JMESPath Examples Library

60+ practical JMESPath examples for filtering, projections, functions, and AWS-style JSON. Load any example into the playground and try it online.

Open playground JMESPath create CSV

Basic selections

10 examples

Select all names

Simple projection

Expression

people[].name

Input JSON

{
  "people": [
    { "name": "Alice", "age": 34, "city": "NYC", "skills": ["aws", "python"] },
    { "name": "Bob", "age": 28, "city": "Seattle", "skills": ["node"] },
    { "name": "Cara", "age": 41, "city": "NYC", "skills": ["go", "aws", "k8s"] }
  ]
}

Output

["Alice","Bob","Cara"]

First person

Index access

Expression

people[0]

Input JSON

{
  "people": [
    { "name": "Alice", "age": 34, "city": "NYC", "skills": ["aws", "python"] },
    { "name": "Bob", "age": 28, "city": "Seattle", "skills": ["node"] },
    { "name": "Cara", "age": 41, "city": "NYC", "skills": ["go", "aws", "k8s"] }
  ]
}

Output

{"name":"Alice","age":34,"city":"NYC","skills":["aws","python"]}

Slice first two

Array slicing

Expression

people[:2].name

Input JSON

{
  "people": [
    { "name": "Alice", "age": 34, "city": "NYC", "skills": ["aws", "python"] },
    { "name": "Bob", "age": 28, "city": "Seattle", "skills": ["node"] },
    { "name": "Cara", "age": 41, "city": "NYC", "skills": ["go", "aws", "k8s"] }
  ]
}

Output

["Alice","Bob"]

All skills nested

Nested projection

Expression

people[].skills[]

Input JSON

{
  "people": [
    { "name": "Alice", "age": 34, "city": "NYC", "skills": ["aws", "python"] },
    { "name": "Bob", "age": 28, "city": "Seattle", "skills": ["node"] },
    { "name": "Cara", "age": 41, "city": "NYC", "skills": ["go", "aws", "k8s"] }
  ]
}

Output

["aws","python","node","go","aws","k8s"]

Cities unique

Unique values

Expression

people[].city | unique(@)

Input JSON

{
  "people": [
    { "name": "Alice", "age": 34, "city": "NYC", "skills": ["aws", "python"] },
    { "name": "Bob", "age": 28, "city": "Seattle", "skills": ["node"] },
    { "name": "Cara", "age": 41, "city": "NYC", "skills": ["go", "aws", "k8s"] }
  ]
}

Output

["NYC","Seattle"]

Count people

length function

Expression

length(people)

Input JSON

{
  "people": [
    { "name": "Alice", "age": 34, "city": "NYC", "skills": ["aws", "python"] },
    { "name": "Bob", "age": 28, "city": "Seattle", "skills": ["node"] },
    { "name": "Cara", "age": 41, "city": "NYC", "skills": ["go", "aws", "k8s"] }
  ]
}

Output

Check first skill

Nested index

Expression

people[0].skills[0]

Input JSON

{
  "people": [
    { "name": "Alice", "age": 34, "city": "NYC", "skills": ["aws", "python"] },
    { "name": "Bob", "age": 28, "city": "Seattle", "skills": ["node"] },
    { "name": "Cara", "age": 41, "city": "NYC", "skills": ["go", "aws", "k8s"] }
  ]
}

Output

"aws"

Hash projection

Rename keys

Expression

people[].{Name: name, City: city}

Input JSON

{
  "people": [
    { "name": "Alice", "age": 34, "city": "NYC", "skills": ["aws", "python"] },
    { "name": "Bob", "age": 28, "city": "Seattle", "skills": ["node"] },
    { "name": "Cara", "age": 41, "city": "NYC", "skills": ["go", "aws", "k8s"] }
  ]
}

Output

[{"Name":"Alice","City":"NYC"},{"Name":"Bob","City":"Seattle"},{"Name":"Cara","City":"NYC"}]

Deep wildcard

Wildcard on arrays

Expression

people[*]

Input JSON

{
  "people": [
    { "name": "Alice", "age": 34, "city": "NYC", "skills": ["aws", "python"] },
    { "name": "Bob", "age": 28, "city": "Seattle", "skills": ["node"] },
    { "name": "Cara", "age": 41, "city": "NYC", "skills": ["go", "aws", "k8s"] }
  ]
}

Output

[...]

