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Session hashtag: #SAISEco10
Hudi: Large-Scale, Near Real-Time Pipelines at Uber with Nishith Agarwal and Vinoth Chandar
Hudi: Large-Scale, Near Real-Time Pipelines at Uber with Nishith Agarwal and Vinoth Chandar
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Hudi: Large-Scale, Near Real-Time Pipelines at Uber with Nishith Agarwal and Vinoth Chandar
Hudi: Large-Scale, Near Real-Time Pipelines at Uber with Nishith Agarwal and Vinoth Chandar
Hudi: Large-Scale, Near Real-Time Pipelines at Uber with Nishith Agarwal and Vinoth Chandar
Hudi: Large-Scale, Near Real-Time Pipelines at Uber with Nishith Agarwal and Vinoth Chandar
Hudi: Large-Scale, Near Real-Time Pipelines at Uber with Nishith Agarwal and Vinoth Chandar
Changelog Changelog
Normal Table
(Hive/Spark/Presto)
Dataset
Hudi: Large-Scale, Near Real-Time Pipelines at Uber with Nishith Agarwal and Vinoth Chandar
Hudi
DataSource
(Spark)
Index
Data Files
Timeline
Metadata
Hive
Queries
Dataset On
Hadoop FS
Presto
Queries
Spark
DAGs
Store & Index
Data
Read data
Storage
Type
Views
REALTIME
READ
OPTIMIZED
Cost
Latency
Hudi: Large-Scale, Near Real-Time Pipelines at Uber with Nishith Agarwal and Vinoth Chandar
Hudi: Large-Scale, Near Real-Time Pipelines at Uber with Nishith Agarwal and Vinoth Chandar
Hudi: Large-Scale, Near Real-Time Pipelines at Uber with Nishith Agarwal and Vinoth Chandar
Hudi: Large-Scale, Near Real-Time Pipelines at Uber with Nishith Agarwal and Vinoth Chandar
// Command to extract incrementals using sqoop
bin/sqoop import 
-Dmapreduce.job.user.classpath.first=true 
--connect jdbc:mysql://localhost/users 
--username root 
--password ******* 
--table users 
--as-avrodatafile 
--target-dir 
s3:///tmp/sqoop/import-1/users
// Spark Datasource
Import com.uber.hoodie.DataSourceWriteOptions._
// Use Spark datasource to read avro
Dataset<Row> inputDataset
spark.read.avro(‘s3://tmp/sqoop/import-1/users/*’);
// save it as a Hoodie dataset
inputDataset.write.format(“com.uber.hoodie”)
.option(HoodieWriteConfig.TABLE_NAME, “hoodie.users”)
.option(RECORDKEY_FIELD_OPT_KEY(), "userID")
.option(PARTITIONPATH_FIELD_OPT_KEY(),"country")
.option(PRECOMBINE_FIELD_OPT_KEY(), "last_mod")
.option(OPERATION_OPT_KEY(), UPSERT_OPERATION_OPT_VAL())
.mode(SaveMode.Append);
.save(“/path/on/dfs”)
// Deltastreamer command to ingest sqoop incrementals
spark-submit 
--class com.uber.hoodie.utilities.deltastreamer.HoodieDeltaStreamer 
/path/to/hoodie-utilities-*-SNAPSHOT.jar` 
--props s3://path/to/dfs-source.properties 
--schemaprovider-class com.uber.hoodie.utilities.schema.FilebasedSchemaProvider 
--source-class com.uber.hoodie.utilities.sources.AvroDFSSource 
--source-ordering-field last_mod 
--target-base-path s3:///path/on/dfs
--target-table uber.employees 
--op UPSERT
// dfs-source-properties
include=base.properties
# Key generator props
hoodie.datasource.write.recordkey.field=_userID
hoodie.datasource.write.partitionpath.field=country
# Schema provider props
hoodie.deltastreamer.filebased.schemaprovider.source.schema.file=s3:///path/to/users.avsc
# DFS Source
hoodie.deltastreamer.source.dfs.root=s3:///tmp/sqoop
