Apache Hudi is a data lake platform, that provides streaming primitives (upserts/deletes/change streams) on top of data lake storage. Hudi powers very large data lakes at Uber, Robinhood and other companies, while being pre-installed on four major cloud platforms. Hudi supports exactly-once, near real-time data ingestion from Apache Kafka to cloud storage, which is typically used in-place of a S3/HDFS sink connector to gain transactions and mutability. While this approach is scalable and battle-tested, it can only ingest data in mini batches, leading to lower data freshness. In this talk, we introduce a Kafka Connect Sink Connector for Apache Hudi, which writes data straight into Hudi's log format, making the data immediately queryable, while Hudi's table services like indexing, compaction, clustering work behind the scenes, to further re-organize for better query performance.

1. Streaming Data Lakes Using
Kafka Connect +Apache Hudi
Balaji Varadarajan, Vinoth Chandar

2. Speakers
Vinoth Chandar
PMC Chair/Creator of Hudi
Sr.Staff Eng @ Uber (Data
Infra/Platforms, Networking)
Principal Eng @ Confluent (ksqlDB,
Kafka/Streams)
Staff Eng @ Linkedin (Voldemort,
DDS)
Sr Eng @ Oracle
(CDC/Goldengate/XStream)
Balaji Varadarajan
PMC Member, Apache Hudi
Sr. Staff Eng @ Robinhood, Data Infra
Tech Lead @Uber, Data Platform
Staff Engineer @Linkedin, Databus CDC

3. Agenda
1) Background
2) Hudi 101
3) Hudi’s Spark Writers (existing)
4) Kafka Connect Sink (new)
5) Onwards

4. Background
Event Streams, Data Lakes

5. Data Lakes are now essential
Architectural Pattern for Analytical Data
❏ Data Lake != Spark, Flink
❏ Data Lake != Files on S3
❏ Raw data (OLTP schema)
❏ Derived Data (OLAP/BI, ML schema)
Open Storage + Scalable Compute
❏ Avoid data lock-in, Open formats (data +
metadata)
❏ Efficient, Universal (Analytics, Data
Science)
Lot of exciting progress
❏ Lakehouse = Lake + Warehouse
❏ Data meshes on Lakes => Need for streams
Source:
https://martinfowler.com/bliki/images/dataLake/context.png

6. Event Streams are the new norm
Events come in many flavors
Database change Events
❏ High fidelity, High value, update/deletes
❏ E.g: Debezium changelogs into Kafka
Application/Service business events
❏ High volume, Immutable or Deltas,
❏ E.g: Emit Uber app events, emit changes from IoT sensors
SaaS Data Sources
❏ Lower volume, mutable
❏ E.g: polling Github events API

7. Database
Kafka
Cluster
Apps/
Services
Event Firehose
External
Sources
Extracting Event Streams
Kafka
Connect
Sources

8. Why not just Connect File Sinks?
Queries
DFS/Cloud Storage
Data Lake??
Files
Kafka
Cluster
Kafka
Connect
Sinks
(S3/HDFS)

9. Challenges
Working at the file abstraction level is painful
❏ Transactional, Concurrency Control
❏ Updates subset of data, indexing for faster access
Scalability, Operational Overhead
❏ Writing columnar files is resource intensive
❏ Partitioned data increases memory overhead
Lack of management
❏ Control file sizes, Deletes for GDPR/Compliance
❏ Re-align storage for better query performance

10. Apache Hudi
Transactional Writes, MVCC/OCC
❏ Work with tables and records
❏ Automatic compaction, clustering, sizing
First class support for Updates, Deletes
❏ Record level Update/Deletes inspired by stream
processors
CDC Streams From Lake Storage
❏ Storage Layout optimized for incremental fetches
❏ Hudi’s unique contribution in the space

11. Hudi 101
Components, APIs, Architecture

12. Stream processing + Batch data
The Incremental Stack
+ Intelligent, Incremental
+ Fast, Efficient
+ Scans, Columnar formats
+ Scalable Compute
https://www.oreilly.com/content/ubers-case-for-
incremental-processing-on-hadoop/; 2016