Map with pipe

Pipe to map

Expression

people | map(&name, @)

Input JSON

{
  "people": [
    { "name": "Alice", "age": 34, "city": "NYC", "skills": ["aws", "python"] },
    { "name": "Bob", "age": 28, "city": "Seattle", "skills": ["node"] },
    { "name": "Cara", "age": 41, "city": "NYC", "skills": ["go", "aws", "k8s"] }
  ]
}

Output

["Alice","Bob","Cara"]

Filters & conditions

15 examples

Age > 30

Numeric filter

Expression

people[?age > `30`].name

Input JSON

{
  "people": [
    { "name": "Alice", "age": 34, "city": "NYC", "skills": ["aws", "python"] },
    { "name": "Bob", "age": 28, "city": "Seattle", "skills": ["node"] },
    { "name": "Cara", "age": 41, "city": "NYC", "skills": ["go", "aws", "k8s"] }
  ]
}

Output

["Alice","Cara"]

City equals

String match

Expression

people[?city=='NYC']

Input JSON

{
  "people": [
    { "name": "Alice", "age": 34, "city": "NYC", "skills": ["aws", "python"] },
    { "name": "Bob", "age": 28, "city": "Seattle", "skills": ["node"] },
    { "name": "Cara", "age": 41, "city": "NYC", "skills": ["go", "aws", "k8s"] }
  ]
}

Output

[...]

Has skill aws

contains on array

Expression

people[?contains(skills, 'aws')].name

Input JSON

{
  "people": [
    { "name": "Alice", "age": 34, "city": "NYC", "skills": ["aws", "python"] },
    { "name": "Bob", "age": 28, "city": "Seattle", "skills": ["node"] },
    { "name": "Cara", "age": 41, "city": "NYC", "skills": ["go", "aws", "k8s"] }
  ]
}

Output

["Alice","Cara"]

Missing skill

Negated contains

Expression

people[? !contains(skills, 'aws')].name

Input JSON

{
  "people": [
    { "name": "Alice", "age": 34, "city": "NYC", "skills": ["aws", "python"] },
    { "name": "Bob", "age": 28, "city": "Seattle", "skills": ["node"] },
    { "name": "Cara", "age": 41, "city": "NYC", "skills": ["go", "aws", "k8s"] }
  ]
}

Output

["Bob"]

Between totals

Numeric range

Expression

orders[?total > `50` && total < `180`].id

Input JSON

{
  "orders": [
    { "id": "o-100", "total": 120.5, "items": [{"sku":"sku-1","qty":2},{"sku":"sku-2","qty":1}] },
    { "id": "o-101", "total": 42, "items": [{"sku":"sku-3","qty":5}] },
    { "id": "o-102", "total": 200, "items": [] }
  ]
}

Output

["o-100"]

Empty items

length filter

Expression

orders[?length(items)==`0`].id

Input JSON

{
  "orders": [
    { "id": "o-100", "total": 120.5, "items": [{"sku":"sku-1","qty":2},{"sku":"sku-2","qty":1}] },
    { "id": "o-101", "total": 42, "items": [{"sku":"sku-3","qty":5}] },
    { "id": "o-102", "total": 200, "items": [] }
  ]
}

Output

["o-102"]

Out of stock

Zero stock

Expression

inventory[?stock==`0`].name

Input JSON

{
  "inventory": [
    { "name": "widget", "stock": 10, "price": 5.5, "tags": ["sale","blue"] },
    { "name": "gizmo", "stock": 0, "price": 15, "tags": ["featured"] },
    { "name": "bolt", "stock": 44, "price": 0.2, "tags": [] }
  ]
}

Output

["gizmo"]

Stock low

< 5 units

Expression

inventory[?stock < `5`].name

Input JSON

{
  "inventory": [
    { "name": "widget", "stock": 10, "price": 5.5, "tags": ["sale","blue"] },
    { "name": "gizmo", "stock": 0, "price": 15, "tags": ["featured"] },
    { "name": "bolt", "stock": 44, "price": 0.2, "tags": [] }
  ]
}

Output

["gizmo"]

Tagged sale

Tag filter

Expression

inventory[?contains(tags, 'sale')]

Input JSON

{
  "inventory": [
    { "name": "widget", "stock": 10, "price": 5.5, "tags": ["sale","blue"] },
    { "name": "gizmo", "stock": 0, "price": 15, "tags": ["featured"] },
    { "name": "bolt", "stock": 44, "price": 0.2, "tags": [] }
  ]
}

Output

[...]

