Dynamic SQL (dy-sql)

This project consists of a set of python decorators that eases integration with SQL databases. These decorators may trigger queries, inserts, updates, and deletes.

The decorators are a way to help us map our data in python to SQL queries and vice versa. When we select, insert, update, or delete the queries, we pass the data we want to insert along with a well defined query.

This is designed to be done with minimal setup and coding. You need to specify the database connection parameters and annotate any SQL queries/updates you have with the decorator that fits your needs.

Installation

pip install dy-sql

Component Breakdown

• set_default_connection_parameters - this function needs to be used to set the database parameters on initialization so that when a decorator function is called, it can setup a connection pool to a correct database
• is_set_current_database_supported - this function may be used to determine if the *_current_database methods may be used or not
• set_current_database - (only supported on Python 3.7+) this function may be used to set the database name for the current async context (not thread), this is especially useful for multitenant applications
• reset_current_database - (only supported on Python 3.7+) helper method to reset the current database after set_current_database has been used in an async context
• set_database_init_hook - sets a method to call whenever a new database is initialized
• QueryData - a class that may be returned or yielded from sql* decorated methods which contains query information
• DbMapResult - base class that can be used when selecting data that helps to map the results of a query to an object in python
• DbMapResultModel - pydantic version of DbMapResult that allows easy mapping to pydantic models
• @sqlquery - decorator for select queries that can return a SQL result in a DbMapResult
• @sqlupdate - decorator for any queries that can change data in the database, this can take a set of values and yield multiple operations back for insertions or updates inside of a transaction
• @sqlexists - decorator for a simplified select query that will return true if a record exists and false otherwise
• XDbTestManager - test manager classes that may be used for testing purposes

Database Preparation

In order to initialize a connection pool for the sql* decorators, the database needs to first be set up using the set_default_connection_parameters method.

from dysql import set_database_parameters

def set_database_from_config():
    maria_db_config = {...}
    set_database_parameters(
        maria_db_host,
        maria_db_user,
        maria_db_password,
        maria_db_database,
        port=maria_db_port,
        charset=maria_db_charset
    )

Note: the keyword arguments are not required and have standard default values, the port for example defaults to 3306

Database Init Hook

At times, it is necessary to perform post-initialization tasks on the database engine after it has been created. The set_database_init_hook method may be used in this case. As an example, to instrument the engine using opentelemetry-instrumentation-sqlalchemy, the following code may be used:

from typing import Optional
# Used for type-hints only
import sqlalchemy
from opentelemetry.instrumentation.sqlalchemy import SQLAlchemyInstrumentor
from dysql import set_database_init_hook

def _instrument_engine(database_name: Optional[str], engine: sqlalchemy.engine.Engine) -> None:
    # The database name is unused in this case
    _ = database_name
    SQLAlchemyInstrumentor().instrument(engine=engine)

set_database_init_hook(_instrument_engine)

Multitenancy

In some applications, it may be useful to set a database other than the default database in order to support database-per-tenant configurations. This may be done using the set_current_database and reset_current_database methods.

from dysql import reset_current_database, set_current_database

def use_database_for_query():
    set_database_parameters(
        ...
        'db1',
    )
    set_current_database('db2')
    try:
        # Queries db2 and not db1
        query_database()
    finally:
        reset_current_database()

Warning

These methods are only supported in Python 3.7+ due to their use of the contextvars module. The is_set_current_database_supported method is provided to help tell if these methods may be used.

Decorators

Decorators are an easy way for us to tell a function to be a 'query' and return a result without having to have a big chunk of boiler plate code. Once the database has been prepared, calling a sql* decorated function will initialize the database, parse the value returned in your function, make a corresponding parameterized query and return the results.

