dataloom 1.0.2

dataloom stands as a bespoke Object-Relational Mapping (ORM) solution meticulously crafted to empower Python developers in efficiently managing diverse databases. Unlike conventional ORMs, Dataloom has been built from the ground up, providing native support for SQLite3, PostgreSQL, and MySQL. Navigate effortlessly between database engines while enjoying a tailored and performant ORM experience.

dataloom

dataloom is a lightweight and versatile Object-Relational Mapping (ORM) library for Python. With support for PostgreSQL, MySQL, and SQLite3 databases, dataloom simplifies database interactions, providing a seamless experience for developers.

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Why choose dataloom?

1. Ease of Use: dataloom offers a user-friendly interface, making it straightforward to work with.
2. Flexible SQL Driver: Write one codebase and seamlessly switch between PostgreSQL, MySQL, and SQLite3 drivers as needed.
3. Lightweight: Despite its powerful features, dataloom remains lightweight, ensuring efficient performance.
4. Comprehensive Documentation: Benefit from extensive documentation that guides users through various functionalities and use cases.
5. Active Maintenance: dataloom is actively maintained, ensuring ongoing support and updates for a reliable development experience.
6. Cross-platform Compatibility: dataloom works seamlessly across different operating systems, including Windows, macOS, and Linux.
7. Scalability: Scale your application effortlessly with dataloom, whether it's a small project or a large-scale enterprise application.

⚠️ Warning

⚠️ Experimental Status of dataloom: The dataloom module is currently in an experimental phase. As such, we strongly advise against using it in production environments until a major version is officially released and stability is ensured. During this experimental phase, the dataloom module may undergo significant changes, and its features are subject to refinement. We recommend monitoring the project updates and waiting for a stable release before incorporating it into production systems. Please exercise caution and consider alternative solutions for production use until the module reaches a stable release.

Table of Contents

• dataloom
  • Why choose dataloom?
• ⚠️ Warning
• Table of Contents
• Key Features:
• Installation
• Python Version Compatibility
• Usage
• Connection
• Dataloom Classes
  • Model Class
  • Column Class
  • PrimaryKeyColumn Class
  • ForeignKeyColumn Class
  • CreatedAtColumn Class
  • UpdatedAtColumn Class
  • Filter Class
  • ColumnValue Class
  • Order Class
  • Include
• Syncing Tables
  • 1. The sync method.
  • 2. The connect_and_sync method.
• CRUD Operations with Dataloom
  • 1. Creating a Record
    • 1. insert_one()
    • 2. insert_bulk().
  • 2. Reading records
    • 1. find_all()
    • 2. find_many()
    • 3. find_one()
    • 4. find_by_pk()
  • 3. Updating a record
    • 1. update_by_pk()
    • 2. update_one()
    • 3. update_bulk()
  • 4. Deleting a record
    • 1. delete_by_pk()
    • 2. delete_one()
    • 3. delete_bulk()
      • Warning: Potential Risk with delete_bulk()
      • Guidelines for Safe Usage
• Ordering
• Filters
• Utilities
  • inspect
• What is coming next?
• Contributing
• License

Key Features:

• Lightweight: dataloom is designed to be minimalistic and easy to use, ensuring a streamlined ORM experience without unnecessary complexities.

• Database Support: dataloom supports popular relational databases such as PostgreSQL, MySQL, and SQLite3, making it suitable for a variety of projects.

• Simplified Querying: The ORM simplifies the process of database querying, allowing developers to interact with the database using Python classes and methods rather than raw SQL queries.

• Intuitive Syntax: dataloom's syntax is intuitive and Pythonic, making it accessible for developers familiar with the Python language.

• Flexible Data Types: The ORM seamlessly handles various data types, offering flexibility in designing database schemas.

Installation

To install dataloom, you just need to run the following command using pip:

pip install dataloom

Python Version Compatibility

dataloom supports Python version 3.12 and above. Ensure that you are using a compatible version of Python before installing or using dataloom.

You can check your Python version by running:

python --version

Usage

In this section we are going to go through how you can use our orm package in your project.

Connection

To use Dataloom, you need to establish a connection with a specific database dialect. The available dialect options are mysql, postgres, and sqlite. The following is an example of how you can establish a connection with postgres database.

from dataloom import Dataloom

# Create a Dataloom instance with PostgreSQL configuration
pg_loom = Dataloom(
    dialect="postgres",
    database="hi",
    password="root",
    user="postgres",
    host="localhost",
    sql_logger="console",
    logs_filename="logs.sql",
    port=5432,
)

# Connect to the PostgreSQL database
conn = pg_loom.connect()


# Close the connection when the script completes
if __name__ == "__main__":
    conn.close()

To establish a connection with a MySQL database using Dataloom, you can use the following example:

from dataloom import Dataloom

# Create a Dataloom instance with MySQL configuration
mysql_loom = Dataloom(
    dialect="mysql",
    database="hi",
    password="root",
    user="root",
    host="localhost",
    sql_logger="console",
    logs_filename="logs.sql",
    port=3306,
)

