etielle 2.1.0

A declarative, type-safe Python DSL for mapping complex nested JSON to relational database schemas

etielle: Declarative JSON-to-Relational Mapping in Python

etielle is a simple, powerful Python library for reshaping nested JSON data, typically from an API, into relational tables that fit your database schema. Think of etielle as a “JSON extractor” that you program with clear instructions: “Go here in the JSON, pull this data, and put it in that table.” The library’s name is a play on ETL (“Extract, Transform, Load”), which is the technical term for this set of operations.

• Repository: Promptly-Technologies-LLC/etielle
• PyPI: etielle
• Python: ≥ 3.13

Why Use etielle? (For Beginners)

JSON data from APIs (Application Program Interfaces—web services that typically return JSON) is often deeply nested and requires complicated parsing. etielle helps by:

• Declaring what you want: Write Python code to describe your tables and how to fill them.
• Traversing nested structures: Walk through arrays-within-dictionaries-within-arrays to any arbitrary depth.
• Performing arbitrary transformations: Use the provided functions to perform common operations (like getting the key or index of the current item or its parent), or define your own.
• Building relationships: Link records across your different output tables and emit ORM relationships or foreign keys.
• Being beginner-friendly: Everything is type-safe (Python checks your types), composable (build complex things from simple pieces), and easy to debug.

Learning Path

1. Start here: Follow the Quick Start example below to see basic mapping
2. Understand the pieces: Read Core Concepts to learn about Context, Transforms, and TraversalSpec
3. Go deeper: Explore the detailed examples for nesting and merging
4. Advanced features: Check out the docs/ folder for instance emission, relationships, and more

Installation

We recommend using uv for faster installs, but pip works too.

With uv (Recommended for Speed)

For your project:

uv add etielle

For one-off use:

uv pip install etielle

With pip

pip install etielle

Optional: SQLAlchemy adapter

If you plan to bind relationships and flush via SQLAlchemy in one go, install the optional extra:

uv add "etielle[sqlalchemy]"

Optional: SQLModel adapter

If you plan to bind relationships and flush via SQLModel in one go, install the optional extra:

uv add "etielle[sqlmodel]"

Quick Start: Your First Mapping

Let’s start with a simple example. Suppose you have this JSON:

import json

data = {
  "users": [
    {"id": "u1", "name": "Alice", "posts": [{"id": "p1", "title": "Hello"}, {"id": "p2", "title": "World"}]},
    {"id": "u2", "name": "Bob", "posts": []}
  ]
}

We want two tables: “users” (id, name) and “posts” (id, user_id, title).

Here’s the code:

from etielle.core import MappingSpec, TraversalSpec, TableEmit, Field  # Core building blocks
from etielle.transforms import get, get_from_parent  # Functions to pull data from JSON
from etielle.executor import run_mapping  # The engine that runs everything

# A TraversalSpec tells etielle how to walk through your JSON. Think of it as
# giving directions: "Start at the 'users' key, then loop through each item in that array."

# Traverse users array
users_traversal = TraversalSpec(
    path=["users"],  # Path to the array
    mode="auto",  # Iterate automatically based on container
    emits=[
        # The join_keys identify each unique row—like a primary key in a database.
        # Rows with matching keys will be merged together.
        TableEmit(
            table="users",
            join_keys=[get("id")],  # Unique key for the row
            fields=[
                Field("id", get("id")),
                Field("name", get("name"))
            ]
        )
    ]
)

# This second traversal is nested: first we navigate to each user,
# then for each user we go into their posts array using inner_path.
posts_traversal = TraversalSpec(
    path=["users"],
    mode="auto",
    inner_path=["posts"],  # Nested path inside each user
    inner_mode="auto",
    emits=[
        TableEmit(
            table="posts",
            join_keys=[get("id")],
            fields=[
                Field("id", get("id")),
                Field("user_id", get_from_parent("id")),  # Link to parent user
                Field("title", get("title"))
            ]
        )
    ]
)

spec = MappingSpec(traversals=[users_traversal, posts_traversal])
result = run_mapping(data, spec)

# result is a dict: {"users": MappingResult, "posts": MappingResult}
# Each MappingResult has .instances (a dict keyed by join_keys)
# Let's convert to simple lists for display:
out = {table: list(mr.instances.values()) for table, mr in result.items()}
print(json.dumps(out, indent=2))

{
  "users": [
    {
      "id": "u1",
      "name": "Alice"
    },
    {
      "id": "u2",
      "name": "Bob"
    }
  ],
  "posts": [
    {
      "id": "p1",
      "user_id": "u1",
      "title": "Hello"
    },
    {
      "id": "p2",
      "user_id": "u1",
      "title": "World"
    }
  ]
}

Congrats! You’ve mapped your first JSON.

Core Concepts: Breaking It Down

Let’s explain the building blocks like you’re learning for the first time.

1. Context: Your “Location” in the JSON

Imagine traversing a JSON tree—Context is your GPS:

• root: The entire JSON.
• node: The current spot (e.g., a user object).
• path: Directions to get here (e.g., (“users”, 0)).
• parent: The previous spot (for looking “up”).
• key/index: If in a dict/list, the current key or index.
• slots: A notepad for temporary notes.

Contexts are created automatically as you traverse and are immutable (unchangeable) for safety.

2. Transforms: Smart Data Extractors

Transforms are like mini-functions that pull values from Context. They’re “lazy”—they don’t run until needed, and they adapt to the current Context.

