The timeline refresh on table initialization invokes the fs view #sync, which has two actions now:
1. reload the timeline of the fs view, so that the next fs view request is based on this timeline metadata
2. if this is a local fs view, clear all the local states; if this is a remote fs view, send request to sync the remote fs view
But, let's see the construction, the meta client is instantiated freshly so the timeline is already the latest,
the table is also constructed freshly, so the fs view has no local states, that means, the #sync is unnecessary totally.
In this patch, the metadata lifecycle and data set fs view are kept in sync, when the fs view is refreshed, the underneath metadata
is also refreshed synchronouly. The freshness of the metadata follows the same rules as data fs view:
1. if the fs view is local, the visibility is based on the client table metadata client's latest commit
2. if the fs view is remote, the timeline server would #sync the fs view and metadata together based on the lagging server local timeline
From the perspective of client, no need to care about the refresh action anymore no matter whether the metadata table is enabled or not.
That make the client logic more clear and less error-prone.
Removes the timeline refresh has another benefit: if avoids unncecessary #refresh of the remote fs view, if all the clients send request to #sync the
remote fs view, the server would encounter conflicts and the client encounters a response error.
* Remove the metadata cleaning strategy for flink, that means the multi-modal index may be affected
* Improve the HoodieTable#clearMetadataTablePartitionsConfig to only update table config when necessary
* Remove the modification of read code path in HoodieTableConfig
No need to #sync actively because the table instance is instantiated freshly,
its view manager has empty fiew instantces, the fs view would be synced lazily when
is it requested.
- getDataSize has non-trivial overhead in the current ParquetWriter impl, requiring traversal of already composed Column Groups in memory. Instead we can sample these calls to getDataSize to amortize its cost.
Co-authored-by: sivabalan <n.siva.b@gmail.com>
- Avoid using udf for key generator for SimpleKeyGen and NonPartitionedKeyGen.
- Fixed NonPartitioned Key generator to directly fetch record key from row rather than involving GenericRecord.
- Other minor fixes around using static values instead of looking up hashmap.
- Due to the fact that Spark 3.2.1 is non-BWC w/ 3.2.0, we have to handle all these incompatibilities in Spark32HoodieParquetFileFormat. This PR is addressing that.
Co-authored-by: Raymond Xu <2701446+xushiyan@users.noreply.github.com>
- when columns names are renamed (schema evolution enabled), while copying records from old data file with HoodieMergeHande, renamed columns wasn't handled well.
- Scaffolded `Spark24HoodieParquetFileFormat` extending `ParquetFileFormat` and overriding the behavior of adding partition columns to every row
- Amended `SparkAdapter`s `createHoodieParquetFileFormat` API to be able to configure whether to append partition values or not
- Fallback to append partition values in cases when the source columns are not persisted in data-file
- Fixing HoodieBaseRelation incorrectly handling mandatory columns
Fixing FILENAME_METADATA_FIELD not being correctly updated in HoodieMergeHandle, in cases when old-record is carried over from existing file as is.
- Revisited HoodieFileWriter API to accept HoodieKey instead of HoodieRecord
- Fixed FILENAME_METADATA_FIELD not being overridden in cases when simply old record is carried over
- Exposing standard JVM's debugger ports in Docker setup
* Fixing incorrect selection of MT partitions to be updated
* Ensure that metadata partitions table config is inherited correctly
Co-authored-by: Sagar Sumit <sagarsumit09@gmail.com>
