Approvers

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

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Current State

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title	Under Discussion

Status

colour	Yellow
title	In Progress

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colour	Red
title	ABANDONED

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colour	Green
title	Completed

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colour	Blue
title	INactive

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Index integrations with query engines

Spark

Presto

...

How to apply query predicates in Hudi?

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

Code Block

language	java

public class HudiExpressionParentNode implements HudiExpression {
   HudiExpression left;
   HudiExpression right;
   
   @override
   boolean evaluate() {
        left.evaluate() && right.evaluate()
   }
}

For LEAF nodes we can create expression which contains the operator and value we are comparing to determine whether the file group may have data relevant to this query. The common search expressions for the leaf nodes:

Equal to - if value in search expression greater than or equal to lower bound and is less than or equal to upper bound in file’s column statistics then true, else false
Less than - if value in search expression is greater than lower bound in file’s column statistics then true, else false
Less than or equal to - if value in search expression is greater than or equal to lower bound in file’s column statistics then true, else false
Greater than - if value in search expression is lower than upper bound in file’s column statistics then true, else false
Greater than or equal to - if value in search expression is lower than or equal to upper bound in file’s column statistics then true, else false

True tells us that there is a possibility that the file contains data which matches the search expression and to include in result set. False tells us that there is no possibility this file contains any data which matches the search expression and to exclude from the results.

Code Block

language	java

public class HudiExpressionLeafNode implements HudiExpression {
   
   Operator op; // (EQ, LT, LTEQ, GT, GTEQ)
   T literal; // (INT, DOUBLE, FLOAT value)
   String column;
   
   @override
   boolean evaluate()
}

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.

Hive

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 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.

Code Block

language	java

public interface HiveStorageHandler extends Configurable {
  public Class<? extends InputFormat> getInputFormatClass();
  public Class<? extends OutputFormat> getOutputFormatClass();
  public Class<? extends SerDe> getSerDeClass();
  public HiveMetaHook getMetaHook();
  public void configureTableJobProperties(
    TableDesc tableDesc,
    Map<String, String> jobProperties);
}

Everything will remain same with input format, output format, and serde classes being used in existing Hudi tables registered in Hive (HoodieParquetInputFormat still being used). HudiStorageHandler would implement HiveStorageHandler and HiveStoragePredicateHandler.

Hive adds the query predicate returned by the Storage Handler to the job configuration. This job configuration is then supplied to the Input Format. It can be fetched and deserialized using the following:

    String hiveFilter = jobConf.get(TableScanDesc.FILTER_EXPR_CONF_STR);
    if (hiveFilter != null) {
      ExprNodeGenericFuncDesc exprNodeDesc = SerializationUtilities
              .deserializeObject(hiveFilter, ExprNodeGenericFuncDesc.class);
      SearchArgument sarg = ConvertAstToSearchArg.create(job, exprNodeDesc);

The SearchArgument contains an ExpressionTree and a list of PredicateLeaf. The ExpressionTree is a tree structure used to define the query predicate. If operator is defined as OR, AND, or NOT this indicates there are children expressions, normally LEAFs.

public class ExpressionTree {
  public enum Operator {OR, AND, NOT, LEAF, CONSTANT}
  private final Operator operator;
  private final List<ExpressionTree> children;
  private int leaf;

If operator in ExpressionTree is defined as LEAF it corresponds to a PredicateLeaf defined in the Search Argument. PredicateLeaf will contain information about the query predicate such as operator, column name, and literal which is being compared

        private final org.apache.hadoop.hive.ql.io.sarg.PredicateLeaf.Operator operator;
        private final Type type;
        private String columnName;
        private final Object literal;
        private final List<Object> literalList;

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

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 14

New Version Current

Key

Approvers

Index integrations with query engines

Spark

Presto

How to apply query predicates in Hudi?

Hive

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

Page tree

Page History

Versions Compared

Old Version 14

New Version Current

Key

Approvers

Index integrations with query engines

Spark

Presto

How to apply query predicates in Hudi?

Hive

Spark

Presto

Rollout/Adoption Plan

Appendix

Generating Parquet metadata table

Querying Parquet metadata table

Generating HFile metadata table

Querying HFile Metadata

Test Plan