Optimizing Keyword Filtering: Merge Terms or Use Direct List Checking?

Below is one approach to simplify your filtering without merging all terms into a single string:

Use a List/Array Filter

Instead of combining all terms into one string and then doing one "contains" check, load the filter terms into a list/array and then check if any term appears in your message. For example, in Python you might do:

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# Assume filter_terms is a list of strings from your SELECT query
filter_terms = ['term1', 'term2', 'term3']

def is_valid(message_body):
    return not any(term in message_body for term in filter_terms)

# Use the function to filter messages
messages = [...]  # list of messages with a 'body'
valid_messages = [msg for msg in messages if is_valid(msg['body'])]

This way, you are iterating over the list and checking each term separately without the need to merge them.

Use a Compiled Regular Expression

If you have many terms or need more flexible pattern matching, you can compile a regex that checks for any term at once:

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import re

# Escape terms if needed to match literals and join with '|'
pattern = re.compile('|'.join(re.escape(term) for term in filter_terms))

def is_valid(message_body):
    # Return False if any filter term is found
    return pattern.search(message_body) is None

# Filter messages as before
valid_messages = [msg for msg in messages if is_valid(msg['body'])]

Using a regex can be more efficient when checking multiple terms over many messages since the regular expression engine handles the alternation.

Consider Pushing Filtering to the Database

If it fits your use-case, you could also refactor the process to have PostgreSQL perform the filtering, using a query that filters messages based on the filter terms. For example:

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-- Assuming there is a messages table with a text column 'body'
SELECT m.*
FROM messages m
WHERE NOT EXISTS (
    SELECT 1
    FROM terms_table t
    WHERE m.body ILIKE '%' || t.term || '%'
);

This way, you offload the work to the database engine which might be optimized for such operations.

Summary

• Do not merge terms into a single string: Instead, iterate through the list or compile a regex.
• List filtering: Use a simple loop (or language-specific functions like Python's any()).
• Compiled regex: Provides a single check with alternatives.
• Database filtering: When possible, push the filtering logic into PostgreSQL.

Each of these approaches has its own trade-offs depending on the size of data, language, and performance requirements.