PartiQL CLI
Command line interface for executing PartiQL queries. Can be run in an interactive (REPL) mode or non-interactive.
Examples:
To run in REPL mode simply execute the executable without any arguments:
partiql
In non-interactive mode we use Ion as the format for input data which is bound to a global variable
named "input_data", in the example below /logs/log.ion is bound to "input_data":
partiql --query="SELECT * FROM input_data" --input=/logs/log.ion
The cli can output using PartiQL syntax or Ion using the --output-format option, e.g. to output binary ion:
partiql --query="SELECT * FROM input_data" --output-format=ION_BINARY --input=/logs/log.ion
To pipe input data in via stdin:
cat /logs/log.ion | partiql --query="SELECT * FROM input_data" --format=ION_BINARY > output.10n
Option Description
------ -----------
-e, --environment <File> initial global environment (optional)
-h, --help prints this help
-i, --input <File> input file, requires the query option (default: stdin)
-o, --output <File> output file, requires the query option (default: stdout)
--of, --output-format <OutputFormat: output format, requires the query option (default: PARTIQL)
(ION_TEXT|ION_BINARY|PARTIQL)>
-q, --query <String> PartiQL query, triggers non interactive mode
The CLI is built during the main Gradle build. To build it separately execute:
./gradlew :cli:build
After building, distributable jars are located in the cli/build/distributions directory (relative to the
project root).
Be sure to include the correct relative path to gradlew if you are not in the project root.
The following command will build any dependencies before starting the CLI.
./gradlew :cli:run -q --args="<command line arguments>"
To start an interactive read, eval, print loop (REPL) execute:
rlwrap ./gradlew :cli:run --console=plain
rlwrap provides command history support. It allows
the use of the up and down arrow keys to cycle through recently executed commands and remembers commands entered into
previous sessions. rlwrap is available as an optional package in all major Linux distributions and in
Homebrew on MacOS. rlwrap is not required but is highly recommended.
You will see a prompt that looks as follows:
Welcome to the PartiQL REPL!
PartiQL>
At this point you can type in SQL and press enter twice to execute it:
PartiQL> SELECT id FROM `[{id: 5, name:"bill"}, {id: 6, name:"bob"}]` WHERE name = 'bob'
|
==='
<<
{
'id': 6
}
>>
---
OK! (282 ms)
The result of previous expression is stored in the variable named _, so you can then run subsequent
expressions based on the last one.
PartiQL> SELECT id + 4 AS name FROM _
|
==='
<<
{
'name': 10
}
>>
---
OK! (16 ms)
Press control-D to exit the REPL.
To view the AST of an SQL statement, type one and press enter only once, then type !! and press enter:
PartiQL> 1 + 1
| !!
==='
(
ast
(
version
1
)
(
root
(
+
(
lit
1
)
(
lit
1
)
)
)
)
---
OK! (23 ms)
To view the AST with metadata information of an SQL statement, type one and press enter only once,
then type !? and press enter:
PartiQL> 1 + 1
| !?
==='
(
ast
(
version
1
)
(
root
(
term
(
exp
(
+
(
term
(
exp
(
lit
1
)
)
(
meta
(
$source_location
(
{
line_num:1,
char_offset:1
}
)
)
)
)
(
term
(
exp
(
lit
1
)
)
(
meta
(
$source_location
(
{
line_num:1,
char_offset:5
}
)
)
)
)
)
)
(
meta
(
$source_location
(
{
line_num:1,
char_offset:3
}
)
)
)
)
)
)
---
OK! (1 ms)
The initial environment for the REPL can be setup with a configuration file, which should be an PartiQL file with a
single struct containing the initial global environment.
For example a file named config.sql, containing the following:
{
'animals':[
{'name': 'Kumo', 'type': 'dog'},
{'name': 'Mochi', 'type': 'dog'},
{'name': 'Lilikoi', 'type': 'unicorn'}
],
'types':[
{'id': 'dog', 'is_magic': false},
{'id': 'cat', 'is_magic': false},
{'id': 'unicorn', 'is_magic': true}
]
}
Could be loaded into the REPL with animals and types bound list of struct values.
The REPL could be started up with:
$ ./gradlew :cli:run -q --console=plain --args='-e config.sql'
(Note that shell expansions such as ~ do not work within the value of the args argument.)