Hudi: Large-Scale, Near Real-Time Pipelines at Uber with Nishith Agarwal and Vinoth Chandar
Hudi: Large-Scale, Near Real-Time Pipelines at Uber with Nishith Agarwal and Vinoth Chandar
// Deltastreamer command to ingest kafka events, dedupe, ingest
spark-submit --class com.uber.hoodie.utilities.deltastreamer.HoodieDeltaStreamer 
/path/to/hoodie-utilities-*-SNAPSHOT.jar` 
--props s3://path/to/kafka-source.properties 
--schemaprovider-class com.uber.hoodie.utilities.schema.SchemaRegistryProvider 
--source-class com.uber.hoodie.utilities.sources.AvroKafkaSource 
--source-ordering-field time 
--target-base-path s3:///hoodie-deltastreamer/impressions --target-table uber.impressions 
--op BULK_INSERT
--filter-dupes
// kafka-source-properties
include=base.properties
# Key fields, for kafka example
hoodie.datasource.write.recordkey.field=id
hoodie.datasource.write.partitionpath.field=datestr
# schema provider configs
hoodie.deltastreamer.schemaprovider.registry.url=http://localhost:8081/subjects/impressions-value/v
ersions/latest
# Kafka Source
hoodie.deltastreamer.source.kafka.topic=impressions
#Kafka props
metadata.broker.list=localhost:9092
auto.offset.reset=smallest
schema.registry.url=http://localhost:8081
Hudi: Large-Scale, Near Real-Time Pipelines at Uber with Nishith Agarwal and Vinoth Chandar
New Data
Unaffected Data
Updated Datachanges
Source table ETL table A ETL table B
// Spark Datasource
Import com.uber.hoodie.{DataSourceWriteOptions, DataSourceReadOptions}._
// Use Spark datasource to read avro
Dataset<Row> hoodieIncViewDF = spark.read().format("com.uber.hoodie")
.option(VIEW_TYPE_OPT_KEY(), VIEW_TYPE_INCREMENTAL_OPT_VAL())
.option(DataSourceReadOptions.BEGIN_INSTANTTIME_OPT_KEY(),commitInstantFor8AM)
.load(“s3://tables/raw_trips”);
Dataset<Row> stdDF = standardize_fare(hoodieIncViewDF)
// save it as a Hoodie dataset
inputDataset.write.format(“com.uber.hoodie”)
.option(HoodieWriteConfig.TABLE_NAME, “hoodie.std_trips”)
.option(RECORDKEY_FIELD_OPT_KEY(), "id")
.option(PARTITIONPATH_FIELD_OPT_KEY(),"datestr")
.option(PRECOMBINE_FIELD_OPT_KEY(), "time")
.option(OPERATION_OPT_KEY(), UPSERT_OPERATION_OPT_VAL())
.mode(SaveMode.Append);
.save(“/path/on/dfs”)
Hudi: Large-Scale, Near Real-Time Pipelines at Uber with Nishith Agarwal and Vinoth Chandar
Hudi: Large-Scale, Near Real-Time Pipelines at Uber with Nishith Agarwal and Vinoth Chandar
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Hudi: Large-Scale, Near Real-Time Pipelines at Uber with Nishith Agarwal and Vinoth Chandar

  • 12. Hudi DataSource (Spark) Index Data Files Timeline Metadata Hive Queries Dataset On Hadoop FS Presto Queries Spark DAGs Store & Index Data Read data Storage Type Views
  • 18. // Command to extract incrementals using sqoop bin/sqoop import -Dmapreduce.job.user.classpath.first=true --connect jdbc:mysql://localhost/users --username root --password ******* --table users --as-avrodatafile --target-dir s3:///tmp/sqoop/import-1/users // Spark Datasource Import com.uber.hoodie.DataSourceWriteOptions._ // Use Spark datasource to read avro Dataset<Row> inputDataset spark.read.avro(‘s3://tmp/sqoop/import-1/users/*’); // save it as a Hoodie dataset inputDataset.write.format(“com.uber.hoodie”) .option(HoodieWriteConfig.TABLE_NAME, “hoodie.users”) .option(RECORDKEY_FIELD_OPT_KEY(), "userID") .option(PARTITIONPATH_FIELD_OPT_KEY(),"country") .option(PRECOMBINE_FIELD_OPT_KEY(), "last_mod") .option(OPERATION_OPT_KEY(), UPSERT_OPERATION_OPT_VAL()) .mode(SaveMode.Append); .save(“/path/on/dfs”)