# Connect to the MySQL database
conn = mysql_loom.connect()

# Close the connection when the script completes
if __name__ == "__main__":
    conn.close()

To establish a connection with an SQLite database using Dataloom, you can use the following example:

from dataloom import Dataloom

# Create a Dataloom instance with SQLite configuration
sqlite_loom = Dataloom(
    dialect="sqlite",
    database="hi.db",
    logs_filename="sqlite-logs.sql",
    logging=True,
    sql_logger="console",
)

# Connect to the SQLite database
conn = sqlite_loom.connect()

# Close the connection when the script completes
if __name__ == "__main__":
    conn.close()

The Dataloom class takes in the following options:

Parameter	Description	Value Type	Default Value	Required
dialect	Dialect for the database connection. Options are mysql, postgres, or sqlite	str or None	None	Yes
database	Name of the database for mysql and postgres, filename for sqlite	str or None	None	Yes
password	Password for the database user (only for mysql and postgres)	str or None	None	No
user	Database user (only for mysql and postgres)	str or None	None	No
host	Database host (only for mysql and postgres)	str or None	localhost	No
sql_logger	Enable logging for the database queries. If you don't want to see the sql logs you can set this option to None which is the default value. If you set it to file then you will see the logs in the default dataloom.sql file, you can overide this by passing a logs_filename option. Setting this option to console, then sql statements will be printed on the console.	consoleor file or None	True	No
logs_filename	Filename for the query logs	str or None	dataloom.sql	No
port	Port number for the database connection (only for mysql and postgres)	int or None	None	No

Dataloom Classes

Model Class

A model in Dataloom is a top-level class that facilitates the creation of complex SQL tables using regular Python classes. This example demonstrates how to define two tables, User and Post, by creating classes that inherit from the Model class.

from dataloom import (
    Dataloom,
    Model,
    PrimaryKeyColumn,
    Column,
    CreatedAtColumn,
    UpdatedAtColumn,
    TableColumn,
    ForeignKeyColumn,
)

class User(Model):
    __tablename__:TableColumn = TableColumn(name="users")
    id = PrimaryKeyColumn(type="int", auto_increment=True)
    name = Column(type="text", nullable=False, default="Bob")
    username = Column(type="varchar", unique=True, length=255)

    # timestamps
    createdAt = CreatedAtColumn()
    updatedAt = UpdatedAtColumn()


class Post(Model):
    __tablename__: TableColumn = TableColumn(name="posts")
    id = PrimaryKeyColumn(type="int", auto_increment=True, nullable=False, unique=True)
    completed = Column(type="boolean", default=False)
    title = Column(
        type="varchar",
        length=255,
        nullable=False,
    )
    # timestamps
    createdAt = CreatedAtColumn()
    updatedAt = UpdatedAtColumn()

    # relations
    userId = ForeignKeyColumn(
        User, type="int", required=True, onDelete="CASCADE", onUpdate="CASCADE"
    )

• Within the User model definition, the table name is explicitly specified using the __tablename__ property, set to "users". This informs dataloom to use the provided name instead of automatically deriving it from the class name. If TableColumn is not specified, the class name becomes the default table name during the synchronization of tables. To achieve this, the TableColumn class is used, accepting the specified table name as an argument.

👉:Note: When defining a table name, it's not necessary to specify the property as __tablename__. However, it's considered good practice to name your table column like that to avoid potential clashes with other columns in the table.

• Every table must include exactly one primary key column. To define this, the PrimaryKeyColumn class is employed, signaling to dataloom that the specified field is a primary key.
• The Column class represents a regular column, allowing the inclusion of various options such as type and whether it is required.
• The CreatedAtColumn and UpdatedAt column types are automatically generated by the database as timestamps. If timestamps are unnecessary or only one of them is needed, they can be omitted.
• The ForeignKeyColumn establishes a relationship between the current (child) table and a referenced (parent) table.

Column Class

In the context of a database table, each property marked as a column in a model is treated as an individual attribute. Here's an example of how to define a column in a table using the Column class:

username = Column(type="text", nullable=False, default="Hello there!!")