The basic structure is to decorate a method that returns information about the query. There are multiple options for returning a query, below is a summary of some of the possibilities:

• return a QueryData object that possibly contains query_params and/or template_params
• (not available for all sql* decorators) yield one or more QueryData objects, each containing query_params and/or template_params

DbMapResult

This class is used in the default mapper (see below) for any sqlquery decorated method. This class may also be overridden as shown below. The default class wraps and returns the results of a query for easy access to the data from the query. For example, if you use the query SELECT id, name FROM table, it would return a list of DbMapResult objects where each contains the id and name fields. You could then easily loop through and access the properties as shown in the following example:

@sqlquery()
def get_items_from_sql_query():
    return QueryData("SELECT id, name FROM table")

def get_and_process_items():
    for item in get_items_from_sql_query():
        # we are able to access properties on the object
        print('{name} goes with {id}'.format(item.name, item.id))

We can inherit from DbMapResult and override the way our data maps into the object. This is primarily helpful in cases where we end up with multiple rows such as a query for a 1-to-many relationship.

class ExampleMap(DbMapResult):
    def map_result(self, result):
        # we know we are mapping multiple rows to a single result
        if self.id is None:
            # in our case we know the id is the same so we only set it the first time
            self.id = result['id']
            # initialize our array
            self.item_names = []

    # we know that every result for a given id has a unique item_name
    self.item_names.append(result['item_name'])

@sqlquery(mapping=ExampleMap)
def get_table_items()
    return QueryData("""
        SELECT id, name, item_name FROM table
            JOIN table_item ON table.id = table_item.table_id
            JOIN item ON item.id = table_item.item_id
    """)

def print_item_names()
    for table_item in get_table_items():
        for item_name in table_item.item_names:
            print(f'table name {table_item.name} has item {item_name}')

DbMapResultModel (pydantic)

If pydantic models are desired to be used, there is a record mapper available. Note that pydantic must be installed, which is available as an extra package:

pip install dy-sql[pydantic]

This model attempts to make mapping records easier, but there are shortcomings of it in more complex cases. Most fields will "just work" as defined by the type annotations.

from dysql.pydantic_mappers import DbMapResultModel

class PydanticDbModel(DbMapResultModel):
    id: int
    field_str: str
    field_int: int
    field_bool: bool

Mapping a record onto this class will automatically convert types as defined by the type annotations. No map_record method needs to be defined since the pydantic model has everything necessary to map database fields.

Lists, sets, dicts, csv strings, and json strings (when using the RecordCombiningMapper) require additional configuration on the model class.

from dysql.pydantic_mappers import DbMapResultModel

class ComplexDbModel(DbMapResultModel):
    # if any data has been aggregated or saved into a string as a comma delimited list, this will convert to a list
    # NOTE this only does simple splitting and is not fully rfc4180 compatible
    _csv_list_fields: Set[str] = {'list_from_string'}
    # List fields (type does not matter)
    _list_fields: Set[str] = {'list1'}
    # Set fields (type does not matter)
    _set_fields: Set[str] = {'set1'}
    # Dictionary key fields as DB field name => model field name
    _dict_key_fields: Dict[str, str] = {'key1': 'dict1', 'key2': 'dict2'}
    # Dictionary value fields as model field name => DB field name (this is reversed from _dict_key_fields!)
    _dict_value_mappings: Dict[str, str] = {'dict1': 'val1', 'dict2': 'val2'}
    # JSON string fields. Type can be any dictionary type but for larger json objects its safe to stay with `dict`
    _json_fields: Set[str] = {'json1', 'json2'}

    id: int = None
    list_from_string: List[str]
    list1: List[str]
    set1: Set[str] = set()
    dict1: Dict[str, Any] = {}
    dict2: Dict[str, int] = {}
    json1: dict
    json2: dict

Note

csv strings can be useful in queries where you want to group by an id and then group_concat some field

json strings are a handy way to extract json blobs into a python dictionary for ease of use without manually processing each field everytime you need something.

In this case, the _ prefixed properties tell the model which fields should be treated differently when combining multiple rows into a single object. For an example of how this works with database rows, see the test_pydantic_mappers.py file in the source repository.

Note that validation does occur the very first time map_record is called, but not on subsequent runs. Therefore if you desire better validation for list, set, or dict fields, this must most likely be done outside of dysql/pydantic. Additionally, lists, sets, and dicts will ignore null values from the database. Therefore you must provide default values for these fields when used or else validation will fail.

