jsonjsdb 0.8.9

Python library for JSONJS database loading

jsonjsdb

Python library for JSONJS databases with full CRUD support and relational queries.

Features

• Read & Write: Full CRUD operations
• Typed API: Optional TypedDict support with autocompletion
• Relational queries: having.{table}(id) for one-to-many and many-to-many
• Filtering: where() with operators (==, !=, >, in, is_null, etc.)
• TypeScript compatible: Same file format as the TypeScript jsonjsdb library

Installation

pip install jsonjsdb

Quick Start

from jsonjsdb import Jsonjsdb

db = Jsonjsdb("path/to/db")

# Read
user = db["user"].get("user_1")
active = db["user"].where("status", "==", "active")

# Write
db["user"].add({"id": "u1", "name": "Alice", "tag_ids": []})
db["user"].update("u1", name="Alice Updated")
db.save()

Typed Access

With TypedDict (dict-style)

from typing import TypedDict
from jsonjsdb import Jsonjsdb, Table

class User(TypedDict):
    id: str
    name: str
    tag_ids: list[str]

class MyDB(Jsonjsdb):
    user: Table[User]

db = MyDB("path/to/db")
user = db.user.get("user_1")  # Returns User | None
print(user["name"])           # Dict-style access

With Dataclass (attribute-style)

from dataclasses import dataclass
from jsonjsdb import Jsonjsdb, Table

@dataclass
class User:
    id: str
    name: str
    tag_ids: list[str]

# Pass entity_type to get dataclass instances
table: Table[User] = Table("user", entity_type=User)

user = table.get("user_1")    # Returns User dataclass
print(user.name)              # Attribute-style access

table.add(User(id="u2", name="Bob", tag_ids=[]))

API Reference

CRUD

db.user.add({"id": "u1", "name": "Alice", ...})  # Add row (id required)
db.user.add_all([...])                           # Add multiple rows (batch)
db.user.upsert({"id": "u1", ...})                # Add or update → bool (True=added)

db.user.get("u1")                                # → User | None
db.user.get_by("email", "[e-mail]")        # → User | None (by column)
db.user.exists("u1")                             # → bool
db.user.all()                                    # → list[User]
db.user.count                                    # → int (number of rows)
db.user.is_empty                                 # → bool

db.user.update("u1", name="New Name")            # Update fields
db.user.update_many(["u1", "u2"], status="x")   # Batch update → int (count)
db.user.remove("u1")                             # → bool
db.user.remove_all(["u1", "u2"])                 # → int (count)
db.user.remove_where("status", "==", "inactive") # → int (count)

Filtering

db.user.where("status", "==", "active")          # Equality
db.user.where("age", ">", 18)                    # Comparison (>, >=, <, <=)
db.user.where("status", "in", ["a", "b"])        # In list
db.user.where("email", "is_null")                # Null check (is_not_null)

db.user.ids_where("status", "==", "active")      # → list[str] (IDs only, faster)

Relations

db.email.having.user("user_1")      # One-to-many: where user_id == "user_1"
db.user.having.tag("tag_1")         # Many-to-many: where tag_ids contains "tag_1"
db.folder.having.parent("folder_1") # Hierarchy: where parent_id == "folder_1"

db.email.ids_having.user("user_1")  # Same as above, returns IDs only (faster)

Save / New Database

db.save()                # Save to original path
db.save("new/path")      # Save to new location

db = MyDB()              # Create empty in-memory DB
db.user.add({...})
db.save("path/to/db")    # Path required on first save

Evolution Tracking

Changes are automatically tracked when saving. An evolution.json file logs all additions, deletions, and updates:

# Tracking enabled by default
db.save()

# Disable tracking
db.save(track_evolution=False)

# Skip .json.js files (faster, smaller output)
db.save(write_js=False)

# Use Excel as source (for easy editing of logs)
db.save(evolution_xlsx=Path("path/to/evolution.xlsx"))

# Override timestamp for deterministic outputs (useful for testing)
db.save(timestamp=1741186800)

Cascade Filtering

When a parent entity is added or deleted, all child entities are also added/deleted. By default, this creates noise in the evolution log. Use parent_relations to automatically filter out cascade entries:

db.save(
    parent_relations={
        "variable": "dataset",    # variable.dataset_id → dataset
        "freq": "variable",       # freq.variable_id → variable
    }
)

With cascade filtering:

• Adding a dataset with 50 variables logs only 1 entry (the dataset add)
• Deleting a dataset logs only the parent delete, not all child deletes
• Updates are always logged (no filtering)
• Explicit child additions (to existing parent) are still logged

When evolution_xlsx is provided:

• The xlsx file becomes the source of truth (read from xlsx if it exists)
• User edits made in Excel are preserved on subsequent saves
• Both evolution.json and evolution.xlsx are written to stay in sync

Evolution format:

[
  {
    "timestamp": 1741186800,
    "type": "add",
    "entity": "user",
    "entity_id": "user_2",
    "parent_entity_id": null,
    "variable": null,
    "old_value": null,
    "new_value": null,
    "name": null
  },
  {
    "timestamp": 1741186800,
    "type": "update",
    "entity": "variable",
    "entity_id": "var_1",
    "parent_entity_id": "ds_1",
    "variable": "name",
    "old_value": "Old Name",
    "new_value": "New Name",
    "name": null
  }
]

Runtime Fields

Exclude fields from persistence (in-memory only):

from jsonjsdb import Table

# Option 1: Via constructor
table: Table[dict] = Table("user", runtime_fields={"_seen", "_processed"})

# Option 2: Via subclass
class UserTable(Table[User]):
    runtime_fields = {"_seen", "_processed"}

table.add({"id": "1", "name": "Alice", "_seen": True})

table.get("1")["_seen"]           # → True (in memory)
table.get_persistable_df()        # → DataFrame without _seen
# On save(), runtime_fields are automatically excluded

File Format

• __table__.json — Index of tables with metadata
• {table}.json — Data as array of objects
• {table}.json.js — Same data for browser (JavaScript)
• evolution.json — Change history (auto-generated on save)

Column Conventions

Column	Description
id	Primary key (always string)
xxx_id	Foreign key to table xxx
xxx_ids	Many-to-many (comma-separated in file, list[str] in API)
parent_id	Self-reference for hierarchies

License

MIT