This is an automated email from the ASF dual-hosted git repository.
danny0405 pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/hudi.git
The following commit(s) were added to refs/heads/master by this push:
new df20ed805022 docs: RFC-95 - New Hudi Flink Source implementation
(#13381)
df20ed805022 is described below
commit df20ed805022fdc287808c70ca912768bb5cae38
Author: Peter Huang <[email protected]>
AuthorDate: Tue Nov 18 18:44:26 2025 -0800
docs: RFC-95 - New Hudi Flink Source implementation (#13381)
---
rfc/rfc-95/hudi_flink_source.png | Bin 0 -> 65432 bytes
rfc/rfc-95/rfc-95.md | 319 +++++++++++++++++++++++++++++++++++++++
2 files changed, 319 insertions(+)
diff --git a/rfc/rfc-95/hudi_flink_source.png b/rfc/rfc-95/hudi_flink_source.png
new file mode 100644
index 000000000000..4398bc12d0ca
Binary files /dev/null and b/rfc/rfc-95/hudi_flink_source.png differ
diff --git a/rfc/rfc-95/rfc-95.md b/rfc/rfc-95/rfc-95.md
new file mode 100644
index 000000000000..2988f909fa00
--- /dev/null
+++ b/rfc/rfc-95/rfc-95.md
@@ -0,0 +1,319 @@
+<!--
+ Licensed to the Apache Software Foundation (ASF) under one or more
+ contributor license agreements. See the NOTICE file distributed with
+ this work for additional information regarding copyright ownership.
+ The ASF licenses this file to You under the Apache License, Version 2.0
+ (the "License"); you may not use this file except in compliance with
+ the License. You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+-->
+# RFC-95: Hudi Flink Source Implementation
+
+## Proposers
+
+- HuangZhenQiu
+
+## Approvers
+ - Danny Chan
+
+## Status
+
+JIRA: [HUDI-9483](https://issues.apache.org/jira/browse/HUDI-9483)
+
+## Abstract
+This RFC proposes support for new Flink source API for Flink hudi source. Hudi
currently supports reading data via Flink Source Function APIs. However, it
lacks a native Flink
+Source API implementation for consuming Hudi tables as a first-class Flink
source. The proposal aims to fill that gap by implementing a Flink Source that
adheres to Flink's Source API
+(introduced in Flink 1.11 and enhanced in 1.13+) to enable efficient, scalable
and consistent reading of Hudi dataset in both streaming and batch modes.
+
+## Background
+Flink FLIP-27 solve several problems/shortcomings in the streaming source
interface (SourceFunction) and simultaneously to unify the source interfaces
between the batch and streaming APIs.
+By adopting the new source interface, Flink can read hudi data in batch mode
for better processing efficiency of bounded data. It also unblocks Flink Hudi
user to use Flink hybrid source to seamless
+switch reading data from hudi to kafka for back-fill use cases.
+
+## Implementation
+In the FLIP-27, the new source api split the reading logic into two major
parts, SplitEnumerator, Reader. In the proposal, the implementation of native
FlinkHudiSource using Flink's unified Source API
+will be discussed. It supports both snapshot and incremental modes, integrate
with Hudi's metadata and file indexing, provide fault-tolerant Flink sources.
For the Flink SQL user, the split order and
+watermark emitter for each split is very important for the correctness of
result of window aggregation and window join. Thus, watermark emit and
event-time processing will also be discussed below.
+
+
+
+### Hudi Source
+
+```java
+public class HudiSource implements Source<RowData, HudiSourceSplit,
HudiEnumeratorState> {
+
+ //...
+ @Override
+ public Boundedness getBoundedness() {
+ return scanContext.isStreaming() ? Boundedness.CONTINUOUS_UNBOUNDED :
Boundedness.BOUNDED;
+ }
+
+ @Override
+ public SourceReader<T, HudiSourceSplit> createReader(SourceReaderContext
readerContext) {
+ HudiSourceReaderMetrics metrics =
+ new HudiSourceReaderMetrics(readerContext.metricGroup(), tableName);
+ return new HudiSourceReader<>(
+ emitter, metrics, readerFunction, splitComparator, readerContext);
+ }
+
+ @Override
+ public SplitEnumerator<HudiSourceSplit, HudiEnumeratorState>
createEnumerator(
+ SplitEnumeratorContext<HudiSourceSplit> enumContext) {
+ return createEnumerator(enumContext, null);
+ }
+
+ @Override
+ public SplitEnumerator<HudiSourceSplit, HudiEnumeratorState>
restoreEnumerator(
+ SplitEnumeratorContext<HudiSourceSplit> enumContext, HudiEnumeratorState
enumState) {
+ return createEnumerator(enumContext, enumState);
+ }
+
+ @Override
+ public SimpleVersionedSerializer<HudiSourceSplit> getSplitSerializer() {
+ return new HudiSourceSplitSerializer(scanContext.caseSensitive());
+ }
+
+ @Override
+ public SimpleVersionedSerializer<HudiEnumeratorState>
getEnumeratorCheckpointSerializer() {
+ return new HudiEnumeratorStateSerializer(scanContext.caseSensitive());
+ }
+
+ // ...
