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SQLAlchemy-Nested-Mutable

An advanced SQLAlchemy column type factory that helps map compound Python types (e.g. list, dict, Pydantic Model and their hybrids) to database types (e.g. ARRAY, JSONB), And keep track of mutations in deeply nested data structures so that SQLAlchemy can emit proper UPDATE statements.

SQLAlchemy-Nested-Mutable is highly inspired by SQLAlchemy-JSON[0][1]. However, it does not limit the mapped Python type to be dict or list.

Why this package?

  • By default, SQLAlchemy does not track in-place mutations for non-scalar data types such as list and dict (which are usually mapped with ARRAY and JSON/JSONB).

  • Even though SQLAlchemy provides an extension to track mutations on compound objects, it's too shallow, i.e. it only tracks mutations on the first level of the compound object.

  • There exists the SQLAlchemy-JSON package to help track mutations on nested dict or list data structures. However, the db type is limited to JSON(B).

  • Also, I would like the mapped Python types can be subclasses of the Pydantic BaseModel, which have strong schemas, with the db type be schema-less JSON.

Installation

pip install sqlalchemy-nested-mutable

Usage

NOTE the example below is first updated in examples/user-addresses.py and then updated here.

fromtypingimportOptional, Listimportpydanticimportsqlalchemyassafromsqlalchemy.ormimportSession, DeclarativeBase, Mapped, mapped_columnfromsqlalchemy_nested_mutableimportMutablePydanticBaseModelclassBase(DeclarativeBase):
passclassAddresses(MutablePydanticBaseModel):
"""A container for storing various addresses of users. NOTE: for working with pydantic model, use a subclass of `MutablePydanticBaseModel` for column mapping. However, the nested models (e.g. `AddressItem` below) should be direct subclasses of `pydantic.BaseModel`. """classAddressItem(pydantic.BaseModel):
street: strcity: strarea: Optional[str]
preferred: AddressItemwork: Optional[AddressItem]
home: Optional[AddressItem]
others: List[AddressItem] = []
classUser(Base):
__tablename__="user_account"id: Mapped[int] =mapped_column(primary_key=True)
name: Mapped[str] =mapped_column(sa.String(30))
addresses: Mapped[Addresses] =mapped_column(Addresses.as_mutable(), nullable=True)
engine=sa.create_engine("sqlite://")
Base.metadata.create_all(engine)
withSession(engine) ass:
s.add(u:=User(name="foo", addresses={"preferred": {"street": "bar", "city": "baz"}}))
assertisinstance(u.addresses, MutablePydanticBaseModel)
s.commit()
u.addresses.preferred.street="bar2"s.commit()
assertu.addresses.preferred.street=="bar2"u.addresses.others.append(Addresses.AddressItem.parse_obj({"street": "bar3", "city": "baz3"}))
s.commit()
assertisinstance(u.addresses.others[0], Addresses.AddressItem)
print(u.addresses.dict())

For more usage, please refer to the following test files:

  • tests/test_mutable_list.py
  • tests/test_mutable_dict.py
  • tests/test_mutable_pydantic_type.py

About

An advanced SQLAlchemy column type factory that helps map complex Python types (e.g. List, Dict, Pydantic Model and their hybrids) to database types (e.g. ARRAY, JSONB), And keep track of mutations in deeply nested data structures so that SQLAlchemy can emit proper UPDATE statements.

