This is a good topic, thanks for raising this. Overall our reliance on spark classes/APIs that are declared experimental is an issue on paper. But there is few other ways to get right performance without relying on these. This has been the tricky issue IMO. Thoughts?
I ll review the code organization more carefully and report back. On Fri, Jun 2, 2023 at 04:23 Rahil C <rchert...@gmail.com> wrote: > Thanks Shawn for writing this, I would like to also add on to the Spark > Discussion. > > Currently I think our integration with Spark is too tight, and brings up > serious issues when upgrading. > > I will describe one example(however there are many more) but one area is we > extend Spark's *ParquetFileFormat* in the following classes. > > > https://github.com/apache/hudi/blob/master/hudi-spark-datasource/hudi-spark-common/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/HoodieParquetFileFormat.scala > > https://github.com/apache/hudi/blob/master/hudi-spark-datasource/hudi-spark3.2plus-common/src/main/scala/org/apache/spark/sql/execution/datasources/parquet/Spark32PlusHoodieParquetFileFormat.scala > > and specifically the main logic changes is we override > *buildReaderWithPartitionValues > *method > *.* > I understand the pro of reusability of spark's code, but the con is that we > dont then get the latest changes from the latest implementation of these > methods. This gets more complex as we then need to understand which spark > changes are required to cherry pick over as spark upgrades, such as these > issues. > > For spark 3.3.2 we faced several issues documented here > https://github.com/apache/hudi/pull/8082, > and for spark 3.4.0 we have encountered several issues as well. > https://github.com/apache/hudi/pull/8682 > > We also are not keeping up to date with certain spark features as a result > of the integration we have made. I have created a JIRA that goes more into > this in-depth to this. > https://issues.apache.org/jira/browse/HUDI-6262 > > Would be happy to sync with other hudi spark committers/experts, or anyone > interested in revisiting this integration so that future spark work will be > more achievable. > > Regards, > Rahil Chertara > > On Tue, May 23, 2023 at 8:16 PM Shawn Chang <yc2...@cornell.edu> wrote: > > > Hi Hudi developers, > > > > I am writing to discuss the current code structure of the existing > > hudi-spark-datasource and propose a more scalable approach for supporting > > multiple Spark versions. The current structure involves common code > shared > > by several Spark versions, such as hudi-spark-common, hudi-spark3-common, > > hudi-spark3.2plus-common, etc. (a detailed description can be found in > the > > readme here: > > > https://github.com/apache/hudi/blob/master/hudi-spark-datasource/README.md > > ). > > This setup aims to minimize duplicate code in Hudi. Hudi currently > utilizes > > the SparkAdapter to invoke specific code based on the Spark version, > > allowing different Spark versions to trigger different logic. > > > > However, this code structure proves to be complex and hampers the process > > of adding support for newer Spark versions. The current approach involves > > the following steps: > > 1) Identify breaking changes introduced by the new Spark version and > patch > > affected Hudi classes. > > 2) Separate affected Hudi classes into different folders so that older > > Spark versions can continue using the existing logic, while the new Spark > > version can work with the updated Hudi classes. > > 3) Connect SparkAdapter to these Hudi classes, enabling Hudi to utilize > the > > correct code based on the Spark version. > > 4) Collect common code and place it in a new folder, such as > > hudi-spark3.2plus-common, to reduce duplicate code. > > > > This convoluted process has significantly slowed down the pace of adding > > support for newer Spark versions in Hudi. Fortunately, there is a simpler > > alternative that can streamline the process. I propose removing the > common > > modules and having only one folder for each Spark version. For example: > > > > > > > > > > > > > *hudi-spark-datasource/---hudi-spark2.4.0/---hudi-spark3.2.0/---hudi-spark3.3.0/...* > > > > With this revised code structure, each Spark version will have its own > > corresponding Hudi module. The process of adding Spark support will be > > simplified as follows: > > 1) Copy the latest existing hudi-spark module to a new module, > > hudi-spark<new_Spark_version>. > > 2) Identify breaking changes introduced by the new Spark version and > patch > > affected Hudi classes. > > > > Let's consider some pros and cons of this new code structure: > > *Pros:* > > -A more readable codebase, with each Spark version having its individual > > module. > > -Easier addition of support for new Spark versions by duplicating the > most > > recent module and making necessary modifications. > > -Simpler implementation of improvements specific to a particular Spark > > version. > > *Cons:* > > -Increased duplicate code (though this shouldn't impact the Hudi jar size > > during runtime, as the jar will still only contain support for one Spark > > version). > > -When applying a general fix for multiple Spark versions, the fix needs > to > > be applied to different Spark modules instead of a common codebase. > > > > Please feel free to share your opinion, any feedback would be welcome! > > > > Thank you. > > > > Best, > > Shawn > > >
