Hi devs, While thinking about metrics improvements, I doubt how many users know that what 'exactly' is complete latency. In fact, it's somewhat complicated because additional waiting time could be added to complete latency because of single-thread model event loop of spout.
Long running nextTuple() / ack() / fail() can affect complete latency but it's behind the scene. No latency information provided, and someone even didn't know about this characteristic. Moreover, calling nextTuple() could be skipped due to max spout waiting, which will make us harder to guess when avg. latency of nextTuple() will be provided. I think separation of threads (tuple handler to separate thread, as JStorm provides) would resolve the gap, but it requires our spout logic to be thread-safe, so I'd like to find workaround first. My sketched idea is let Acker decides end time for root tuple. There're two subsequent ways to decide start time for root tuple, 1. when Spout about to emit ACK_INIT to Acker (in other words, keep it as it is) - Acker sends ack / fail message to Spout with timestamp, and Spout calculates time delta - pros. : It's most accurate way since it respects the definition of 'complete latency'. - cons. : The sync of machine time between machines are very important. Milliseconds of precision would be required. 2. when Acker receives ACK_INIT from Spout - Acker calculates time delta itself, and sends ack / fail message to Spout with time delta - pros. : No requirement to sync the time between servers so strictly. - cons. : It doesn't contain the latency to send / receive ACK_INIT between Spout and Acker. Sure we could leave it as is if we decide it doesn't hurt much. What do you think? Thanks, Jungtaek Lim (HeartSaVioR)