Here are some other options that you can pass to the Column:

Argument	Description	Type	Default
type	Required datatype of a column	any datatype
nullable	Optional to specify if the column will allow null values or not.	bool	False
length	Optional to specify the length of the type. If passed as N with type T, it yields an SQL statement with type T(N).	int | None	None
auto_increment	Optional to specify if the column will automatically increment or not.	bool	False
default	Optional to specify the default value in a column.	any	None
unique	Optional to specify if the column will contain unique values or not.	bool	False

Talking about data types, each dialect has its own accepted values. Here is a list of types supported by each and every dialect:

1. mysql

   • "int" - Integer data type.
   • "smallint" - Small integer data type.
   • "bigint" - Big integer data type.
   • "float" - Floating-point number data type.
   • "double" - Double-precision floating-point number data type.
   • "numeric" - Numeric or decimal data type.
   • "text" - Text data type.
   • "varchar" - Variable-length character data type.
   • "char" - Fixed-length character data type.
   • "boolean" - Boolean data type.
   • "date" - Date data type.
   • "time" - Time data type.
   • "timestamp" - Timestamp data type.
   • "json" - JSON (JavaScript Object Notation) data type.
   • "blob" - Binary Large Object (BLOB) data type.

2. postgres

   • "int" - Integer data type (Alias: "INTEGER").
   • "smallint" - Small integer data type (Alias: "SMALLINT").
   • "bigint" - Big integer data type (Alias: "BIGINT").
   • "serial" - Auto-incrementing integer data type (Alias: "SERIAL").
   • "bigserial" - Auto-incrementing big integer data type (Alias: "BIGSERIAL").
   • "smallserial" - Auto-incrementing small integer data type (Alias: "SMALLSERIAL").
   • "float" - Real number data type (Alias: "REAL").
   • "double precision" - Double-precision floating-point number data type (Alias: "DOUBLE PRECISION").
   • "numeric" - Numeric data type (Alias: "NUMERIC").
   • "text" - Text data type.
   • "varchar" - Variable-length character data type.
   • "char" - Fixed-length character data type.
   • "boolean" - Boolean data type.
   • "date" - Date data type.
   • "time" - Time data type.
   • "timestamp" - Timestamp data type.
   • "interval" - Time interval data type.
   • "uuid" - UUID (Universally Unique Identifier) data type.
   • "json" - JSON (JavaScript Object Notation) data type.
   • "jsonb" - Binary JSON (JavaScript Object Notation) data type.
   • "bytea" - Binary data type (Array of bytes).
   • "array" - Array data type.
   • "inet" - IP network address data type.
   • "cidr" - Classless Inter-Domain Routing (CIDR) address data type.
   • "macaddr" - MAC (Media Access Control) address data type.
   • "tsvector" - Text search vector data type.
   • "point" - Geometric point data type.
   • "line" - Geometric line data type.
   • "lseg" - Geometric line segment data type.
   • "box" - Geometric box data type.
   • "path" - Geometric path data type.
   • "polygon" - Geometric polygon data type.
   • "circle" - Geometric circle data type.
   • "hstore" - Key-value pair store data type.

3. sqlite

   • "int" - Integer data type (Alias: "INTEGER").
   • "smallint" - Small integer data type (Alias: "SMALLINT").
   • "bigint" - Big integer data type (Alias: "BIGINT").
   • "float" - Real number data type (Alias: "REAL").
   • "double precision" - Double-precision floating-point number data type (Alias: "DOUBLE").
   • "numeric" - Numeric data type (Alias: "NUMERIC").
   • "text" - Text data type.
   • "varchar" - Variable-length character data type.
   • "char" - Fixed-length character data type.
   • "boolean" - Boolean data type.
   • "date" - Date data type.
   • "time" - Time data type.
   • "timestamp" - Timestamp data type.
   • "json" - JSON (JavaScript Object Notation) data type.
   • "blob" - Binary Large Object (BLOB) data type.

Note: Every table is required to have a primary key column and this column should be 1. Let's talk about the PrimaryKeyColumn

PrimaryKeyColumn Class

This class is used to create a unique index in every table you create. In the context of a table that inherits from the Model class, exactly one PrimaryKeyColumn is required. Below is an example of creating an id column as a primary key in a table named Post:

class Post(Model):
    __tablename__: Optional[TableColumn] = TableColumn(name="users")
    id = PrimaryKeyColumn(type="int", auto_increment=True)
    #...rest of your columns

The following are the arguments that the PrimaryKeyColumn class accepts.

Argument	Description	Type	Default
type	The datatype of your primary key.	str	"bigserial"
length	Optional to specify the length of the type. If passed as N with type T, it yields an SQL statement with type T(N).	int | None	None
auto_increment	Optional to specify if the column will automatically increment or not.	bool	False
default	Optional to specify the default value in a column.	any	None
nullable	Optional to specify if the column will allow null values or not.	bool	False
unique	Optional to specify if the column will contain unique values or not.	bool	True

ForeignKeyColumn Class

This class is utilized when informing dataloom that a column has a relationship with a primary key in another table. Consider the following model definition of a Post:

class Post(Model):
    __tablename__: Optional[TableColumn] = TableColumn(name="posts")
    id = PrimaryKeyColumn(type="int", auto_increment=True, nullable=False, unique=True)
    completed = Column(type="boolean", default=False)
    title = Column(type="varchar", length=255, nullable=False)
    # timestamps
    createdAt = CreatedAtColumn()
    updatedAt = UpdatedAtColumn()
    # relations
    userId = ForeignKeyColumn(
        User, type="int", required=True, onDelete="CASCADE", onUpdate="CASCADE"
    )

• userId is a foreign key in the table posts, indicating it has a relationship with a primary key in the users table.

