')) %}
    {% if days_interval > 180 %}
        toStartOfWeek(date)
    {% elif days_interval > 31 %}
        toStartOfDay(date)
    {% elif days_interval > 7 %}
        toStartOfInterval(date, INTERVAL 4 HOUR)
    {% elif days_interval > 2 %}
        toStartOfHour(date)
    {% else %}
        toStartOfFifteenMinutes(date)
    {% end %} AS t,
    round(sum(price), 2) AS sales
FROM buy_events_with_price
WHERE date BETWEEN
    toDateTime(toDate({{Date(start_date, '2018-10-01')}})) AND
    toDateTime(toDate({{Date(end_date, '2020-11-01')}}) + 1)
GROUP BY t
ORDER BY t

This query makes use of Tinybird's templating language:

  • It defines a couple of Date parameters and adds some default values to be able to test the Pipe while you build it ({{Date(start_date, '2018-01-01')}} and {{Date(end_date, '2018-12-31')}}).
  • It computes the number of days in the interval defined by start_date and end_date: days_interval = day_diff(...).
  • It uses the days_interval variable to decide the best granularity for the data.

Using the templating language additions might look a little complicated at a first glance, but you'll quickly become familiar with it!

2. Publish your API Endpoint

Selecting the Publish button > buy_events_dynamic_agg Node makes your API accessible immediately. Once it's published you can directly test it using the snippets available in the API Endpoint page or using Tinybird's REST API. Just change the start_date and the end_date parameters to see how the aggregation window changes dynamically.

Testing the API Endpoint using cURL
TOKEN=<your_token>

curl -s "https://api.tinybird.co/v0/pipes/ecommerce_events_dynamic.json?start_date=2018-10-01&end_date=2018-11-01&token=$TOKEN" \
    | jq '.data[:2]'
[
  {
    "t": "2018-10-01 00:00:00",
    "sales": 66687.38
  },
  {
    "t": "2018-10-01 04:00:00",
    "sales": 50821.24
  }
]

Use a Token with the right scope. Replace <your_token> with a Token whose scope is READ or higher.

To sum up: With Tinybird, you can dynamically return different responses from your analytics API Endpoints depending on the request's parameters. This can give you more granular control over the data you send to the client, either for performance or privacy reasons.

Next steps