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.
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.
- 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>
- 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
* Filter out empty string (for non-partitioned table) being added to "__all_partitions__" record
* Instead of filtering, transform empty partition-id to `NON_PARTITIONED_NAME`
* Cleaned up `HoodieBackedTableMetadataWriter`
* Make sure REPLACE_COMMITS are handled as well
- Adding capability to fetch Metadata Records by key prefix so that Data Skipping could fetch only Column Stats
- Index records pertaining to the columns being queried by, instead of reading out whole Index.
- Fixed usages of HFileScanner in HFileReader. few code paths uses cached scanner if available. Other code paths uses its own HFileScanner w/ positional read.
Brief change log
- Rebasing ColumnStatsIndexSupport to rely on HoodieBackedTableMetadata in lieu of reading t/h Spark DS
- Adding methods enabling key-prefix lookups to HoodiFileReader, HoodieHFileReader
- Wiring key-prefix lookup t/h LogRecordScanner impls
- Cleaning up HoodieHFileReader impl
Co-authored-by: sivabalan <n.siva.b@gmail.com>
Co-authored-by: Sagar Sumit <sagarsumit09@gmail.com>
* [HUDI-3290] Different file formats for the partition metadata file.
Partition metadata files are stored in each partition to help identify the base path of a table. These files are saved in the properties file format. Some query engines do not work when non Parquet/ORC files are found in a partition.
Added a new table config 'hoodie.partition.metafile.use.data.format' which when enabled (default false for backward compatibility) ensures that partition metafiles will be saved in the same format as the base files of a dataset.
For new datasets, the config can be set via hudi-cli. Deltastreamer has a new parameter --partition-metafile-use-data-format which will create a table with this setting.
* Code review comments
- Adding a new command to migrate from text to base file formats for meta file.
- Reimplementing readFromFS() to first read the text format, then base format
- Avoid extra exists() checks in readFromFS()
- Added unit tests, enabled parquet format across hoodie-hadoop-mr
- Code cleanup, restructuring, naming consistency.
* Wiring in all the other Spark code paths to respect this config
- Turned on parquet meta format for COW data source tests
- Removed the deltastreamer command line to keep it shorter
* populate HoodiePartitionMetadata#format after readFromFS()
Co-authored-by: Vinoth Chandar <vinoth@apache.org>
Co-authored-by: Raymond Xu <2701446+xushiyan@users.noreply.github.com>
* Add checks for metadata table init to avoid possible out-of-sync
* Revise the logic to reuse existing table config
* Revise docs and naming
Co-authored-by: yuezhang <yuezhang@freewheel.tv>
Co-authored-by: Y Ethan Guo <ethan.guoyihua@gmail.com>
* [HUDI-2560] introduce id_based schema to support full schema evolution.
* add test for FileBasedInternalSchemaStorageManger and rebase code
* add support for change column type and fix some test case
* fix some bugs encountered in the production env and delete useless code
* fix test error
* rebase code
* fixed some nested schema change bugs
* [HUDI-2429][Stacked On HUDI-2560]Support full schema evolution for spark
* [use dummyInternalSchema instead of null]
* add support for spark3.1.x
* remove support for spark3.1.x , sicne some compile fail
* support spark3.1.x
* rebase and prepare solve all comments
* address all comments
* rebase code
* fixed the count(*) bug
* try to get internalSchema by parser commit file/history file directly, not use metaclient which is time cost
address some comments
* fixed all comments
* fix new comments
* rebase code,fix UT failed
* fixed mistake
* rebase code ,fixed new comments
* rebase code , and prepare for address new comments
* address commits
* address new comments
* fix new issues
* control fallback original write logical
In order to drop any metadata partition (index), we can reuse the DELETE_PARTITION operation in metadata table. Subsequent to this, we can support drop index (with table config update) for async metadata indexer.
- Add a new API in HoodieTableMetadataWriter
- Current only supported for Spark metadata writer
- Add a new action called INDEX, whose state transition is described in the RFC.
- Changes in timeline to support the new action.
- Add an index planner in ScheduleIndexActionExecutor.
- Add index plan executor in RunIndexActionExecutor.