City not NYC

Inequality

Expression

people[?city!='NYC'].name

Input JSON

{
  "people": [
    { "name": "Alice", "age": 34, "city": "NYC", "skills": ["aws", "python"] },
    { "name": "Bob", "age": 28, "city": "Seattle", "skills": ["node"] },
    { "name": "Cara", "age": 41, "city": "NYC", "skills": ["go", "aws", "k8s"] }
  ]
}

Output

["Bob"]

Age and city

Multi condition

Expression

people[?age > `30` && city=='NYC'].name

Input JSON

{
  "people": [
    { "name": "Alice", "age": 34, "city": "NYC", "skills": ["aws", "python"] },
    { "name": "Bob", "age": 28, "city": "Seattle", "skills": ["node"] },
    { "name": "Cara", "age": 41, "city": "NYC", "skills": ["go", "aws", "k8s"] }
  ]
}

Output

["Alice","Cara"]

Exists check

Truthy key

Expression

people[?skills]

Input JSON

{
  "people": [
    { "name": "Alice", "age": 34, "city": "NYC", "skills": ["aws", "python"] },
    { "name": "Bob", "age": 28, "city": "Seattle", "skills": ["node"] },
    { "name": "Cara", "age": 41, "city": "NYC", "skills": ["go", "aws", "k8s"] }
  ]
}

Output

[...]

Length of skills > 2

length on array

Expression

people[?length(skills) > `2`].name

Input JSON

{
  "people": [
    { "name": "Alice", "age": 34, "city": "NYC", "skills": ["aws", "python"] },
    { "name": "Bob", "age": 28, "city": "Seattle", "skills": ["node"] },
    { "name": "Cara", "age": 41, "city": "NYC", "skills": ["go", "aws", "k8s"] }
  ]
}

Output

["Cara"]

Price above 1

Numeric filter inventory

Expression

inventory[?price > `1`].name

Input JSON

{
  "inventory": [
    { "name": "widget", "stock": 10, "price": 5.5, "tags": ["sale","blue"] },
    { "name": "gizmo", "stock": 0, "price": 15, "tags": ["featured"] },
    { "name": "bolt", "stock": 44, "price": 0.2, "tags": [] }
  ]
}

Output

["widget","gizmo"]

Not null city

Filter non-null

Expression

people[?city!=null]

Input JSON

{
  "people": [
    { "name": "Alice", "age": 34, "city": "NYC", "skills": ["aws", "python"] },
    { "name": "Bob", "age": 28, "city": "Seattle", "skills": ["node"] },
    { "name": "Cara", "age": 41, "city": "NYC", "skills": ["go", "aws", "k8s"] }
  ]
}

Output

[...]

Projections & transforms

12 examples

Order totals

Project totals only

Expression

orders[].{OrderId:id, Total:total}

Input JSON

{
  "orders": [
    { "id": "o-100", "total": 120.5, "items": [{"sku":"sku-1","qty":2},{"sku":"sku-2","qty":1}] },
    { "id": "o-101", "total": 42, "items": [{"sku":"sku-3","qty":5}] },
    { "id": "o-102", "total": 200, "items": [] }
  ]
}

Output

[{"OrderId":"o-100","Total":120.5},{"OrderId":"o-101","Total":42},{"OrderId":"o-102","Total":200}]

Flatten order items

Flatten nested arrays

Expression

orders[].items[].{Sku: sku, Qty: qty}

Input JSON

{
  "orders": [
    { "id": "o-100", "total": 120.5, "items": [{"sku":"sku-1","qty":2},{"sku":"sku-2","qty":1}] },
    { "id": "o-101", "total": 42, "items": [{"sku":"sku-3","qty":5}] },
    { "id": "o-102", "total": 200, "items": [] }
  ]
}

Output

[{"Sku":"sku-1","Qty":2},{"Sku":"sku-2","Qty":1},{"Sku":"sku-3","Qty":5}]

