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In the Merge-On-Read storage model, there are 2 logical components - one for ingesting data (both inserts/updates) into the dataset and another for creating compacted views. The former is hereby referred to as `Writer` while the later
is referred as `Compactor`this def~table-type, records written to the def~table, are quickly first written to def~log-files, which are at a later time merged with the def~base-file, using a def~compaction action on the timeline. Various def~query-types can be supported depending on whether the query reads the merged snapshot or the change stream in the logs or the un-merged base-file alone.

At a high level, Merge-On-Read Writer  135860486 writer goes through same stages as Copydef~copy-On-Write writer on-write (COW) writer in ingesting data. The key difference here is that updates are appended to latest log (delta) file belonging to the latest file slice without merging. For inserts, Hudi supports 2 modes:1.

1. Inserts to Log Files - This is done for

1. def~tables that have an indexable log files (for

1. eg def~hbase-index)

1. Inserts to parquet files - This is done for

1. def~tables that do not have indexable log files, for

1. eg def~bloom-index

As in the case of Copydef~copy-On-Writeon-write (COW), the input tagged records are partitioned such that all upserts destined to a `file id` a def~file-id are grouped together. This upsert-batch is written as one or more log-blocks written to log def~log-files. Hudi allows clients to control log file sizes (See [Storage Configs](. ./configurations))
The WriteClient API is same for both Copyboth def~copy-On-Write and Merge-On-Read on-write (COW) and 135860486 writers. With Merge-On-Read 135860486, several rounds of data-writes would have resulted in accumulation of one or more log-files. All these log-files along with base-parquet (if exists) constitute a `file slice` a  def~file-slice which represents one complete version of the file.

This table type is the most versatile, highly advanced and offers much flexibility for writing (ability specify different compaction policies, absorb bursty write traffic etc) and querying (e.g: tradeoff data freshness and query performance). At the same time, it can involve a learning curve for mastering it operationally.