Topics

databasejsonbormpydanticpythonsqlalchemy

Resources

Readme
MIT license
Activity

Stars

33 stars

Watchers

1 watching

Forks

10 forks
Report repository

Releases

Sponsor this project

Packages

Used by

Contributors

Languages

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(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '
GitHub - wonderbeyond/SQLAlchemy-Nested-Mutable: An advanced SQLAlchemy column type factory that helps map complex Python types (e.g. List, Dict, Pydantic Model and their hybrids) to database types (e.g. ARRAY, JSONB), And keep track of mutations in deeply nested data structures so that SQLAlchemy can emit proper UPDATE statements. · GitHub
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SQLAlchemy-Nested-Mutable
An advanced SQLAlchemy column type factory that helps map compound Python types (e.g. list, dict, Pydantic Model and their hybrids) to database types (e.g. ARRAY, JSONB),
And keep track of mutations in deeply nested data structures so that SQLAlchemy can emit proper UPDATE statements.
SQLAlchemy-Nested-Mutable is highly inspired by SQLAlchemy-JSON[0][1].
However, it does not limit the mapped Python type to be dict or list.
Why this package?
  • By default, SQLAlchemy does not track in-place mutations for non-scalar data types
such as list and dict (which are usually mapped with ARRAY and JSON/JSONB).
  • Even though SQLAlchemy provides an extension
to track mutations on compound objects, it's too shallow, i.e. it only tracks mutations on the first level of the compound object.
  • There exists the SQLAlchemy-JSON package
to help track mutations on nested dict or list data structures.
However, the db type is limited to JSON(B).
  • Also, I would like the mapped Python types can be subclasses of the Pydantic BaseModel,
which have strong schemas, with the db type be schema-less JSON.
Installation
pip install sqlalchemy-nested-mutable
Usage
NOTE the example below is first updated in examples/user-addresses.py and then updated here.
fromtypingimportOptional, Listimportpydanticimportsqlalchemyassafromsqlalchemy.ormimportSession, DeclarativeBase, Mapped, mapped_columnfromsqlalchemy_nested_mutableimportMutablePydanticBaseModelclassBase(DeclarativeBase):
passclassAddresses(MutablePydanticBaseModel):
"""A container for storing various addresses of users. NOTE: for working with pydantic model, use a subclass of `MutablePydanticBaseModel` for column mapping. However, the nested models (e.g. `AddressItem` below) should be direct subclasses of `pydantic.BaseModel`. """classAddressItem(pydantic.BaseModel):
street: strcity: strarea: Optional[str]
preferred: AddressItemwork: Optional[AddressItem]
home: Optional[AddressItem]
others: List[AddressItem] = []
classUser(Base):
__tablename__="user_account"id: Mapped[int] =mapped_column(primary_key=True)
name: Mapped[str] =mapped_column(sa.String(30))
addresses: Mapped[Addresses] =mapped_column(Addresses.as_mutable(), nullable=True)
engine=sa.create_engine("sqlite://")
Base.metadata.create_all(engine)
withSession(engine) ass:
s.add(u:=User(name="foo", addresses={"preferred": {"street": "bar", "city": "baz"}}))
assertisinstance(u.addresses, MutablePydanticBaseModel)
s.commit()
u.addresses.preferred.street="bar2"s.commit()
assertu.addresses.preferred.street=="bar2"u.addresses.others.append(Addresses.AddressItem.parse_obj({"street": "bar3", "city": "baz3"}))
s.commit()
assertisinstance(u.addresses.others[0], Addresses.AddressItem)
print(u.addresses.dict())
For more usage, please refer to the following test files:
  • tests/test_mutable_list.py
  • tests/test_mutable_dict.py
  • tests/test_mutable_pydantic_type.py
About
An advanced SQLAlchemy column type factory that helps map complex Python types (e.g. List, Dict, Pydantic Model and their hybrids) to database types (e.g. ARRAY, JSONB), And keep track of mutations in deeply nested data structures so that SQLAlchemy can emit proper UPDATE statements.
Topics
databasejsonbormpydanticpythonsqlalchemy
Resources
Readme
MIT license
Activity
Stars
33 stars
Watchers
1 watching
Forks
10 forks
Report repository
Releases
Sponsor this project
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
