Approvers

TBD (This is a WIP RFC, Approvers to be added after it is ready to review)

...

Current State

Status

title	Under Discussion

Status

colour	Yellow
title	In Progress

Status


colour	Red
title	ABANDONED

Status

colour	Green
title	Completed

Status


colour	Blue
title	INactive

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Query predicates are normally constructed in a tree like structure so this will follow same pattern. The proposal is create a mapping utility from “Engine” query predicates to a HudiExpression. This way filtering logic is engine agnostic

For AND and OR operators we can translate to a tree node with left and right expressions. An example is shown below of what the structure would look

...

This way we can call evaluate on the root HudiExpression tree and it will determine whether the entire expression is satisfied for the file group.

...

In order for us to implement predicate push down in Hive we need to have access to the query predicate. Query predicate is not passed to Hive InputFormat by default. HiveStoragePredicateHandler interface needs to be implemented in order to provide query predicate to InputFormat and for this we need to create a custom HiveStorageHandler. Therefore we will be creating new storage handler HudiStorageHandler.

...

We can use this information and the SearchArgument to generate our HudiExpression. Then in HoodieParquetInputFormat.listStatus() after fetching files from FileSystemView we for the remaining file groups we can apply data filter using HudieExpression using column metadata for the remaining file groups.

Spark

Presto

Rollout/Adoption Plan

<What impact (if any) will there be on existing users?>
<If we are changing behavior how will we phase out the older behavior?>
<If we need special migration tools, describe them here.>
<When will we remove the existing behavior?>

Appendix

The below code can be run in spark-shell (or jupyter notebook) to quickly iterate on different formats (TODO integrate with hudi metadata table writer code)

Generating Parquet metadata table

Code Block

title	generate range metadata in parquet format

import org.apache.hudi.common.table.HoodieTableMetaClient
import java.util.Properties

val tableName="test.metadata_test_parquet"
val props = new Properties()
props.put("hoodie.table.name", tableName)
val destPath="hdfs://tables/my_metadata_table_parquet"
HoodieTableMetaClient.initTableAndGetMetaClient(spark.sparkContext.hadoopConfiguration, destPath, props)

import org.apache.hudi.common.table.HoodieSnapshotMetadataClient
import org.apache.hudi.common.table.HoodieTableMetaClient
import java.util.stream.Collectors
import org.apache.parquet.hadoop.ParquetFileReader
import org.apache.parquet.hadoop.metadata.ColumnChunkMetaData
import org.apache.parquet.column.statistics.Statistics
import scala.collection.JavaConverters._
import spark.implicits._


case class FileStats(val minVal: String, val maxVal: String)

case class ColumnFileStats(val partitionPath: String, val fileName: String, val colName: String, val minVal: String, val maxVal: String)

val basePath = "hdfs://tables/my_table1"
val metaClient = new HoodieTableMetaClient(spark.sparkContext.hadoopConfiguration, basePath)
val snapshotClient = new HoodieSnapshotMetadataClient(metaClient)

val partition="2021/02/03"
val files = snapshotClient.getLatestSnapshotFiles(partition).collect(Collectors.toList()).asScala.toSeq


val allmetadata = files.flatMap(filePath => {
    val blocks = ParquetFileReader.readFooter(spark.sparkContext.hadoopConfiguration, filePath).getBlocks().asScala

    blocks.flatMap(b => b.getColumns().asScala.
               map(col => (col.getPath().toDotString(), 
                           FileStats(Objects.toString(col.getStatistics().minAsString()),
                                     Objects.toString(col.getStatistics().maxAsString()))))).toSeq.
               groupBy(x => x._1).mapValues(v => v.map(vv => vv._2).toSeq).
               mapValues(value => FileStats(value.map(_.minVal).min, value.map(_.maxVal).max)).toSeq.
               map(x => ColumnFileStats(partition, filePath.getName(), x._1, x._2.minVal, x._2.maxVal))
})

val sqlContext = new org.apache.spark.sql.SQLContext(spark.sparkContext) 
val rdd = spark.sparkContext.parallelize(allmetadata)
rdd.toDF().write.format("org.apache.hudi").
    option("hoodie.datasource.write.recordkey.field", "partitionPath,fileName,colName").
    option("hoodie.datasource.write.precombine.field", "colName").
    option("hoodie.table.name", "test.metadata_test").
    option("hoodie.datasource.write.operation", "insert").
    option("hoodie.parquet.small.file.limit", "107374182400").
    option("hoodie.datasource.write.keygenerator.class", "org.apache.hudi.keygen.ComplexKeyGenerator").
    mode("Append").
    save(destPath)