This column accepts the following arguments:

Argument	Description	Type	Default
table	Required. This is the parent table that the current model references. In our example, this is referred to as User.	Model
type	Optional. Specifies the data type of the foreign key. If not provided, dataloom can infer it from the parent table.	str | None	None
required	Optional. Indicates whether the foreign key is required or not.	bool	False
onDelete	Optional. Specifies the action to be taken when the associated record in the parent table is deleted.	str ["NO ACTION", "SET NULL", "CASCADE"]	"NO ACTION"
onUpdate	Optional. Specifies the action to be taken when the associated record in the parent table is updated.	str ["NO ACTION", "SET NULL", "CASCADE"]	"NO ACTION"

It is crucial to specify the actions for onDelete and onUpdate to ensure that dataloom manages your model's relationship actions appropriately. The available actions are:

1. "NO ACTION" - If you delete or update the parent table, no changes will occur in the child table.
2. "SET NULL" - If you delete or update the parent table, the corresponding value in the child table will be set to null.
3. "CASCADE" - If you delete or update the table, the same action will also be applied to the child table.

CreatedAtColumn Class

When a column is designated as CreatedAtColumn, its value will be automatically generated each time you create a new record in a database, serving as a timestamp.

UpdatedAtColumn Class

When a column is designated as UpdatedAtColumn, its value will be automatically generated each time you create a new record or update an existing record in a database table, acting as a timestamp.

Filter Class

This Filter class in dataloom is designed to facilitate the application of filters when executing queries and mutations. It allows users to specify conditions that must be met for the operation to affect certain rows in a database table. Below is an example demonstrating how this class can be used:

affected_rows = pg_loom.update_one(
    Post,
    values=[
        ColumnValue(name="title", value="Hey"),
        ColumnValue(name="completed", value=True),
    ],
    filters=[
        Filter(column="id", value=1, join_next_filter_with="AND"),
        Filter(column="userId", value=1, join_next_filter_with="AND"),
    ],
)

So from the above example we are applying filters while updating a Post here are the options that you can pass on that filter class:

Argument	Description	Type	Default
column	The name of the column to apply the filter on	String	-
value	The value to filter against	Any	-
operator	The comparison operator to use for the filter	'eq', 'neq'. 'lt', 'gt', 'leq', 'geq', 'in', 'notIn', 'like'	'eq'
join_next_filter_with	The logical operator to join this filter with the next one	'AND', 'OR'	'AND'

👉 : Note: You can apply either a list of filters or a single filter when filtering records.

ColumnValue Class

Just like the Filter class, dataloom also provides a ColumnValue class. This class acts as a setter to update the values of columns in your database table.

The following code snippet demonstrates how the ColumnValue class is used to update records in the database:

re = pg_loom.update_one(
    Post,
    values=[
        ColumnValue(name="title", value="Hey"),
        ColumnValue(name="completed", value=True),
    ],
    filters=[
        Filter(column="id",  value=1, join_next_filter_with="AND"),
        Filter(column="userId", value=1, join_next_filter_with="AND"),
    ],
)

It accepts two arguments: name and value. name represents the column name, while value corresponds to the new value to be assigned to that column.

Argument	Description	Type	Default
name	The name of the column to be updated or inserted.	str	-
value	The value to assign to the column during update or insert.	Any	-

Order Class

The Order class enables us to specify the desired order in which documents should be returned. Below is an example illustrating its usage:

posts = pg_loom.find_all(
    Post,
    select=["id", "completed", "title", "createdAt"],
    limit=3,
    offset=0,
    order=[
        Order(column="createdAt", order="ASC"),
        Order(column="id", order="DESC"),
    ]
)

👉 Note: When utilizing a list of orders, they are applied sequentially, one after the other:

Argument	Description	Type	Default
column	The name of the column to order by.	str	-
order	The order direction, either "ASC" (ascending) or "DESC" (descending).	"ASC" or "DESC"	"ASC"

Include

Syncing Tables

Syncing tables involves the process of creating tables from models and saving them to a database. After defining your tables, you will need to synchronize your database tables using the sync method.

1. The sync method.

This method enables you to create and save tables into the database. For instance, if you have two models, User and Post, and you want to synchronize them with the database, you can achieve it as follows:

tables = sqlite_loom.sync([Post, User], drop=True, force=True)
print(tables)

The method returns a list of table names that have been created or that exist in your database. The sync method accepts the following arguments:

Argument	Description	Type	Default
models	A list of your table classes that inherit from the Model class.	list	[]
drop	Whether to drop tables during syncing or not.	bool	False
force	Forcefully drop tables during syncing or not.	bool	False
alter	Alter tables instead of dropping them during syncing or not.	bool	False

We've noticed two steps involved in starting to work with our orm. Initially, you need to create a connection and then synchronize the tables in another step.

