UQL (Unstructured query language) is advance query format in infinity datasource which will consolidate JSON, CSV, XML, GraphQL formats. UQL also provides ability to customize the results.

UQL is an opinionated query language designed for in-memory operations. UQL query can be formed with list of commands joined by |, in a line each. Most of the times, fields are referred within double quotes and string values are referred with single quotes. UQL was inspired by kusto query language and follows similar syntax.

UQL is still in BETA.

if your data looks like this,

[ { "id": 1, "name": { "firstName": "john", "lastName": "doe" }, "dob": "1985-01-01", "city": "chennai" }, { "id": 2, "name": { "firstName": "alice", "lastName": "bob" }, "dob": "1990-12-31", "city": "london" } ]

then the following UQL query

parse-json | extend "full name"=strcat("name.firstName",' ',"name.lastName"), "dob"=todatetime("dob") | project-away "name" | order by "full name" asc

will produce four column table (id, dob, city, full name).

Basic UQL commands

following are the basic UQL commands. All these commands are available in all the version unless specified.


project command is used to select the columns to include in the results. If you want to select a property inside a nested object, you can use dot notation. Optionally, you can also alias the fields.

parse-json | project "id", "name.firstName", "date of birth"="dob"


project-away command is exactly opposite as project. It just drops specific columns from the data. It doesn't support alias or dot notation selector.

parse-json | project-away "id", "city"

order by

order by command sorts the input based on any column. sort direction should be either asc or desc

parse-json | order by "full name" asc


extend command is similar to project. but instead of selecting the columns, it just adds/replace columns in existing data. extends expects an alias and a function.

parse-json | extend "dob"=todatetime("dob"), "city"=toupper("city")

following are some of the available functions

function keyword syntax description available from
trim trim("name") trims the string 0.8.0
trim_start trim_start("name") removes the space before 0.8.0
trim_end trim_end("name") removes the space after 0.8.0
tonumber tonumber("age") converts a string into number 0.8.0
tostring tostring("age") converts a number into string 0.8.0
todatetime todatetime("age") converts a datetime string into datetime 0.8.0
unixtime_seconds_todatetime unixtime_seconds_todatetime("dob") converts unix epoch s timestamp to datetime 0.8.0
unixtime_nanoseconds_todatetime unixtime_nanoseconds_todatetime("dob") converts unix epoch ns timestamp to datetime 0.8.0
unixtime_milliseconds_todatetime unixtime_milliseconds_todatetime("dob") converts unix epoch ms timestamp to datetime 0.8.0
unixtime_microseconds_todatetime unixtime_microseconds_todatetime("dob") converts unix epoch microsecond timestamp to datetime 0.8.0
format_datetime format_datetime("dob",'DD/MM/YYYY') converts datetime to a specific format 0.8.0
add_datetime add_datetime("dob",'-1d') adds duration to a datetime field 0.8.0
startofminute startofminute("dob") rounds the datetime field to the starting minute 0.8.0
startofhour startofhour("dob") rounds the datetime field to the starting hour 0.8.0
startofday startofday("dob") rounds the datetime field to the starting day 0.8.0
startofmonth startofmonth("dob") rounds the datetime field to the starting month 0.8.0
startofweek startofweek("dob") rounds the datetime field to the starting week 0.8.0
startofyear startofyear("dob") rounds the datetime field to the starting year 0.8.0
extract extract('regex',index,"col1") extracts part of the string field using regex and match index (0/1/..) 1.0.0
sum sum("col1","col2") sum of two or more columns 0.8.0
diff diff("col1","col2") difference between two columns 0.8.0
mul mul("col1","col2") multiplication of two columns 0.8.0
div div("col1","col2") division of two columns (col1/col2) 0.8.0
percentage percentage("col1","col2") percentage of two columns ((col1/col2)*100) 1.0.0
strcat strcat("col1","col2") concatenates two or more columns 0.8.0
split split("col1",'delimiter') splits a string using delimiter 1.0.0
replace_string replace_string("col1",'src','replacer') replace a portion of string with another 1.0.0
reverse revers("col1") reverse a string 1.0.0
floor floor("col1") calculates the floor value of given numeric field 0.8.7
ceil ceil("col1") calculates the ceil value of given numeric field 0.8.7
round round("col1") calculates the round value of given numeric field 0.8.7
sign sign("col1") calculates the sign value of given numeric field 0.8.7
pow pow("col1",3) calculates the pow value of given numeric field 0.8.7
sin sin("col1") calculates the sin value of given numeric field 0.8.7
cos cos("col1") calculates the cos value of given numeric field 0.8.7
tan tan("col1") calculates the tan value of given numeric field 0.8.7
log log("col1") calculates the log value of given numeric field 0.8.7
log2 log2("col1") calculates the log2 value of given numeric field 0.8.7
log10 log10("col1") calculates the log10 value of given numeric field 0.8.7
parse_url parse_url("col1") parses the col1 as URL 0.8.6
parse_url("col1",'pathname') returns the pathname of the URL. Options are host,hash,origin,href,protocol and search 0.8.6
parse_url("col1",'search','key1') returns the query string value for key1. 2nd arg is always search 0.8.6