Querying Parquet metadata table

Code Block

title	Querying parquet metadata

import System.nanoTime
def profile[R](code: => R, t: Long = nanoTime) = (code, (((nanoTime - t)/1000)).toInt)

val parquetBasePath = new Path("hdfs://tables/my_metadata_table1/default/d95d3fbe-fe66-48fc-8bdc-7db923496916-0_0-55-98_20210327202848.parquet")
val fullSchema = ParquetUtils.readAvroSchema(spark.sparkContext.hadoopConfiguration, parquetBasePath)
val schema = HoodieAvroUtils.generateProjectionSchema(fullSchema,
    java.util.Arrays.asList("partitionPath", "fileName", "colName", "minVal", "maxVal"))

val  keys = new scala.collection.mutable.ListBuffer[String]()

val (count, time) = profile {
    
    AvroReadSupport.setAvroReadSchema(spark.sparkContext.hadoopConfiguration, schema)
    AvroReadSupport.setRequestedProjection(spark.sparkContext.hadoopConfiguration, schema)
    val filterToUse = FilterCompat.get(
        FilterApi.or(
            FilterApi.or(
                  FilterApi.eq(FilterApi.binaryColumn("colName"), Binary.fromString("BASE.city_id")),
                  FilterApi.eq(FilterApi.binaryColumn("colName"), Binary.fromString("ACTIONS.actions.array.source.type"))
            ),
            FilterApi.or(
                FilterApi.eq(FilterApi.binaryColumn("colName"), Binary.fromString("ACTIONS.actions.array.contactType")),
                FilterApi.eq(FilterApi.binaryColumn("colName"), Binary.fromString("ACTIONS.actions.array.destination.type"))
            )
                
        )
    )
    val parquetReader = AvroParquetReader.builder(parquetBasePath).
       withConf(spark.sparkContext.hadoopConfiguration).
       withFilter(filterToUse).
       build()

    var record1 : GenericRecord = parquetReader.read().asInstanceOf[GenericRecord]  
    var count: Long = 1
    while (record1 != null) {
        record1 = parquetReader.read().asInstanceOf[GenericRecord] 
        count+=1
    }
    count
}
"" + (time/1000) + " ms to READ column info from parquet " + count

Generating HFile metadata table

Code Block

title	generate HFile metadata format

import org.apache.hudi.common.table.HoodieTableMetaClient
import java.util.Properties

val tableName=""test.metadata_test_hfile"
val destPath="hdfs://tables/my_metadata_table_hfile"
val props = new Properties()
props.put("hoodie.table.name", tableName)
props.put("hoodie.table.base.file.format", "HFILE")
HoodieTableMetaClient.initTableAndGetMetaClient(spark.sparkContext.hadoopConfiguration, destPath, props)

import java.text.DecimalFormat
import org.apache.hudi.common.table.HoodieSnapshotMetadataClient
import org.apache.hudi.common.table.HoodieTableMetaClient
import java.util.stream.Collectors
import java.util.Objects
import org.apache.parquet.hadoop.ParquetFileReader
import org.apache.parquet.hadoop.metadata.ColumnChunkMetaData
import org.apache.parquet.column.statistics.Statistics
import scala.collection.JavaConverters._
import spark.implicits._


val basePath = "hdfs://tables/my_table1"

case class FileStats(val minVal: String, val maxVal: String)

case class ColumnFileStats(val partitionPath: String, val fileName: String, val colName: String, val minVal: String, val maxVal: String)
    
val metaClient = new HoodieTableMetaClient(spark.sparkContext.hadoopConfiguration, basePath)
val snapshotClient = new HoodieSnapshotMetadataClient(metaClient)

val partition="2021/02/03"
val files = snapshotClient.getLatestSnapshotFiles(partition).collect(Collectors.toList()).asScala.toSeq

