zuyanton opened a new issue #1829:
URL: https://github.com/apache/hudi/issues/1829
Hudi MoR reading performance gets slower on tables with many (1000+)
partitions stored in S3. When running simple ```spark.sql("select * from
table_ro).count``` command, we observe in spark UI that first 2.5 minutes no
spark jobs gets scheduled and the load on cluster during that period is almost
non existing.
When looking into logs to figure out what is going on during that period we
observe that first two and a half minutes Hudi is busy running
```HoodieParquetInputFormat.listStatus``` [code
link](https://github.com/apache/hudi/blob/master/hudi-hadoop-mr/src/main/java/org/apache/hudi/hadoop/HoodieParquetInputFormat.java#L68).
I placed timer logs lines around various parts of that function and was able
to narrow down to this line
https://github.com/apache/hudi/blob/f5dc8ca733014d15a6d7966a5b6ae4308868adfa/hudi-hadoop-mr/src/main/java/org/apache/hudi/hadoop/HoodieParquetInputFormat.java#L103
this line execution takes over 2/3 of all time.
If I understand correctly what this line does it lists all files in a single
partition.
Looks like this "overhead" is linearly depends on number of partitions as
increasing number of partitions to 2000 almost doubles the overhead and cluster
just runs ```HoodieParquetInputFormat.listStatus``` before starting executing
any spark jobs.
**To Reproduce**
see code snippet bellow
* Hudi version : master branch
* Spark version : 2.4.4
* Hive version : 2.3.6
* Hadoop version : 2.8.5
* Storage (HDFS/S3/GCS..) : S3
* Running on Docker? (yes/no) : no
**Additional context**
```
import org.apache.spark.sql.functions._
import org.apache.hudi.hive.MultiPartKeysValueExtractor
import org.apache.hudi.QuickstartUtils._
import scala.collection.JavaConversions._
import org.apache.spark.sql.SaveMode
import org.apache.hudi.DataSourceReadOptions._
import org.apache.hudi.DataSourceWriteOptions._
import org.apache.hudi.DataSourceWriteOptions
import org.apache.hudi.config.HoodieWriteConfig._
import org.apache.hudi.config.HoodieWriteConfig
import org.apache.hudi.keygen.ComplexKeyGenerator
import org.apache.hadoop.hive.conf.HiveConf
val hiveConf = new HiveConf()
val hiveMetastoreURI =
hiveConf.get("hive.metastore.uris").replaceAll("thrift://", "")
val hiveServer2URI = hiveMetastoreURI.substring(0,
hiveMetastoreURI.lastIndexOf(":"))
var hudiOptions = Map[String,String](
HoodieWriteConfig.TABLE_NAME → "testTable1",
"hoodie.consistency.check.enabled"->"true",
"hoodie.compact.inline.max.delta.commits"->"100",
"hoodie.compact.inline"->"true",
DataSourceWriteOptions.STORAGE_TYPE_OPT_KEY -> "MERGE_ON_READ",
DataSourceWriteOptions.RECORDKEY_FIELD_OPT_KEY -> "pk",
DataSourceWriteOptions.KEYGENERATOR_CLASS_OPT_KEY ->
classOf[ComplexKeyGenerator].getName,
DataSourceWriteOptions.PARTITIONPATH_FIELD_OPT_KEY ->"partition",
DataSourceWriteOptions.PRECOMBINE_FIELD_OPT_KEY -> "sort_key",
DataSourceWriteOptions.HIVE_SYNC_ENABLED_OPT_KEY → "true",
DataSourceWriteOptions.HIVE_TABLE_OPT_KEY → "testTable1",
DataSourceWriteOptions.HIVE_PARTITION_FIELDS_OPT_KEY → "partition",
DataSourceWriteOptions.HIVE_PARTITION_EXTRACTOR_CLASS_OPT_KEY →
classOf[MultiPartKeysValueExtractor].getName,
DataSourceWriteOptions.HIVE_URL_OPT_KEY
->s"jdbc:hive2://$hiveServer2URI:10000"
)
spark.sql("drop table if exists testTable1_ro")
spark.sql("drop table if exists testTable1_rt")
var seq = Seq((1, 2, 3))
for (i<- 2 to 1000) {
seq = seq :+ (i, i , 1)
}
var df = seq.toDF("pk", "partition", "sort_key")
//create table
df.write.format("org.apache.hudi").options(hudiOptions).mode(SaveMode.Append).save("s3://testBucket/test/hudi/zuyanton/1/testTable1")
//update table couple times
df.write.format("org.apache.hudi").options(hudiOptions).mode(SaveMode.Append).save("s3://testBucket/test/hudi/zuyanton/1/testTable1")
df.write.format("org.apache.hudi").options(hudiOptions).mode(SaveMode.Append).save("s3://testBucket/test/hudi/zuyanton/1/testTable1")
//read table
spark.sql("select * from testTable_ro").count
```
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