Added annotations when using DbMapResultModel

When using the DbMapResultModel mapper, there are some additional annotations that may be used to help with mapping. These annotations are not required, but may be helpful in some cases.

• FromCSVToList - this annotation will convert a comma separated string into a list. This is useful when you have a column containing a csv or a query that uses group_concat to combine multiple rows into a single row. This annotation may be used on any field that is a list. For example:

  from dysql.pydantic_mappers import DbMapResultModel, FromCSVToList
  
  class CsvModel(DbMapResultModel):
      id: int
      name: str
      # This annotation will convert the string into a list of ints
      list_from_string_int: FromCSVToList[List[int]]
      # This annotation will convert the string into a list of strings
      list_from_string: FromCSVToList[List[str]]
      # This annotation will convert the string into a list of ints or None if the string is null or empty
      list_from_string_int_nullable: FromCSVToList[List[int] | None]
      # This annotation will convert the string into a list of strings or None if the string is null or empty
      list_from_string_nullable: FromCSVToList[List[str] | None]
  
      # if using python <= 3.9, you can use typing.Union instead of the pipe operator
      # list_from_string_nullable: FromCSVToList[Union[List[str],None]]

@sqlquery

This is for making SQL select calls. An optional mapper may be specified to change the behavior of what is returned from a decorated method. The default mapper can combine multiple records into a single result if there is an id field present in each record. Mappers available:

• RecordCombiningMapper (default) - Returns a list of results where multiple records that can be combined with the same unique identifer. An optional record_mapper value may be passed to the constructor to change how records are mapped to result. By default the record_mapper used is DbMapResult. The base identifier is the column id but an array of columns can be used to create a unique key lookup for combining records.

Note

The _key_columns field of the DbMapResultModel is an array containing only the id but can be overriden in derived classes. For example, setting _key_columns = [ 'a', 'b' ] in your derived class would make it so you class would use the values of columns a and b in order to uniquely identify records when being combined.

• SingleRowMapper - returns an object for the first record from the database (even if multiple records are returned). An optional record_mapper value may be passed to the constructor to change how this first record is mapped to the result.
• SingleColumnMapper - Returns a list of scalars with the first column from every record, even if multiple columns are returned from the database.
• SingleRowAndColumnMapper - Returns a single scalar value even if multiple records and columns are returned from the database.
• CountMapper - alias for SingleRowAndColumnMapper to make it clear that it may be used for count queries.
• KeyValueMapper - returns a dictionary mapping 1 column to the keys and 1 column to the values. By default the key is mapped to the first column and value is mapped to the second column. You can override the key_column and value_columns by specifying the name of the columns you want for each. You can also pass in a has_multiple_values which defaults to False. Doing so will allow you to get a dictionary of lists based on the keys and values you specify.
• Custom mappers may be made by extending the BaseMapper class and implementing the map_records method.

basic query with conditions hardcoded into query and default mapper

def get_items():
    items = select_items_for_joe()
    # ... work on items

@sqlquery()
def select_items_for_joe()
    return QueryData("SELECT * FROM table WHERE name='joe'")

basic query with params passed as a dict

def get_items():
    items = select_items_for_name('joe')
    # ... work on items, which contains all records matching the name

@sqlquery()
def select_items_for_name(name)
    return QueryData("SELECT * FROM table WHERE name=:name", query_params={'name': name})

query that only returns a single result from the first row

def get_joe_id():
    result = get_item_for_name('joe')
    return result.get('id')

# Either an instance or class may be used as the mapper parameter
@sqlquery(mapper=SingleRowMapper())
def get_item_for_name(name)
    return QueryData("SELECT id, name FROM table WHERE name=:name", query_params={'name': name})

alternative to the above query that returns the id directly

def get_joe_id():
    return get_id_for_name('joe')

@sqlquery(mapper=SingleRowAndColumnMapper)
def get_id_for_name(name)
    return QueryData("SELECT id FROM table WHERE name=:name", query_params={'name': name})

query that returns a list of scalar values containing the list of distinct names available

def get_unique_names():
    return get_names_from_items()

