Triggers
1. Triggers
>> You have seen how the water mark is useful to have an idea of data completeness and how to use metrics to see how the water mark is evolving, the default behavior is to trigger the results when the water mark is sparse. But that can be often a very long time. Do you have to wait that long before you can see the results coming out of your windows? No, you don't. Three years are useful to define in precise detail when we want to see the results of our window. Let's see how things work. By using three years, you can control the Lattanzi to produce a result or you can ensure the data completeness before you emit a result or a combination of both triggers can be based on Aventine, for instance, immediate results after 30 seconds as measured by the messages, timestamps or on processing time. For instance, Emet results every 30 seconds as measured by the workers clock, regardless of their messages thunderstorms. And they can also be based on data, for instance, and meet some results after seeing 25 messages. Or you can use any combination of the above three years with a composite trigger, we can employ and implement a very complex logic for deciding when and how many times to trigger the results of our windows. The default behavior is to trigger the watermark. So if you don't specify a trigger, you are actually using the trigger after watermark after watermark is an event time trigger. We could also apply any other triggers event time. The message is that are used to measure time with these triggers, but we could also add custom triggers if the trigger is based on processing time. The actual clock real time is used to decide when to meet the results. For instance, you could decide to meet exactly every 30 seconds, regardless of the timestamps of the messages that have arrived. The window after count is an example of a data driven trigger rather than immediate results based on time here with trigger based on the amount of data that has arrived within the window. The combination of several types of triggers, openside worth of possibilities with a streaming pipeline, we may need some results using after processing time and then again at the watermark when data's complete and then for the next five messages that arrive late after the watermark. In summary, you can integrate to make sure that Doumitt results early, which is to say with minimal latency, or you can use them to make sure that you process data and that your results include all the relevant messages, even if those messages are delayed. Or you can combine the two conditions. For instance, make sure that the immediate results early and late repeat the calculation and new results when data is complete. But when you hand me the results several times, how will that aperture have been rapid? The calculation, you can actually control that. Let's talk about accumulation modes. When you trigger the the window several times, you have to decide on the desired accumulation mode. There are two accumulation modes in a batch of accumulate and this car with accumulate every time you trigger it again in the same window. The calculation is just repeated with all the messages that have been included in the window so far with this car. Once some messages have been used for a calculation. Those messages are discarded. If new messages arrive later and there is a new trigger, the result will only include the new messages and those messages will be discarded again. Sure, there will be any additional triggers later. Let's see how these models work with an example. This example is using fixed windows of 10 minutes, but you don't want to wait so long until you see results. So the trigger is set to Aventine every couple of minutes in the first trigger, the window has only seen two messages and we immediately at the containing just two messages by the ten. Then by the time the next trigger fires up, the window has received four more messages. Now the trigger amidst the list, again containing the previous messages and the new messages. The third trigger, again, includes all the previous messages and the new messages. If your window is very wide, using accumulate as the accumulation mode may consume considerable resources as the accumulated output has to be stored when the window is still open, the windows and the message here are the same as in the previous slide. But now we have said the accumulation mode to discard every new trigger will only use the new messages that the window has received so far. And once the result is emitted, it will discard those messages. If the calculation you need to make with the windows is associative and commutative, you can safely update that calculation. Using this commode without any loss of accuracy in the output storage where you store the partial results, you should be able to aggregate the partial results to get the actual calculation value. The main advantage of using the discard mode is that the performance will not suffer even if you use a very wide window, because no state, no accumulation is stored for very long, only until the next trigger is released. Let's see how the specified triggers in a butterbean, these examples are in Python, in the example at the top, the pipeline triggers more than once per window. There will be a trigger 30 seconds after opening the window and then again once the watermark is reached after that, for every late message within the first two days, there will be an additional three or so. Every window will produce more than one output. Bear that in mind when designing Utøya index sample at the bottom, the window is not trigger the watermark, but whenever the window sees a hundred messages or 60 seconds have passed, whichever happens first. The trigger only computes that dealt us. Once it really is calculated, the previous messages are discarded because the window allows for two days to wait for little data. The trigger will produce output if we have left messages within two days after the watermark. So even if we are not triggering the watermark, the watermark is still important. You may have heard, by the way, that the Python SDK for Baturin doesn't support setting a value for allowed liveness. That was the case sometime ago, but they're now allowed. The place is fully supported in Python, in dataflow and other runit. These are the same examples, but in Java here, you need to specify the type of the collection we are assuming this extreme battery could be actually any other type, even a custom class. The API is different than in the case of Python, more adapted to the customs of Java. But the concepts are exactly the same as those shown in the producers like you have learned how to process detainee stimming with a Butterbean instrument is not only about the continuity of data, it has other important features as well. The most prominent lack of order when receiving data windows can help with that window by event time lets you recover the natural order of the data. But you can also use processing time, which is more like MacRobertson to omit results in a window. Watermarks are important to know if our data is complete or whether we should still wait for more data. If data arrives after the watermark, that data will be considered late. That's not a big deal with triggers. We can decide how to deal with data. Remember that Couston triggers. Let you decide when to meet results early, when the data is complete and so on, and the window may have several triggers.2. Let's practice!
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