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(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - wonderbeyond/SQLAlchemy-Nested-Mutable: An advanced SQLAlchemy column type factory that helps map complex Python types (e.g. List, Dict, Pydantic Model and their hybrids) to database types (e.g. ARRAY, JSONB), And keep track of mutations in deeply nested data structures so that SQLAlchemy can emit proper UPDATE statements. · GitHub
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BranchesTags
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SQLAlchemy-Nested-Mutable
An advanced SQLAlchemy column type factory that helps map compound Python types (e.g. list, dict, Pydantic Model and their hybrids) to database types (e.g. ARRAY, JSONB),
And keep track of mutations in deeply nested data structures so that SQLAlchemy can emit proper UPDATE statements.
SQLAlchemy-Nested-Mutable is highly inspired by SQLAlchemy-JSON[0][1].
However, it does not limit the mapped Python type to be dict or list.
Why this package?
  • By default, SQLAlchemy does not track in-place mutations for non-scalar data types
such as list and dict (which are usually mapped with ARRAY and JSON/JSONB).
  • Even though SQLAlchemy provides an extension
to track mutations on compound objects, it's too shallow, i.e. it only tracks mutations on the first level of the compound object.
  • There exists the SQLAlchemy-JSON package
to help track mutations on nested dict or list data structures.
However, the db type is limited to JSON(B).
  • Also, I would like the mapped Python types can be subclasses of the Pydantic BaseModel,
which have strong schemas, with the db type be schema-less JSON.
Installation
pip install sqlalchemy-nested-mutable
Usage
NOTE the example below is first updated in examples/user-addresses.py and then updated here.
fromtypingimportOptional, Listimportpydanticimportsqlalchemyassafromsqlalchemy.ormimportSession, DeclarativeBase, Mapped, mapped_columnfromsqlalchemy_nested_mutableimportMutablePydanticBaseModelclassBase(DeclarativeBase):
passclassAddresses(MutablePydanticBaseModel):
"""A container for storing various addresses of users. NOTE: for working with pydantic model, use a subclass of `MutablePydanticBaseModel` for column mapping. However, the nested models (e.g. `AddressItem` below) should be direct subclasses of `pydantic.BaseModel`. """classAddressItem(pydantic.BaseModel):
street: strcity: strarea: Optional[str]
preferred: AddressItemwork: Optional[AddressItem]
home: Optional[AddressItem]
others: List[AddressItem] = []
classUser(Base):
__tablename__="user_account"id: Mapped[int] =mapped_column(primary_key=True)
name: Mapped[str] =mapped_column(sa.String(30))
addresses: Mapped[Addresses] =mapped_column(Addresses.as_mutable(), nullable=True)
engine=sa.create_engine("sqlite://")
Base.metadata.create_all(engine)
withSession(engine) ass:
s.add(u:=User(name="foo", addresses={"preferred": {"street": "bar", "city": "baz"}}))
assertisinstance(u.addresses, MutablePydanticBaseModel)
s.commit()
u.addresses.preferred.street="bar2"s.commit()
assertu.addresses.preferred.street=="bar2"u.addresses.others.append(Addresses.AddressItem.parse_obj({"street": "bar3", "city": "baz3"}))
s.commit()
assertisinstance(u.addresses.others[0], Addresses.AddressItem)
print(u.addresses.dict())
For more usage, please refer to the following test files:
  • tests/test_mutable_list.py
  • tests/test_mutable_dict.py
  • tests/test_mutable_pydantic_type.py
About
An advanced SQLAlchemy column type factory that helps map complex Python types (e.g. List, Dict, Pydantic Model and their hybrids) to database types (e.g. ARRAY, JSONB), And keep track of mutations in deeply nested data structures so that SQLAlchemy can emit proper UPDATE statements.
Topics
databasejsonbormpydanticpythonsqlalchemy
Resources
Readme
MIT license
Activity
Stars
33 stars
Watchers
1 watching
Forks
10 forks
Report repository
Releases
Sponsor this project
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Highlight search terms from Google/DuckDuckGo/Bing referrer
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var ref = document.referrer;