Examples:

• get("name"): Get “name” from current node → "Alice" when node is {"name": "Alice"}
• get_from_parent("id"): Get “id” from parent context → "u1" when processing a post under user u1
• index(): Current list position → 0 for first item, 1 for second, etc.
• concat(literal("user_"), get("id")): Combine strings → "user_u1"

Full list in the Cheatsheet below.

3. TraversalSpec: How to Walk the JSON

This says: “Start here, then go deeper if needed, and do this for each item.”

• path: Starting path (list of strings, e.g., [“users”]).
• mode: Iteration mode for the outer container: “auto” (default), “items”, or “single”.
• inner_path: Optional deeper path (e.g., [“posts”] for nesting).
• inner_mode: Iteration mode for the inner container: “auto” (default), “items”, or “single”.
• emits: What tables to create from each item.

You can have multiple Traversals in one MappingSpec—they run independently.

Here’s a visual representation of how traversals work:

JSON structure:
root
└── users []                    ← path=["users"]
    ├── [0] {"id": "u1", ...}
    │   └── posts []            ← inner_path=["posts"]
    │       ├── [0] {"id": "p1", "title": "Hello"}
    │       └── [1] {"id": "p2", "title": "World"}
    └── [1] {"id": "u2", ...}

4. TableEmit and Fields: Building Your Tables

• table: Name of the table.
• fields: List of Field(name, transform) – columns and how to compute them.
• join_keys: List of transforms for unique row IDs (like primary keys). Same keys across traversals merge rows.

5. Executor: Running It All

run_mapping(json_data, spec) executes everything and returns a dict of tables.

Detailed Examples

Example 1: Composite Keys for Merging Data

Merge user info from two parts of JSON:

spec = MappingSpec(traversals=[
    TraversalSpec(  # Basic user data
        path=["users"],
        mode="auto",
        emits=[TableEmit(
            table="users",
            join_keys=[get("id")],
            fields=[Field("id", get("id")), Field("name", get("name"))]
        )]
    ),
    TraversalSpec(  # Add email from another section
        path=["profiles"],
        mode="auto",
        emits=[TableEmit(
            table="users",  # Same table!
            join_keys=[get("user_id")],  # Matches previous keys
            fields=[Field("email", get("email"))]
        )]
    )
])

Rows with matching keys merge: e.g., add “email” to existing user row.

Example 2: Deep Nesting (Arbitrary Depth)

No limit to depth—use longer inner_path. The depth parameter controls how many levels up to look:

• get_from_parent("id") or depth=1 → immediate parent
• get_from_parent("id", depth=2) → grandparent
• get_from_parent("id", depth=3) → great-grandparent

spec = MappingSpec(traversals=[
    TraversalSpec(
        path=["servers"],
        mode="auto",
        inner_path=["channels", "messages", "reactions"],  # 3 levels deep!
        inner_mode="auto",
        emits=[TableEmit(
            table="reactions",
            join_keys=[get_from_parent("id", depth=3), get_from_parent("id", depth=2), get_from_parent("id"), get("id")],
            fields=[
                Field("server_id", get_from_parent("id", depth=3)),
                Field("channel_id", get_from_parent("id", depth=2)),
                Field("message_id", get_from_parent("id")),
                Field("reaction", get("emoji"))
            ]
        )]
    )
])

Transform Cheatsheet

• get(path): From current node (dot notation or list, e.g., “user.name” or [“user”, 0]).
• get_from_parent(path, depth=1): From ancestor.
• get_from_root(path): From top-level JSON.
• key(): Current dict key.
• index(): Current list index.
• literal(value): Constant value.
• concat(*parts): Join strings.
• format_id(*parts, sep="_"): Join non-empty parts with separator.
• coalesce(*transforms): First non-None value.
• len_of(inner): Length of a list/dict/string.

Pro Tip: Transforms are lazy—they run in the “context” of where they’re used, making them super flexible.

Transforms compose naturally:

user_key = concat(literal("user_"), get("id"))           # "user_123"
full_name = concat(get("first"), literal(" "), get("last"))  # "Alice Smith"

Common Mistakes

• Empty results?
  • Check your path matches the JSON structure exactly
  • Verify the data type at that path matches expectations
• Missing parent data?
  • Check the depth parameter in get_from_parent()
  • Ensure the parent context exists in your traversal
• Duplicate or missing rows?
  • Verify join_keys are unique for each row
  • Check that join_keys don’t contain None values (these rows are skipped)

Advanced Topics

• Lazy Evaluation: Transforms don’t compute until executed, adapting to the current spot in JSON.
• Custom Transforms: Define your own functions that take Context and return values.
• Row Merging Rules: Last write wins for duplicate fields; missing keys skip rows.
• Field selectors: Type-safe field references. See Field selectors.
• Instance emission: Build Pydantic/TypedDict/ORM instances directly instead of dicts. See Instance emission.
• Merge policies: Sum/append/min/max instead of overwrite when multiple traversals update the same field. See Merge policies.
• Error reporting: Per-key diagnostics in results. See Error reporting.
• Relationships without extra round trips: Bind in-memory, flush once. See Relationships and SQLAlchemy adapter.
• Performance: Efficient for large JSON; traversals are independent.

Roadmap Ideas

• Database integrations (e.g., SQLAlchemy).
• More examples and benchmarks.
• Visual mapping tools.

Glossary

• Context: Your current position while traversing the JSON tree
• Transform: A function that extracts values from a Context
• Traversal: Instructions for walking through part of the JSON
• Emit: Creating a table row from the current context
• Join keys: Values that uniquely identify a row (like primary keys)
• Depth: How many parent levels to traverse upward

License

MIT

Need help? Open an issue on GitHub!