Using jless to explore complex json files

[I'm still thinking of this as a work in progress, but I'm also aiming to get better at putting writing out a bit quicker. That said, the post might change a bit in the future, and I'm also very open to feedback/suggestions!]

I stumbled across jless a while ago, and I've used it for a few months for viewing json files in the terminal.[1] I always found it helpful, but more recently I've encountered use-cases where it's really come into its own. Thinking through why I found it helpful in these cases also led me to realise some more general things about exploring large json structures, which I will try to sketch here.

There are plenty of effective ways to explore data structures, and no single approach is going to suit everyone. I wanted to share an approach that I've found valuable, but I'd love to hear how other people have solved similar problems!

The challenges

It's worth mentioning the kind of task I have in mind, namely: exploratory analysis of large, complex json files.

A good example is the json object which describes the structure and content of a 'front page' on The Guardian newspaper's website, such as the homepage for the UK edition ([2]

The majority of the information needed to render one of the pages is contained within a single json file which can be used to generate front pages via a range of different rendering methods.

If this file is formatted using prettier, it typically comes out to around 140,000 lines. At the top level it has 8 distinct keys. In total, there are around 230 unique keys in the structure, and some of the leaf nodes are nested 20 layers deep.[3]

There are certainly larger json files out there, but at this size the Fronts json already presents a couple of challenges:

  1. Speed. The file in question isn't that big in itself -- a few megabytes of plain text. But if you're going to explore json then you'll want it to be pretty printed in some way or another. I've found that many solutions get quite sluggish when they try to parse a json object of this size and structure.
  2. Enabling the simultaneous discovery of content and structure. json is a fairly simple format insofar as there are only a few basic data types which make it up: objects, arrays, numbers, strings, booleans, and null. But json objects can end up being very deeply nested, and because most uses of json address values via a path from the root, dealing effectively with the content often requires a decent grasp of the structure of the whole object (or at minimum the whole branch leading to the values of interest).


To be honest I don't have too much to say about the issue of speed. I think that it's important for the interface not to lag so far behind your window of attention that you end up losing track of where you are. And I'm sure that there are plenty of tools which will clear that bar. I'll just say that I've had trouble with the speed of other tools before, but for the data structures I've needed to explore, jless has more than cleared my bar for speed. If you've had trouble with other tools, it might be worth trying out.

Exploring complex json

I'll try to say a bit more about how I understand the cognitive challenges of understanding a complex json object.

Take a line from the current Guardian UK homepage:

Spooky shipwrecks and singing sands: 10 of the UK's weirdest beaches

A regular text search in the json file will easily find this phrase and confirm that there are only 4 occurrences, pretty close together. So now we know that this piece of content on the rendered page is connected to this part of the json object, and in particular we can guess that it's coming from the field with the key webTitle.

This is a good start in understanding the object. But there's a lot that we still don't know at this point. Let's say we want to find the headlines for all of the cards on the front page.

If we're lucky, then the key webTitle will only exist on representations of cards. But we can't just assume that, and in many contexts we'd be lucky to find such a descriptive key in the first place; some APIs typically use value as the final key for the value of interest, and a more descriptive key might only be found a few branches higher up in the tree, if at all.

But let's say we are lucky, and webTitle reliably gives us card titles. If we find all of these values, it won't necessarily tell us much more about how cards and their properties are stored. (Are all of the article titles contained in a single array? Or are they nested inside objects which contain other card properties? Are the cards stored in an array? Or an array of arrays? Or an array inside a 'section' object, which itself is in an array of sections? etc.) In this case, looking at the top-level keys doesn't give us too many clues, and expanding the pretty-printed json tree until we can see the various occurrences of webTitle in context leaves us with tens of thousands of rows to scroll through.

It's at this point that I've found jless really comes into its own. jless implements a number of 'vim-inspired' commands for navigating json structures. There are three sets in particular that I've found very useful for solving the challenge I've just described.

Collapse and expand siblings

You can collapse and expand an individual array or object in jless by using the arrow or space keys, or by clicking. But pressing c will collapse the currently focused node and all of its siblings. (Pressing e will expand the node and its siblings.) Using these together, you can rapidly toggle the state of any set of sibling nodes.

The upshot of this is that you can easily confirm that two nodes are in fact siblings, without having to count indents. It also allows you to explore the contents of a node's siblings, without having to either expand and collapse a lot of nodes individually, or scroll through thousands of lines. If you're anything like me, both of the more 'manual' solutions make it very easy to lose track of your place and your train of thought.

Jump to parent node

H (or, shift+h) will jump the cursor to the parent of the currently focused node. This is helpful especially if you're focused on a node that's part of an array or object with a lot of children. Instead of scrolling up and trying to keep track of indents, you can jump straight to the parent. Combined with c from above, you can very quickly navigate from a given value to the root node by hopping through the tree structurally, rather than navigating by number of lines in the way you'd get with scrolling or using PgUp.

Copying paths

When we're actually doing things with a json object, we almost always need to know how to access the data we're interested in via a path which starts at the root of the object tree. Again, if you're anything like me then reconstructing the path to a particular piece of data visually is an error-prone process.

jless provides three shortcuts which allow you to easily copy the path to the currently focused node:


As with pretty much any new tool which makes extensive use of keyboard shortcuts, it can take a bit of practice before using these commands feels fluid. But at least in my own case I've found that it's really helped me to navigate through complex data structures without losing my bearings. If you decide to give it a go, I hope it works for you, too!


  1. It also works for YAML! But I haven't had any reason to explore complex YAML files yet, so I don't know much about how it works for those. [back to text]

  2. These json files are publicly accessible, so you can query them if you want to, but please note that this is not maintained as a public API as such, so there are no guarantees that the content or structure will be stable over time. If you're interested in building something with Guardian data, you'd be better off looking at the paper's Content API. [back to text]

  3. We can think of a json object as having a tree-like structure. In a tree data structure, the 'leaves' are all the nodes which don't have any 'children' nodes. [back to text]