The Ultimate Interactive JQ Guide

Cover Photo by Pixabay

Has this ever happened to you?

You’ve just received a massive JSON file that looks like it was designed to confuse you. Or maybe you entered a command, and you got so much JSON that it looks incomprehensible.

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The data you need is buried inside, and you’re dreading the hours it’ll take to extract and clean it up.

I’ve been there. I’ve grepped my way through JSON and written ad-hoc Python scripts to process it for me.

But things don’t have to be like this.

#Introduction

jq is one of the best-kept secrets in the data processing world.

Here are some scenarios where jq could swoop in to save your day (and saves mine regularly):

1. Integrating with APIs in shell scripts often means handling JSON responses, requiring data extraction and manipulation.

2. Data from different sources may need to be converted to or from JSON format for compatibility.

3. Managing software configuration files in JSON format can be a regular task.

4. Extracting data from websites often results in dealing with JSON data that requires parsing and filtering.

5. Server logs and monitoring data often use JSON, necessitating parsing and analysis.

6. Infra as Code tools like Ansible and Terraform use JSON-like configurations, requiring management. JSON is a subset of YAML, so every valid JSON file is also a valid YAML file.

All examples are ✨fully interactive✨, so I encourage you to play around! In fact, I’ll be downright heartbroken if you don’t, because I put a lot of effort into it. You can edit both the input JSON data, and the jq program as well.

Let’s dive in! We’ll start off easy, and get slowly deeper into the weeds.

#Basic Operations

#Selecting values

Everything in jq is a filter. The dot . is used to select the current object or element, and we can put the property name after it to access a key from an object:

Click Run or press ⌘Enter

#Filtering Arrays

The .[] notation is used to iterate over the elements of an array in a JSON document. It allows you to access each element of an array and perform operations on them.

The select() function is used to filter JSON data based on a specified condition or criteria. It is a powerful tool for extracting specific elements from a JSON document that meet certain conditions.

Similiar to shell scripting, jq works on a pipes-and-filters manner. We use the | to send the data from one filter to the next.

Click Run or press ⌘Enter

#Mapping Arrays

We can use the map function to run any operation on every element of the array and return a new array containing the outputs of that operation:

Click Run or press ⌘Enter

#Combining Filters

The pipe operator | can be used to chain as many filters or functions as we want:

Click Run or press ⌘Enter

#Splitting Strings

We can use the split() function to a split a string on a particular separator character. Note also the usage of .[0] to select the first index from the split array.

Click Run or press ⌘Enter

#Conditional Logic

We can use if to create expressions

Click Run or press ⌘Enter

#Handling Null Values

Null values can often mess up logic in our scripts, so we can filter them all out using map and select

Click Run or press ⌘Enter

#Formatting Output

Sometimes we don’t want JSON output. We want it in a particular string format. Note the use of the -r flag, it makes the output raw. Without it, it would be displayed with quote marks around it.

Click Run or press ⌘Enter

#Multiple Outputs

Curly braces create a new object, which we can use for multiple outputs:

Click Run or press ⌘Enter

#Dealing with Nested Items

Photo by cottonbro studio

JSON is very commonly used to store nested objects, and we often need to traverse or manipulate such structures. jq gives us all the tools we need to make it easy:

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#Recursive Descent

We can use .. to recursively descend through a tree of an object.

Click Run or press ⌘Enter

#Filtering Nested Arrays

Click Run or press ⌘Enter

#Flattening Nested JSON Objects

Often, we just want all the key-values, and flattening the object may be the most convenient way to go.

This is an example where the operation we want to do is fairly straightforward, but the program looks way too scary.

Let’s try to break it down:

