Optimizing Custom Query Names In Morpc-py Passing A Custom Parameter

Aug 1, 2025 by Pedro Alvarez 69 views

Optimizing Custom Query Naming in morpc-py: A Comprehensive Guide

Hey guys! Let's dive into a common challenge in software development: how to name custom queries effectively, especially when dealing with dynamic parameters like dates. In this article, we'll explore the issue highlighted in the morpc-py library and discuss strategies to improve the naming of custom queries. We'll break down the problem, propose solutions, and provide a comprehensive guide to help you implement better naming conventions in your projects. So, buckle up and let’s get started!

Understanding the Challenge

The Current Implementation

In the morpc-py library, specifically within the census.py file, there’s a section of code that deals with naming custom queries. Currently, when custom query parameters are passed to the .query method, the name of the resource is generated dynamically, incorporating the date. Here’s the snippet we’re focusing on:

            self.VARS = temp

        # If custom query parameters are passed to .query then the name of the resource is custom and includes date.
        # TODO: Find a better way of naming custom queries, possibly by passing a custom parameter.
        if scope is None:
            self.NAME = f"morpc-acs{self.SURVEY}-{self.YEAR}-custom-ucgid-{self.GROUP}-{datetime.now().strftime(format='%Y%m%d')}".lower()

The critical line here is:

self.NAME = f"morpc-acs{self.SURVEY}-{self.YEAR}-custom-ucgid-{self.GROUP}-{datetime.now().strftime(format='%Y%m%d')}".lower()

This line constructs the name using several components:

morpc-acs: A static prefix indicating the library and data source.
self.SURVEY: The survey type.
self.YEAR: The year of the data.
custom-ucgid: A static string indicating a custom query.
self.GROUP: The group or category of the data.
datetime.now().strftime(format='%Y%m%d'): The current date in YYYYMMDD format.

While this approach ensures that each custom query has a unique name, it also introduces a few challenges that we need to address. Let's dive deeper into these challenges.

Issues with the Current Naming Strategy

The current method of naming custom queries, while functional, presents several potential issues. Understanding these issues is crucial for developing a more robust and maintainable solution. Let’s break down the primary concerns:

Readability and Maintainability: The generated name, while unique, can become quite long and difficult to read. Long names make it harder to quickly identify the purpose or nature of the query. When debugging or reviewing code, developers might struggle to understand the naming convention at a glance, leading to increased cognitive load and potential errors. Over time, this can significantly impact the maintainability of the codebase.
Scalability: Including the date in the name ensures uniqueness but can lead to an excessive number of names over time. As the application generates more custom queries, the list of names will grow, making it harder to manage and search. Imagine trying to find a specific query from months ago – the date-based naming makes it challenging to locate the exact query you need quickly. This issue becomes more pronounced in large-scale applications with frequent data requests.
Lack of Context: The current naming scheme provides limited context about the query itself. While components like SURVEY and GROUP offer some information, they don't convey the specific parameters or filters used in the query. This lack of context can be problematic when you need to understand the specifics of a query without diving into the code. For example, knowing the exact filters applied or the specific variables requested would be beneficial without having to trace back the query logic.
Potential for Naming Conflicts: Although the inclusion of the date aims to prevent naming conflicts, there's still a slight chance of conflicts if multiple queries are generated within the same day with identical parameters. While the probability is low, it's a potential edge case that a more robust solution should address. Additionally, relying solely on the date for uniqueness can be limiting, especially if you want to run similar queries on different dates.
Debugging Difficulties: When debugging, having a clear and descriptive name is invaluable. The current naming strategy, while unique, doesn’t inherently provide much insight into the query's purpose or parameters. This can make it harder to trace issues back to specific queries, increasing the time and effort required for debugging. A more informative naming scheme would allow developers to quickly identify the query in question and understand its context.

To overcome these challenges, we need to explore alternative naming strategies that balance uniqueness with readability, scalability, context, and ease of debugging. This leads us to the core question: How can we enhance the naming of custom queries in morpc-py to make it more efficient and developer-friendly?

The Need for a Better Solution

As we’ve seen, the current approach has some limitations. The core issue revolves around creating names that are both unique and informative. The TODO comment in the code explicitly acknowledges this, stating: “Find a better way of naming custom queries, possibly by passing a custom parameter.” This is exactly what we aim to address.