13. The Hudi Stack
❏ Complete “data” lake platform
❏ Tightly integrated, Self managing
❏ Write using Spark, Flink
❏ Query using Spark, Flink, Hive,
Presto, Trino, Impala, AWS
Athena/Redshift, Aliyun DLA etc
❏ Out-of-box tools/services for data ops
http://hudi.apache.org/blog/2021/07/21/st
reaming-data-lake-platform

14. Storage Layout

15. ❏ Powers arguably the largest transactional data
lake on the planet @ Uber
❏ (Database CDC) Robinhood’s near-realtime
data lake
❏ (ML Feature stores) @ Logical Clocks
❏ (Event Deletions/De-Duping) @ Moveworks
❏ Many more companies, pre-installed by 5
major cloud providers
1000+
Slack members
150+
Contributors
1000+
GH Engagers
~10-20
PRs/week
20+
Committers
10+
PMCs
The Community

16. Hudi DeltaStreamer
Efficient, Micro-batched

17. Event
Streams
DFS/Cloud Storage
Tables
Pull using Spark
Kafka
De-Dupe Indexing
Txn
DeltaStreamer Utility,
Spark Streaming
Cluster
Optimize
Compact
Apply
Pull
Cleaning

18. Current Kafka to Hudi Options
- Ingest streaming data to Data Lake - Raw Tables
- Current Solutions through Spark:
- Hudi DeltaStreamer
- Spark Structured Streaming
Kafka
Cluster
Hudi
DeltaStreamer
Spark Structured
Streaming
DFS/Cloud
Storage
Tables
Apply

19. Structured Streaming Sink
// Read data from stream
Dataset<Row> streamingInput = spark.readStream()...
// Write to Hudi in a streaming fashion
DataStreamWriter<Row> writer = streamingInput.writeStream()
.format("org.apache.hudi")
.option(DataSourceWriteOptions.TABLE_TYPE.key(), tableType)
.option(DataSourceWriteOptions.RECORDKEY_FIELD.key(), "_row_key")
.option(DataSourceWriteOptions.PARTITIONPATH_FIELD.key(), "partition")
.option(DataSourceWriteOptions.PRECOMBINE_FIELD.key(), "timestamp")
.option(HoodieWriteConfig.TABLE_NAME.key(), tableName)
.option("checkpointLocation", checkpointLocation)
.outputMode(OutputMode.Append());
String tablePath = “s3://…."
// Schedule the job
StreamingQuery query = ...
writer.trigger(Trigger.ProcessingTime(500)).start(tablePath);
query.awaitTermination(streamingDurationInMs);

20. DeltaStreamer Utility
❏ Fully Managed Ingestion
and ETL service
❏ Integration with various
Streaming and batch
sources
❏ Table State &
Checkpoints
transactionally consistent
❏ Pluggable
Transformations for ETL
use cases.

21. DeltaStreamer Example
spark-submit
--master yarn
--packages org.apache.hudi:hudi-utilities-bundle_2.12:0.8.0
--class org.apache.hudi.utilities.deltastreamer.HoodieDeltaStreamer
--conf spark.scheduler.mode=FAIR
--conf spark.task.maxFailures=5
...
--enable-sync
--hoodie-conf auto.offset.reset=latest
--hoodie-conf hoodie.avro.schema.validate=true
….
--table-type MERGE_ON_READ
--source-class org.apache.hudi.utilities.sources.AvroKafkaSource
--schemaprovider-class org.apache.hudi.utilities.schema.SchemaRegistryProvider
--props /path/job.properties
--transformer-class com.some.someTransformer
--continuous ← Enables async compaction, clustering & cleaning along with streaming writes
Streaming Data Lake without writing any code!