val allmetadata = files.flatMap(filePath => {
    val blocks = ParquetFileReader.readFooter(spark.sparkContext.hadoopConfiguration, filePath).getBlocks().asScala

    blocks.flatMap(b => b.getColumns().asScala.
               map(col => (col.getPath().toDotString(), 
                           FileStats(Objects.toString(col.getStatistics().minAsString()),
                                     Objects.toString(col.getStatistics().maxAsString()))))).toSeq.
               groupBy(x => x._1).mapValues(v => v.map(vv => vv._2).toSeq).
               mapValues(value => FileStats(value.map(_.minVal).min, value.map(_.maxVal).max)).toSeq.
               map(x => ColumnFileStats(partition, filePath.getName(), x._1, x._2.minVal, x._2.maxVal))
})

val sqlContext = new org.apache.spark.sql.SQLContext(spark.sparkContext) 
val rdd = spark.sparkContext.parallelize(allmetadata)
rdd.toDF().write.format("org.apache.hudi").
    option("hoodie.datasource.write.recordkey.field", "partitionPath,fileName,colName").
    option("hoodie.datasource.write.precombine.field", "colName").
    option("hoodie.table.name", tableName).
    option("hoodie.datasource.write.operation", "bulk_insert").
    option("hoodie.parquet.small.file.limit", "1073741824").
    option("hoodie.parquet.max.file.size", "1073741824").
    option("hoodie.bulkinsert.shuffle.parallelism", "1").
    option("hoodie.datasource.write.keygenerator.class", "org.apache.hudi.keygen.ComplexKeyGenerator").
    mode("Append").
    save(destPath)
})

Querying HFile Metadata

Note that HFile format key could be stored differently. This is a simple example to measure time taken to read 'N' keys. N could be different for different key formats. (For example, if we choose key = partitionPath and value = range_for_all_files_all_columns_in_the_partition, then we only have to read few keys compared to key = partition+file_name, value = range_for_all_columns_in_file)

Code Block

title	querying hfile range metadata

import System.nanoTime
def profile[R](code: => R, t: Long = nanoTime) = (code, (((nanoTime - t)/1000)).toInt)

import org.apache.hadoop.hbase.io.hfile.CacheConfig
import org.apache.hudi.io.storage.HoodieHFileReader
import org.apache.hadoop.fs.Path
import scala.collection.JavaConverters._


val cacheConfig = new CacheConfig(spark.sparkContext.hadoopConfiguration);
cacheConfig.setCacheDataInL1(false);

val hfilePath = new Path("hdfs://tables/my_metadata_table_hfile/default/53dd4e23-012c-4e3e-91fc-9d5ff6a3bf83-0_0-48-91_20210327201656.hfile")
val reader = new HoodieHFileReader(spark.sparkContext.hadoopConfiguration, hfilePath, cacheConfig);

val keys = reader.readAllRecords().asScala.map(x => x.getFirst())
val keyRange = keys.slice(95000, 100000) // pick desired number of keys

var totalTime: Long = 0
var totalRecords: Long = 0
val reader = new HoodieHFileReader(spark.sparkContext.hadoopConfiguration, hfilePath, cacheConfig)
(keyRange).map { k =>
    val (record, time) = profile { reader.getRecordByKey(k) }
    totalTime += time
    if (record.isPresent()) { totalRecords += 1L}
}
"" + (totalTime/1000) + " ms to LOOKUP HFile, #records: " + totalRecords

Test Plan

<Describe in few sentences how the RFC will be tested. How will we know that the implementation works as expected? How will we know nothing broke?>

...

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Versions Compared

Old Version 16

New Version Current

Key

Approvers

Spark

Presto

Rollout/Adoption Plan

Appendix

Generating Parquet metadata table

Querying Parquet metadata table

Generating HFile metadata table

Querying HFile Metadata

Test Plan

Space shortcuts

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Page History

Versions Compared

Old Version 16

New Version Current

Key

Approvers

Spark

Presto

Rollout/Adoption Plan

Appendix

Generating Parquet metadata table

Querying Parquet metadata table

Generating HFile metadata table

Querying HFile Metadata

Test Plan