The goal is to develop a naming strategy that:

Ensures uniqueness without relying solely on the date.
Provides context about the query parameters.
Improves readability and maintainability.
Scales well as the application grows.
Simplifies debugging.

To achieve this, we'll explore several alternative approaches, including the suggestion of passing a custom parameter for naming. This approach allows developers to have more control over how queries are named, making the system more flexible and user-friendly. Let's dive into some potential solutions.

Potential Solutions for Enhanced Query Naming

Now that we understand the challenges with the current naming strategy, let's brainstorm some potential solutions. Our goal is to find a method that balances uniqueness, readability, and context. Here are a few ideas to consider, each with its own set of pros and cons:

1. Passing a Custom Name Parameter

One of the most straightforward and flexible solutions is to allow developers to pass a custom name parameter when making a query. This approach is explicitly mentioned in the TODO comment in the code, indicating its relevance. By allowing a custom name, developers can provide a descriptive and meaningful identifier for each query.

How it Works:

Modify the .query method to accept an optional name parameter.
If a name is provided, use it as the base name for the query.
If no name is provided, fall back to the current naming strategy or another default method.

Pros:

Flexibility: Developers have complete control over the naming, allowing for clear and descriptive names.
Context: Custom names can include specific details about the query parameters or purpose.
Readability: Names can be crafted to be easily readable and understandable.

Cons:

Responsibility on Developers: Relies on developers to provide meaningful names, which might not always happen.
Potential for Duplicates: Requires a mechanism to handle potential naming conflicts if custom names are not unique.

Example Implementation:

def query(self, scope=None, name=None):
    if name:
        self.NAME = name.lower()
    else:
        self.NAME = f"morpc-acs{self.SURVEY}-{self.YEAR}-custom-ucgid-{self.GROUP}-{datetime.now().strftime(format='%Y%m%d')}".lower()

2. Hashing Query Parameters

Another approach is to generate a unique name by hashing the query parameters. This ensures uniqueness and provides some context based on the parameters used. A hash function takes the query parameters as input and produces a fixed-size string that serves as the name.

How it Works:

Serialize the query parameters into a string.
Use a hashing algorithm (e.g., SHA-256) to generate a hash of the string.
Use the hash as the query name.

Pros:

Uniqueness: Hash functions produce unique outputs for different inputs, ensuring unique names.
Context: The name is derived from the parameters, providing implicit context.
Automation: The naming process is automated, reducing the burden on developers.

Cons:

Readability: Hashes are not human-readable, making it difficult to understand the query's purpose at a glance.
Debugging: Difficult to trace issues back to specific queries without additional tools to map hashes to parameters.

Example Implementation:

import hashlib
import json

def generate_hash_name(params):
    params_str = json.dumps(params, sort_keys=True).encode('utf-8')
    hash_object = hashlib.sha256(params_str)
    return hash_object.hexdigest()

def query(self, scope=None, custom_params=None):
    if custom_params:
        self.NAME = generate_hash_name(custom_params)
    else:
        self.NAME = f"morpc-acs{self.SURVEY}-{self.YEAR}-custom-ucgid-{self.GROUP}-{datetime.now().strftime(format='%Y%m%d')}".lower()

3. Combining Custom Names with a Counter

To address the potential for naming conflicts when using custom names, we can combine custom names with a counter. This approach allows developers to provide a base name, and the system appends a counter to ensure uniqueness.

How it Works:

Accept a custom name parameter.
Maintain a counter for each base name.
If a name already exists, increment the counter and append it to the name.

Pros:

Flexibility: Developers can provide meaningful names.
Uniqueness: The counter ensures unique names even with identical base names.
Readability: Names remain relatively readable, especially with small counter values.

Cons:

Complexity: Requires managing counters, which can add complexity to the system.
Potential for Long Names: If counters become large, names can become long and less readable.