@sqlquery(mapper=SingleColumnMapper)
def get_names_from_items()
    return QueryData("SELECT DISTINCT(name) FROM table")

basic count query that only returns the scalar value returned for the count

def get_count_for_joe():
    return get_count_for_name('joe')

@sqlquery(mapper=CountMapper)
def get_count_for_name(name):
    return QueryData("SELECT COUNT(*) FROM table WHERE name=:name", query_params={'name': name})

basic query returning dictionary

@sqlquery(mapper=KeyValueMapper())
def get_status_by_name():
    return QueryData("SELECT name, status FROM table")

query returning a dictionary where we are specifying the keys. Note that the columns are returning in a different order

@sqlquery(mapper=KeyValueMapper(key_column='name', value_column='status'))
def get_status_by_name():
    return QueryData("SELECT status, name FROM table")

query returning a dictionary where there are multiple results under each key. Note that here we are essentially grouping under status

@sqlquery(mapper=KeyValueMapper(key_column='status', value_column='name', has_multiple_values=True))
def get_status_by_name():
    return QueryData("SELECT status, name FROM table")

@sqlupdate

Handles any SQL that is not a select. This is primarily, but not limited to, insert, update, and delete.

@sqlupdate()
def insert_items(item_dict):
    return QueryData("INSERT INTO", template_params={'in__item_id':item_id_list})

multiple queries in a transaction

You can yield multiple QueryData objects. This is done in a transaction and it can be helpful for data integrity or just a nice clean way to run a set of updates.

@sqlupdate()
def insert_items(item_dict):
    insert_values_1, insert_params_1 = TemplateGenerator.values('table1values', _get_values_for_1_from_items(item_dict))
    insert_values_2, insert_params_2 = TemplateGenerator.values('table2values', _get_values_for_2_from_items(item_dict))
    yield QueryData(f'INSERT INTO table_1 {insert_values_1}', query_params=insert_values_params_1)
    yield QueryData(f'INSERT INTO table_2 {insert_values_2}', query_params=insert_values_params_2)

getting the last insert id

You can assign a callback to be ran after a query or set of queries completes successfully. This is useful when you need to get the last insert id for a table that has an auto incrementing id field. This allows you to set it as a parameter on a follow up relational table within the same transaction scope.

@sqlupdate()
def insert_items_with_callback(item_dict):
    insert_values_1, insert_params_1 = TemplateGenerator.values('table1values', _get_values_for_1_from_items(item_dict))
    insert_values_2, insert_params_2 = TemplateGenerator.values('table2values', _get_values_for_2_from_items(item_dict))
    yield QueryData(f'INSERT INTO table_1 {insert_values_1}', query_params=insert_values_params_1)
    yield QueryData(f'INSERT INTO table_2 {insert_values_2}', query_params=insert_values_params_2)

def _handle_insert_success(item_dict):
    #  callback logic here happens after the transaction is complete

get_last_insert_id is a placeholder kwarg that will be automatically overwritten by the sqlupdate decorator at run time. Therefore, the assigned value in the function definition does not matter.

Using get_last_insert_id gives you the most recently set id. You can leverage this for later queries yielded, or you could use it and set ids in a reference object passed in for access to the ides outside of the sqlupdate function.

@sqlupdate()
def insert_item_with_get_last_insert(get_last_insert_id=None, item_dict):
    insert_values, insert_params = TemplateGenerator.values('table1values', _get_values_from_items(item_dict))
    yield QueryData(f'INSERT INTO table_1 {insert_values}', query_params=insert_values_params)
    last_id = get_last_insert_id()
    yield QueryData(f'INSERT INTO related_table_1 (table_1_id, value) VALUES (:table_1_id, :value)',
            query_params={'table_1_id': last_id, 'value': 'some_value'})

Note

get_last_insert_id will get you the last inserted id from the most recently table inserted with an autoincrement. Be sure to call get_last_insert_id right after you yield the query that inserts the record you need the id for.

class Item(BaseModel):
    id: int | None = None
    name: str

@sqlupdate()
def insert_items_and_update_ids(items: List[Item], get_last_insert_id = None)
    for item in items:
        yield QueryData("INSERT INTO table (name) VALUES (:name)", query_params={'name': item.name})
        last_id = get_last_insert_id()
        item.id = last_id

@sqlexists

This wraps a SQL query to determine if a row exists or not. If at least one row is returned from the query, it will return True, otherwise False. The query you give here can return anything you want but as good practice, try to always select as little as possible. For example, below we are just returning 1 because the value itself isn't used, we just need to know there are records available.

