add new config key hoodie.deltastreamer.source.kafka.enable.failOnDataLoss
when failOnDataLoss=false (current behaviour, the default), log a warning instead of seeking to earliest silently
when failOnDataLoss is set, fail explicitly
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>
- Upgrade junit to 5.7.2
- Downgrade surefire and failsafe to 2.22.2
- Fix test failures that were previously not reported
- Improve azure pipeline configs
Co-authored-by: liujinhui1994 <965147871@qq.com>
Co-authored-by: Y Ethan Guo <ethan.guoyihua@gmail.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.
* HiveConf needs to load fs conf to allow instantiation via AWSGlueClientFactory
* Resolve metastore uri config before loading fs conf
* Skip hiveql due to CI issue
Co-authored-by: Sagar Sumit <sagarsumit09@gmail.com>
- 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;