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} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - wonderbeyond/SQLAlchemy-Nested-Mutable: An advanced SQLAlchemy column type factory that helps map complex Python types (e.g. List, Dict, Pydantic Model and their hybrids) to database types (e.g. ARRAY, JSONB), And keep track of mutations in deeply nested data structures so that SQLAlchemy can emit proper UPDATE statements. · GitHub
Skip to content
BranchesTags
Repository files navigation
SQLAlchemy-Nested-Mutable
An advanced SQLAlchemy column type factory that helps map compound Python types (e.g. list, dict, Pydantic Model and their hybrids) to database types (e.g. ARRAY, JSONB),
And keep track of mutations in deeply nested data structures so that SQLAlchemy can emit proper UPDATE statements.
SQLAlchemy-Nested-Mutable is highly inspired by SQLAlchemy-JSON[0][1].
However, it does not limit the mapped Python type to be dict or list.
Why this package?
  • By default, SQLAlchemy does not track in-place mutations for non-scalar data types
such as list and dict (which are usually mapped with ARRAY and JSON/JSONB).
  • Even though SQLAlchemy provides an extension
to track mutations on compound objects, it's too shallow, i.e. it only tracks mutations on the first level of the compound object.
  • There exists the SQLAlchemy-JSON package
to help track mutations on nested dict or list data structures.
However, the db type is limited to JSON(B).
  • Also, I would like the mapped Python types can be subclasses of the Pydantic BaseModel,
which have strong schemas, with the db type be schema-less JSON.
Installation
pip install sqlalchemy-nested-mutable
Usage
NOTE the example below is first updated in examples/user-addresses.py and then updated here.
fromtypingimportOptional, Listimportpydanticimportsqlalchemyassafromsqlalchemy.ormimportSession, DeclarativeBase, Mapped, mapped_columnfromsqlalchemy_nested_mutableimportMutablePydanticBaseModelclassBase(DeclarativeBase):
passclassAddresses(MutablePydanticBaseModel):
"""A container for storing various addresses of users. NOTE: for working with pydantic model, use a subclass of `MutablePydanticBaseModel` for column mapping. However, the nested models (e.g. `AddressItem` below) should be direct subclasses of `pydantic.BaseModel`. """classAddressItem(pydantic.BaseModel):
street: strcity: strarea: Optional[str]
preferred: AddressItemwork: Optional[AddressItem]
home: Optional[AddressItem]
others: List[AddressItem] = []
classUser(Base):
__tablename__="user_account"id: Mapped[int] =mapped_column(primary_key=True)
name: Mapped[str] =mapped_column(sa.String(30))
addresses: Mapped[Addresses] =mapped_column(Addresses.as_mutable(), nullable=True)
engine=sa.create_engine("sqlite://")
Base.metadata.create_all(engine)
withSession(engine) ass:
s.add(u:=User(name="foo", addresses={"preferred": {"street": "bar", "city": "baz"}}))
assertisinstance(u.addresses, MutablePydanticBaseModel)
s.commit()
u.addresses.preferred.street="bar2"s.commit()
assertu.addresses.preferred.street=="bar2"u.addresses.others.append(Addresses.AddressItem.parse_obj({"street": "bar3", "city": "baz3"}))
s.commit()
assertisinstance(u.addresses.others[0], Addresses.AddressItem)
print(u.addresses.dict())
For more usage, please refer to the following test files:
  • tests/test_mutable_list.py
  • tests/test_mutable_dict.py
  • tests/test_mutable_pydantic_type.py
About
An advanced SQLAlchemy column type factory that helps map complex Python types (e.g. List, Dict, Pydantic Model and their hybrids) to database types (e.g. ARRAY, JSONB), And keep track of mutations in deeply nested data structures so that SQLAlchemy can emit proper UPDATE statements.
Topics
databasejsonbormpydanticpythonsqlalchemy
Resources
Readme
MIT license
Activity
Stars
33 stars
Watchers
1 watching
Forks
10 forks
Report repository
Releases
Sponsor this project
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Strip utm_, fbclid, gclid, etc. from all links on page
(function() {
var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',
'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',
'ref', 'ref_src', 'source', 'medium', 'campaign'];
function cleanUrl(url) {
try {
var u = new URL(url, window.location.origin);
var changed = false;
trackingParams.forEach(function(p) {
if (u.searchParams.has(p)) {
u.searchParams.delete(p);
changed = true;
}
});
return changed ? u.toString() : url;
} catch (e) {
return url;
}
}
function cleanLinks() {
