1. BUILDING ROBUST CDC PIPELINE WITH
APACHE HUDI AND DEBEZIUM @SCALE
• PRATYAKSH
• PURUSHOTHAM
• SYED
• SHAIK
Hadoop Meetup Bangalore
(Dec-2019)

2. What is CDC?
Benefits of CDC
Comparison of CDC Streaming Systems
Comparison of Reconciler Systems
CDC Platform Architecture @ Tathastu
Challenges
Contribution
Roadmap
Questions

3. CHANGE DATA CAPTURE (CDC): A set of
software design patterns used to determine
(and track) the data that has changed so that
action can be taken using the changed data.

4. Low latency
Event processing
Real time analytics and Dashboarding
Audit logging
Distribute the load round the clock

5. Method Log-Based Query-Based
Tools Debezium JDBC Connector
Schema Evolution Yes Yes
Processing Stream Batch
Audit Track Preserved Partially Preserved
Latency Low High
Cost High Low
Delete Track Yes No

6. Solution Maxwell Apache NiFi Debezium
Bootstrap Yes No Yes
Formats JSON JSON JSON, Avro
Message Queues
Kafka, Kinesis, SQS, Google
Pub/Sub, RabbitMQ, Redis, Custom
Producer
NiFi connections Kafka
Schema Evolution Yes No Yes
Latency Low Medium Low
Supported Databases MySQL MySQL
MySQL, PostgreSQL, Oracle,
SQL Server, MongoDB,
Cassandra
Onboarding Command Driven Config and API Driven Purely API Driven
State
Storage/checkpoints
External Database
Zookeeper, External
Cache
Kafka topics

7. Solution
Delta.io
(Databricks)
Apache
HUDI
Apache Hive
(LLAP)
Updates / Deletes Yes Yes Yes
Compactions
Manual cleanup
No Compaction
Automatic
Manual
Automatic
Manual
File Format Parquet
Parquet
AVRO
ORC
Engine
Spark
Presto (Recently)
Spark
Presto
Hive
EMR
Athena (with workaround)
Hive
Spark(LLAP)
SQL DML NO NO YES
Write Amplification HIGH LOW LOW
Apache Governance YES (Recently) YES YES
Credits Qubole

9. Hadoop Upserts Deletes and Incrementals
Consists of a self-contained spark library
Hudi key = Record key + Partition key
Storage types – COPY_ON_WRITE and MERGE_ON_READ
Query Engines – SparkSQL, Hive, Presto
Multiple Cleaning and Compaction policies supported
Key classes – HoodieDeltaStreamer, HiveSyncTool

11. Schema evolution
Handling datatypes (JDBC)
Handling RDS internal commands
Making libraries compatible with latest versions of Kafka and Spark
Multi-table support in DeltaStreamer
Enhancing Kafka Batch read for Bootstrapping (Source Limit)
Hive Metastore settings
Queriable HUDI dataset – making compatible with Athena

12. CONTRIBUTION
• HUDI-288
• HUDI-340
• HUDI-259
• HUDI-114
• HUDI-118
• HUDI-245
• DBZ-1521
• DBZ-1492
• 563
• 311
• NIFI-6501
• NIFI-6914
• NIFI-6119

13. • Build the single click UI for Orchestration
• Data profiler UI for validation and alerts
• Config-store for configs and credential
• ACL for table and databases (via Ranger)
• Managing the subscriber list for notifications
and alerts

14. • QUBOLE CDC RECONCILER COMPARISION
• HUDI DETAILED ARCHITECTURE DISCUSSION
• ADVANTAGES OF LOG-BASED OVER QUERY-BASED

15. spark-submit --name debz_futurepay --queue etl --files jaas.conf,custom_config.json
--master yarn --deploy-mode cluster --driver-memory 4g --executor-memory 4g
--num-executors 50 --class org.apache.hudi.utilities.deltastreamer.CDCStreamer hudi-
utilities-bundle-0.5.1-SNAPSHOT.jar
--source-class org.apache.hudi.utilities.sources.AvroKafkaSource
--storage-type COPY_ON_WRITE --source-ordering-field __ts_ms --target-base-path
s3://{BASE_PATH}/hudi/${DATABASE}/${TABLE}/ --target-table cdc_flat_cow --props
${HUDI_CONFIG} --enable-hive-sync --custom-props custom_config.json --continuous --
source-limit 1000000
hive.metastore.disallow.incompatible.col.type.changes=false;
parquet.column.index.access='false'
HUDI Command
Hive Metastore Properties

16. #Cleanup policy
hoodie.cleaner.policy=KEEP_LATEST_FILE_VERSIONS
hoodie.cleaner.fileversions.retained=1
HUDI Properties (For Athena )