MLAlchemy is a Python-based utility library aimed at allowing relatively safe conversion from YAML/JSON to SQLAlchemy read-only queries. One use case here is to allow RESTful web applications (written in Python) to receive YAML- or JSON-based queries for data, e.g. from a front-end JavaScript-based application.
The name "MLAlchemy" is an abbreviation for "Markup Language for SQLAlchemy".
Installation via PyPI:
> pip install mlalchemyTo get a feel for what MLAlchemy queries look like, take a look at the
following. Note: All field names are converted from camelCase or kebab-case
to snake_case prior to query execution.
Fetching all the entries from a table called Users:
from: UsersLimiting the users to only those with the last name "Michaels":
from: Users
where:
last-name: MichaelsA more complex YAML query:
from: Users
where:
$or:
last-name: Michaels
first-name: Michael
$gt:
date-of-birth: 1984-01-01The raw SQL query for the above would look like:
SELECT * FROM users WHERE
(last_name = "Michaels" OR first_name = "Michael") AND
(date_of_birth > "1984-01-01")The same queries as above, but in JSON format. To fetch all entries
in the Users table:
{
"from": "Users"
}Limiting the users to only those with the last name "Michaels":
{
"from": "Users",
"where": {
"lastName": "Michaels"
}
}And finally, the more complex query:
{
"from": "Users",
"where": {
"$or": {
"lastName": "Michaels",
"firstName": "Michael"
},
"$gt": {
"dateOfBirth": "1984-01-01"
}
}
}A simple example of how to use MLAlchemy:
from sqlalchemy import create_engine, Column, Integer, String, Date
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import sessionmaker
from mlalchemy import parse_yaml_query, parse_json_query
Base = declarative_base()
class User(Base):
__tablename__ = "users"
id = Column(Integer, primary_key=True)
first_name = Column(String)
last_name = Column(String)
date_of_birth = Column(Date)
# use an in-memory SQLite database for this example
engine = create_engine("sqlite:///:memory:")
Base.metadata.create_all(engine)
Session = sessionmaker(bind=engine)
session = Session()
# add a couple of dummy users
user1 = User(first_name="Michael", last_name="Anderson", date_of_birth=date(1980, 1, 1))
user2 = User(first_name="James", last_name="Michaels", date_of_birth=date(1976, 10, 23))
user3 = User(first_name="Andrew", last_name="Michaels", date_of_birth=date(1988, 8, 12))
session.add_all([user1, user2, user3])
session.commit()
# we need a lookup table for MLAlchemy
tables = {
"User": User
}
# try a simple YAML-based query first
all_users = parse_yaml_query("from: User").to_sqlalchemy(session, tables).all()
print(all_users)
# same query, but this time in JSON
all_users = parse_json_query("""{"from": "User"}""").to_sqlalchemy(session, tables).all()
print(all_users)
# a slightly more complex query
young_users = parse_yaml_query("""from: User
where:
$gt:
date-of-birth: 1988-01-01
""").to_sqlalchemy(session, tables).all()
print(young_users)As mentioned before, queries can either be supplied in YAML format or in JSON format to one of the respective parsers.
At present, MLAlchemy can only support selecting data from a single
table (multi-table support is planned in future). Here, the from
parameter allows you to specify the name of the table from which
to select data.
The where parameter defines, in hierarchical fashion, the structure
of the logical query to perform. There are 3 kinds of key types in
the JSON/YAML structures, as described in the following table.
| Kind | Description | Options |
|---|---|---|
| Operators | Logical (boolean) operators for combining sub-clauses | $and, $or, $not |
| Comparators | Comparative operators for comparing fields to values | $eq, $gt, $gte, $lt, $lte, $like, $neq, $in, $nin, $is |
| Field Names | The name of a field in the from table |
(Depends on table) |
Provides the ordering for the resulting query. Must either be a single field name or a list of field names, with the direction specifier in front of the field name. For example:
# Order by "field2" in ascending order
order-by: field2Another example:
# Order by "field2" in *descending* order
order-by: "-field2"A more complex example:
# Order first by "field1" in ascending order, then by "field2" in
# descending order
order-by:
- field1
- "-field2"Specifies the number of results to skip before providing results. If not specified, no results are skipped.
Specifies the maximum number of results to return. If not specified, there will be no limit to the number of returned results.
The following is an example of a relatively simple query in YAML format:
from: SomeTable
where:
- $gt:
field1: 5
- $lt:
field2: 3
order-by:
- field1
offset: 2
limit: 10This would translate into the following SQLAlchemy query:
from sqlalchemy.sql.expression import and_
session.query(SomeTable).filter(
and_(SomeTable.field1 > 5, SomeTable.field2 < 3)
) \
.order_by(SomeTable.field1) \
.offset(2) \
.limit(10)The following is an example of a more complex query in YAML format:
from: SomeTable
where:
- $or:
field1: 5
field2: something
- $not:
$like:
field3: "else%"This would translate into the following SQLAlchemy query:
from sqlalchemy.sql.expression import and_, or_, not_
session.query(SomeTable) \
.filter(
and_(
or_(
SomeTable.field1 == 5,
SomeTable.field2 == "something"
),
not_(
SomeTable.field3.like("else%")
)
)
)The following is an example of a relatively complex query in JSON format:
{
"from": "SomeTable",
"where": [
{
"$or": [
{"field1": 10},
{
"$gt": {
"field2": 5
}
}
],
"$and": [
{"field3": "somevalue"},
{"field4": "othervalue"},
{
"$or": {
"field5": 5,
"field6": 6
}
}
]
}
],
"orderBy": [
"field1",
"-field2"
],
"offset": 2,
"limit": 10
}This query would be translated into the following SQLAlchemy code:
from sqlalchemy.sql.expression import and_, or_, not_
session.query(SomeTable) \
.filter(
and_(
or_(
SomeTable.field1 == 10,
SomeTable.field2 > 5
),
and_(
SomeTable.field3 == "somevalue",
SomeTable.field4 == "othervalue",
or_(
SomeTable.field5 == 5,
SomeTable.field6 == 6
)
)
)
) \
.order_by(SomeTable.field1, SomeTable.field2.desc()) \
.offset(2) \
.limit(10)The MIT License (MIT)
Copyright (c) 2017 Thane Thomson
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