Example Implementation:

class QueryNaming:
    def __init__(self):
        self.name_counters = {}

    def generate_unique_name(self, base_name):
        if base_name in self.name_counters:
            self.name_counters[base_name] += 1
            return f"{base_name}-{self.name_counters[base_name]}"
        else:
            self.name_counters[base_name] = 1
            return base_name

naming_manager = QueryNaming()

def query(self, scope=None, name=None):
    if name:
        self.NAME = naming_manager.generate_unique_name(name.lower())
    else:
        self.NAME = f"morpc-acs{self.SURVEY}-{self.YEAR}-custom-ucgid-{self.GROUP}-{datetime.now().strftime(format='%Y%m%d')}".lower()

4. Metadata-Driven Naming

Another approach is to generate names based on metadata associated with the query. This could include parameters, filters, or other relevant information. By incorporating metadata, the names can provide more context about the query's purpose.

How it Works:

Collect relevant metadata about the query (e.g., filters, variables, scope).
Construct a name by combining key metadata elements.
Ensure uniqueness by adding a hash or counter if necessary.

Pros:

Context: Names provide rich context about the query.
Readability: Metadata elements can be chosen to create relatively readable names.
Flexibility: Can be adapted to include different types of metadata.

Cons:

Complexity: Requires careful selection and formatting of metadata elements.
Potential for Long Names: Including too much metadata can result in long and unwieldy names.

Example Implementation:

def query(self, scope=None, filters=None, variables=None):
    name_parts = [
        f"scope-{scope}" if scope else None,
        f"filters-{'-'.join(filters)}" if filters else None,
        f"vars-{'-'.join(variables)}" if variables else None
    ]
    name_parts = [part for part in name_parts if part]
    base_name = "morpc-custom-" + "-".join(name_parts) if name_parts else "morpc-custom-default"
    self.NAME = base_name.lower()

Choosing the Right Solution

Selecting the best approach for naming custom queries depends on the specific requirements and constraints of your project. Each of the solutions we’ve discussed has its strengths and weaknesses. Let's compare them to help you make an informed decision.

Comparison Table

Solution	Uniqueness	Readability	Context	Flexibility	Complexity	Scalability	Debugging
Custom Name Parameter	Medium	High	High	High	Low	Medium	High
Hashing Query Parameters	High	Low	Medium	Low	Medium	High	Low
Custom Names with Counter	High	Medium	High	Medium	Medium	Medium	Medium
Metadata-Driven Naming	Medium	Medium	High	Medium	Medium	Medium	Medium

Recommendations

For Maximum Flexibility and Readability: Passing a custom name parameter is an excellent choice. It empowers developers to create meaningful names, but it requires discipline to avoid naming conflicts. You can mitigate this by combining it with a counter or a naming convention.
For Automated Uniqueness: Hashing query parameters ensures uniqueness automatically. However, the resulting names are not human-readable, making this approach less suitable for debugging and manual inspection.
For a Balance of Uniqueness and Readability: Combining custom names with a counter strikes a good balance. It allows developers to provide descriptive names while ensuring uniqueness through the counter mechanism.
For Rich Contextual Information: Metadata-driven naming is ideal when you need names to convey detailed information about the query. This approach can improve traceability and understanding but might lead to longer names.

Best Practices

Regardless of the solution you choose, consider these best practices for naming custom queries:

Consistency: Adopt a consistent naming convention across your project. Consistency makes it easier to understand and maintain the codebase.
Descriptive Names: Use names that clearly indicate the query's purpose and parameters. Avoid ambiguous or generic names.
Uniqueness: Ensure that names are unique to prevent conflicts and confusion.
Brevity: Keep names as concise as possible while maintaining clarity. Long names can be cumbersome and difficult to manage.
Documentation: Document your naming conventions to ensure that all developers follow them consistently.

Implementing a Custom Name Parameter in `morpc-py`

Let's walk through implementing the custom name parameter solution in morpc-py. This approach offers a great balance of flexibility and readability, making it a strong choice for many projects.

Step-by-Step Implementation

Modify the .query Method: The first step is to modify the .query method in the census.py file to accept an optional name parameter.
```
def query(self, scope=None, name=None):
    if name:
        self.NAME = name.lower()
    else:
        self.NAME = f"morpc-acs{self.SURVEY}-{self.YEAR}-custom-ucgid-{self.GROUP}-{datetime.now().strftime(format='%Y%m%d')}".lower()
```
In this code snippet, we’ve added a name parameter to the query method. If a name is provided, it is used as the query name (converted to lowercase). If no name is provided, the code falls back to the original naming strategy.

