lamber-ken edited a comment on issue #1469: [HUDI-686] Implement BloomIndexV2 that does not depend on memory caching URL: https://github.com/apache/incubator-hudi/pull/1469#issuecomment-612500968 Also, index performance has been greatly improved, your idea and design is amazing 👍 👍 👍 @vinothchandar I tested `upsert` 500,0000 records, `bulk_insert` first, then do `upsert` operation with the same dataset 1. Download CSV data with 5M records ``` https://drive.google.com/open?id=1uwJ68_RrKMUTbEtsGl56_P5b_mNX3k2S ``` 2. Run demo command ``` export SPARK_HOME=/work/BigData/install/spark/spark-2.4.4-bin-hadoop2.7 ${SPARK_HOME}/bin/spark-shell \ --driver-memory 6G \ --packages org.apache.hudi:hudi-spark-bundle_2.11:0.5.1-incubating,org.apache.spark:spark-avro_2.11:2.4.4 \ --conf 'spark.serializer=org.apache.spark.serializer.KryoSerializer' export SPARK_HOME=/work/BigData/install/spark/spark-2.4.4-bin-hadoop2.7 ${SPARK_HOME}/bin/spark-shell \ --driver-memory 6G \ --jars `ls packaging/hudi-spark-bundle/target/hudi-spark-bundle_*.*-*.*.*-SNAPSHOT.jar` \ --conf 'spark.serializer=org.apache.spark.serializer.KryoSerializer' import org.apache.spark.sql.functions._ val tableName = "hudi_mor_table" val basePath = "file:///tmp/hudi_mor_table" var inputDF = spark.read.format("csv").option("header", "true").load("file:///work/hudi-debug/2.csv") val hudiOptions = Map[String,String]( "hoodie.insert.shuffle.parallelism" -> "10", "hoodie.upsert.shuffle.parallelism" -> "10", "hoodie.delete.shuffle.parallelism" -> "10", "hoodie.bulkinsert.shuffle.parallelism" -> "10", "hoodie.datasource.write.recordkey.field" -> "tds_cid", "hoodie.datasource.write.partitionpath.field" -> "hit_date", "hoodie.table.name" -> tableName, "hoodie.datasource.write.precombine.field" -> "hit_timestamp", "hoodie.datasource.write.operation" -> "upsert", "hoodie.memory.merge.max.size" -> "2004857600000", "hoodie.index.type" -> "BLOOM_V2", "hoodie.bloom.index.v2.buffer.max.size" -> "102000000000" ) inputDF.write.format("org.apache.hudi"). options(hudiOptions). mode("Append"). save(basePath) spark.read.format("org.apache.hudi").load(basePath + "/2020-03-19/*").count(); ``` <br> ### Performance comparison #### `HoodieBloomIndex`: cost about 20min  #### `HoodieBloomIndexV2`: cost about 3min 
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