Got it. I'll look into implementation choices for creating a new data source. Appreciate all the feedback.
On Mon, Jun 1, 2020 at 7:53 PM Vinoth Chandar <[email protected]> wrote: > >Is it to separate data and metadata access? > Correct. We already have modes for querying data using format("hudi"). I > feel it will get very confusing to mix data and metadata in the same > source.. for e.g a lot of options we support for data may not even make > sense for the TimelineRelation. > > >This class seems like a list of static methods, I'm not seeing where these > are accessed from > That's the public API for obtaining this information for Scala/Java Spark. > If you have a way of calling this from python through some bridge without > painful bridges (e.g jython), might be a tactical solution that can meet > your needs. > > On Mon, Jun 1, 2020 at 5:07 PM Satish Kotha <[email protected]> > wrote: > > > Thanks for the feedback. > > > > What is the advantage of doing > > spark.read.format(“hudi-timeline”).load(basepath) as opposed to doing new > > relation? Is it to separate data and metadata access? > > > > Are you looking for similar functionality as HoodieDatasourceHelpers? > > > > > This class seems like a list of static methods, I'm not seeing where > these > > are accessed from. But, I need a way to query metadata details easily > > in pyspark. > > > > > > On Mon, Jun 1, 2020 at 8:02 AM Vinoth Chandar <[email protected]> wrote: > > > > > Also please take a look at > > > > > > https://urldefense.proofpoint.com/v2/url?u=https-3A__issues.apache.org_jira_browse_HUDI-2D309&d=DwIFaQ&c=r2dcLCtU9q6n0vrtnDw9vg&r=4xNSsHvHqd0Eym5a_ZpDVwlq_iJaZ0Rdk0u0SMLXZ0c&m=NLHsTFjPharIb29R1o1lWgYLCr1KIZZB4WGPt4IQnOE&s=fGOaSc8PxPJ8yqczQyzYtsqWMEXAbWdeKh-5xltbVG0&e= > > > . > > > > > > This was an effort to make the timeline more generalized for querying > > (for > > > a different purpose).. but good to revisit now.. > > > > > > On Sun, May 31, 2020 at 11:04 PM [email protected] < > [email protected]> > > > wrote: > > > > > > > > > > > I strongly recommend using a separate datasource relation (option 1) > to > > > > query timeline. It is elegant and fits well with spark APIs. > > > > Thanks.Balaji.V On Saturday, May 30, 2020, 01:18:45 PM PDT, Vinoth > > > > Chandar <[email protected]> wrote: > > > > > > > > Hi satish, > > > > > > > > Are you looking for similar functionality as HoodieDatasourceHelpers? > > > > > > > > We have historically relied on cli to inspect the table, which does > not > > > > lend it self well to programmatic access.. overall in like option 1 - > > > > allowing the timeline to be queryable with a standard schema does > seem > > > way > > > > nicer. > > > > > > > > I am wondering though if we should introduce a new view. Instead we > can > > > use > > > > a different data source name - > > > > spark.read.format(“hudi-timeline”).load(basepath). We can start by > just > > > > allowing querying of active timeline and expand this to archive > > timeline? > > > > > > > > What do other Think? > > > > > > > > > > > > > > > > > > > > On Fri, May 29, 2020 at 2:37 PM Satish Kotha > > > <[email protected] > > > > > > > > > wrote: > > > > > > > > > Hello folks, > > > > > > > > > > We have a use case to incrementally generate data for hudi table > (say > > > > > 'table2') by transforming data from other hudi table(say, table1). > > We > > > > want > > > > > to atomically store commit timestamps read from table1 into table2 > > > commit > > > > > metadata. > > > > > > > > > > This is similar to how DeltaStreamer operates with kafka offsets. > > > > However, > > > > > DeltaStreamer is java code and can easily query kafka offset > > processed > > > by > > > > > creating metaclient for target table. We want to use pyspark and I > > > don't > > > > > see a good way to query commit metadata of table1 from DataSource. > > > > > > > > > > I'm considering making one of the below changes to hoodie to make > > this > > > > > easier. > > > > > > > > > > Option1: Add new relation in hudi-spark to query commit metadata. > > This > > > > > relation would present a 'metadata view' to query and filter > > metadata. > > > > > > > > > > Option2: Add other DataSource options on top of incremental > querying > > to > > > > > allow fetching from source table. For example, users can specify > > > > > 'hoodie.consume.metadata.table: table2BasePath' and issue > > incremental > > > > > query on table1. Then, IncrementalRelation would go read table2 > > > metadata > > > > > first to identify 'consume.start.timestamp' and start incremental > > read > > > on > > > > > table1 with that timestamp. > > > > > > > > > > Option 2 looks simpler to implement. But, seems a bit hacky because > > we > > > > are > > > > > reading metadata from table2 when data souce is table1. > > > > > > > > > > Option1 is a bit more complex. But, it is cleaner and not tightly > > > coupled > > > > > to incremental reads. For example, use cases other than incremental > > > reads > > > > > can leverage same relation to query metadata > > > > > > > > > > What do you guys think? Let me know if there are other simpler > > > solutions. > > > > > Appreciate any feedback. > > > > > > > > > > Thanks > > > > > Satish > > > > > > > > > > >
