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The first part refactors the scheduler and FlushPolicy into a new DatasetCoordinatorActor, which coordinates all ingestion, reprojection, and columnar flushing.  It also makes the scheduling event-based, rather than polling or time-based.   Unlike the previous scheduler, which was global, the DatasetCoordinatorActor coordinates writes for only one (dataset, version) at a time.  In turn, the NodeCoordinatorActor spins up DatasetCoordinatorActors, and acts as the one external API entry point for all ingestion and client interactions (other than Spark queries).  The implications:\r\n\r\n* Both the CLI and Spark modules now use Actor client based ingestion, and talk to NodeCoordinatorActor through `RowSource`, instead of calling into a MemTable directly.  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