+}
+```
+
+### Hudi Split
+Similar to existing MergeOnReadInputSplit that implements InputSplit API, A
new MergeOnReadSourceSplit and CdcSourceSplit will be implemented with the new
SourceSplit interface.
+FlinkRowDataReaderContext will be extended to be able to apply HudiSourceSplit
and return a CloseableIterator that iterates `RecordsWithSplitIds`.
RecordsWithSplitIds is a Flink interface
+that will be extended to support HudiSourceSplit. Base on the change, the
HudiSourceSplitReader will be skeleton as below.
+
+```java
+
+class HudiSourceSplitReader implements SplitReader<RecordAndPosition<RowData>,
HudiSourceSplit> {
+ private final FlinkRowDataReaderContext readerContext;
+ private final SerializableComparator<HudiSourceSplit> splitComparator;
+ private final int indexOfSubtask;
+ private final Queue<HudiSourceSplit> splits;
+
+ private CloseableIterator<RecordsWithSplitIds<RecordAndPosition<RowData>>>
currentReader;
+ private HudiSourceSplit currentSplit;
+ private String currentSplitId;
+
+ HudiSourceSplitReader(
+ FlinkRowDataReaderContext readerContext,
+ SerializableComparator<HudiSourceSplit> splitComparator,
+ SourceReaderContext context) {
+ this.readerContext = readerContext;
+ this.splitComparator = splitComparator;
+ this.indexOfSubtask = context.getIndexOfSubtask();
+ this.splits = Queues.newArrayDeque();
+ }
+
+ @Override
+ public RecordsWithSplitIds<RecordAndPosition<T>> fetch() throws IOException {
+ metrics.incrementSplitReaderFetchCalls(1);
+ if (currentReader == null) {
+ HudiSourceSplit nextSplit = splits.poll();
+ if (nextSplit != null) {
+ currentSplit = nextSplit;
+ currentSplitId = nextSplit.splitId();
+ currentReader = readerContext.apply(currentSplit);
+ } else {
+ return new RecordsBySplits<>(Collections.emptyMap(),
Collections.emptySet());
+ }
+ }
+
+ if (currentReader.hasNext()) {
+ // Because Iterator#next() doesn't support checked exception,
+ // we need to wrap and unwrap the checked IOException with
UncheckedIOException
+ try {
+ return currentReader.next();
+ } catch (UncheckedIOException e) {
+ throw e.getCause();
+ }
+ } else {
+ return finishSplit();
+ }
+ }
+
+ @Override
+ public void handleSplitsChanges(SplitsChange<HudiSourceSplit> splitsChange) {
+ if (!(splitsChange instanceof SplitsAddition)) {
+ throw new UnsupportedOperationException(
+ String.format("Unsupported split change: %s",
splitsChange.getClass()));
+ }
+
+ if (splitComparator != null) {
+ List<HudiSourceSplit> newSplits =
Lists.newArrayList(splitsChange.splits());
+ newSplits.sort(splitComparator);
+ splits.addAll(newSplits);
+ } else {
+ splits.addAll(splitsChange.splits());
+ }
+ }
+
+ @Override
+ public void wakeUp() {
+ }
+
+ @Override
+ public void close() throws Exception {
+ currentSplitId = null;
+ if (currentReader != null) {
+ currentReader.close();
+ }
+ }
+
+ @Override
+ public void pauseOrResumeSplits(
+ Collection<HudiSourceSplit> splitsToPause, Collection<HudiSourceSplit>
splitsToResume) {
+ }
+}
+```
+
+### Hudi Source Reader
+```java
+public class HudiSourceReader<T>
+ extends SingleThreadMultiplexSourceReaderBase<
+ RecordAndPosition<T>, T, HudiSourceSplit, HudiSourceSplit> {
+
+ // ...
+ @Override
+ public void start() {
+ // We request a split only if we did not get splits during the checkpoint
restore.
+ // Otherwise, reader restarts will keep requesting more and more splits.
+ if (getNumberOfCurrentlyAssignedSplits() == 0) {
+ requestSplit(Collections.emptyList());
+ }
+ }
+
+ @Override
+ protected void onSplitFinished(Map<String, HudiSourceSplit>
finishedSplitIds) {
+ requestSplit(Lists.newArrayList(finishedSplitIds.keySet()));
+ }
+
+ @Override
+ protected HudiSourceSplit initializedState(HudiSourceSplit split) {
+ return split;
+ }
+
+ @Override
+ protected HudiSourceSplit toSplitType(String splitId, HudiSourceSplit
splitState) {
+ return splitState;
+ }
+
+ private void requestSplit(Collection<String> finishedSplitIds) {
+ context.sendSourceEventToCoordinator(new
SplitRequestEvent(finishedSplitIds));
+ }
+
+ // ...
+}
+```
+
+### Hudi SplitEnumerator
+Hudi split enumerator is responsible to monitor new hudi commit, get
IncrementalInputSplits and converts to HudiSourceSplit. It functions the same
as StreamReadMonitoringFunction, the difference is that
+Hudi split enumerator runs in job manager. To need the requirement of
streaming ETL and back-fill data from Hudi tables within a time range.
