def METHOD_NAME(
    benchmark_time: int, timestream_database_and_table: str, df_timestream: pd.DataFrame, path: str, path2: str, request
) -> None:
    name = timestream_database_and_table
    df_timestream["time"] = round(time.time()) * 1_000
    df_timestream["index"] = range(0, len(df_timestream))
    df_cpu = df_timestream[df_timestream.measure_kind == "cpu_utilization"]
    df_memory = df_timestream[df_timestream.measure_kind == "memory_utilization"]
    error_bucket, error_prefix = wr._utils.parse_path(path2)
    kwargs = {
        "path": path,
        "database": name,
        "table": name,
        "time_col": "time",
        "dimensions_cols": ["index", "region", "az", "hostname"],
        "measure_cols": ["measure"],
        "measure_name_col": "measure_kind",
        "report_s3_configuration": {"BucketName": error_bucket, "ObjectKeyPrefix": error_prefix},
    }
    with ExecutionTimer(request) as timer:
        for df in [df_cpu, df_memory]:
            response = wr.timestream.batch_load(df=df, **kwargs)
            assert response["BatchLoadTaskDescription"]["ProgressReport"]["RecordIngestionFailures"] == 0
    assert timer.elapsed_time < benchmark_time
    df = wr.timestream.query(f'SELECT COUNT(*) AS counter FROM "{name}"."{name}"')
    assert df["counter"].iloc[0] == 126_000