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Timestamp is set to the new value, but the event time field itself is not changed. This adjustment is the only situation where an event's System.
Timestamp can be different from the value in the event time field and may cause unexpected results to be generated. The heuristic watermark generation mechanism described works well in most of cases where time is mostly synchronized between the various event senders.
However, in real life, especially in many IoT scenarios, the system has little control over the clock on the event senders.
The event senders could be all sorts of devices in the field, perhaps on different versions of hardware and software.
Instead of using a watermark that is global to all events in an input partition, Stream Analytics has another mechanism called substreams.
You can utilize substreams in your job by writing a job query that uses the TIMESTAMP BY clause and the keyword OVER. To designate the substream, provide a key column name after the OVER keyword, such as a deviceid , so that system applies time policies by that column.
Each substream gets its own independent watermark. This mechanism is useful to allow timely output generation, when dealing with large clock skews or network delays among event senders.
Substreams are a unique solution provided by Azure Stream Analytics, and are not offered by other streaming data processing systems.
When you use substreams, Stream Analytics applies the late arrival tolerance window to incoming events. The late arrival tolerance decides the maximum amount by which different substreams can be apart from each other.
For example, if Device 1 is at Timestamp 1, and Device 2 is at Timestamp 2, the at most late arrival tolerance is Timestamp 2 minus Timestamp 1.
The default setting is 5 seconds and is likely too small for devices with divergent timestamps. We recommend that you start with 5 minutes and make adjustments according to their device clock skew pattern.
You may have noticed another concept called early arrival window that looks like the opposite of late arrival tolerance window.
This window is fixed at 5 minutes and serves a different purpose from the late arrival tolerance window. Because Azure Stream Analytics guarantees complete results, you can only specify job start time as the first output time of the job, not the input time.
The job start time is required so that the complete window is processed, not just from the middle of the window. Stream Analytics derives the start time from the query specification.
However, because the input event broker is only indexed by arrival time, the system has to translate the starting event time to arrival time.
The system can start processing events from that point in the input event broker. With the early arriving window limit, the translation is straightforward: starting event time minus the 5-minute early arriving window.
This calculation also means that the system drops all events that are seen as having an event time 5 minutes earlier than the arrival time.
The early input events metric is incremented when the events are dropped. This concept is used to ensure the processing is repeatable no matter where you start to output from.
Without such a mechanism, it would not be possible to guarantee repeatability, as many other streaming systems claim they do.
Stream Analytics jobs have several Event ordering options. Two can be configured in the Azure portal: the Out of order events setting out-of-order tolerance , and the Events that arrive late setting late arrival tolerance.
The early arrival tolerance is fixed and cannot be adjusted. These time policies are used by Stream Analytics to provide strong guarantees. However, these settings do have some sometimes unexpected implications:.
Early events should not be outputted normally. All early arriving events are dropped, so you will not see any of them from the output.
While old events may seem harmless at first, because of the application of the late arrival tolerance, the old events may be dropped.
If the events are too old, the System. Timestamp value is altered during event ingestion. Due to this behavior, currently Azure Stream Analytics is more suited for near-real-time event processing scenarios, instead of historical event processing scenarios.
You can set the Events that arrive late time to the largest possible value 20 days to work around this behavior in some cases.
The first watermark is generated at the calculated time: the maximum event time the system has observed so far, minus the out-of-order tolerance window size.
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