@sqlexists()
def item_exists(item_id)
    return QueryData("SELECT 1 FROM table WHERE id=:id", query_params={'id': item_id})

Ultimately, the above query becomes SELECT EXISTS (SELECT 1 FROM table WHERE id=:id). You'll notice the inner select value isn't actually used in the return.

Decorator templates

Templates and generators for these templates are also provided to simplify SQL query strings.

in template - this template will allow you to pass a list as a single parameter and have the IN condition build out for you. This allows you to more dynamically include values in your queries.

@sqlquery()
def select_items(item_id_list):
    return QueryData("SELECT * FROM table WHERE {in__item_id}",
                    template_params={'in__item_id': item_id_list})

you can also use the TemlpateGenerate.in_column method to get back a tuple of query and params

@sqlquery()
def select_items(item_id_list):
    in_query, in_params = TemplateGenerators.in_column('key', item_id_list)
    # NOTE: the query string is using an f-string and passing into query_params instead of template_params
    return QueryData(f"SELECT * FROM table WHERE {in_query}", query_params=in_params)

in and not in multi column - this template works the same as the in and not in template but it will allow you to pass a list of tuples to an in clause allowing you to match against multiple columns. NOTE: this is only available through the TemplateGenerators using query_params and not through the the template_params method

@sqlquery()
def select_multi(tuple_list):
    in_query, in_params = TemplateGenerators.in_multi_column('(key1, key2)', tuple_list)
    return QueryData(f"SELECT * FROM table WHERE {in_query}", query_params=in_params)

@sqlquery()
def select_multi(tuple_list):
    in_query, in_params = TemplateGenerators.not_in_multi_column('(key1, key2)', tuple_list)
    return QueryData(f"SELECT * FROM table WHERE {in_query}", query_params=in_params)

not_in template - this template will allow you to pass a list as a single parameter and have the NOT IN condition build out for you. This allows you more dynamically exclude values in your queries.

@sqlquery()
def select_items(item_id_list)
    return QueryData("SELECT * FROM table WHERE {not_in__item_id}",
                    template_params={'not_in__item_id': item_id_list})

you can also use the TemplateGenerators.not_in_column method to get back a tuple of query and params

@sqlquery()
def select_items(item_id_list):
    not_in_query, not_in_params = TemplateGenerators.not_in_column('key', item_id_list)
    # NOTE: the query string is using an f-string and passing into query_params instead of template_params
    return QueryData(f"SELECT * FROM table WHERE {not_in_query}", query_params=not_in_params)

values template - when inserting and you have multiple records to insert, this allows you to pass multiple records for insert in a single INSERT statement.

@sqlquery()
def insert_items(items):
    return QueryData("INSERT_INTO table(column_a, column_b) {values__items}",
                    template_params={'values__items': item_id_list})

You can write queries that combine template_params and query_params as well..

@sqlquery()
def select_items(item_id_list, name):
    return QueryData("SELECT * FROM table WHERE {in__item_id} and name=:name",
                    template_params={'in__item_id': item_id_list},
                    query_params={'name': name})

Testing with Managers

During testing, it may be useful to hook up a real database to the tests. However, this can be difficult to maintain schema and isolate databases during testing. Database test managers exist for this reason. Usage is very simple with pytest.

@pytest.fixture(scope='module', autouse=True)
def setup_db(self):
    # Pass in the database name and any optional params
    with MariaDbTestManager(f'testdb_{self.__class__.__name__.lower()}'):
        yield

The Maria database test manager is shown used above, but future implementations may be added for other SQL backends.