(rows, metrics, key)
return list(queries_by_key.values())
Given a list of query rows, merge all duplicate rows as determined by the key function into a single row
    with the sum of the stats of all duplicates. This is motivated by database integrations such as postgres
    that can report many instances of a query that are considered the same after the agent normalization.

    - **rows** (_List[dict]_) - rows from current check run
    - **metrics** (_List[str]_) - the metrics to compute for each row
    - **key** (_callable_) - function for an ID which uniquely identifies a query row across runs
Given a list of query rows, merge all duplicate rows as determined by the key function into a single row
    with the sum of the stats of all duplicates. This is motivated by database integrations such as postgres
    that can report many instances of a query that are considered the same after the agent normalization.
Given a list of query rows merge all duplicate rows as determined by the key function into a single row with the sum of the stats of all duplicates . This is motivated by database integrations such as postgres that can report many instances of a query that are considered the same after the agent normalization .
def _merge_duplicate_rows(rows, metrics, key):
    """
    Given a list of query rows, merge all duplicate rows as determined by the key function into a single row
    with the sum of the stats of all duplicates. This is motivated by database integrations such as postgres
    that can report many instances of a query that are considered the same after the agent normalization.

    - **rows** (_List[dict]_) - rows from current check run
    - **metrics** (_List[str]_) - the metrics to compute for each row
    - **key** (_callable_) - function for an ID which uniquely identifies a query row across runs
    """

    queries_by_key = {}
    for row in rows:
        merged_row = dict(row)

        query_key = key(merged_row)

        if query_key in queries_by_key:
            merged_state = queries_by_key[query_key]
            queries_by_key[query_key] = {
                k: merged_row[k] + merged_state[k] if k in metrics else merged_state[k] for k in merged_state.keys()
            }
        else:
            queries_by_key[query_key] = merged_row

    return list(queries_by_key.values())
def _merge_duplicate_rows ( rows , metrics , key ) : queries_by_key = { } for row in rows : merged_row = dict ( row ) query_key = key ( merged_row ) if query_key in queries_by_key : merged_state = queries_by_key [ query_key ] queries_by_key [ query_key ] = { k : merged_row [ k ] + merged_state [ k ] if k in metrics else merged_state [ k ] for k in merged_state . keys ( ) } else : queries_by_key [ query_key ] = merged_row return list ( queries_by_key . values ( ) )