SQL Query Result Caching

Many applications perform frequent, identical database queries. Executing these queries repeatedly can put unnecessary strain on the database and slow down application performance. This challenge asks you to implement a system that caches the results of SQL queries to avoid redundant database calls.

Problem Description

Your task is to create a QueryCache system that intercepts SQL queries, checks if the result for that specific query is already stored in the cache, and if so, returns the cached result. Otherwise, it executes the query against a simulated database and stores the result in the cache before returning it.

Key Requirements:

• Cache Storage: Implement a mechanism to store query results. A key-value store is appropriate, where the key is derived from the query and its parameters, and the value is the query result.
• Cache Lookup: Before executing a query, check if a valid cached result exists for the given query and its parameters.
• Cache Invalidation (Implicit): For simplicity in this challenge, we will assume a "time-to-live" (TTL) mechanism for cache entries. After a certain duration, a cached entry is considered stale and a new query execution is required.
• Query Execution: If a result is not found in the cache or is stale, the system must execute the query against a simulated database and retrieve the actual results.
• Cache Storage: After successful retrieval from the database, the result should be stored in the cache with an appropriate TTL.
• Language-Agnostic: The solution should be described using pseudocode and concepts applicable to any programming language and SQL database.

Expected Behavior:

1. A get_query_result(query_string, parameters) function is called.
2. The system generates a unique cache key based on query_string and parameters.
3. It checks the cache for an entry associated with this key.
4. If a valid (non-expired) entry exists, it returns the cached data.
5. If no entry exists or the entry has expired, it executes the query_string with parameters against the database.
6. The returned data from the database is stored in the cache with the generated key and an expiration timestamp.
7. The data from the database is then returned.

Edge Cases:

• Queries with no parameters.
• Queries with different parameter orders but effectively the same parameters.
• Handling of empty result sets.
• Cache key generation for complex parameter types (though for this challenge, assume simple scalar types or string representations).

Examples

Example 1:

Input:
query_string = "SELECT name, age FROM users WHERE id = ?"
parameters = [123]
cache_ttl_seconds = 300 // 5 minutes

Output:
[{'name': 'Alice', 'age': 30}]

Explanation:
This is the first time this query is executed. The system generates a cache key (e.g., "SELECT name, age FROM users WHERE id = ?::123"). It doesn't find a cached result, so it executes the query against the database. The result is [{'name': 'Alice', 'age': 30}]. This result is then stored in the cache with an expiration time 5 minutes from now. Finally, the result is returned.

Example 2:

Input:
// Assume Example 1 was run just before this.
query_string = "SELECT name, age FROM users WHERE id = ?"
parameters = [123]
cache_ttl_seconds = 300

Output:
[{'name': 'Alice', 'age': 30}]

Explanation:
The system generates the same cache key as in Example 1. It finds a valid (non-expired) entry in the cache. It immediately returns the cached result without querying the database.

Example 3:

Input:
// Assume Example 1 was run, and 6 minutes have passed.
query_string = "SELECT name, age FROM users WHERE id = ?"
parameters = [123]
cache_ttl_seconds = 300

Output:
[{'name': 'Alice', 'age': 30}]

Explanation:
The system generates the same cache key. It finds an entry in the cache, but the entry has expired (6 minutes have passed since its creation, exceeding the 5-minute TTL). Therefore, it executes the query against the database again. The result is retrieved and used to update the cache with a new expiration time. The result is then returned.

Constraints

• The cache_ttl_seconds will be an integer greater than 0.
• query_string will be a valid SQL query string.
• parameters will be a list/array of simple data types (integers, strings, booleans, etc.).
• The simulated database will always return consistent results for the same query and parameters.
• Performance: For repeated identical queries, the cache hit rate should be high, resulting in significantly faster response times compared to direct database access.

Notes

• Consider how to generate a robust cache key that uniquely identifies a query along with its specific parameters. A simple approach might involve concatenating the query string and a serialized representation of the parameters.
• Think about the data structure for the cache. A hash map (dictionary) is a good starting point.
• The "simulated database" can be represented by a simple function that returns pre-defined results for specific queries. You do not need to implement actual SQL database logic.
• For a real-world scenario, consider more advanced cache invalidation strategies (e.g., manual invalidation, dependency-based invalidation), but for this challenge, TTL is sufficient.