Name to city map

Object projection

Expression

{NYC: people[?city=='NYC'].name, Seattle: people[?city=='Seattle'].name}

Input JSON

{
  "people": [
    { "name": "Alice", "age": 34, "city": "NYC", "skills": ["aws", "python"] },
    { "name": "Bob", "age": 28, "city": "Seattle", "skills": ["node"] },
    { "name": "Cara", "age": 41, "city": "NYC", "skills": ["go", "aws", "k8s"] }
  ]
}

Output

{"NYC":["Alice","Cara"],"Seattle":["Bob"]}

Array of objects

Multi-select hash

Expression

people[].{Name:name, Skills:skills}

Input JSON

{
  "people": [
    { "name": "Alice", "age": 34, "city": "NYC", "skills": ["aws", "python"] },
    { "name": "Bob", "age": 28, "city": "Seattle", "skills": ["node"] },
    { "name": "Cara", "age": 41, "city": "NYC", "skills": ["go", "aws", "k8s"] }
  ]
}

Output

[...]

Totals summary

Aggregate with sum

Expression

sum(orders[].total)

Input JSON

{
  "orders": [
    { "id": "o-100", "total": 120.5, "items": [{"sku":"sku-1","qty":2},{"sku":"sku-2","qty":1}] },
    { "id": "o-101", "total": 42, "items": [{"sku":"sku-3","qty":5}] },
    { "id": "o-102", "total": 200, "items": [] }
  ]
}

Output

362.5

Inventory value

Multiply fields

Expression

inventory[].{Name:name, Value: price * stock}

Input JSON

{
  "inventory": [
    { "name": "widget", "stock": 10, "price": 5.5, "tags": ["sale","blue"] },
    { "name": "gizmo", "stock": 0, "price": 15, "tags": ["featured"] },
    { "name": "bolt", "stock": 44, "price": 0.2, "tags": [] }
  ]
}

Output

[{"Name":"widget","Value":55},{"Name":"gizmo","Value":0},{"Name":"bolt","Value":8.8}]

Team counts

Nested projection per team

Expression

departments[].teams[].{Team:name, Headcount:headcount}

Input JSON

{
  "departments": [
    { "name": "Engineering", "teams": [{"name":"Platform","headcount":12},{"name":"Data","headcount":7}] },
    { "name": "Support", "teams": [{"name":"Tier1","headcount":15},{"name":"Tier2","headcount":5}] }
  ]
}

Output

[{"Team":"Platform","Headcount":12},{"Team":"Data","Headcount":7},{"Team":"Tier1","Headcount":15},{"Team":"Tier2","Headcount":5}]

Default value

Handle missing tags

Expression

inventory[].{Name:name, FirstTag: tags[0] || 'none'}

Input JSON

{
  "inventory": [
    { "name": "widget", "stock": 10, "price": 5.5, "tags": ["sale","blue"] },
    { "name": "gizmo", "stock": 0, "price": 15, "tags": ["featured"] },
    { "name": "bolt", "stock": 44, "price": 0.2, "tags": [] }
  ]
}

Output

[{"Name":"widget","FirstTag":"sale"},{"Name":"gizmo","FirstTag":"featured"},{"Name":"bolt","FirstTag":"none"}]

Rename and sort

Projection with sort

Expression

sort_by(people, &age)[].{Name:name, Age:age}

Input JSON

{
  "people": [
    { "name": "Alice", "age": 34, "city": "NYC", "skills": ["aws", "python"] },
    { "name": "Bob", "age": 28, "city": "Seattle", "skills": ["node"] },
    { "name": "Cara", "age": 41, "city": "NYC", "skills": ["go", "aws", "k8s"] }
  ]
}

Output

[...]

Cities with counts

Group projection

Expression

{NYC:length(people[?city=='NYC']), Seattle:length(people[?city=='Seattle'])}

Input JSON

{
  "people": [
    { "name": "Alice", "age": 34, "city": "NYC", "skills": ["aws", "python"] },
    { "name": "Bob", "age": 28, "city": "Seattle", "skills": ["node"] },
    { "name": "Cara", "age": 41, "city": "NYC", "skills": ["go", "aws", "k8s"] }
  ]
}

Output

{"NYC":2,"Seattle":1}

Item quantities per order

Map with pipe

Expression

orders[].{id:id, quantities: items[].qty}

Input JSON

{
  "orders": [
    { "id": "o-100", "total": 120.5, "items": [{"sku":"sku-1","qty":2},{"sku":"sku-2","qty":1}] },
    { "id": "o-101", "total": 42, "items": [{"sku":"sku-3","qty":5}] },
    { "id": "o-102", "total": 200, "items": [] }
  ]
}

Output

[...]

