Bug: Fix structured-streaming java example error
Status: Resolved | Priority: Trivial | Resolution: Fixed
Created: 17/Jan/20 15:48
Affects Version/s: 2.1.1

[http://spark.apache.org/docs/latest/structured-streaming-programming-guide.html#handling-late-data-and-watermarking]

I write code according to this by java and scala.

java
{code:java}
    public static void main(String[] args) throws StreamingQueryException {
        SparkSession spark = SparkSession.builder().appName("test").master("local[*]")
                .config("spark.sql.shuffle.partitions", 1)
                .getOrCreate();        Dataset<Row> lines = spark.readStream().format("socket")
                .option("host", "skynet")
                .option("includeTimestamp", true)
                .option("port", 8888).load();
        Dataset<Row> words = lines.select("timestamp", "value");
        Dataset<Row> count = words.withWatermark("timestamp", "10 seconds")
                .groupBy(functions.window(words.col("timestamp"), "10 seconds", "10 seconds")
                        , words.col("value")).count();
        StreamingQuery start = count.writeStream()
                .outputMode("update")
                .format("console").start();
        start.awaitTermination();    }
{code}
scala

 
{code:java}
 def main(args: Array[String]): Unit = {
    val spark = SparkSession.builder.appName("test").
      master("local[*]").
      config("spark.sql.shuffle.partitions", 1)
      .getOrCreate
    import spark.implicits._
    val lines = spark.readStream.format("socket").
      option("host", "skynet").option("includeTimestamp", true).
      option("port", 8888).load
    val words = lines.select("timestamp", "value")
    val count = words.withWatermark("timestamp", "10 seconds").
      groupBy(window($"timestamp", "10 seconds", "10 seconds"), $"value")
      .count()
    val start = count.writeStream.outputMode("update").format("console").start
    start.awaitTermination()
  }
{code}
This is according to official documents. written in Java I found metrics "stateOnCurrentVersionSizeBytes" always increase .but scala is ok.

 

java

 
{code:java}
== Physical Plan ==
WriteToDataSourceV2 org.apache.spark.sql.execution.streaming.sources.MicroBatchWriter@4176a001
+- *(4) HashAggregate(keys=[window#11, value#0], functions=[count(1)], output=[window#11, value#0, count#10L])
   +- StateStoreSave [window#11, value#0], state info [ checkpoint = file:/C:/Users/chenhao/AppData/Local/Temp/temporary-63acf9b1-9249-40db-ab33-9dcadf5736aa/state, runId = d38b8fee-6cd0-441c-87da-a4e3660856a3, opId = 0, ver = 5, numPartitions = 1], Update, 1579274372624, 2
      +- *(3) HashAggregate(keys=[window#11, value#0], functions=[merge_count(1)], output=[window#11, value#0, count#21L])
         +- StateStoreRestore [window#11, value#0], state info [ checkpoint = file:/C:/Users/chenhao/AppData/Local/Temp/temporary-63acf9b1-9249-40db-ab33-9dcadf5736aa/state, runId = d38b8fee-6cd0-441c-87da-a4e3660856a3, opId = 0, ver = 5, numPartitions = 1], 2
            +- *(2) HashAggregate(keys=[window#11, value#0], functions=[merge_count(1)], output=[window#11, value#0, count#21L])
               +- Exchange hashpartitioning(window#11, value#0, 1)
                  +- *(1) HashAggregate(keys=[window#11, value#0], functions=[partial_count(1)], output=[window#11, value#0, count#21L])
                     +- *(1) Project [named_struct(start, precisetimestampconversion(((((CASE WHEN (cast(CEIL((cast((precisetimestampconversion(timestamp#1, TimestampType, LongType) - 0) as double) / 1.0E7)) as double) = (cast((precisetimestampconversion(timestamp#1, TimestampType, LongType) - 0) as double) / 1.0E7)) THEN (CEIL((cast((precisetimestampconversion(timestamp#1, TimestampType, LongType) - 0) as double) / 1.0E7)) + 1) ELSE CEIL((cast((precisetimestampconversion(timestamp#1, TimestampType, LongType) - 0) as double) / 1.0E7)) END + 0) - 1) * 10000000) + 0), LongType, TimestampType), end, precisetimestampconversion(((((CASE WHEN (cast(CEIL((cast((precisetimestampconversion(timestamp#1, TimestampType, LongType) - 0) as double) / 1.0E7)) as double) = (cast((precisetimestampconversion(timestamp#1, TimestampType, LongType) - 0) as double) / 1.0E7)) THEN (CEIL((cast((precisetimestampconversion(timestamp#1, TimestampType, LongType) - 0) as double) / 1.0E7)) + 1) ELSE CEIL((cast((precisetimestampconversion(timestamp#1, TimestampType, LongType) - 0) as double) / 1.0E7)) END + 0) - 1) * 10000000) + 10000000), LongType, TimestampType)) AS window#11, value#0]
                        +- *(1) Filter isnotnull(timestamp#1)
                           +- EventTimeWatermark timestamp#1: timestamp, interval 10 seconds
                              +- LocalTableScan <empty>, [timestamp#1, value#0]