  • 19. // Deltastreamer command to ingest sqoop incrementals spark-submit --class com.uber.hoodie.utilities.deltastreamer.HoodieDeltaStreamer /path/to/hoodie-utilities-*-SNAPSHOT.jar` --props s3://path/to/dfs-source.properties --schemaprovider-class com.uber.hoodie.utilities.schema.FilebasedSchemaProvider --source-class com.uber.hoodie.utilities.sources.AvroDFSSource --source-ordering-field last_mod --target-base-path s3:///path/on/dfs --target-table uber.employees --op UPSERT // dfs-source-properties include=base.properties # Key generator props hoodie.datasource.write.recordkey.field=_userID hoodie.datasource.write.partitionpath.field=country # Schema provider props hoodie.deltastreamer.filebased.schemaprovider.source.schema.file=s3:///path/to/users.avsc # DFS Source hoodie.deltastreamer.source.dfs.root=s3:///tmp/sqoop
  • 22. // Deltastreamer command to ingest kafka events, dedupe, ingest spark-submit --class com.uber.hoodie.utilities.deltastreamer.HoodieDeltaStreamer /path/to/hoodie-utilities-*-SNAPSHOT.jar` --props s3://path/to/kafka-source.properties --schemaprovider-class com.uber.hoodie.utilities.schema.SchemaRegistryProvider --source-class com.uber.hoodie.utilities.sources.AvroKafkaSource --source-ordering-field time --target-base-path s3:///hoodie-deltastreamer/impressions --target-table uber.impressions --op BULK_INSERT --filter-dupes // kafka-source-properties include=base.properties # Key fields, for kafka example hoodie.datasource.write.recordkey.field=id hoodie.datasource.write.partitionpath.field=datestr # schema provider configs hoodie.deltastreamer.schemaprovider.registry.url=http://localhost:8081/subjects/impressions-value/v ersions/latest # Kafka Source hoodie.deltastreamer.source.kafka.topic=impressions #Kafka props metadata.broker.list=localhost:9092 auto.offset.reset=smallest schema.registry.url=http://localhost:8081
  • 24. New Data Unaffected Data Updated Datachanges Source table ETL table A ETL table B
  • 25. // Spark Datasource Import com.uber.hoodie.{DataSourceWriteOptions, DataSourceReadOptions}._ // Use Spark datasource to read avro Dataset<Row> hoodieIncViewDF = spark.read().format("com.uber.hoodie") .option(VIEW_TYPE_OPT_KEY(), VIEW_TYPE_INCREMENTAL_OPT_VAL()) .option(DataSourceReadOptions.BEGIN_INSTANTTIME_OPT_KEY(),commitInstantFor8AM) .load(“s3://tables/raw_trips”); Dataset<Row> stdDF = standardize_fare(hoodieIncViewDF) // save it as a Hoodie dataset inputDataset.write.format(“com.uber.hoodie”) .option(HoodieWriteConfig.TABLE_NAME, “hoodie.std_trips”) .option(RECORDKEY_FIELD_OPT_KEY(), "id") .option(PARTITIONPATH_FIELD_OPT_KEY(),"datestr") .option(PRECOMBINE_FIELD_OPT_KEY(), "time") .option(OPERATION_OPT_KEY(), UPSERT_OPERATION_OPT_VAL()) .mode(SaveMode.Append); .save(“/path/on/dfs”)