Extract totals and ids

Two arrays

Expression

{Ids: orders[].id, Totals: orders[].total}

Input JSON

{
  "orders": [
    { "id": "o-100", "total": 120.5, "items": [{"sku":"sku-1","qty":2},{"sku":"sku-2","qty":1}] },
    { "id": "o-101", "total": 42, "items": [{"sku":"sku-3","qty":5}] },
    { "id": "o-102", "total": 200, "items": [] }
  ]
}

Output

{"Ids":["o-100","o-101","o-102"],"Totals":[120.5,42,200]}

Functions

17 examples

Max total

max_by

Expression

max_by(orders, &total).id

Input JSON

{
  "orders": [
    { "id": "o-100", "total": 120.5, "items": [{"sku":"sku-1","qty":2},{"sku":"sku-2","qty":1}] },
    { "id": "o-101", "total": 42, "items": [{"sku":"sku-3","qty":5}] },
    { "id": "o-102", "total": 200, "items": [] }
  ]
}

Output

"o-102"

Min stock

min_by

Expression

min_by(inventory, &stock).name

Input JSON

{
  "inventory": [
    { "name": "widget", "stock": 10, "price": 5.5, "tags": ["sale","blue"] },
    { "name": "gizmo", "stock": 0, "price": 15, "tags": ["featured"] },
    { "name": "bolt", "stock": 44, "price": 0.2, "tags": [] }
  ]
}

Output

"gizmo"

Sort by price desc

reverse + sort

Expression

reverse(sort_by(inventory, &price))[].name

Input JSON

{
  "inventory": [
    { "name": "widget", "stock": 10, "price": 5.5, "tags": ["sale","blue"] },
    { "name": "gizmo", "stock": 0, "price": 15, "tags": ["featured"] },
    { "name": "bolt", "stock": 44, "price": 0.2, "tags": [] }
  ]
}

Output

["gizmo","widget","bolt"]

Average price

avg

Expression

avg(inventory[].price)

Input JSON

{
  "inventory": [
    { "name": "widget", "stock": 10, "price": 5.5, "tags": ["sale","blue"] },
    { "name": "gizmo", "stock": 0, "price": 15, "tags": ["featured"] },
    { "name": "bolt", "stock": 44, "price": 0.2, "tags": [] }
  ]
}

Output

6.9

Join tags

join

Expression

inventory[].{name:name, tags: join(',', tags)}

Input JSON

{
  "inventory": [
    { "name": "widget", "stock": 10, "price": 5.5, "tags": ["sale","blue"] },
    { "name": "gizmo", "stock": 0, "price": 15, "tags": ["featured"] },
    { "name": "bolt", "stock": 44, "price": 0.2, "tags": [] }
  ]
}

Output

[...]

Type of fields

type function

Expression

people[].{name:name, type:type(age)}

Input JSON

{
  "people": [
    { "name": "Alice", "age": 34, "city": "NYC", "skills": ["aws", "python"] },
    { "name": "Bob", "age": 28, "city": "Seattle", "skills": ["node"] },
    { "name": "Cara", "age": 41, "city": "NYC", "skills": ["go", "aws", "k8s"] }
  ]
}

Output

[...]

Keys of object

keys

Expression

keys(inventory[0])

Input JSON

{
  "inventory": [
    { "name": "widget", "stock": 10, "price": 5.5, "tags": ["sale","blue"] },
    { "name": "gizmo", "stock": 0, "price": 15, "tags": ["featured"] },
    { "name": "bolt", "stock": 44, "price": 0.2, "tags": [] }
  ]
}

Output

["name","stock","price","tags"]

Values of object

values

Expression

values(inventory[0])

Input JSON

{
  "inventory": [
    { "name": "widget", "stock": 10, "price": 5.5, "tags": ["sale","blue"] },
    { "name": "gizmo", "stock": 0, "price": 15, "tags": ["featured"] },
    { "name": "bolt", "stock": 44, "price": 0.2, "tags": [] }
  ]
}

Output

["widget",10,5.5,["sale","blue"]]

Not null selection

not_null

Expression

not_null(inventory[0].tags[0], 'fallback')