document.querySelectorAll('a[href]').forEach(function(a) {
var clean = cleanUrl(a.href);
if (clean !== a.href) a.href = clean;
});
}
cleanLinks();
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mutations.forEach(function(m) {
m.addedNodes.forEach(function(node) {
if (node.nodeType === 1) {
if (node.tagName === 'A') cleanLinks();
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if (clean !== a.href) a.href = clean;
});
}
});
});
});
observer.observe(document.body, { childList: true, subtree: true });
})();
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} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '
GitHub - wonderbeyond/SQLAlchemy-Nested-Mutable: An advanced SQLAlchemy column type factory that helps map complex Python types (e.g. List, Dict, Pydantic Model and their hybrids) to database types (e.g. ARRAY, JSONB), And keep track of mutations in deeply nested data structures so that SQLAlchemy can emit proper UPDATE statements. · GitHub
Skip to content
BranchesTags
Repository files navigation
SQLAlchemy-Nested-Mutable
An advanced SQLAlchemy column type factory that helps map compound Python types (e.g. list, dict, Pydantic Model and their hybrids) to database types (e.g. ARRAY, JSONB),
And keep track of mutations in deeply nested data structures so that SQLAlchemy can emit proper UPDATE statements.
SQLAlchemy-Nested-Mutable is highly inspired by SQLAlchemy-JSON[0][1].
However, it does not limit the mapped Python type to be dict or list.
Why this package?
  • By default, SQLAlchemy does not track in-place mutations for non-scalar data types
such as list and dict (which are usually mapped with ARRAY and JSON/JSONB).
  • Even though SQLAlchemy provides an extension
to track mutations on compound objects, it's too shallow, i.e. it only tracks mutations on the first level of the compound object.
  • There exists the SQLAlchemy-JSON package
to help track mutations on nested dict or list data structures.
However, the db type is limited to JSON(B).
  • Also, I would like the mapped Python types can be subclasses of the Pydantic BaseModel,
which have strong schemas, with the db type be schema-less JSON.
Installation
pip install sqlalchemy-nested-mutable
Usage
NOTE the example below is first updated in examples/user-addresses.py and then updated here.
fromtypingimportOptional, Listimportpydanticimportsqlalchemyassafromsqlalchemy.ormimportSession, DeclarativeBase, Mapped, mapped_columnfromsqlalchemy_nested_mutableimportMutablePydanticBaseModelclassBase(DeclarativeBase):
passclassAddresses(MutablePydanticBaseModel):
"""A container for storing various addresses of users. NOTE: for working with pydantic model, use a subclass of `MutablePydanticBaseModel` for column mapping. However, the nested models (e.g. `AddressItem` below) should be direct subclasses of `pydantic.BaseModel`. """classAddressItem(pydantic.BaseModel):
street: strcity: strarea: Optional[str]
preferred: AddressItemwork: Optional[AddressItem]
home: Optional[AddressItem]
others: List[AddressItem] = []
classUser(Base):
__tablename__="user_account"id: Mapped[int] =mapped_column(primary_key=True)
name: Mapped[str] =mapped_column(sa.String(30))
addresses: Mapped[Addresses] =mapped_column(Addresses.as_mutable(), nullable=True)
engine=sa.create_engine("sqlite://")
Base.metadata.create_all(engine)
withSession(engine) ass:
s.add(u:=User(name="foo", addresses={"preferred": {"street": "bar", "city": "baz"}}))
assertisinstance(u.addresses, MutablePydanticBaseModel)
s.commit()
u.addresses.preferred.street="bar2"s.commit()
assertu.addresses.preferred.street=="bar2"u.addresses.others.append(Addresses.AddressItem.parse_obj({"street": "bar3", "city": "baz3"}))
s.commit()
assertisinstance(u.addresses.others[0], Addresses.AddressItem)
print(u.addresses.dict())
For more usage, please refer to the following test files:
  • tests/test_mutable_list.py
  • tests/test_mutable_dict.py
  • tests/test_mutable_pydantic_type.py
About
An advanced SQLAlchemy column type factory that helps map complex Python types (e.g. List, Dict, Pydantic Model and their hybrids) to database types (e.g. ARRAY, JSONB), And keep track of mutations in deeply nested data structures so that SQLAlchemy can emit proper UPDATE statements.
Topics
databasejsonbormpydanticpythonsqlalchemy
