What principles are the processing and storage of normalized queries in the cache based on?

Processing and storing normalized queries in cache are based on the following principles:

1. Normalization of queries — bringing queries to a uniform form so that different variants of the same query (e.g., with different parameter order or spaces) are perceived as identical. This allows efficient cache use.

2. Hashing — after normalization, the query is transformed into a hash key used for quick lookup in the cache.

3. Parameter identification — query parameters are separated and stored separately, so different values can be substituted without reprocessing the entire query.

4. Cache TTL (Time To Live) — cache data usually has a limited lifetime for relevance.

5. Update strategies — cache is refreshed upon data changes or after TTL expiration.

Example in Go (simplified):

func normalizeQuery(query string) string {
    // Example: remove extra spaces, convert to lowercase
    normalized := strings.ToLower(strings.TrimSpace(query))
    // Additional parameter sorting and processing can be added
    return normalized
}

func cacheKey(query string) string {
    normalized := normalizeQuery(query)
    return fmt.Sprintf("query:%x", md5.Sum([]byte(normalized)))
}

Thus, the cache stores normalized keys and associated results, increasing efficiency of repeated use.