2. The connect_and_sync method.

The connect_and_sync function proves to be very handy as it handles both the database connection and table synchronization. Here is an example demonstrating its usage:

# ....

sqlite_loom = Dataloom(
    dialect="sqlite", database="hi.db", logs_filename="sqlite-logs.sql", logging=True
)
conn, tables = sqlite_loom.connect_and_sync([Post, User], drop=True, force=True)
print(tables)

if __name__ == "__main__":
    conn.close()

Returns a conn and the list of tablenames that exist in the database. The method accepts the same arguments as the sync method.

CRUD Operations with Dataloom

In this section of the documentation, we will illustrate how to perform basic CRUD operations using dataloom on simple Models. Please note that in the following code snippets, I will be utilizing sqlite_loom. However, it's important to highlight that you can use any loom of your choice to follow along.

1. Creating a Record

To insert a single or multiple records in a database you make use of the following functions:

1. insert_one()
2. insert_bulk()

1. insert_one()

The insert_one method allows you to save a single row in a specific table. Upon saving, it will return the primary key (pk) value of the inserted document. The following example shows how the insert_one() method works.

# Example: Creating a user record
userId = pg_loom.insert_one(
    instance=User, values=ColumnValue(name="username", value="@miller")
)

userId = pg_loom.insert_one(
    instance=User,
    values=[
        ColumnValue(name="username", value="@miller"),
        ColumnValue(name="name", value="Jonh"),
    ],
)

This function takes in two arguments which are instance and values. Where values are the column values that you are inserting in a user table or a single column value.

Argument	Description	Type	Required	Default
instance	The instance of the table where the row will be inserted.	Model	Yes	None
values	The column values to be inserted into the table. It can be a single column value or a list of column values.	list[ColumnValue] or ColumnValue	Yes	None

2. insert_bulk().

The insert_bulk method facilitates the bulk insertion of records, as its name suggests. The following example illustrates how you can add 3 posts to the database table simultaneously.

# Example: Inserting multiple posts
rows = pg_loom.insert_bulk(
    User,
    values=[
        [
            ColumnValue(name="username", value="@miller"),
            ColumnValue(name="name", value="Jonh"),
        ],
        [
            ColumnValue(name="username", value="@brown"),
            ColumnValue(name="name", value="Jonh"),
        ],
        [
            ColumnValue(name="username", value="@blue"),
            ColumnValue(name="name", value="Jonh"),
        ],
    ],
)

The argument parameters for the insert_bulk methods are as follows.

Argument	Description	Type	Required	Default
instance	The instance of the table where the row will be inserted.	Model	Yes	None
values	The column values to be inserted into the table. It must be a list of list of column values with the same length, otherwise dataloom will fail to map the values correctly during the insert operation.	list[list[ColumnValue]] or ColumnValue	Yes	None

In contrast to the insert_one method, the insert_bulk method returns the row count of the inserted documents rather than the individual primary keys (pks) of those documents.

2. Reading records

To retrieve documents or a document from the database, you can make use of the following functions:

1. find_all(): This function is used to retrieve all documents from the database.
2. find_by_pk(): This function is used to retrieve a document by its primary key (or ID).
3. find_one(): This function is used to retrieve a single document based on a specific condition.
4. find_many(): This function is used to retrieve multiple documents based on a specific condition.

1. find_all()

This method is used to retrieve all the records that are in the database table. Below are examples demonstrating how to do it:

users = pg_loom.find_all(
    instance=User,
    select=["id", "username"],
    limit=3,
    offset=0,
    order=[Order(column="id", order="DESC")],
)
print(users) # ? [{'id': 1, 'username': '@miller'}]

The find_all() method takes in the following arguments:

Argument	Description	Type	Default	Required
instance	The model class to retrieve documents from.	Model	None	Yes
select	List of fields to select from the documents.	list[str]	None	No
limit	Maximum number of documents to retrieve.	int	None	No
offset	Number of documents to skip before retrieving.	int	0	No
order	List of columns to order the documents by.	list[Order]	None	No
include	List of related models to eagerly load.	list[Model]	None	No

👉 Note: Note that the include argument is not working at the moment. This argument allows us to eagerly load child relationships from the parent model.

