Select¶

Hint

Follow along by installing Piccolo and running piccolo playground run - see Playground.

To get all rows:

>>> await Band.select()
[{'id': 1, 'name': 'Pythonistas', 'manager': 1, 'popularity': 1000},
 {'id': 2, 'name': 'Rustaceans', 'manager': 2, 'popularity': 500}]

To get certain columns:

>>> await Band.select(Band.name)
[{'name': 'Rustaceans'}, {'name': 'Pythonistas'}]

Or use an alias to make it shorter:

>>> b = Band
>>> await b.select(b.name)
[{'name': 'Rustaceans'}, {'name': 'Pythonistas'}]

Hint

All of these examples also work synchronously using run_sync - see Sync and Async.

as_alias¶

By using as_alias, the name of the row can be overriden in the response.

>>> await Band.select(Band.name.as_alias('title'))
[{'title': 'Rustaceans'}, {'title': 'Pythonistas'}]

This is equivalent to SELECT name AS title FROM band in SQL.

Joins¶

One of the most powerful things about select is it’s support for joins.

>>> await Band.select(Band.name, Band.manager.name)
[
    {'name': 'Pythonistas', 'manager.name': 'Guido'},
    {'name': 'Rustaceans', 'manager.name': 'Graydon'}
]

The joins can go several layers deep.

>>> await Concert.select(Concert.id, Concert.band_1.manager.name)
[{'id': 1, 'band_1.manager.name': 'Guido'}]

all_columns¶

If you want all of the columns from a related table you can use all_columns, which is a useful shortcut which saves you from typing them all out:

>>> await Band.select(Band.name, Band.manager.all_columns())
[
    {'name': 'Pythonistas', 'manager.id': 1, 'manager.name': 'Guido'},
    {'name': 'Rustaceans', 'manager.id': 2, 'manager.name': 'Graydon'}
]

In Piccolo < 0.41.0 you had to explicitly unpack all_columns. This is equivalent to the code above:

>>> await Band.select(Band.name, *Band.manager.all_columns())

You can exclude some columns if you like:

>>> await Band.select(
...     Band.name,
...     Band.manager.all_columns(exclude=[Band.manager.id])
... )
[
    {'name': 'Pythonistas', 'manager.name': 'Guido'},
    {'name': 'Rustaceans', 'manager.name': 'Graydon'}
]

Strings are supported too if you prefer:

>>> await Band.select(
...     Band.name,
...     Band.manager.all_columns(exclude=['id'])
... )
[
    {'name': 'Pythonistas', 'manager.name': 'Guido'},
    {'name': 'Rustaceans', 'manager.name': 'Graydon'}
]

You can also use all_columns on the root table, which saves you time if you have lots of columns. It works identically to related tables:

>>> await Band.select(
...     Band.all_columns(exclude=[Band.id]),
...     Band.manager.all_columns(exclude=[Band.manager.id])
... )
[
    {'name': 'Pythonistas', 'popularity': 1000, 'manager.name': 'Guido'},
    {'name': 'Rustaceans', 'popularity': 500, 'manager.name': 'Graydon'}
]

Nested¶

You can also get the response as nested dictionaries, which can be very useful:

>>> await Band.select(Band.name, Band.manager.all_columns()).output(nested=True)
[
    {'name': 'Pythonistas', 'manager': {'id': 1, 'name': 'Guido'}},
    {'name': 'Rustaceans', 'manager': {'id': 2, 'manager.name': 'Graydon'}}
]

String syntax¶

You can specify the column names using a string if you prefer. The disadvantage is you won’t have tab completion, but sometimes it’s more convenient.

await Band.select('name')

# For joins:
await Band.select('manager.name')

Aggregate functions¶

Note

These can all be used in conjunction with the group_by clause.

Count¶

Hint

You can use the count query as a quick way of getting the number of rows in a table.

Returns the number of matching rows.

from piccolo.query.methods.select import Count

>> await Band.select(Count()).where(Band.popularity > 100)
[{'count': 3}]

To find out more about the options available, see Count.

Avg¶

Returns the average for a given column:

>>> from piccolo.query import Avg
>>> response = await Band.select(Avg(Band.popularity)).first()
>>> response["avg"]
750.0

Sum¶

Returns the sum for a given column:

>>> from piccolo.query import Sum
>>> response = await Band.select(Sum(Band.popularity)).first()
>>> response["sum"]
1500

Max¶

Returns the maximum for a given column:

>>> from piccolo.query import Max
>>> response = await Band.select(Max(Band.popularity)).first()
>>> response["max"]
1000

Min¶

Returns the minimum for a given column:

>>> from piccolo.query import Min
>>> response = await Band.select(Min(Band.popularity)).first()
>>> response["min"]
500

Additional features¶

You also can have multiple different aggregate functions in one query:

>>> from piccolo.query import Avg, Sum
>>> response = await Band.select(
...     Avg(Band.popularity),
...     Sum(Band.popularity)
... ).first()
>>> response
{"avg": 750.0, "sum": 1500}

And can use aliases for aggregate functions like this:

# Alternatively, you can use the `as_alias` method.
>>> response = await Band.select(
...     Avg(Band.popularity).as_alias("popularity_avg")
... ).first()
>>> response["popularity_avg"]
750.0

SelectRaw¶

In certain situations you may want to have raw SQL in your select query.

For example, if there’s a Postgres function which you want to access, which isn’t supported by Piccolo:

from piccolo.query import SelectRaw

>>> await Band.select(
...     Band.name,
...     SelectRaw("log(popularity) AS log_popularity")
... )
[{'name': 'Pythonistas', 'log_popularity': 3.0}]

Warning

Only use SQL that you trust.

Query clauses¶

batch¶

See batch.

callback¶

See callback.

columns¶

By default all columns are returned from the queried table.

# Equivalent to SELECT * from band
await Band.select()

To restrict the returned columns, either pass in the columns into the select method, or use the columns method.

# Equivalent to SELECT name from band
await Band.select(Band.name)

# Or alternatively:
await Band.select().columns(Band.name)

The columns method is additive, meaning you can chain it to add additional columns.

await Band.select().columns(Band.name).columns(Band.manager)

# Or just define it one go:
await Band.select().columns(Band.name, Band.manager)

Select¶

as_alias¶

Joins¶

all_columns¶

Nested¶

String syntax¶

Aggregate functions¶

Count¶

Avg¶

Sum¶

Max¶

Min¶

Additional features¶

SelectRaw¶

Query clauses¶

batch¶

callback¶

columns¶

distinct¶

first¶

group_by¶

limit¶

offset¶

order_by¶

output¶

where¶