- Move clean related configuration to HoodieCleanConfig
- Move Archival related configuration to HoodieArchivalConfig
- hoodie.compaction.payload.class move this to HoodiePayloadConfig
* [HUDI-4276] Reconcile schema-inject null values for missing fields and add new fields.
* fix comments
Co-authored-by: public (bdcee5037027) <mengtao0326@qq.com>
Fixed sequence determining whether Parquet's legacy-format writing property should be overridden to only kick in when it has not been explicitly specified by the caller
* Fixed Dictionary encoding config not being properly propagated to Parquet writer (making it unable to apply it, substantially bloating the storage footprint)
TestReaderFilterRowKeys needs to get the key from RECORD_KEY_METADATA_FIELD, but the writer in current UT does not populate the meta field and the schema does not contains meta fields.
This fix writes data with schema which contains meta fields and calls writeAvroWithMetadata for writing.
Co-authored-by: xicm <xicm@asiainfo.com>
There are multiple issues with our current DataSource V2 integrations: b/c we advertise Hudi tables as V2, Spark expects it to implement certain APIs which are not implemented at the moment, instead we're using custom Resolution rule (in HoodieSpark3Analysis) to instead manually fallback to V1 APIs. This commit fixes the issue by reverting DSv2 APIs and making Spark use V1, except for schema evaluation logic.
- Key fetched from metadata table especially from base file reader is not sorted. and hence may result in throwing NPE (key prefix search) or unnecessary seeks to starting of Hfile (full key look ups). Fixing the same in this patch. This is not an issue with log blocks, since sorting is taking care within HoodieHfileDataBlock.
- Commit where the sorting was mistakenly reverted [HUDI-3760] Adding capability to fetch Metadata Records by prefix #5208
- When async indexer is invoked only with "FILES" partition, it fails. Fixing it to work with Async indexer. Also, if metadata table itself is not initialized, and if someone is looking to build indexes via AsyncIndexer, first they are expected to index "FILES" partition followed by other partitions. In general, we have a limitation of building only one index at a time w/ AsyncIndexer and hence. Have added guards to ensure these conditions are met.
Bulk insert row writer code path had a gap wrt hive style partitioning and default partition when virtual keys are enabled with SimpleKeyGen. This patch fixes the issue.
As has been outlined in HUDI-4176, we've hit a roadblock while testing Hudi on a large dataset (~1Tb) having pretty fat commits where Hudi's commit metadata could reach into 100s of Mbs.
Given the size some of ours commit metadata instances Spark's parsing and resolving phase (when spark.sql(...) is involved, but before returned Dataset is dereferenced) starts to dominate some of our queries' execution time.
- Rebased onto new APIs to avoid excessive Hadoop's Path allocations
- Eliminated hasOperationField completely to avoid repeatitive computations
- Cleaning up duplication in HoodieActiveTimeline
- Added caching for common instances of HoodieCommitMetadata
- Made tableStructSchema lazy;
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.
