2cb976355c
## What changes were proposed in this pull request? Currently, the micro-batches in the MicroBatchExecution is not exposed to the user through any public API. This was because we did not want to expose the micro-batches, so that all the APIs we expose, we can eventually support them in the Continuous engine. But now that we have better sense of buiding a ContinuousExecution, I am considering adding APIs which will run only the MicroBatchExecution. I have quite a few use cases where exposing the microbatch output as a dataframe is useful. - Pass the output rows of each batch to a library that is designed only the batch jobs (example, uses many ML libraries need to collect() while learning). - Reuse batch data sources for output whose streaming version does not exists (e.g. redshift data source). - Writer the output rows to multiple places by writing twice for each batch. This is not the most elegant thing to do for multiple-output streaming queries but is likely to be better than running two streaming queries processing the same data twice. The proposal is to add a method `foreachBatch(f: Dataset[T] => Unit)` to Scala/Java/Python `DataStreamWriter`. ## How was this patch tested? New unit tests. Author: Tathagata Das <tathagata.das1565@gmail.com> Closes #21571 from tdas/foreachBatch. |
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| .. | ||
| __init__.py | ||
| context.py | ||
| dstream.py | ||
| flume.py | ||
| kafka.py | ||
| kinesis.py | ||
| listener.py | ||
| tests.py | ||
| util.py | ||