Chaining Dependent Operations with BasicReturns in Python

In multi-step workflows — read a file, then parse it, then validate its contents — if any step fails you want to stop immediately and propagate the failure upward, not continue into the next step with bad data. BasicReturns makes this trivial. Because every function returns the same structure, you can check result.ok after each call and return the failed result directly, keeping the error context intact without any nested try/except blocks or re-raising exceptions.

Early Return Pattern

The simplest chaining technique is the early return: call a function, and if it did not succeed, return its result immediately. The caller receives the same DataAndMsgReturn that the inner function produced — including its error, msg, and ok = False — without any wrapping or translation.

import json
from BasicReturns import DataAndMsgReturn

def read_file(filename: str) -> DataAndMsgReturn:
    """Read raw file content."""
    response = DataAndMsgReturn()

    try:
        with open(filename, "r", encoding="utf-8") as file:
            response.data = file.read()
            response.msg = f"Successfully read file: {filename}"
    except Exception as e:
        response.ok = False
        response.error = e
        response.msg = f"Error reading file: {filename}"

    return response


def read_json(filename: str) -> DataAndMsgReturn:
    """Read and parse a JSON file, propagating any file-read errors."""
    response = DataAndMsgReturn()
    file_result = read_file(filename)

    if not file_result.ok:
        return file_result  # bubble the failure straight up

    try:
        response.data = json.loads(file_result.data)
        response.msg = f"Successfully parsed JSON from: {filename}"
    except json.JSONDecodeError as e:
        response.ok = False
        response.error = e
        response.msg = f"Invalid JSON format in file: {filename}"

    return response

read_json never needs to know why read_file failed — it just passes the result along. The caller of read_json gets back either a valid parsed payload or a fully described failure, regardless of which layer it originated in.

Constructing a New Return from a Failed One

Sometimes you need to add context at the current layer rather than passing the original result unchanged. For example, a higher-level function may want to describe what it was trying to do rather than surface the low-level technical message. In that case, build a new DataAndMsgReturn that preserves the original exception while replacing the message:

from BasicReturns import DataAndMsgReturn

def load_settings(path: str) -> DataAndMsgReturn:
    """Load application settings, adding context on failure."""
    result = read_json(path)

    if not result.ok:
        return DataAndMsgReturn(
            ok=False,
            error=result.error,          # keep the original exception
            msg=f"Failed to load settings from '{path}': {result.msg}"
        )

    return result

The key rule: always forward result.error — never replace the exception object with a plain string. Keeping the original exception preserves the full traceback for logging and debugging.

Full Chain Example

Here is a complete three-step chain that reads a JSON configuration file and then validates its contents, taken directly from the BasicReturns README:

from BasicReturns import DataAndMsgReturn

def validate_config(config: dict) -> DataAndMsgReturn:
    """Validate required fields in a configuration dictionary."""
    response = DataAndMsgReturn()

    try:
        required_keys = {"host", "port", "database"}
        missing = required_keys - config.keys()
        if missing:
            raise ValueError(f"Missing required config keys: {missing}")
        response.data = config
        response.msg = "Configuration is valid"
    except Exception as e:
        response.ok = False
        response.error = e
        response.msg = "Configuration validation failed"

    return response


def load_and_validate_config() -> DataAndMsgReturn:
    """Load config.json and validate its structure."""

    # Step 1 — read and parse the file
    config_result = read_json("config.json")
    if not config_result.ok:
        return config_result  # propagate file/parse error as-is

    # Step 2 — validate the parsed data
    validation_result = validate_config(config_result.data)
    if not validation_result.ok:
        return DataAndMsgReturn(
            ok=False,
            error=validation_result.error,
            msg=f"Configuration validation failed: {validation_result.msg}"
        )

    # All steps succeeded
    return DataAndMsgReturn(
        data=config_result.data,
        msg="Configuration loaded and validated successfully"
    )

Each step is isolated: read_json knows nothing about validation, and validate_config knows nothing about files. load_and_validate_config composes them, checks the result of each, and either short-circuits with the error or assembles the final success return.

This pattern is structurally identical to Rust’s ? operator and to monadic bind (the >>= operator in Haskell). Each step only executes if the previous one succeeded, and errors propagate automatically through the chain without boilerplate. BasicReturns brings that discipline to Python in an explicit, readable form.

Propagating to API Responses

Chained results naturally serialize at the boundary. Call to_dict() once, at the outermost layer, and the full context — including the original error and every message accumulated along the chain — is ready for a JSON response or log entry:

from BasicReturns import DataAndMsgReturn

def handle_startup_request() -> dict:
    """Load config and return a serializable result for the API layer."""
    result = load_and_validate_config()

    # to_dict() returns {"ok": ..., "error": ..., "msg": ..., "data": ...}
    return result.to_dict()


# Example output on failure:
# {
#   "ok": False,
#   "error": FileNotFoundError("config.json not found"),
#   "msg": "Error reading file: config.json",
#   "data": {}
# }

# Example output on success:
# {
#   "ok": True,
#   "error": None,
#   "msg": "Configuration loaded and validated successfully",
#   "data": {"host": "localhost", "port": 5432, "database": "mydb"}
# }

Because the structure is always the same, the API layer does not need to branch on the result type — it calls to_dict() unconditionally and lets the ok field carry the signal to the client.

Get Started

Guides

Chaining Dependent Operations with BasicReturns in Python

Early Return Pattern

Constructing a New Return from a Failed One

Full Chain Example

Propagating to API Responses

​Early Return Pattern

​Constructing a New Return from a Failed One

​Full Chain Example

​Propagating to API Responses

Early Return Pattern

Constructing a New Return from a Failed One

Full Chain Example

Propagating to API Responses