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Ingest multiple tables using Hudi

pratyakshsharma
4 min read
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When building a change data capture pipeline for already existing or newly created relational databases, one of the most common problems that one faces is simplifying the onboarding process for multiple tables. Ingesting multiple tables to Hudi dataset at a single go is now possible using HoodieMultiTableDeltaStreamer class which is a wrapper on top of the more popular HoodieDeltaStreamer class. Currently HoodieMultiTableDeltaStreamer supports COPY_ON_WRITE storage type only and the ingestion is done in a sequential way.

This blog will guide you through configuring and running HoodieMultiTableDeltaStreamer.

Configuration​

  • HoodieMultiTableDeltaStreamer expects users to maintain table wise overridden properties in separate files in a dedicated config folder. Common properties can be configured via common properties file also.
  • By default, hudi datasets are created under the path <base-path-prefix>/<database_name>/<name_of_table_to_be_ingested>. You need to provide the names of tables to be ingested via the property hoodie.deltastreamer.ingestion.tablesToBeIngested in the format <database>.<table>, for example
hoodie.deltastreamer.ingestion.tablesToBeIngested=db1.table1,db2.table2
  • If you do not provide database name, then it is assumed the table belongs to default database and the hudi dataset for the concerned table is created under the path <base-path-prefix>/default/<name_of_table_to_be_ingested>. Also there is a provision to override the default path for hudi datasets. You can create hudi dataset for a particular table by setting the property hoodie.deltastreamer.ingestion.targetBasePath in table level config file
  • There are a lot of properties that one might like to override per table, for example
hoodie.datasource.write.recordkey.field=_row_key
hoodie.datasource.write.partitionpath.field=created_at
hoodie.deltastreamer.source.kafka.topic=topic2
hoodie.deltastreamer.keygen.timebased.timestamp.type=UNIX_TIMESTAMP
hoodie.deltastreamer.keygen.timebased.input.dateformat=yyyy-MM-dd HH:mm:ss.S
hoodie.datasource.hive_sync.table=short_trip_uber_hive_dummy_table
hoodie.deltastreamer.ingestion.targetBasePath=s3:///temp/hudi/table1
  • Properties like above need to be set for every table to be ingested. As already suggested at the beginning, users are expected to maintain separate config files for every table by setting the below property
hoodie.deltastreamer.ingestion.<db>.<table>.configFile=s3:///tmp/config/config1.properties

If you do not want to set the above property for every table, you can simply create config files for every table to be ingested under the config folder with the name - <database>_<table>_config.properties. For example if you want to ingest table1 and table2 from dummy database, where config folder is set to s3:///tmp/config, then you need to create 2 config files on the given paths - s3:///tmp/config/dummy_table1_config.properties and s3:///tmp/config/dummy_table2_config.properties.

  • Finally you can specify all the common properties in a common properties file. Common properties file does not necessarily have to lie under config folder but it is advised to keep it along with other config files. This file will contain the below properties
hoodie.deltastreamer.ingestion.tablesToBeIngested=db1.table1,db2.table2
hoodie.deltastreamer.ingestion.db1.table1.configFile=s3:///tmp/config_table1.properties
hoodie.deltastreamer.ingestion.db2.table2.configFile=s3:///tmp/config_table2.properties

Configuring schema providers​

It is possible to configure different schema providers for different tables or same schema provider class for all tables. All you need to do is configure the property hoodie.deltastreamer.schemaprovider.class accordingly as per your use case as below -

hoodie.deltastreamer.schemaprovider.class=org.apache.hudi.utilities.schema.FilebasedSchemaProvider

Further it is also possible to configure different source and target schema registry urls with SchemaRegistryProvider as the schemaprovider class. Originally HoodieMultiTableDeltaStreamer was designed to cater to use cases where subject naming strategy is set to TopicNameStrategy which is the default provided by Confluent. With this default strategy in place, the subject name is same as the topic name being used in kafka. Source and target schema registry urls can be configured as below with TopicNameStrategy -

hoodie.deltastreamer.schemaprovider.registry.baseUrl=http://localhost:8081/subjects/
hoodie.deltastreamer.schemaprovider.registry.urlSuffix=-value/versions/latest

If you want to consume different versions of your source and target subjects, you can configure as below -

hoodie.deltastreamer.schemaprovider.registry.baseUrl=http://localhost:8081/subjects/
hoodie.deltastreamer.schemaprovider.registry.sourceUrlSuffix=-value/versions/latest
hoodie.deltastreamer.schemaprovider.registry.targetUrlSuffix=-value/versions/1

If you are looking to configure the schema registry urls in the most straight forward way, you can do that as below

hoodie.deltastreamer.schemaprovider.registry.url=http://localhost:8081/subjects/random-value/versions/latest
hoodie.deltastreamer.schemaprovider.registry.targetUrl=http://localhost:8081/subjects/random-value/versions/latest

Run Command​

HoodieMultiTableDeltaStreamer can be run similar to how one runs HoodieDeltaStreamer. Please refer to the example given below for the command.

Example​

Suppose you want to ingest table1 and table2 from db1 and want to ingest the 2 tables under the path s3:///temp/hudi. You can ingest them using the below command

[hoodie]$ spark-submit --class org.apache.hudi.utilities.deltastreamer.HoodieMultiTableDeltaStreamer `ls packaging/hudi-utilities-bundle/target/hudi-utilities-bundle-*.jar` \
  --props s3:///temp/hudi-ingestion-config/kafka-source.properties \
  --config-folder s3:///temp/hudi-ingestion-config \
  --schemaprovider-class org.apache.hudi.utilities.schema.SchemaRegistryProvider \
  --source-class org.apache.hudi.utilities.sources.AvroKafkaSource \
  --source-ordering-field impresssiontime \
  --base-path-prefix s3:///temp/hudi \ 
  --target-table dummy_table \
  --op UPSERT

s3:///temp/config/kafka-source.properties

hoodie.deltastreamer.ingestion.tablesToBeIngested=db1.table1,db1.table2
hoodie.deltastreamer.ingestion.db1.table1.configFile=s3:///temp/hudi-ingestion-config/config_table1.properties
hoodie.deltastreamer.ingestion.db21.table2.configFile=s3:///temp/hudi-ingestion-config/config_table2.properties

#Kafka props
bootstrap.servers=localhost:9092
auto.offset.reset=earliest
schema.registry.url=http://localhost:8081

hoodie.datasource.write.keygenerator.class=org.apache.hudi.keygen.CustomKeyGenerator

s3:///temp/hudi-ingestion-config/config_table1.properties

hoodie.datasource.write.recordkey.field=_row_key1
hoodie.datasource.write.partitionpath.field=created_at
hoodie.deltastreamer.source.kafka.topic=topic1

s3:///temp/hudi-ingestion-config/config_table2.properties

hoodie.datasource.write.recordkey.field=_row_key2
hoodie.datasource.write.partitionpath.field=created_at
hoodie.deltastreamer.source.kafka.topic=topic2

Contributions are welcome for extending multiple tables ingestion support to MERGE_ON_READ storage type and enabling HoodieMultiTableDeltaStreamer ingest multiple tables parallely.

Happy ingesting!