Add Documentation: It’s crucial to document the new name parameter so that other developers know how to use it.

def query(self, scope=None, name=None):
    """Query the Census API.

    Args:
        scope (str, optional): The scope of the query. Defaults to None.
        name (str, optional): A custom name for the query. Defaults to None.

    Returns:
        pd.DataFrame: The query results.
    """
    if name:
        self.NAME = name.lower()
    else:
        self.NAME = f"morpc-acs{self.SURVEY}-{self.YEAR}-custom-ucgid-{self.GROUP}-{datetime.now().strftime(format='%Y%m%d')}".lower()

Here, we’ve added a docstring to the query method that explains the purpose and usage of the name parameter.

Handle Naming Conflicts (Optional): To address the potential for naming conflicts, you can add a mechanism to ensure uniqueness. One way to do this is by using the counter approach we discussed earlier.

class QueryNaming:
    def __init__(self):
        self.name_counters = {}

    def generate_unique_name(self, base_name):
        if base_name in self.name_counters:
            self.name_counters[base_name] += 1
            return f"{base_name}-{self.name_counters[base_name]}"
        else:
            self.name_counters[base_name] = 1
            return base_name

naming_manager = QueryNaming()

def query(self, scope=None, name=None):
    if name:
        self.NAME = naming_manager.generate_unique_name(name.lower())
    else:
        self.NAME = f"morpc-acs{self.SURVEY}-{self.YEAR}-custom-ucgid-{self.GROUP}-{datetime.now().strftime(format='%Y%m%d')}".lower()

In this enhanced implementation, we’ve incorporated a QueryNaming class that manages a counter for each base name. The generate_unique_name method ensures that each name is unique by appending a counter if necessary.

Test the Implementation: After implementing the changes, it’s essential to test them thoroughly. Create several custom queries with different names and verify that the names are generated correctly.

# Example usage
census_data = CensusData(SURVEY='acs5', YEAR='2021', GROUP='B01003', VARS=['B01003_001E'])
census_data.query(name='population-2021')
print(census_data.NAME)  # Output: population-2021

census_data.query(name='population-2021')
print(census_data.NAME)  # Output: population-2021-1 (if counter is implemented)

census_data.query()
print(census_data.NAME)  # Output: morpc-acs5-2021-custom-ucgid-b01003-20240704 (or similar)

These tests demonstrate how to use the custom name parameter and verify that the names are generated as expected.

Benefits of this Approach

By implementing a custom name parameter, you gain several benefits:

Improved Readability: Developers can provide descriptive names that clearly indicate the query's purpose.
Enhanced Flexibility: The naming strategy can be tailored to the specific needs of each query.
Simplified Debugging: Clear names make it easier to trace issues back to specific queries.
Better Maintainability: Consistent and descriptive names improve the overall maintainability of the codebase.

Conclusion

In this article, we’ve explored the challenges of naming custom queries in the morpc-py library and discussed several potential solutions. We’ve seen that while the current date-based naming strategy ensures uniqueness, it falls short in terms of readability, context, and scalability. By implementing a custom name parameter, you can significantly improve the naming of your queries, making your code more maintainable and easier to debug.

Remember, the key to effective query naming is to strike a balance between uniqueness, readability, and context. By considering the specific needs of your project and following best practices, you can develop a naming strategy that works well for you and your team. Whether you choose to pass a custom parameter, hash query parameters, combine custom names with a counter, or use a metadata-driven approach, the goal is to create names that are clear, consistent, and informative. Happy coding, guys!

Understanding the Challenge

The Current Implementation

Issues with the Current Naming Strategy

The Need for a Better Solution

Potential Solutions for Enhanced Query Naming

1. Passing a Custom Name Parameter

2. Hashing Query Parameters

3. Combining Custom Names with a Counter

4. Metadata-Driven Naming

Choosing the Right Solution

Comparison Table

Recommendations

Best Practices

Implementing a Custom Name Parameter in morpc-py

Step-by-Step Implementation

Benefits of this Approach

Conclusion

Implementing a Custom Name Parameter in `morpc-py`