GitHub user marmbrus opened a pull request:
https://github.com/apache/spark/pull/15702
[SPARK-18124] Observed-delay based Even Time Watermarks
This PR adds a new method `withWatermark` to the `Dataset` API, which can
be used specify an _event time watermark_. An event time watermark allows the
streaming engine to reason about the point in time after which we no longer
expect to see late data. This PR also has augmented `StreamExecution` to use
this watermark for several purposes:
- To know when a given time window aggregation is finalized and thus
results can be emitted when using output modes that do not allow updates (e.g.
`Append` mode).
- To minimize the amount of state that we need to keep for on-going
aggregations, by evicting state for groups that are no longer expected to
change. Note that we do still maintain all state if required (i.e. when in
`Complete` mode).
An example that emits windowed counts of records, waiting up to 5 minutes
for late data to arrive.
```scala
df.withWatermark($"eventTime", "5 mintues")
.groupBy(window($"eventTime", "1 minute) as 'window)
.count()
.writeStream
.format("console")
.mode("append") // In append mode, we only output complete aggregations.
.start()
```
### Calculating the watermark.
The current event time is computed by looking at the `MAX(eventTime)` seen
this epoch across all of the partitions in the query minus some user defined
_delayThreshold_. Note that since we must coordinate this value across
partitions occasionally, the actual watermark used is only guaranteed to be at
least `delay` behind the actual event time. In some cases we may still process
records that arrive more than delay late.
This mechanism was chosen for the initial implementation over processing
time for two reasons:
- it is robust to downtime that could affect processing delay
- it does not require syncing of time or timezones across
### Other notable implementation details
- A new trigger metric `eventTimeWatermark` outputs the current value of
the watermark.
- We mark the event time column in the `Attribute` metadata using the key
`spark.watermarkDelay`. This allows downstream operations to know which column
holds the event time. Operations like `window` propagate this metadata.
- `explain()` marks the watermark with a suffix of `-T${delayMs}` to ease
debugging of how this information is propagated.
- Currently, we don't filter out late records, but instead rely on the
state store to avoid emitting records that are both added and filtered in the
same epoch.
### Remaining in this PR
- [ ] The test for recovery is currently failing as we don't record the
watermark used in the offset log. We will need to do so to ensure determinism,
but this is deferred until #15626 is merged.
### Other follow-ups
There are some natural additional features that we should consider for
future work:
- Ability to write records that arrive too late to some external store in
case any out-of-band remediation is required.
- `Update` mode so you can get partial results before a group is evicted.
- Other mechanisms for calculating the watermark. In particular a
watermark based on quantiles would be more robust to outliers.
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/marmbrus/spark watermarks
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/spark/pull/15702.patch
To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:
This closes #15702
----
commit e6e3bbe9ca2d2081264b5bff68293572af7778a7
Author: Michael Armbrust <mich...@databricks.com>
Date: 2016-10-28T04:11:19Z
first test passing
commit 92320720492f192fe6791d0fea90495ea5db94a7
Author: Michael Armbrust <mich...@databricks.com>
Date: 2016-10-28T07:55:57Z
cleanup
commit 5b921323092c5730f816795193a6e0d985d7e430
Author: Michael Armbrust <mich...@databricks.com>
Date: 2016-10-31T22:00:32Z
Merge remote-tracking branch 'origin/master' into watermarks
----
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