For example, the data [ { "a": 12, "b" : 20 }, { "a" : 6, "b": 32} ] and the following uql query

parse-json | project "a", "triple"=sum("a","a","a"),"thrice"=mul("a",3), sum("a","b"), diff("a","b"), mul("a","b")

wil produce the following output

a,triple,thrice,sum,diff,mul 12,36,36,32,-8,240 6,18,18,38,-26,192

To apply multiple transformations over a field, repeat them with the same field name. For example, the uql query extend "name"=tolower("name"), "name"=trim("name") will apply tolower function and then trim function over the name field.

There are few other extend/project methods also available to deal with array


pack method converts array of key value pairs into a map. Example extend "foo"=pack('key1',"value1",'key1',"value2") will yield a object {key1:value1,key2:value2}


array_from_entries method builds an array of objects from entries. Example extend "foo"=array_from_entries('timestamp',[2010,2020,2030]) will yield an array [{timestamp:2010},{timestamp:2020},{timestamp:2030}]


array_to_map converts an array of entries to a map. Optionally, one can provide alias for keys instead of index. Example extend "foo"=array_to_map(['chennai','india'],'city','country') will yield { 'city': 'chennai', 'country':'india'}


summarize command aggregates the data by a string column. summarize command expects alias, summarize by fields and summarize function. Following are the valid summarize functions.

function keyword syntax description available from
count count() count of values 0.8.0
sum sum("age") sum of age 0.8.0
min min("population") min of population 0.8.0
max max("foo") max of foo 0.8.0
mean mean("foo") mean of foo 0.8.0

For example, the following data

[ { "city": "tokyo", "country": "japan", "population": 200 }, { "city": "newyork", "country": "usa", "population": 60 }, { "city": "oslo", "country": "usa", "population": 40 }, { "city": "new delhi", "country": "india", "population": 180 }, { "city": "mumbai", "country": "india", "population": 150 } ]

and the following uql query

parse-json | summarize "number of cities"=count(), "total population"=sum("population") by "country" | extend "country"=toupper("country") | order by "total population" desc

will produce the output table like this

country,number of cities,total population INDIA,2,330 JAPAN,1,200 USA,2,100


parse-json is the command to instruct the UQL to parse the response as JSON


parse-csv is the command to instruct the UQL to parse the response as CSV


parse-xml is the command to instruct the UQL to parse the response as XML


parse-yaml is used to specify that the results are in xml format


count gives the number of results.

parse-json | count


limit command restricts the number of results returned. For example, below query returns only 10 results

parse-json | limit 10


scope commands sets the context of the output data. It is useful when the results are insides nested json object.


{ "meta": { "last-updated": "2021-08-09" }, "count": 2, "users": [{ "name": "foo" }, { "name": "bar" }] }

and the following uql query just results the "users" and ignores the other root level properties.

parse-json | scope "users"


mv-expand expands multi-value properties into their own records. For example, the command mv-expand "user"="users" over following data

[ { "group": "A", "users": ["user a1", "user a2"] }, { "group": "B", "users": ["user b1"] } ]

will produce results like

[ { "group": "A", "user": "user a1" }, { "group": "A", "user": "user a2" }, { "group": "B", "user": "user b1" } ]

mv-expand should also work for non string arrays.

project kv()

project kv() command is used to convert the given object into key-value pairs.

Example: For the data { "a": {"name":"a1"}, "b": {"name":"b1"}, "c": {"name":"c1"} } and the query parse-json | project kv() will yield the following table

key value
a {"name":"a1"}
b {"name":"b1"}
c {"name":"c1"}

this command can be also used with arguments

Example: For the data { "data": { "a": {"name":"a1"}, "b": {"name":"b1"}, "c": {"name":"c1"} } } and the query parse-json | project kv("data") will yield the same results

project kv() command is available only from 0.8.7 of the plugin


jsonata command accepts a JSONata query and apply over the previous input

parse-json | scope "library" | jsonata "library.loans@$L.books@$B[$L.isbn=$B.isbn].customers[$L.customer=id].{ 'customer': name, 'book': $B.title, 'due': $L.return}" | count

Like any other command, jsonata command can be combined/piped with multiple commands. You can use JSONata for filtering the data as well.

JSONata support is available from 0.8.8 version


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