What is Data Weight?

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Data Weight is a Bubble term we introduced in our book The Ultimate Guide to Bubble Performance in the chapter about Database Structuring, as a way to visualize how the setup of a Data Type can affect your app’s performance

In essence, any Data Type that contains a larger volume of data will take Bubble’s server longer to search through and download, thus degrading your app’s performance. We call these Data Types heavy, i.e. they have a high data weight.

Data Weight illustrated with a scale
Imagine your data types as having mass – the heavier they are, the more they slow down your app.

The term itself makes intuitive sense – we’re used to big files taking longer to download and long documents taking longer to search through after all, and a database is no different. But what exactly is it that adds to a Data Type’s weight? To understand that, we’ll first need to talk about two other common database terms: structured and unstructured data.

What is structured data?

Structured data is any kind of data that follows a set structure. We’ll use the Contacts app on your phone as an example: let’s say each contact has a field for first name, last name, phone number and birthday. Each of these fields are structured, in the sense that they follow a common pattern:

  • First/last name: Short text
  • Phone number: Short text
  • Birthday: Datetime (saved as a 10-digit number in Unix Time)

These are fields that you could easily include and format correctly in a spreadsheet.

Structured data is lightweight. With the current fields we have set up, one record in this data type wouldn’t contain more than about 17 bytes of data.

First name: John (4 bytes)
Last name: Doe (3 bytes)
Birthday: 1/1/2000 00:00 (saved as 1633028845 or 10 bytes)

If we were to download a list of 1,000 contacts, its total size would be a mere 17,000 bytes or 17 kb – smaller than a single average image. Bubble adds a bit extra information to your data type for searching and other purposes, so this isn’t an exact size, but it’ll do for this example.

So what happens if you add something like an actual 17kb image to the mix, like a portrait photo. Does that double the size? Well, keep in mind that the image file is not actually stored in the database – only the URL. So even adding a huge JPG would still only add a few bytes extra information to the database (though the file download can of course still add to your page’s loading time, but it’s unrelated to the database)

It’s when we add unstructured data that the size of the database records starts to grow.

What is unstructured data?

Unstructured data, as you may have guessed, is any kind of data that does not follow any predetermined structure. The article you are currently reading is a typical example of this. This website uses WordPress, but the database works in a similar way – the article is saved as HTML code in a field on a Data Type just like it would in Bubble.

Now, an article is a pretty lightweight piece of data in itself – it’s just text. Compared to images, audio and video, it’s not much, and if we were talking about a file, we wouldn’t give it much thought. But when we’re talking about databases, we’re moving from singular to plural. The questions is not how much data one article contains, but how all articles in a list take up. Let’s look at another example to illustrate. This time, we’re not setting up a phone catalogue, but a blog. We have a data type called Articles, which contains the following fields:

Header (short text)
(unstructured text)

Note that we’re saying unstructured, not long, since the article can of course be short. The point is, while an email address or a first name will always be short, an article can be a single word or the length of a book – we don’t know. In this way, unstructured data adds two challenges: long passages of text can take Bubble’s servers longer to search through, and the total size of each record can grow as the number of lengthy articles increases.

Let’s say we have an article of 5,000 characters (remember, this includes spaces). This is about the length of an average newspaper article.

Header: 20 bytes
Article: 5 kilobytes

Now, this may not seem much, but let’s again multiply this by a thousand articles, and you’ll have a total download size of more than 5 megabytes.

In most cases you will not download all articles of course – the whole purpose of using search constraints is to download only what you need – but it serves as an illustration that small amounts of data can quickly grow when multiplied. by the number of records. Not everyone is aware that using a plugin like Zeroqode’s Fuzzy Search for example, will actually download all records in your initial search, and then perform the fuzzy search client-side – unknowingly, you may be downloading a lot of data on page load.

Data Weight and structuring

Going back to our original term Data Weight, we can say that our first example has a low data weight, and the second has a higher one, helping us to determine how we should set up our structure in an efficient way. A high data weight is not a bad thing on its own – saving unstructured data is of course a necessity for all sorts of purposes like blogs, product descriptions, news articles and cake recipes. It’s only a challenge once it starts affecting performance, in which case we can choose to set up our database structure in a different way. One method is the use of Satellite Data Types to speed up searches and minimize download speed.

In the book, we go over a step-by-step process on how to plan and structure your database for optimal performance, and use the Data Weight concept and Satellite Data Types to help your app run smoothly.

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