ApacheHudi性能提升三倍的查询优化

./bin/spark-shell --master 'local[4]' --driver-memory 8G --executor-memory 8G \
  --jars ../../packaging/hudi-spark-bundle/target/hudi-spark3-bundle_2.12-0.10.0.jar \
  --packages org.apache.spark:spark-avro_2.12:2.4.4 \
  --conf 'spark.serializer=org.apache.spark.serializer.KryoSerializer'

import org.apache.hadoop.fs.{FileStatus, Path}
import Scala.collection.JavaConversions._
import org.apache.spark.sql.SaveMode._
import org.apache.hudi.{DataSourceReadOptions, DataSourceWriteOptions}
import org.apache.hudi.DataSourceWriteOptions._
import org.apache.hudi.common.fs.FSUtils
import org.apache.hudi.common.table.HoodieTableMetaClient
import org.apache.hudi.common.util.ClusteringUtils
import org.apache.hudi.config.HoodieClusterinGConfig
import org.apache.hudi.config.HoodieWriteConfig._
import org.apache.spark.sql.DataFrame
import java.util.stream.Collectors
val layoutOptStrategy = "z-order"; // OR "hilbert"
val inputPath = s"file:///${System.getProperty("user.home")}/datasets/amazon_reviews_parquet"
val tableName = s"amazon_reviews_${layoutOptStrategy}"
val outputPath = s"file:///tmp/hudi/$tableName"
def safeTableName(s: String) = s.replace('-', '_')
val commonOpts =
  Map(
    "hoodie.compact.inline" -> "false",
    "hoodie.bulk_insert.shuffle.parallelism" -> "10"
  )
////////////////////////////////////////////////////////////////
// Writing to Hudi
////////////////////////////////////////////////////////////////
val df = spark.read.parquet(inputPath)
df.write.fORMat("hudi")
  .option(DataSourceWriteOptions.TABLE_TYPE.key(), COW_TABLE_TYPE_OPT_VAL)
  .option("hoodie.table.name", tableName)
  .option(PRECOMBINE_FIELD.key(), "review_id")
  .option(RECORDKEY_FIELD.key(), "review_id")
  .option(DataSourceWriteOptions.PARTITIONPATH_FIELD.key(), "product_cateGory")
  .option("hoodie.clustering.inline", "true")
  .option("hoodie.clustering.inline.max.commits", "1")
  // NOTE: Small file limit is intentionally kept _ABOVE_ target file-size max threshold for Clustering,
  // to force re-clustering
  .option("hoodie.clustering.plan.strategy.small.file.limit", String.valueOf(1024 * 1024 * 1024)) // 1Gb
  .option("hoodie.clustering.plan.strategy.target.file.max.bytes", String.valueOf(128 * 1024 * 1024)) // 128Mb
  // NOTE: We're increasing cap on number of file-groups produced as part of the Clustering run to be able to accommodate for the 
  // whole dataset (~33Gb)
  .option("hoodie.clustering.plan.strategy.max.num.groups", String.valueOf(4096))
  .option(HoodieClusteringConfig.LAYOUT_OPTIMIZE_ENABLE.key, "true")
  .option(HoodieClusteringConfig.LAYOUT_OPTIMIZE_STRATEGY.key, layoutOptStrategy)
  .option(HoodieClusteringConfig.PLAN_STRATEGY_SORT_COLUMNS.key, "product_id,customer_id")
  .option(DataSourceWriteOptions.OPERATION.key(), DataSourceWriteOptions.BULK_INSERT_OPERATION_OPT_VAL)
  .option(BULK_INSERT_SORT_MODE.key(), "NONE")
  .options(commonOpts)
  .mode(ErrorIfExists)

////////////////////////////////////////////////////////////////
// Reading
///////////////////////////////////////////////////////////////

// Temp Table w/ Data Skipping DISABLED
val readDf: DataFrame =
  spark.read.option(DataSourceReadOptions.ENABLE_DATA_SKIPPING.key(), "false").format("hudi").load(outputPath)

val rawSnapshotTableName = safeTableName(s"${tableName}_sql_snapshot")

readDf.createOrReplaceTempView(rawSnapshotTableName)


// Temp Table w/ Data Skipping ENABLED
val readDfSkip: DataFrame =
  spark.read.option(DataSourceReadOptions.ENABLE_DATA_SKIPPING.key(), "true").format("hudi").load(outputPath)

val dataSkippingSnapshotTableName = safeTableName(s"${tableName}_sql_snapshot_skipping")

readDfSkip.createOrReplaceTempView(dataSkippingSnapshotTableName)

