- Added upgrade and downgrade step to and from 0.9.0. Upgrade adds few table properties. Downgrade recreates timeline server based marker files if any.
- Rollback infers the directory structure and does rollback based on the strategy used while markers were written. "write markers type" in write config is used to determine marker strategy only for new writes.
* [HUDI-2119] Ensure the rolled-back instance was previously synced to the Metadata Table when syncing a Rollback Instant.
If the rolled-back instant was synced to the Metadata Table, a corresponding deltacommit with the same timestamp should have been created on the Metadata Table timeline. To ensure we can always perfomr this check, the Metadata Table instants should not be archived until their corresponding instants are present in the dataset timeline. But ensuring this requires a large number of instants to be kept on the metadata table.
In this change, the metadata table will keep atleast the number of instants that the main dataset is keeping. If the instant being rolled back was before the metadata table timeline, the code will throw an exception and the metadata table will have to be re-bootstrapped. This should be a very rare occurance and should occur only when the dataset is being repaired by rolling back multiple commits or restoring to an much older time.
* Fixed checkstyle
* Improvements from review comments.
Fixed checkstyle
Replaced explicit null check with Option.ofNullable
Removed redundant function getSynedInstantTime
* Renamed getSyncedInstantTime and getSyncedInstantTimeForReader.
Sync is confusing so renamed to getUpdateTime() and getReaderTime().
* Removed getReaderTime which is only for testing as the same method can be accessed during testing differently without making it part of the public interface.
* Fix compilation error
* Reverting changes to HoodieMetadataFileSystemView
Co-authored-by: Vinoth Chandar <vinoth@apache.org>
Main functions:
Support create table for hoodie.
Support CTAS.
Support Insert for hoodie. Including dynamic partition and static partition insert.
Support MergeInto for hoodie.
Support DELETE
Support UPDATE
Both support spark2 & spark3 based on DataSourceV1.
Main changes:
Add sql parser for spark2.
Add HoodieAnalysis for sql resolve and logical plan rewrite.
Add commands implementation for CREATE TABLE、INSERT、MERGE INTO & CTAS.
In order to push down the update&insert logical to the HoodieRecordPayload for MergeInto, I make same change to the
HoodieWriteHandler and other related classes.
1、Add the inputSchema for parser the incoming record. This is because the inputSchema for MergeInto is different from writeSchema as there are some transforms in the update& insert expression.
2、Add WRITE_SCHEMA to HoodieWriteConfig to pass the write schema for merge into.
3、Pass properties to HoodieRecordPayload#getInsertValue to pass the insert expression and table schema.
Verify this pull request
Add TestCreateTable for test create hoodie tables and CTAS.
Add TestInsertTable for test insert hoodie tables.
Add TestMergeIntoTable for test merge hoodie tables.
Add TestUpdateTable for test update hoodie tables.
Add TestDeleteTable for test delete hoodie tables.
Add TestSqlStatement for test supported ddl/dml currently.
Make the intermediate files of FlinkMergeAndReplaceHandle hidden, when
committing the instant, clean these files in case there was some
corrupted files left(in normal case, the intermediate files should be cleaned
by the FlinkMergeAndReplaceHandle itself).
The `FlinkMergeHande` creates a marker file under the metadata path
each time it initializes, when a write task restarts from killing, it
tries to create the existing file and reports error.
To solve this problem, skip the creation and use the original data file
as base file to merge.
Currently we assign the buckets by record partition path which could
cause hotspot if the partition field is datetime type. Changes to assign
buckets by grouping the record whth their key first, the assignment is
valid if only there is no conflict(two task write to the same bucket).
This patch also changes the coordinator execution to be asynchronous.
Current we did a soft delete for DELETE row data when writes into hoodie
table. For streaming read of MOR table, the Flink reader detects the
delete records and still emit them if the record key semantics are still
kept.
This is useful and actually a must for streaming ETL pipeline
incremental computation.
* [HUDI-845] Added locking capability to allow multiple writers
1. Added LockProvider API for pluggable lock methodologies
2. Added Resolution Strategy API to allow for pluggable conflict resolution
3. Added TableService client API to schedule table services
4. Added Transaction Manager for wrapping actions within transactions