Or if you have extracted one of the compressed archives:
$ ./bin/partiql -e config.sql
Expressions can then use the environment defined by config.sql:
PartiQL> SELECT name, type, is_magic FROM animals, types WHERE type = id
|
==='
<<
{
'name': 'Kumo',
'type': 'dog',
'is_magic': false
},
{
'name': 'Mochi',
'type': 'dog',
'is_magic': false
},
{
'name': 'Lilikoi',
'type': 'unicorn',
'is_magic': true
}
>>
---
OK! (55 ms)
To see the current REPL environment you can use !global_env, for example for the file above:
PartiQL> !global_env
|
==='
{
'types': [
{
'id': 'dog',
'is_magic': false
},
{
'id': 'cat',
'is_magic': false
},
{
'id': 'unicorn',
'is_magic': true
}
],
'animals': [
{
'name': 'Kumo',
'type': 'dog'
},
{
'name': 'Mochi',
'type': 'dog'
},
{
'name': 'Lilikoi',
'type': 'unicorn'
}
]
}
---
OK! (2 ms)
You can also add new values to the global environment or replace existing values using !add_to_global_env. The
example bellow replaces the value bound to types
PartiQL> !add_to_global_env {'types': []}
|
==='
{
'types': []
}
---
OK! (1 ms)
PartiQL> !global_env
|
==='
{
'types': [],
'animals': [
{
'name': 'Kumo',
'type': 'dog'
},
{
'name': 'Mochi',
'type': 'dog'
},
{
'name': 'Lilikoi',
'type': 'unicorn'
}
]
}
---
OK! (0 ms)
Let's consider the following initial environment:
{
'stores':[
{
'id': 5,
'books': [
{'title':'A', 'price': 5.0, 'categories':['sci-fi', 'action']},
{'title':'B', 'price': 2.0, 'categories':['sci-fi', 'comedy']},
{'title':'C', 'price': 7.0, 'categories':['action', 'suspense']},
{'title':'D', 'price': 9.0, 'categories':['suspense']}
]
},
{
'id': 6,
'books': [
{'title':'A', 'price': 5.0, 'categories':['sci-fi', 'action']},
{'title':'E', 'price': 9.5, 'categories':['fantasy', 'comedy']},
{'title':'F', 'price': 10.0, 'categories':['history']}
]
}
]
}
If we wanted to find all books as their own rows with a price greater than 7 we can use paths on the FROM for this:
PartiQL> SELECT * FROM stores[*].books[*] AS b WHERE b.price > 7
|
==='
<<
{
'title': 'D',
'price': 9.0,
'categories': [
'suspense'
]
},
{
'title': 'E',
'price': 9.5,
'categories': [
'fantasy',
'comedy'
]
},
{
'title': 'F',
'price': 10.0,
'categories': [
'history'
]
}
>>
---
OK! (28 ms)
If you wanted to also de-normalize the store ID and title into the above rows:
PartiQL> SELECT s.id AS store, b.title AS title FROM stores AS s, @s.books AS b WHERE b.price > 7
|
==='
<<
{
'store': 5,
'title': 'D'
},
{
'store': 6,
'title': 'E'
},
{
'store': 6,
'title': 'F'
}
>>
---
OK! (30 ms)
We can also use sub-queries with paths to predicate on sub-structure without changing the
cardinality. So if we wanted to find all stores with books having prices greater than
9.5
PartiQL> SELECT * FROM stores AS s
| WHERE EXISTS(
| SELECT * FROM @s.books AS b WHERE b.price > 9.5
| )
|
==='
<<
{
'id': 6,
'books': [
{
'title': 'A',
'price': 5.0,
'categories': [
'sci-fi',
'action'
]
},
{
'title': 'E',
'price': 9.5,
'categories': [
'fantasy',
'comedy'
]
},
{
'title': 'F',
'price': 10.0,
'categories': [
'history'
]
}
]
}
>>
---
OK! (5 ms)
The REPL provides the read_file function to stream data from a file. The files needs to be placed in the folder cli.
For example:
PartiQL> SELECT city FROM read_file('data.ion') AS c, `["HI", "NY"]` AS s WHERE c.state = s
|
==='
<<
{
'city': 'Honolulu'
},
{
'city': 'Rochester'
}
>>
------
OK! (45 ms)
The REPL also has the capability to write files with the write_file function:
PartiQL> write_file('out.ion', SELECT * FROM _)
|
==='
true
------
OK! (20 ms)
Functions and expressions can be used in the global configuration as well. Consider
the following config.ion:
{
'data': read_file('data.ion')
}
The data variable will now be bound to file containing Ion:
PartiQL> SELECT * FROM data
|
==='
<<
{
'city: ;Seattle;,
'state: 'WA;
},
{
'city: 'Bellevue',
'state: 'WA'
},
{
'city: 'Honolulu',
'state: 'HI'
},
{
'city: 'Rochester',
'state: 'NY'
}
>>
------
OK! (75 ms)
The read_file function supports an optional struct argument to add additional parsing options.
Parsing delimited files can be specified with the type field with a string tsv or csv
to parse tab or comma separated values respectively.
PartiQL> read_file('simple.tsv', {'type':'tsv'})
|
==='
<<
{
_0:'title',
_1:'category',
_2:'price'
},
{
_0:'harry potter',
_1:'book',
_2:'7.99'
},
{
_0:'dot',
_1:'electronics',
_2:'49.99'
},
{
_0:'echo',
_1:'electronics',
_2:'99.99'
}
>>
----
OK! (83 ms)
The options struct can also define if the first row for delimited data should be the
column names with the header field.
PartiQL> read_file('simple.tsv', {'type': 'tsv', 'header': true})
|
==='
<<
{
'title': 'harry potter',
'category': 'book',
'price': '7.99'
},
{
'title': 'dot',
'category': 'electronics',
'price': '49.99'
},
{
'title': 'echo',
'category': 'electronics',
'price': '99.99'
}
>>
----
OK! (87 ms)
Auto conversion can also be specified numeric and timestamps in delimited data.
PartiQL> read_file('simple.tsv', {'type':'tsv', 'header':true, 'conversion':'auto'})
|
==='
<<
{
'title':' harry potter',
'category': 'book',
'price': 7.99
},
{
'title: 'dot',
'category': 'electronics',
'price': 49.99
},
{
'title: 'echo',
'category': 'electronics',
'price': 99.99
}
>>
----
OK! (96 ms)
Writing TSV/CSV data can be done by specifying the optional struct argument to specify output
format to the write_file function. Similar to the read_file function, the type field
can be used to specify tsv, csv, or ion output.
PartiQL> write_file('out.csv', {'type':'csv'}, SELECT name, type FROM animals)
|
==='
true
----
OK! (41 ms)
This would produce the following file:
$ cat out.csv
Kumo,dog
Mochi,dog
Lilikoi,unicorn
The options struct can also specify a header Boolean field to indicate whether the output
TSV/CSV should have a header row.
PartiQL> write_file('out.csv', {'type':'csv', 'header':true}, SELECT name, type FROM animals)
|
==='
true
----
OK! (39 ms)
Which would produce the following file:
$ cat out.csv
name,type
Kumo,dog
Mochi,dog
Lilikoi,unicorn