2. find_many()

Here is an example demonstrating the usage of the find_many() function with specific filters.

users = mysql_loom.find_many(
    User,
    filters=[Filter(column="username", value="@miller")],
    select=["id", "username"],
    offset=0,
    limit=10,
)

print(users) # ? [{'id': 1, 'username': '@miller'}]

The find_many() method takes in the following arguments:

Argument	Description	Type	Default	Required
instance	The model class to retrieve documents from.	Model	None	Yes
filters	List of filters to apply to the query.	list[Filter] | Filter	None	No
select	List of fields to select from the documents.	list[str]	None	No
limit	Maximum number of documents to retrieve.	int	None	No
offset	Number of documents to skip before retrieving.	int	0	No
order	List of columns to order the documents by.	list[Order]	None	No
include	List of related models to eagerly load.	list[Model]	None	No

The distinction between the find_all() and find_many() methods lies in the fact that find_many() enables you to apply specific filters, whereas find_all() retrieves all the documents within the specified model.

3. find_one()

Here is an example showing you how you can use find_by_pk() locate a single record in the database.

user = mysql_loom.find_one(
    User,
    filters=[Filter(column="username", value="@miller")],
    select=["id", "username"],
)
print(user) # ? {'id': 1, 'username': '@miller'}

This method take the following as arguments

Argument	Description	Type	Default	Required
instance	The model class to retrieve instances from.	Model		Yes
filters	Filter or list of filters to apply to the query.	Filter | list[Filter] | None	None	No
select	List of fields to select from the instances.	list[str]	[]	No
include	List of related models to eagerly load.	list[Include]	[]	No
return_dict	Flag indicating whether to return the result as a dictionary or not.	bool	True	No
offset	Number of instances to skip before retrieving.	int | None	None	No

4. find_by_pk()

Here is an example showing how you can use the find_by_pk() to locate a single record in the database.

user = mysql_loom.find_by_pk(User, pk=userId, select=["id", "username"])
print(user) # ? {'id': 1, 'username': '@miller'}

The method takes the following as arguments:

Argument	Description	Type	Default	Required
instance	The model class to retrieve instances from.	Model		Yes
pk	The primary key value to use for retrieval.	Any		Yes
select	List of fields to select from the instances.	list[str]	[]	No
include	List of related models to eagerly load.	list[Include]	[]	No
return_dict	Flag indicating whether to return the result as a dictionary or not.	bool	True	No

3. Updating a record

To update records in your database table you make use of the following functions:

1. update_by_pk()
2. update_one()
3. update_bulk()

1. update_by_pk()

The update_pk() method can be used as follows:

affected_rows = mysql_loom.update_by_pk(
    instance=Post,
    pk=1,
    values=[
        ColumnValue(name="title", value="Updated?"),
    ],
)

The above method takes in the following as arguments:

Argument	Description	Type	Default	Required
instance	The model class for which to update the instance.	Model		Yes
pk	The primary key value of the instance to update.	Any		Yes
values	Single or list of column-value pairs to update in the instance.	ColumnValue | list[ColumnValue]		Yes

2. update_one()

Here is an example illustrating how to use the update_one() method:

affected_rows = mysql_loom.update_one(
    instance=Post,
    filters=[
        Filter(column="id", value=8, join_next_filter_with="OR"),
        Filter(column="userId", value=1, join_next_filter_with="OR"),
    ],
    values=[
        ColumnValue(name="title", value="Updated?"),
    ],
)

The method takes the following as arguments:

Argument	Description	Type	Default	Required
instance	The model class for which to update the instance(s).	Model		Yes
filters	Filter or list of filters to apply to the update query.	Filter | list[Filter] | None		Yes
values	Single or list of column-value pairs to update in the instance.	ColumnValue | list[ColumnValue]		Yes

3. update_bulk()

The update_bulk() method updates all records that match a filter in a database table.

affected_rows = mysql_loom.update_bulk(
    instance=Post,
    filters=[
        Filter(column="id", value=8, join_next_filter_with="OR"),
        Filter(column="userId", value=1, join_next_filter_with="OR"),
    ],
    values=[
        ColumnValue(name="title", value="Updated?"),
    ],
)

The above method takes in the following as argument:

Argument	Description	Type	Default	Required
instance	The model class for which to update instances.	Model		Yes
filters	Filter or list of filters to apply to the update query.	Filter | list[Filter] | None		Yes
values	Single or list of column-value pairs to update in the instance.	ColumnValue | list[ColumnValue]		Yes

4. Deleting a record

To delete a record or records in a database table you make use of the following functions:

1. delete_by_pk()
2. delete_one()
3. delete_bulk()

1. delete_by_pk()

Using the delete_by_pk() method, you can delete a record in a database based on the primary-key value.

affected_rows = mysql_loom.delete_by_pk(instance=User, pk=1)

The above take the following as arguments:

Argument	Description	Type	Default	Required
instance	The model class from which to delete the instance.	Model		Yes
pk	The primary key value of the instance to delete.	Any		Yes

2. delete_one()

You can also use filters to delete a record in a database. The delete_one() function enables you to delete a single record in a database that matches a filter.

affected_rows = mysql_loom.delete_one(
    instance=User, filters=[Filter(column="username", value="@miller")]
)

The method takes in the following arguments:

Argument	Description	Type	Default	Required
instance	The model class from which to delete the instance(s).	Model		Yes
filters	Filter or list of filters to apply to the deletion query.	Filter | list[Filter] | None	None	No
offset	Number of instances to skip before deleting.	int | None	None	No
order	List of columns to order the instances by before deletion.	list[Order] | None	[]	No

3. delete_bulk()

You can also use the delete_bulk() method to delete a multitude of records that match a given filter:

affected_rows = mysql_loom.delete_bulk(
    instance=User, filters=[Filter(column="username", value="@miller")]
)

The method takes the following as arguments:

Argument	Description	Type	Default	Required
instance	The model class from which to delete instances.	Model		Yes
filters	Filter or list of filters to apply to the deletion query.	Filter | list[Filter] | None	None	No
limit	Maximum number of instances to delete.	int | None	None	No
offset	Number of instances to skip before deleting.	int | None	None	No
order	List of columns to order the instances by before deletion.	list[Order] | None	[]	No

Warning: Potential Risk with delete_bulk()

⚠️ Warning: When using the delete_bulk() function, exercise caution as it can be aggressive. If the filter is not explicitly provided, there is a risk of mistakenly deleting all records in the table.

Guidelines for Safe Usage

To mitigate the potential risks associated with delete_bulk(), follow these guidelines:

1. Always Provide a Filter:

   • When calling delete_bulk(), make sure to provide a filter to specify the subset of records to be deleted. This helps prevent unintentional deletions.

   # Example: Delete records where 'status' is 'inactive'
   sqlite_loom.delete_bulk(filter={'status': 'inactive'})
   

2. Consider Usage When Necessary:

• When contemplating data deletion, it is advisable to consider more targeted methods before resorting to delete_bulk(). Prioritize the use of delete_one() or delete_by_pk() methods to remove specific records based on your needs. This ensures a more precise and controlled approach to data deletion.

3. Use limit and offsets options

• You can consider using the limit and offset options during invocation of delete_bulk

affected_rows = mysql_loom.delete_bulk(
    instance=Post,
    order=[Order(column="id", order="DESC"), Order(column="createdAt", order="ASC")],
    filters=[Filter(column="id", operator="gt", value=0)],
    offset=0,
    limit=10,
)

By following these guidelines, you can use the delete_bulk() function safely and minimize the risk of unintended data loss. Always exercise caution and adhere to best practices when performing bulk deletion operations.

Ordering

In dataloom you can order documents in either DESC (descending) or ASC (ascending) order using the helper class Order.

posts = mysql_loom.find_all(
    instance=Post,
    order=[Order(column="id", order="DESC")],
)

You can apply multiple and these orders will ba applied in sequence of application here is an example:

posts = mysql_loom.find_all(
    instance=Post,
    order=[Order(column="id", order="DESC"), Order(column="createdAt", order="ASC")],
)

Filters

There are different find of filters that you can use when filtering documents for mutations and queries. Filters are very important to use when updating and deleting documents as they give you control on which documents should be updated or deleted. When doing a mutation you can use a single or multiple filters. Bellow is an example that shows you how you can use a single filter in deleting a single record that has an id greater than 1 from the database.

res2 = mysql_loom.delete_one(
    instance=Post,
    offset=0,
    order=[Order(column="id", order="DESC")],
    filters=Filter(column="id", value=1, operator="gt"),
)

Or you can use it as follows:

res2 = mysql_loom.delete_one(
    instance=Post,
    offset=0,
    order=[Order(column="id", order="DESC")],
    filters=[Filter(column="id", value=1, operator="gt")],
)

As you have noticed, you can join your filters together and they will be applied sequentially using the join_next_filter_with which can be either OR or AND te default value is AND. Here is an of filter usage in sequential.

res2 = mysql_loom.delete_one(
    instance=Post,
    offset=0,
    order=[Order(column="id", order="DESC")],
    filters=[
        Filter(column="id", value=1, operator="gt"),
        Filter(column="userId", value=1, operator="eq", join_next_filter_with="OR"),
        Filter(
            column="title",
            value='"What are you doing general?"',
            operator="=",
            join_next_filter_with="AND",
        ),
    ],
)

You can use the operator to match the values. Here is the table of description for these filters.