Input JSON

{
  "inventory": [
    { "name": "widget", "stock": 10, "price": 5.5, "tags": ["sale","blue"] },
    { "name": "gizmo", "stock": 0, "price": 15, "tags": ["featured"] },
    { "name": "bolt", "stock": 44, "price": 0.2, "tags": [] }
  ]
}

Output

"sale"

Merge objects

merge

Expression

merge({a:1}, {b:2})

Input JSON

{
  "people": [
    { "name": "Alice", "age": 34, "city": "NYC", "skills": ["aws", "python"] },
    { "name": "Bob", "age": 28, "city": "Seattle", "skills": ["node"] },
    { "name": "Cara", "age": 41, "city": "NYC", "skills": ["go", "aws", "k8s"] }
  ]
}

Output

{"a":1,"b":2}

To array

to_array

Expression

to_array(people[0].name)

Input JSON

{
  "people": [
    { "name": "Alice", "age": 34, "city": "NYC", "skills": ["aws", "python"] },
    { "name": "Bob", "age": 28, "city": "Seattle", "skills": ["node"] },
    { "name": "Cara", "age": 41, "city": "NYC", "skills": ["go", "aws", "k8s"] }
  ]
}

Output

["Alice"]

To string

to_string number

Expression

to_string(inventory[0].stock)

Input JSON

{
  "inventory": [
    { "name": "widget", "stock": 10, "price": 5.5, "tags": ["sale","blue"] },
    { "name": "gizmo", "stock": 0, "price": 15, "tags": ["featured"] },
    { "name": "bolt", "stock": 44, "price": 0.2, "tags": [] }
  ]
}

Output

"10"

To number

to_number

Expression

to_number('42')

Input JSON

{
  "people": [
    { "name": "Alice", "age": 34, "city": "NYC", "skills": ["aws", "python"] },
    { "name": "Bob", "age": 28, "city": "Seattle", "skills": ["node"] },
    { "name": "Cara", "age": 41, "city": "NYC", "skills": ["go", "aws", "k8s"] }
  ]
}

Output

Ceiling price

ceil

Expression

ceil(inventory[0].price)

Input JSON

{
  "inventory": [
    { "name": "widget", "stock": 10, "price": 5.5, "tags": ["sale","blue"] },
    { "name": "gizmo", "stock": 0, "price": 15, "tags": ["featured"] },
    { "name": "bolt", "stock": 44, "price": 0.2, "tags": [] }
  ]
}

Output

Floor price

floor

Expression

floor(inventory[0].price)

Input JSON

{
  "inventory": [
    { "name": "widget", "stock": 10, "price": 5.5, "tags": ["sale","blue"] },
    { "name": "gizmo", "stock": 0, "price": 15, "tags": ["featured"] },
    { "name": "bolt", "stock": 44, "price": 0.2, "tags": [] }
  ]
}

Output

Starts with tag

starts_with

Expression

inventory[0].tags[?starts_with(@, 's')]

Input JSON

{
  "inventory": [
    { "name": "widget", "stock": 10, "price": 5.5, "tags": ["sale","blue"] },
    { "name": "gizmo", "stock": 0, "price": 15, "tags": ["featured"] },
    { "name": "bolt", "stock": 44, "price": 0.2, "tags": [] }
  ]
}

Output

["sale"]

Ends with check

ends_with

Expression

people[?ends_with(city, 'C')].name

Input JSON

{
  "people": [
    { "name": "Alice", "age": 34, "city": "NYC", "skills": ["aws", "python"] },
    { "name": "Bob", "age": 28, "city": "Seattle", "skills": ["node"] },
    { "name": "Cara", "age": 41, "city": "NYC", "skills": ["go", "aws", "k8s"] }
  ]
}

Output

["Alice","Cara"]

Complex queries

10 examples

Pipeline filter then project

Pipe usage

Expression

people | [?age > `30`] | [].{Name:name, City:city}

Input JSON

{
  "people": [
    { "name": "Alice", "age": 34, "city": "NYC", "skills": ["aws", "python"] },
    { "name": "Bob", "age": 28, "city": "Seattle", "skills": ["node"] },
    { "name": "Cara", "age": 41, "city": "NYC", "skills": ["go", "aws", "k8s"] }
  ]
}

Output

[...]

