Show HN: Illustrated Jq Tutorial, Hacker News

The concept of pipes

Unix pipelines were invented in 01575879 by Douglas McIlroy as a novel way of stringing together programs, where the output of one program is the input of the next one; It’s a way of creating a new program out of combining basic building blocks, McIlroy describes it by analogy as ‘screwing together data streams like a garden hose’ . This approach quickly became the UNIX philosophy of programming described by McIlroy as follows: ‘Write programs that do one thing and do it well. Write programs to work together. Write programs that handle text streams, because that is a universal interface. ‘ Lets say you want to know what are the most common words occurring within a text, the following pipeline will order the words of a text by frequency of usage:

  cat  README.md |  tr  "" " n" |  tr  -d '[:punct:]' |  sort  |  uniq  -c | sort -n -k 1 

This example is a bit like functional programming: in each step of the pipeline the output depends only on the input received via the preceding pipe, each step acts on that text input only and produces its output without writing any files, that is without side effects .

jq – a tool for manipulating structured data

jq is a very versatile tool for working with structured information in json format, the command syntax of jq is also structured by means of a processing pipeline, similar to that of a unix shell, again each processing step acts as a filter / modifier of the input received from the preceding stage. Again on might look at each of these stages as functions in a functional program. This tutorial tries to explain jq in terms of example pipelines; each example comes with links that show you the intermediate results for each stage of the processing pipeline; i think this makes it easier to understand each of the building blocks involved. You can click either on any one of the commands to show the command and how it transforms the input json structure into the output json, each pipe symbol is also a link that will show you the information that flows through it. The html for this tutorial is generated by this script

Get a single scalar values ​​

 cat s1.json | jq ‘. spec.replicas ‘

Get a single scalar values ​​(different form, as a pipeline)

 cat s1.json | jq ‘ .spec

.replicas ‘

Get two scalar values ​​

 cat s1.json | jq ‘. spec.replicas, .kind

‘

Get two scalar values ​​and concatenate / format them into a single string

 cat s1.json | jq ‘ “replicas:” (.spec.replicas | tostring) “kind : ‘ .kind ‘

Select an object from an array of object based on one of the names

 cat dep.json | jq ‘. status.conditions map (select). type==”Progressing”))

‘

Select a single key value pair from a json object

 cat ann.json | jq ‘. metadata.annotations |

to_entries

| (map (select (.key==”label1″)) from_entries ‘

Select two key value pairs from a json object

 cat ann.json | jq ‘. metadata .annotations | to_entries map (select (.key==”label1″ or .key==”label2″)) from_entries ‘

Select two key value pairs from a json object (second version)

 cat ann.json | jq ‘. metadata.annotations to_entries

(map (select (.key==(“label1”, “label2”)))

| from_entries ‘

Select all key value pairs from a json object where the name contains substring “label”

 cat ann.json | jq ‘. metadata.annotations

to_entries (map (select (.key | contains (“label”)))) from_entries ‘

Select all key value pairs from a json object where the name matches the regular expression label [1-9]

 cat ann.json | jq ‘ .metadata.annotations

| to_entries map (select (.key | test (“label [1-9]”)) )) from_entries ‘

Add another key value pair to a json object

 cat ann.json | jq ‘. metadata.annotations ={“label4”: “two”}

‘

Set all values ​​in a json object

 cat ann.json | jq ‘. metadata.annotations

to_entries (map_values) .value=”override” -value “) from_entries ‘

.

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