Resources
Readme
MIT license
Activity
Stars
33 stars
Watchers
1 watching
Forks
10 forks
Report repository
Releases
Sponsor this project
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Auto-enable theater mode on YouTube
(function() {
function tryTheater() {
var btn = document.querySelector('button[aria-label="Theater mode"], ytd-player #player button[title="Theater mode"]');
if (btn && !btn.classList.contains('activated')) {
btn.click();
}
}
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tryTheater();
// Try after navigation (SPA)
var lastUrl = location.href;
setInterval(function() {
if (location.href !== lastUrl) {
lastUrl = location.href;
setTimeout(tryTheater, 500);
}
}, 1000);
// Also try on player load
var observer = new MutationObserver(tryTheater);
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
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GitHub - wonderbeyond/SQLAlchemy-Nested-Mutable: An advanced SQLAlchemy column type factory that helps map complex Python types (e.g. List, Dict, Pydantic Model and their hybrids) to database types (e.g. ARRAY, JSONB), And keep track of mutations in deeply nested data structures so that SQLAlchemy can emit proper UPDATE statements. · GitHub
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SQLAlchemy-Nested-Mutable
An advanced SQLAlchemy column type factory that helps map compound Python types (e.g. list, dict, Pydantic Model and their hybrids) to database types (e.g. ARRAY, JSONB),
And keep track of mutations in deeply nested data structures so that SQLAlchemy can emit proper UPDATE statements.
SQLAlchemy-Nested-Mutable is highly inspired by SQLAlchemy-JSON[0][1].
However, it does not limit the mapped Python type to be dict or list.
Why this package?
  • By default, SQLAlchemy does not track in-place mutations for non-scalar data types
such as list and dict (which are usually mapped with ARRAY and JSON/JSONB).
  • Even though SQLAlchemy provides an extension
to track mutations on compound objects, it's too shallow, i.e. it only tracks mutations on the first level of the compound object.
  • There exists the SQLAlchemy-JSON package
to help track mutations on nested dict or list data structures.
However, the db type is limited to JSON(B).
  • Also, I would like the mapped Python types can be subclasses of the Pydantic BaseModel,
which have strong schemas, with the db type be schema-less JSON.
Installation
pip install sqlalchemy-nested-mutable
Usage
NOTE the example below is first updated in examples/user-addresses.py and then updated here.
fromtypingimportOptional, Listimportpydanticimportsqlalchemyassafromsqlalchemy.ormimportSession, DeclarativeBase, Mapped, mapped_columnfromsqlalchemy_nested_mutableimportMutablePydanticBaseModelclassBase(DeclarativeBase):
passclassAddresses(MutablePydanticBaseModel):
"""A container for storing various addresses of users. NOTE: for working with pydantic model, use a subclass of `MutablePydanticBaseModel` for column mapping. However, the nested models (e.g. `AddressItem` below) should be direct subclasses of `pydantic.BaseModel`. """classAddressItem(pydantic.BaseModel):
street: strcity: strarea: Optional[str]
preferred: AddressItemwork: Optional[AddressItem]
home: Optional[AddressItem]
others: List[AddressItem] = []
classUser(Base):
__tablename__="user_account"id: Mapped[int] =mapped_column(primary_key=True)
name: Mapped[str] =mapped_column(sa.String(30))
addresses: Mapped[Addresses] =mapped_column(Addresses.as_mutable(), nullable=True)
engine=sa.create_engine("sqlite://")
Base.metadata.create_all(engine)
withSession(engine) ass:
s.add(u:=User(name="foo", addresses={"preferred": {"street": "bar", "city": "baz"}}))
assertisinstance(u.addresses, MutablePydanticBaseModel)
s.commit()
u.addresses.preferred.street="bar2"s.commit()
assertu.addresses.preferred.street=="bar2"u.addresses.others.append(Addresses.AddressItem.parse_obj({"street": "bar3", "city": "baz3"}))
s.commit()
assertisinstance(u.addresses.others[0], Addresses.AddressItem)
print(u.addresses.dict())
For more usage, please refer to the following test files:
  • tests/test_mutable_list.py
  • tests/test_mutable_dict.py
  • tests/test_mutable_pydantic_type.py
About