// Query 1: Total votes by product_category, for 6 months
def runQuery1(tableName: String) = {
  // Query 1: Total votes by product_category, for 6 months
  spark.sql(s"SELECT sum(total_votes), product_category FROM $tableName WHERE review_date > '2013-12-15' AND review_date < '2014-06-01' GROUP BY product_category").show()
}

// Query 2: Average star rating by product_id, for some product
def runQuery2(tableName: String) = {
  spark.sql(s"SELECT avg(star_rating), product_id FROM $tableName WHERE product_id in ('B0184XC75U') GROUP BY product_id").show()
}

// Query 3: Count number of reviews by customer_id for some 5 customers
def runQuery3(tableName: String) = {
  spark.sql(s"SELECT count(*) as num_reviews, customer_id FROM $tableName WHERE customer_id in ('53096570','10046284','53096576','10000196','21700145') GROUP BY customer_id").show()
}

//
// Query 1: Is a "wide" query and hence it's expected to touch a lot of files
//
scala> runQuery1(rawSnapshotTableName)
+----------------+--------------------+
|sum(total_votes)|    product_category|
+----------------+--------------------+
|         1050944|                  PC|
|          867794|             Kitchen|
|         1167489|                Home|
|          927531|            Wireless|
|            6861|               Video|
|           39602| Digital_Video_Games|
|          954924|Digital_Video_Dow...|
|           81876|             Luggage|
|          320536|         Video_Games|
|          817679|              Sports|
|           11451|  Mobile_Electronics|
|          228739|  Home_Entertainment|
|         3769269|Digital_Ebook_Pur...|
|          252273|                Baby|
|          735042|             Apparel|
|           49101|    Major_Appliances|
|          484732|             Grocery|
|          285682|               Tools|
|          459980|         Electronics|
|          454258|            Outdoors|
+----------------+--------------------+
only showing top 20 rows

scala> runQuery1(dataSkippingSnapshotTableName)
+----------------+--------------------+
|sum(total_votes)|    product_category|
+----------------+--------------------+
|         1050944|                  PC|
|          867794|             Kitchen|
|         1167489|                Home|
|          927531|            Wireless|
|            6861|               Video|
|           39602| Digital_Video_Games|
|          954924|Digital_Video_Dow...|
|           81876|             Luggage|
|          320536|         Video_Games|
|          817679|              Sports|
|           11451|  Mobile_Electronics|
|          228739|  Home_Entertainment|
|         3769269|Digital_Ebook_Pur...|
|          252273|                Baby|
|          735042|             Apparel|
|           49101|    Major_Appliances|
|          484732|             Grocery|
|          285682|               Tools|
|          459980|         Electronics|
|          454258|            Outdoors|
+----------------+--------------------+
only showing top 20 rows

//
// Query 2: Is a "pointwise" query and hence it's expected that data-skipping should substantially reduce number 
// of files scanned (as compared to Baseline)
//
// NOTE: That Linear Ordering (as compared to Space-curve based on) will have similar effect on performance reducing
// total # of Parquet files scanned, since we're querying on the prefix of the ordering key
//
scala> runQuery2(rawSnapshotTableName)
+----------------+----------+
|avg(star_rating)|product_id|
+----------------+----------+
|             1.0|B0184XC75U|
+----------------+----------+


scala> runQuery2(dataSkippingSnapshotTableName)
+----------------+----------+
|avg(star_rating)|product_id|
+----------------+----------+
|             1.0|B0184XC75U|
+----------------+----------+

//
// Query 3: Similar to Q2, is a "pointwise" query, but querying other part of the ordering-key (product_id, customer_id)
// and hence it's expected that data-skipping should substantially reduce number of files scanned (as compared to Baseline, Linear Ordering).
//
// NOTE: That Linear Ordering (as compared to Space-curve based on) will _NOT_ have similar effect on performance reducing
// total # of Parquet files scanned, since we're NOT querying on the prefix of the ordering key
//
scala> runQuery3(rawSnapshotTableName)
+-----------+-----------+
|num_reviews|customer_id|
+-----------+-----------+
|         50|   53096570|
|          3|   53096576|
|         25|   10046284|
|          1|   10000196|
|         14|   21700145|
+-----------+-----------+

scala> runQuery3(dataSkippingSnapshotTableName)
+-----------+-----------+
|num_reviews|customer_id|
+-----------+-----------+
|         50|   53096570|
|          3|   53096576|
|         25|   10046284|
|          1|   10000196|
|         14|   21700145|
+-----------+-----------+