ContinuousHudiSplitEnumerator and StaticHudiSplitEnumerator
+will be implemented accordingly.
+
+### Hudi Split Assignment
+Hudi split assigner assign new discovered or unassigned discovered split to
each of split requesters. Split will be sorted by using internal hudi commit
time. Thus, splits from the same hudi commit will be processed
+first. It will be helpful for align watermark cross each of split reader.
+
+### Hudi Watermark Emitter
+If there is an event time column stats in parquet file, we can emit watermark
base on the column stats. Otherwise, we can emit watermark by using hudi commit
time.
+
+### Hudi Table Source Integration
+With the HudiSource implemented, it can be used for both streaming and batch
mode in HudiTableSource. A new flink option USE_SOURCE_V2 will be defined to
let user specify whether
+to use the new HudiSource in the HudiTableService. Below is the
getScanRuntimeProvider function that needs to changed accordingly for the
integration.
+
+```java
+
+ @Override
+ public ScanRuntimeProvider getScanRuntimeProvider(ScanContext scanContext) {
+ return new DataStreamScanProviderAdapter() {
+
+ @Override
+ public boolean isBounded() {
+ return !conf.getBoolean(FlinkOptions.READ_AS_STREAMING);
+ }
+
+ @Override
+ public DataStream<RowData>
produceDataStream(StreamExecutionEnvironment execEnv) {
+ @SuppressWarnings("unchecked")
+ TypeInformation<RowData> typeInfo =
+ (TypeInformation<RowData>)
TypeInfoDataTypeConverter.fromDataTypeToTypeInfo(getProducedDataType());
+ OptionsInference.setupSourceTasks(conf, execEnv.getParallelism());
+ if (conf.getBoolean(FlinkOptions.READ_AS_STREAMING)) {
+ if (conf.getBoolean(FlinkOptions.USE_SOURCE_V2)) {
+ return HudiSource
+ .properties(properties)
+ .metaClient(metaClient)
+ .schemaManager(internalSchemaManager)
+ .project(getProjectedSchema())
+ .limit(limit)
+ .filters(filters)
+ .flinkConfig(readableConfig)
+ .buildStream(env);
+ } else {
+ // original logic
+ }
+ } else {
+ if (conf.getBoolean(FlinkOptions.USE_SOURCE_V2)) {
+ return HudiSource
+ .properties(properties)
+ .metaClient(metaClient)
+ .schemaManager(internalSchemaManager)
+ .project(getProjectedSchema())
+ .limit(limit)
+ .filters(filters)
+ .flinkConfig(readableConfig)
+ .buildStream(env);
+ } else {
+ // original logic
+ }
+ }
+ }
+ };
+ }
+```
+
+
+## Rollout/Adoption Plan
+
+ - There is no impact to existing users who are using
StreamingReadMonitoringFunction. User will need to switch use new source API to
adopt the proposed solution.
+ - There is no plan to phase out the existing source function based solution.
+ - No migration is needed
+
+## Test Plan
+We can write normal junit tests for each of the new components. Further adhoc
testing will include the following scenarios:
+
+### Continuous Reading from Hudi Merge On Read Table
+
+Run a long-running Flink streaming read process that continuously read from
merge on read Hudi table. Ensure that Flink Hudi Source can continuously read
from data of new commits. Compare the data between original source table and
sink table to ensure the data correctness.
+
+### Continuous Reading from Hudi CDC Table
+
+Run a long-running Flink streaming read process that continuously read from
CDC Hudi table. Ensure that Flink Hudi Source can continuously read from data
of new commits. Compare the data between original source table and sink table
to ensure the data correctness.
+
+### Time Range Reading from Merge On Read Table
+Run a long-running Flink streaming read process that continuously read from
merge on read Hudi table. Ensure that Flink Hudi Source can read only from data
of commits between a time range. Compare the data between original source table
and sink table to ensure the data correctness within the time period.
+
+### Time Range Reading from CDC Table
+Run a long-running Flink streaming read process that continuously read from
CDC Hudi table. Ensure that Flink Hudi Source can read only from data of
commits between a time range. Compare the data between original source table
and sink table to ensure the data correctness within the time period.
+
+### Flink SQL Test with event time Window Aggregation
+Run a long-running Flink streaming read process that continuously read from
merge on read Hudi table with a Flink Tumbling window query. Ensure that Flink
Hudi Source can emit watermark correctly for each hudi commit and window can be
fired correctly in streaming mode.
+
+### Flink SQL Test with event time Window Join
+Run a long-running Flink streaming read process that continuously read from
two merge on read Hudi tables with a Flink Window Join query. Ensure that Flink
Hudi Source can emit watermark correctly for each hudi commit and join result
can be fired correctly in streaming mode.
+
+### Long-Running Stream Stability Test
+Initiate one or more continuous streaming processes that run for an extended
period (few days). Monitor these processes for issues such as connection leaks,
resource exhaustion, or performance degradation over time.
+