An advanced SQLAlchemy column type factory that helps map complex Python types (e.g. List, Dict, Pydantic Model and their hybrids) to database types (e.g. ARRAY, JSONB), And keep track of mutations in deeply nested data structures so that SQLAlchemy can emit proper UPDATE statements.
Topics
databasejsonbormpydanticpythonsqlalchemy
Resources
Readme
MIT license
Activity
Stars
33 stars
Watchers
1 watching
Forks
10 forks
Report repository
Releases
Sponsor this project
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GitHub - wonderbeyond/SQLAlchemy-Nested-Mutable: An advanced SQLAlchemy column type factory that helps map complex Python types (e.g. List, Dict, Pydantic Model and their hybrids) to database types (e.g. ARRAY, JSONB), And keep track of mutations in deeply nested data structures so that SQLAlchemy can emit proper UPDATE statements. · GitHub
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SQLAlchemy-Nested-Mutable
An advanced SQLAlchemy column type factory that helps map compound Python types (e.g. list, dict, Pydantic Model and their hybrids) to database types (e.g. ARRAY, JSONB),
And keep track of mutations in deeply nested data structures so that SQLAlchemy can emit proper UPDATE statements.
SQLAlchemy-Nested-Mutable is highly inspired by SQLAlchemy-JSON[0][1].
However, it does not limit the mapped Python type to be dict or list.
Why this package?
  • By default, SQLAlchemy does not track in-place mutations for non-scalar data types
such as list and dict (which are usually mapped with ARRAY and JSON/JSONB).
  • Even though SQLAlchemy provides an extension
to track mutations on compound objects, it's too shallow, i.e. it only tracks mutations on the first level of the compound object.
  • There exists the SQLAlchemy-JSON package
to help track mutations on nested dict or list data structures.
However, the db type is limited to JSON(B).
  • Also, I would like the mapped Python types can be subclasses of the Pydantic BaseModel,
which have strong schemas, with the db type be schema-less JSON.
Installation
pip install sqlalchemy-nested-mutable
Usage
NOTE the example below is first updated in examples/user-addresses.py and then updated here.
fromtypingimportOptional, Listimportpydanticimportsqlalchemyassafromsqlalchemy.ormimportSession, DeclarativeBase, Mapped, mapped_columnfromsqlalchemy_nested_mutableimportMutablePydanticBaseModelclassBase(DeclarativeBase):
passclassAddresses(MutablePydanticBaseModel):
"""A container for storing various addresses of users. NOTE: for working with pydantic model, use a subclass of `MutablePydanticBaseModel` for column mapping. However, the nested models (e.g. `AddressItem` below) should be direct subclasses of `pydantic.BaseModel`. """classAddressItem(pydantic.BaseModel):
street: strcity: strarea: Optional[str]
preferred: AddressItemwork: Optional[AddressItem]
home: Optional[AddressItem]
others: List[AddressItem] = []
classUser(Base):
__tablename__="user_account"id: Mapped[int] =mapped_column(primary_key=True)
name: Mapped[str] =mapped_column(sa.String(30))
addresses: Mapped[Addresses] =mapped_column(Addresses.as_mutable(), nullable=True)
engine=sa.create_engine("sqlite://")
Base.metadata.create_all(engine)
withSession(engine) ass:
s.add(u:=User(name="foo", addresses={"preferred": {"street": "bar", "city": "baz"}}))
assertisinstance(u.addresses, MutablePydanticBaseModel)
s.commit()
u.addresses.preferred.street="bar2"s.commit()
assertu.addresses.preferred.street=="bar2"u.addresses.others.append(Addresses.AddressItem.parse_obj({"street": "bar3", "city": "baz3"}))
s.commit()
assertisinstance(u.addresses.others[0], Addresses.AddressItem)
print(u.addresses.dict())
For more usage, please refer to the following test files:
  • tests/test_mutable_list.py
  • tests/test_mutable_dict.py
  • tests/test_mutable_pydantic_type.py
About
An advanced SQLAlchemy column type factory that helps map complex Python types (e.g. List, Dict, Pydantic Model and their hybrids) to database types (e.g. ARRAY, JSONB), And keep track of mutations in deeply nested data structures so that SQLAlchemy can emit proper UPDATE statements.
Topics
databasejsonbormpydanticpythonsqlalchemy
Resources
Readme
MIT license
Activity
Stars
33 stars
Watchers
1 watching
Forks
10 forks
Report repository
Releases
Sponsor this project
Packages
Used by
Contributors
Languages