Operator	Explanation	Expect
'eq'	Indicates equality. It checks if the value is equal to the specified criteria.	Value == Criteria
'lt'	Denotes less than. It checks if the value is less than the specified criteria.	Value < Criteria
'gt'	Denotes greater than. It checks if the value is greater than the specified criteria.	Value > Criteria
'leq'	Denotes less than or equal to. It checks if the value is less than or equal to the specified criteria.	Value <= Criteria
'geq'	Denotes greater than or equal to. It checks if the value is greater than or equal to the specified criteria.	Value >= Criteria
'in'	Checks if the value is included in a specified list of values.	Value in List
'notIn'	Checks if the value is not included in a specified list of values.	Value not in List
'like'	Performs a pattern matching operation. It checks if the value is similar to a specified pattern.	Value matches Pattern
'neq'	Indicates non-equality. It checks if the value is not equal to the specified criteria.	Value != Criteria

Let's talk about these filters in detail of code by example. Let's say you want to update a Post where the id matches 1 you can do it as follows:

res2 = mysql_loom.update_one(
    instance=Post,
    filters=Filter(
        column="id",
        value=1,
        operator="eq",
    ),
    values=[ColumnValue(name="title", value="Bob")],
)

What if you want to update a post where id is not equal to 1 you can do it as follows

res2 = mysql_loom.update_bulk(
    instance=Post,
    filters=Filter(
        column="id",
        value=1,
        operator="neq",
    ),
    values=[ColumnValue(name="title", value="Bob")],
)

What if i want to update the records that have an id less than 3?

res2 = mysql_loom.update_bulk(
    instance=Post,
    filters=Filter(
        column="id",
        value=3,
        operator="lt",
    ),
    values=[ColumnValue(name="title", value="Bob")],
)

What if i want to update the records that have an id less than or equal 3?

res2 = mysql_loom.update_bulk(
    instance=Post,
    filters=Filter(
        column="id",
        value=1,
        operator="neq",
    ),
    values=[ColumnValue(name="title", value="Bob")],
)

What if i want to update the records that have an id greater than 3?

res = mysql_loom.update_bulk(
    instance=Post,
    filters=Filter(
        column="id",
        value=3,
        operator="gt",
    ),
    values=[ColumnValue(name="title", value="Bob")],
)

What if i want to update the records that have an id greater or equal to 3?

res = mysql_loom.update_bulk(
    instance=Post,
    filters=Filter(
        column="id",
        value=3,
        operator="geq",
    ),
    values=[ColumnValue(name="title", value="Bob")],
)

You can use the in to update or query records that matches values in a specified list of values or tuple. Here is an example showing you how you can update records that does matches id in [1, 2].

res = mysql_loom.update_bulk(
    instance=Post,
    filters=Filter(
        column="id",
        value=[1, 2],
        operator="in",
    ),
    values=[ColumnValue(name="title", value="Bob")],
)

You can use the notIn to update or query records that does not matches values in a specified list of values or tuple. Here is an example showing you how you can update records that does not matches id in [1, 2].

res = mysql_loom.update_bulk(
    instance=Post,
    filters=Filter(
        column="id",
        value=[1, 2],
        operator="notIn",
    ),
    values=[ColumnValue(name="title", value="Bob")],
)

You can use the like operator to match some patens in your query filters. Let's say we want to match a post that has the title ends with general we can use the like operator as follows

general = mysql_loom.find_one(
    instance=Post,
    filters=Filter(
        column="title",
        value="% general?",
        operator="like",
    ),
    select=["id", "title"],
)

print(general) # ?  {'id': 1, 'title': 'What are you doing general?'}

The following table show you some expression that you can use with this like operator.

Value	Description
%pattern	Finds values that end with the specified pattern.
pattern%	Finds values that start with the specified pattern.
%pattern%	Finds values that contain the specified pattern anywhere within the string.
_pattern	Finds values that have any single character followed by the specified pattern.
pattern_	Finds values that have the specified pattern followed by any single character.
[charlist]%	Finds values that start with any character in the specified character list.
[!charlist]%	Finds values that start with any character not in the specified character list.
_pattern_	Finds values that have any single character followed by the specified pattern and then followed by any single character.

Utilities

Dataloom comes up with some utility functions that works on an instance of a model. This is very useful when debuging your tables to see how do they look like. These function include:

1. inspect()

inspect

This function takes in a model as argument and inspect the model fields or columns. The following examples show how we can use this handy function in inspecting table names.

table = mysql_loom.inspect(instance=User, fields=["name", "type"], print_table=False)
print(table)

The above snippet returns a list of dictionaries containing the column name and the arguments that were passed.

[{'id': {'type': 'int'}}, {'name': {'type': 'varchar'}}, {'tokenVersion': {'type': 'int'}}, {'username': {'type': 'varchar'}}]

You can print table format these fields with their types as follows

mysql_loom.inspect(instance=User)

Output:

+--------------+---------+----------+---------+
| name         | type    | nullable | default |
+--------------+---------+----------+---------+
| id           | int     | NO       | None    |
| name         | varchar | NO       | Bob     |
| tokenVersion | int     | YES      | 0       |
| username     | varchar | YES      | None    |
+--------------+---------+----------+---------+

The inspect function take the following arguments.

What is coming next?

1. Associations
2. Grouping
3. Altering tables

Contributing

Contributions to dataloom are welcome! Feel free to submit bug reports, feature requests, or pull requests on GitHub.

License

This project is licensed under the MIT License - see the LICENSE file for details.