In a JSON file that contains many tags and parameters, how do you determine which fields to fetch? How do you decide what data to extract from the source file?

When faced with a large JSON payload, the first step is to understand the schema or structure of the data. If a formal schema (e.g., JSON Schema, OpenAPI) is available, you can programmatically inspect it to identify required versus optional fields. In the absence of a schema, you can use a sample payload to build a map of keys and their data types. Once you have a map, you can decide which fields are relevant based on business logic or downstream consumers. Common strategies include:

1. Configuration‑driven selection – maintain a list of keys to include in a config file or environment variable.
2. Metadata tags – embed a "fetch" flag or priority level in the JSON itself.
3. Lazy loading – fetch the entire object but only deserialize the needed fields.
4. Streaming parsing – use a streaming JSON parser (e.g., ijson in Python) to read only the required paths.

The complexity of selective extraction depends on the method. A simple dictionary lookup is O(1) per key, while streaming parsing can be O(n) in the size of the payload but avoids loading the whole object into memory. The goal is to balance performance, memory usage, and maintainability.