- Add 3 APIs in HoodieTableMetadataWriter; a) scheduleIndex: will generate an index plan based on latest completed instant, initialize file groups and add a requested INDEX instant, b) index: executes the index plan and also takes care of writes that happened after indexing was requested, c) dropIndex: will drop index by removing the given metadata partition.
- Add 2 new table configs to serve as the source of truth for inflight and completed indexes.
- Support upgrade/downgrade taking care of the newly added configs.
- Add tool to trigger indexing in HoodieIndexer.
- Handle corner cases related to partial failures.
- Abort gracefully after deleting partition and instant.
- Handle other actions in timeline to consider before catching up
As of now, delete partitions will ensure all file groups are deleted, but the partition as such is not deleted. So, get all partitions might be returning the deleted partitions as well. but no data will be served since all file groups are deleted. With this patch, we are fixing it. We are letting cleaner take care of deleting the partitions when all file groups pertaining to a partitions are deleted.
- Fixed the CleanPlanActionExecutor to return meta info about list of partitions to be deleted. If there are no valid file groups for a partition, clean planner will include the partition to be deleted.
- Fixed HoodieCleanPlan avro schema to include the list of partitions to be deleted
- CleanActionExecutor is fixed to delete partitions if any (as per clean plan)
- Same info is added to HoodieCleanMetadata
- Metadata table when applying clean metadata, will check for partitions to be deleted and will update the "all_partitions" record for the deleted partitions.
Co-authored-by: sivabalan <n.siva.b@gmail.com>
Hudi will be taking on promise for it bundles to stay compatible with Spark minor versions (for ex 2.4, 3.1, 3.2): meaning that single build of Hudi (for ex "hudi-spark3.2-bundle") will be compatible with ALL patch versions in that minor branch (in that case 3.2.1, 3.2.0, etc)
To achieve that we'll have to remove (and ban) "spark-avro" as a dependency, which on a few occasions was the root-cause of incompatibility b/w consecutive Spark patch versions (most recently 3.2.1 and 3.2.0, due to this PR).
Instead of bundling "spark-avro" as dependency, we will be copying over some of the classes Hudi depends on and maintain them along the Hudi code-base to make sure we're able to provide for the aforementioned guarantee. To workaround arising compatibility issues we will be applying local patches to guarantee compatibility of Hudi bundles w/in the Spark minor version branches.
Following Hudi modules to Spark minor branches is currently maintained:
"hudi-spark3" -> 3.2.x
"hudi-spark3.1.x" -> 3.1.x
"hudi-spark2" -> 2.4.x
Following classes hierarchies (borrowed from "spark-avro") are maintained w/in these Spark-specific modules to guarantee compatibility with respective minor version branches:
AvroSerializer
AvroDeserializer
AvroUtils
Each of these classes has been correspondingly copied from Spark 3.2.1 (for 3.2.x branch), 3.1.2 (for 3.1.x branch), 2.4.4 (for 2.4.x branch) into their respective modules.
SchemaConverters class in turn is shared across all those modules given its relative stability (there're only cosmetical changes from 2.4.4 to 3.2.1).
All of the aforementioned classes have their corresponding scope of visibility limited to corresponding packages (org.apache.spark.sql.avro, org.apache.spark.sql) to make sure broader code-base does not become dependent on them and instead relies on facades abstracting them.
Additionally, given that Hudi plans on supporting all the patch versions of Spark w/in aforementioned minor versions branches of Spark, additional build steps were added to validate that Hudi could be properly compiled against those versions. Testing, however, is performed against the most recent patch versions of Spark with the help of Azure CI.
Brief change log:
- Removing spark-avro bundling from Hudi by default
- Scaffolded Spark 3.2.x hierarchy
- Bootstrapped Spark 3.1.x Avro serializer/deserializer hierarchy
- Bootstrapped Spark 2.4.x Avro serializer/deserializer hierarchy
- Moved ExpressionCodeGen,ExpressionPayload into hudi-spark module
- Fixed AvroDeserializer to stay compatible w/ both Spark 3.2.1 and 3.2.0
- Modified bot.yml to build full matrix of support Spark versions
- Removed "spark-avro" dependency from all modules
- Fixed relocation of spark-avro classes in bundles to assist in running integ-tests.