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GitHub - wonderbeyond/SQLAlchemy-Nested-Mutable: An advanced SQLAlchemy column type factory that helps map complex Python types (e.g. List, Dict, Pydantic Model and their hybrids) to database types (e.g. ARRAY, JSONB), And keep track of mutations in deeply nested data structures so that SQLAlchemy can emit proper UPDATE statements. · GitHub
Skip to content
BranchesTags
Repository files navigation
SQLAlchemy-Nested-Mutable
An advanced SQLAlchemy column type factory that helps map compound Python types (e.g. list, dict, Pydantic Model and their hybrids) to database types (e.g. ARRAY, JSONB),
And keep track of mutations in deeply nested data structures so that SQLAlchemy can emit proper UPDATE statements.
SQLAlchemy-Nested-Mutable is highly inspired by SQLAlchemy-JSON[0][1].
However, it does not limit the mapped Python type to be dict or list.
Why this package?
  • By default, SQLAlchemy does not track in-place mutations for non-scalar data types
such as list and dict (which are usually mapped with ARRAY and JSON/JSONB).
  • Even though SQLAlchemy provides an extension
to track mutations on compound objects, it's too shallow, i.e. it only tracks mutations on the first level of the compound object.
  • There exists the SQLAlchemy-JSON package
to help track mutations on nested dict or list data structures.
However, the db type is limited to JSON(B).
  • Also, I would like the mapped Python types can be subclasses of the Pydantic BaseModel,
which have strong schemas, with the db type be schema-less JSON.
Installation
pip install sqlalchemy-nested-mutable
Usage
NOTE the example below is first updated in examples/user-addresses.py and then updated here.
fromtypingimportOptional, Listimportpydanticimportsqlalchemyassafromsqlalchemy.ormimportSession, DeclarativeBase, Mapped, mapped_columnfromsqlalchemy_nested_mutableimportMutablePydanticBaseModelclassBase(DeclarativeBase):
passclassAddresses(MutablePydanticBaseModel):
"""A container for storing various addresses of users. NOTE: for working with pydantic model, use a subclass of `MutablePydanticBaseModel` for column mapping. However, the nested models (e.g. `AddressItem` below) should be direct subclasses of `pydantic.BaseModel`. """classAddressItem(pydantic.BaseModel):
street: strcity: strarea: Optional[str]
preferred: AddressItemwork: Optional[AddressItem]
home: Optional[AddressItem]
others: List[AddressItem] = []
classUser(Base):
__tablename__="user_account"id: Mapped[int] =mapped_column(primary_key=True)
name: Mapped[str] =mapped_column(sa.String(30))
addresses: Mapped[Addresses] =mapped_column(Addresses.as_mutable(), nullable=True)
engine=sa.create_engine("sqlite://")
Base.metadata.create_all(engine)
withSession(engine) ass:
s.add(u:=User(name="foo", addresses={"preferred": {"street": "bar", "city": "baz"}}))
assertisinstance(u.addresses, MutablePydanticBaseModel)
s.commit()
u.addresses.preferred.street="bar2"s.commit()
assertu.addresses.preferred.street=="bar2"u.addresses.others.append(Addresses.AddressItem.parse_obj({"street": "bar3", "city": "baz3"}))
s.commit()
assertisinstance(u.addresses.others[0], Addresses.AddressItem)
print(u.addresses.dict())
For more usage, please refer to the following test files:
  • tests/test_mutable_list.py
  • tests/test_mutable_dict.py
  • tests/test_mutable_pydantic_type.py
About
An advanced SQLAlchemy column type factory that helps map complex Python types (e.g. List, Dict, Pydantic Model and their hybrids) to database types (e.g. ARRAY, JSONB), And keep track of mutations in deeply nested data structures so that SQLAlchemy can emit proper UPDATE statements.
Topics
databasejsonbormpydanticpythonsqlalchemy
Resources
Readme
MIT license
Activity
Stars
33 stars
Watchers
1 watching
Forks
10 forks
Report repository
Releases
Sponsor this project
Packages
Used by
Contributors
Languages