22. Case Study: Robinhood Data Lake
Master
RDS
Replica
RDS
Table Topic
DeltaStreamer
(Live)
DeltaStreamer
(Bootstrap)
DATA LAKE
(s3://xxx/…
Update schema
and partition
Write incremental data
and checkpoint offsets

23. Case Study: Robinhood Data Lake
❏ 1000s of CDC based Streaming ingest pipelines supported by Apache Hudi
DeltaStreamer.
❏ Data Lake freshness Latency down to 5-15 mins from hours.
❏ Powers critical dashboards and use-cases

24. End-to-End Streaming Data Lake
❏ Data Lake has both raw tables and derived tables built through ETLs.
❏ Streaming Data-lake - Needs streaming semantics supported for both kinds of tables.
❏ The Missing Primitive : Derived Tables need Changelog view of the upstream dataset ->
Apache Hudi Incremental Read to rescue

25. The Big Picture
Pull
Database Event
Streams
Apps/
Service
s
External
Sources
CDC
Push
Streaming Data Lake
Raw Tables
DeltaStreamer
Spark Streaming Hudi Change log
Derived Tables
DeltaStreamer
Spark
Streaming

26. Connect Hudi Sink
Kafkaesque, Commit protocol, Transactional

27. Motivations
Integration with Kafka Connect
❏ Separation of concerns (writing vs optimization/management)
❏ Streamline operationally, just one framework for ingesting
❏ Less need for Spark expertise
Faster data
❏ Amortize startup costs (containers, queue delays)
❏ Commit frequently i.e every 1 minute (every N secs in near
future)
❏ E.g avro records in Kafka log to Hudi’s log format

28. Putting it all together
Event
Streams
DFS/Cloud Storage
Tables
Kafka
De-Dupe
Indexing Txn
Hudi Connect Sink
(Writing)
Commit
Pull
Compact
Cluster
Hudi’s Table Services
(Optimization, management)
Clean
Deletes

29. Design Challenges
Determining Transaction Boundaries
❏ No co-ordination via driver process like Spark/Flink
❏ Workers doing their own commits => horrible
concurrency bottlenecks
Connect APIs cannot express DAGs
❏ Meant to be simple `putRecords()`/`preCommit()`
❏ Indexing, De-duplication, Storage optimization all
shuffle data

30. Design Overview
Central Transaction Co-ordination
❏ Use Kafka to elect co-
ordinator.
❏ Runs in one of the workers
Kafka as control channel
❏ Consume from latest control
topic offsets
https://cwiki.apache.org/confluence/display/HUDI/RFC-32+Kafka+Connect+Sink+for+Hudi

31. Design Overview
Transaction Coordinator
❏ Daemon thread on owner of
partition 0
❏ Sends commands to participants
Embedded Hudi Java Writer
❏ Lands data into set of file groups,
mapped to a partition
❏ Hudi’s commit fencing guards
from failures/partial writes

32. Co-ordinator State Machine
Paxos-like two phase commit
❏ Co-ordinator process to start, end commits
❏ Safety > liveness, abort after timeout
Participants “pause” at each commit boundary
❏ Return latest write offsets to co-ordinator
❏ Resume again on start of next commit

33. Example Sink Configuration
# hudi table properties
target.base.path
target.table.name
target.database.name
schemaprovider.class
partition.field.name
hoodie.table.base.file.format
Pre-release, subject to change.
Refer to official Hudi docs, for actual config names.
# controller properties
control.topic.name
coordinator.writestatus.timeout
write.retry.timeout

34. Choosing Right
Delta Streamer Connect Sink
Provides full set of Hudi features Insert only for now, indexes/updates coming
as enhancements
Offers better elasticity for merging/writing
columnar data
i.e copy-on-write tables
Great impedance match with Kafka, for
landing avro/row-oriented data i.e merge-on-
read tables
Data freshness of several minutes, if not
running in continuous mode
Approach ~1 min freshness
Need experience with Spark/Flink Operate all data ingestion in a single
framework.

35. What’s to come
Onwards

36. Kafka + Hudi
Support for mutable, keyed updates/deletes
❏ Need to implement a new index ala Flink writer
❏ preCombine, buffering/batching
What if : Back Kafka’s tiered storage using Hudi
❏ Map offsets to Hudi commit_seq_no
❏ Columnar reads for historical/catch-up reads

37. Engage With Our Community
User Docs : https://hudi.apache.org
Technical Wiki : https://cwiki.apache.org/confluence/display/HUDI
Github : https://github.com/apache/hudi/
Twitter : https://twitter.com/apachehudi
Mailing list(s) : dev-subscribe@hudi.apache.org (send an empty email to subscribe)
dev@hudi.apache.org (actual mailing list)
Slack : https://join.slack.com/t/apache-hudi/signup

38. Questions?
Thanks!