[GitHub] [hudi] codope commented on a diff in pull request #4563: [HUDI-3211][RFC-44] Add RFC for Hudi Connector for Presto

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codope commented on code in PR #4563:
URL: https://github.com/apache/hudi/pull/4563#discussion_r862679414


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rfc/rfc-44/rfc-44.md:
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+
+# RFC-44: Hudi Connector for Presto
+
+## Proposers
+
+- @7c00
+
+## Approvers
+
+- @
+
+## Status
+
+JIRA: [HUDI-3210](https://issues.apache.org/jira/browse/HUDI-3210)
+
+> Please keep the status updated in `rfc/README.md`.
+
+## Abstract
+
+The support for querying Hudi tables in Presto is provided by Presto Hive connector. The implementation is built on the
+InputFormat interface from hudi-hadoop-mr module. This approach has known performance and stability issues. It's also
+hard to adopt new Hudi features due to the restrictions from current code. A separate Hudi connector would make it
+efficient to make specific optimization, add new functionalities, integrate advanced features, and evolve rapidly with
+the upstream project.
+
+## Background
+
+The current Presto integration reads Hudi tables as regular Hive tables with some additional processing. For a COW or
+MOR-RO table, Presto applies a dedicated file filter (`HoodieROTablePathFilter`) to prune invalid files during split
+generation. For an MOR-RT table, Presto delegates split generation to `HoodieParquetRealtimeInputFormat#getSplits`, 
+and data loading to the reader created by `HoodieParquetRealtimeInputFormat#getRecordReader`.
+
+This implementation could take advantage of existing code. But there are some drawbacks.
+
+- Due to the MVCC design, file layouts of Hudi tables are quite different from those of regular Hive tables. 
+  Mixing the split generation for Hudi and non-Hudi tables makes it hard to extend to integrate custom split scheduling
+  strategies. For example, HoodieParquetRealtimeInputFormat generates a split for a file slice, which is not performant
+  and could be improved by fine-grained splitting.
+- In order to transport Hudi-only split properties, CustomSplitConverter is applied. The restrictions from current code
+  make it necessary to have such hacking and tricky code, and increases the difficulty of testing. It would continue to
+  affect the new code to adopt new Hudi features in the future.
+- By delegating most work to HoodieParquetRealtimeInputFormat, memory usage is out of control of Presto memory 
+  management mechanism. That hurts the system robust: workers tend to crash for OOM when querying a large MOR-RT table.
+
+With a new separator connector, Hudi integration could be improved without the restriction of current code in Hive
+connector. A separate connector also separates Hudi's bugs away from Hive connector. It becomes more confident to add
+new code, since it would never break Hive connector.
+
+## Implementation
+
+Presto provides a set of service provider interfaces (SPI) to allow developers to create plugins that would be
+dynamically loaded into Presto runtime system to add custom functionalities. The connector is a kind of plugin, 
+which can read and sink data from/to external data sources.
+
+To create a connector for Hudi, SPIs below are to be implements:
+
+- **Plugin**: the entry class to create plugin components (including connectors), instantiated and invoked by Presto
+  internal services when booting. `HudiPlugin` which implements Plugin interface is to be added for Hudi connector.
+- **ConnectorFactory**: the factory class to create connector instances. `HudiConnectorFactory` which implements
+  ConnectorFactory is to be added for Hudi connector.
+- **Connector**: the facade class of all service classes for a connector. `HudiConnector` which implements Connector
+  is to be added for Hudi connector. Primary service classes for Hudi connector are listed below.
+    - **ConnectorMetadata**: the service class to retrieve (and update if possible) metadata from/to a data source. 
+      `HudiMetadata` which implements ConnectorMetadata is to be added to Hudi connector.
+    - **ConnectorSplitManager**: the service class to generate ConnectorSplit for accessing the data source. 
+      A ConnectorSplit is similar to a Split in Hadoop MapReduce computation paradigm. `HudiConnectorSplitManager`
+      which implements ConnectorSplitManager, is to be added to Hudi connector.
+    - **ConnectorPageSourceProvider**: the service class to generate a reader to load data from the data source.
+      `HudiPageSourceProvider` which implements ConnectorPageSourceProvider, is to be added to Hudi connector. 
+      
+There are other service classes (e.g. `ConnectorPageSinkProvider`), which are not covered in this RFC but might be
+implemented in the future. A class-diagrammatic view of the different components is shown below.
+
+![](presto-connector.png)
+
+### HudiMetadata
+
+HudiMetadata implements ConnectorMetadata interface, and provides methods to access the metadata like databases
+(called schemas in Presto), table and column structures, statistics and properties, and other info.
+As Hudi table metadata is synchronized to Hive MetaStore, HudiMetadata reuses ExtendHiveMetastore from Presto codebase
+to retrieve Hudi table metadata. Besides, HudiMetadata takes use of HoodieTableMetaClient to expose Hudi specified
+metadata, e.g. instants list of a table timeline, represented as SystemTable.
+      
+### HudiSplitManager & HudiSplit
+
+HudiSplitManager implements the ConnectorSplitManager interface. It partitions a Hudi table to read into multiple
+individual chunks (called ConnectorSplit in Presto), so that the data set can be processed in parallel.
+HudiSplitManager also performs partition  pruning if possible.
+
+HudiSplit, which implements ConnectorSplit, describes which files to read, and the file properties (i.e. offset, length,
+location, etc).  For a normal Hudi table, HudiSplitManager generates a split for each FileSlice at certain instant.
+For a Hudi table that contains large base files, HudiSplitManager divides the base file into multiple splits,
+each containing all the log files and part of the base file. This should improve the performance.
+
+For large Hudi tables, generating splits tends to cost lots of time. This can be improved in the following ways. 
+Firstly, increase parallelism. Since each partition is independent of another, processing the partitions in multiple
+threads, one thread per partition, helps reduce the wall time. Secondly, take use of Hudi metadata table or external
+listing services. This should save a large amount of cost to make requests to HDFS.
+
+
+### HudiPageSourceProvider & HudiPageSource
+
+For each HudiSplit, HudiPageSourceProvider generates a reader for it to load data from the data source (e.g. HCFS)
+into memory. Presto organizes the memory data in the form of Pages. Page is implemented in a column-store favor. 
+This design contributes to enhance Presto's query performance. In particular, Presto writes its own readers to
+accelerate the data loading for column-store favored files, such as Parquet and ORC.
+
+For the snapshot query on a COW table, HudiPageSourceProvider takes advantage of Presto's native ParquetPageSource
+as it's well optimized for Parquet files as described above.
+
+For the snapshot query on an MOR table, HudiPageSourceProvider is planned to add a page source that makes mixed uses of
+ParquetPageSource and HoodieRealtimeRecordReader. The new page source takes care of memory usage under Presto's memory

Review Comment:
   +1
   HUDI-4007 to track this task



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