Top spender order

max_by with pipe

Expression

max_by(orders, &total) | {Id: id, Total: total}

Input JSON

{
  "orders": [
    { "id": "o-100", "total": 120.5, "items": [{"sku":"sku-1","qty":2},{"sku":"sku-2","qty":1}] },
    { "id": "o-101", "total": 42, "items": [{"sku":"sku-3","qty":5}] },
    { "id": "o-102", "total": 200, "items": [] }
  ]
}

Output

{"Id":"o-102","Total":200}

Flatten items

Flatten without parent id

Expression

orders[].items[]

Input JSON

{
  "orders": [
    { "id": "o-100", "total": 120.5, "items": [{"sku":"sku-1","qty":2},{"sku":"sku-2","qty":1}] },
    { "id": "o-101", "total": 42, "items": [{"sku":"sku-3","qty":5}] },
    { "id": "o-102", "total": 200, "items": [] }
  ]
}

Output

[{"sku":"sku-1","qty":2},{"sku":"sku-2","qty":1},{"sku":"sku-3","qty":5}]

Group cities

Buckets

Expression

{NYC: people[?city=='NYC'], Others: people[?city!='NYC']}

Input JSON

{
  "people": [
    { "name": "Alice", "age": 34, "city": "NYC", "skills": ["aws", "python"] },
    { "name": "Bob", "age": 28, "city": "Seattle", "skills": ["node"] },
    { "name": "Cara", "age": 41, "city": "NYC", "skills": ["go", "aws", "k8s"] }
  ]
}

Output

[...]

Skills with counts

Flatten then count

Expression

people[].skills[] | group_by(@, @) | [].{skill: @[0], count: length(@)}

Input JSON

{
  "people": [
    { "name": "Alice", "age": 34, "city": "NYC", "skills": ["aws", "python"] },
    { "name": "Bob", "age": 28, "city": "Seattle", "skills": ["node"] },
    { "name": "Cara", "age": 41, "city": "NYC", "skills": ["go", "aws", "k8s"] }
  ]
}

Output

[...]

Ensure CSV-ready shape

Normalize with defaults

Expression

inventory[].{Name:name, Stock: stock || `0`, Price: price || `0`}

Input JSON

{
  "inventory": [
    { "name": "widget", "stock": 10, "price": 5.5, "tags": ["sale","blue"] },
    { "name": "gizmo", "stock": 0, "price": 15, "tags": ["featured"] },
    { "name": "bolt", "stock": 44, "price": 0.2, "tags": [] }
  ]
}

Output

[...]

Filter items over qty 2

Nested filter

Expression

orders[].items[?qty > `2`]

Input JSON

{
  "orders": [
    { "id": "o-100", "total": 120.5, "items": [{"sku":"sku-1","qty":2},{"sku":"sku-2","qty":1}] },
    { "id": "o-101", "total": 42, "items": [{"sku":"sku-3","qty":5}] },
    { "id": "o-102", "total": 200, "items": [] }
  ]
}

Output

[...]

Sum quantities per order

math with sum

Expression

orders[].{id:id, totalQty: sum(items[].qty)}

Input JSON

{
  "orders": [
    { "id": "o-100", "total": 120.5, "items": [{"sku":"sku-1","qty":2},{"sku":"sku-2","qty":1}] },
    { "id": "o-101", "total": 42, "items": [{"sku":"sku-3","qty":5}] },
    { "id": "o-102", "total": 200, "items": [] }
  ]
}

Output

[...]

Sort by headcount

Nested sort

Expression

departments[].teams | [].sort_by(@, &headcount)[-1]

Input JSON

{
  "departments": [
    { "name": "Engineering", "teams": [{"name":"Platform","headcount":12},{"name":"Data","headcount":7}] },
    { "name": "Support", "teams": [{"name":"Tier1","headcount":15},{"name":"Tier2","headcount":5}] }
  ]
}

Output

[...]

Department to team names

map with projection

Expression

map(&{Department: name, Teams: teams[].name}, departments)

Input JSON

{
  "departments": [
    { "name": "Engineering", "teams": [{"name":"Platform","headcount":12},{"name":"Data","headcount":7}] },
    { "name": "Support", "teams": [{"name":"Tier1","headcount":15},{"name":"Tier2","headcount":5}] }
  ]
}

Output

[...]