• It takes a JSON input and applies the paths function, which returns an array of all possible paths to the values in the JSON object. Each path is itself an array of keys or indices that can be used to access the value. For example, if the input is {"a": {"b": 1, "c": [2, 3]}}, then the paths function will return [[], ["a"], ["a", "b"], ["a", "c"], ["a", "c", 0], ["a", "c", 1]].
• The query then assigns this array to a variable $p using the as keyword, which can be used to store intermediate results for later use.
• The query then applies the select function, which filters the array based on a condition. The condition is getpath($p) | type != "object", which means that only the paths that lead to values that are not objects are selected. The getpath function takes a path and returns the value at that path in the JSON input. The type function returns the type of the value, such as "string", "number", "array", or "object". For example, getpath(["a", "b"]) | type will return "number" for the input {"a": {"b": 1, "c": [2, 3]}}.
• The query then applies another pipe, which passes the filtered array to the next filter. The next filter is ($p | join(".")) + " = " + (getpath($p) | tostring), which constructs a string for each path and its corresponding value. The join function takes an array and concatenates its elements with a separator, in this case a dot. The tostring function converts any value to a string representation. The + operator concatenates strings. For example, (["a", "b"] | join(".")) + " = " + (getpath(["a", "b"]) | tostring) will return "a.b = 1" for the input {"a": {"b": 1, "c": [2, 3]}}.
• The query then outputs the resulting strings, one per line, to the standard output.

Click Run or press ⌘Enter

#Recursive Object Manipulation

We can use the recurse as well, to traverse a tree.

Click Run or press ⌘Enter

#Complex Object Transformation

Click Run or press ⌘Enter

#Walk through object and apply a transformation conditionally

The walk() function provides a convenient way to traverse a nested object and apply some transformation to it.

Click Run or press ⌘Enter

#Statistical Operations

Photo by Leeloo Thefirst

jq is incredibly handy for doing quick and dirty statistical analysis in the field. Here’s most of the common operations related to that

#Sorting Arrays

Sorting an array is a basic operation that is useful for many things in statistics.

Click Run or press ⌘Enter

#Extracting Unique Values from an Array

Extracting unique values from an array is another fairly basic operation that we need for many things.

Click Run or press ⌘Enter

#Calculating Averages

Calculating the mean or average of a dataset is a common statistical operation we may often need to do

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#Grouping and Aggregating

We can group an array of objects by a particular key and get an aggregated value of the other keys fairly easily:

Click Run or press ⌘Enter

#Filtering after Aggregation

Click Run or press ⌘Enter

#Custom Aggregation with reduce

We can also use reduce to perform a single-output aggregation from an array

Click Run or press ⌘Enter

#Calculating Histogram Bins

We may want to calculate a histogram from an array of data.

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#Other Common Operations

These are some other common operations I frequently find myself doing every day, but I couldn’t think of a better way to categorize them.

#Extracting Values Based on Multiple Conditions

We can combine multiple conditions in a select call. The test() function is used to check if the passed string contains one of the substrings or not.

Click Run or press ⌘Enter

#Formatting Unix Timestamps

Various tools emit Unix Timestamps, and we can use the handy strftime function to format it so it’s easier to understand at a glace.

Click Run or press ⌘Enter

#Enumerating by Top Level Key and Value

Click Run or press ⌘Enter

#Closing Thoughts

Whew! That’s been a long article 😅 If you’re still here, then I appreciate you staying till the very end.

I hope you’ve learned something new, and that you’ll be able to quickly identify use cases for jq in your current workflow and apply your learnings there.

Important: Enjoying this article so far? Check out this FREE practical project on DataWars

#How Does This Article Work?

• I have used an Astro component JqView to create interactive jq sandboxes.
• The WebAssembly build of jq, as well as all the code for calling out to it is provided by BioWasm.
• When the “Run” button is clicked, it executes jq via WebAssembly and displays the output.
• Since I am not good at front-end, this was a substantial learning experience for me.

#Get In Touch

If you have any suggestions on how this may be improved, errors that I might have made, or you just want to discuss any other topic, please feel free to email me. I always love to hear from you.

#Extra Resources

• JQ Manual: The official JQ manual. Covers everything, but a bit difficult to digest.
• Learn JQ the Hard Way: A good series of blog posts covering introductory theoretical aspects of jq.
• JSON Wrangling with jq: Another interactive series on jq that dives into more detail on the theoretical aspects.

#Changelog

• 2026-02-21
  • Ported interactive examples to a proper Astro JqView component, replacing the old Alpine.js + custom element approach
  • Removed Alpine.js dependency — all interactivity is now vanilla JS
  • Converted article from Markdown to MDX
• 2023-11-29
  • Add a “Reset” button
  • Fixed some examples that were incorrect in the non javascript version of this page
  • Added some more resources
  • Added explanation for Flattening Nested JSON Objects