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A storage type where a dataset's commits are merged into dataset when first written (ingested (by user).
This can be seen as "imperative ingestion", "compaction" of the `delta` happens right away.
#todo: verify)
Design details
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The Spark DAG for this storage, is relatively simpler. The key goal here is to group the tagged Hudi record RDD, into a series of updates and inserts, by using a partitioner. To achieve the goals of maintaining file sizes, we first sample the input to obtain a `workload profile` that understands the spread of inserts vs updates, their distribution among the partitions etc. With this information, we bin-pack the records such that
Any remaining records after that, are again packed into new file id groups, again meeting the size requirements. In this storage, index updation is a no-op, since the bloom filters are already written as a part of committing data. In the case of Copy-On-Write, a single parquet file constitutes one `file slice` which contains one complete version of the file |
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