{code}
 

 

scala 

 

 
{code:java}
WriteToDataSourceV2 org.apache.spark.sql.execution.streaming.sources.MicroBatchWriter@4149892c
+- *(4) HashAggregate(keys=[window#11-T10000ms, value#0], functions=[count(1)], output=[window#6-T10000ms, value#0, count#10L])
   +- StateStoreSave [window#11-T10000ms, value#0], state info [ checkpoint = file:/C:/Users/chenhao/AppData/Local/Temp/temporary-8b17f74b-0963-4fee-82cd-2c1e63a75a98/state, runId = dac4413d-5a82-4d61-b134-c81bfab704d8, opId = 0, ver = 7, numPartitions = 1], Update, 1579275214256, 2
      +- *(3) HashAggregate(keys=[window#11-T10000ms, value#0], functions=[merge_count(1)], output=[window#11-T10000ms, value#0, count#21L])
         +- StateStoreRestore [window#11-T10000ms, value#0], state info [ checkpoint = file:/C:/Users/chenhao/AppData/Local/Temp/temporary-8b17f74b-0963-4fee-82cd-2c1e63a75a98/state, runId = dac4413d-5a82-4d61-b134-c81bfab704d8, opId = 0, ver = 7, numPartitions = 1], 2
            +- *(2) HashAggregate(keys=[window#11-T10000ms, value#0], functions=[merge_count(1)], output=[window#11-T10000ms, value#0, count#21L])
               +- Exchange hashpartitioning(window#11-T10000ms, value#0, 1)
                  +- *(1) HashAggregate(keys=[window#11-T10000ms, value#0], functions=[partial_count(1)], output=[window#11-T10000ms, value#0, count#21L])
                     +- *(1) Project [named_struct(start, precisetimestampconversion(((((CASE WHEN (cast(CEIL((cast((precisetimestampconversion(timestamp#1-T10000ms, TimestampType, LongType) - 0) as double) / 1.0E7)) as double) = (cast((precisetimestampconversion(timestamp#1-T10000ms, TimestampType, LongType) - 0) as double) / 1.0E7)) THEN (CEIL((cast((precisetimestampconversion(timestamp#1-T10000ms, TimestampType, LongType) - 0) as double) / 1.0E7)) + 1) ELSE CEIL((cast((precisetimestampconversion(timestamp#1-T10000ms, TimestampType, LongType) - 0) as double) / 1.0E7)) END + 0) - 1) * 10000000) + 0), LongType, TimestampType), end, precisetimestampconversion(((((CASE WHEN (cast(CEIL((cast((precisetimestampconversion(timestamp#1-T10000ms, TimestampType, LongType) - 0) as double) / 1.0E7)) as double) = (cast((precisetimestampconversion(timestamp#1-T10000ms, TimestampType, LongType) - 0) as double) / 1.0E7)) THEN (CEIL((cast((precisetimestampconversion(timestamp#1-T10000ms, TimestampType, LongType) - 0) as double) / 1.0E7)) + 1) ELSE CEIL((cast((precisetimestampconversion(timestamp#1-T10000ms, TimestampType, LongType) - 0) as double) / 1.0E7)) END + 0) - 1) * 10000000) + 10000000), LongType, TimestampType)) AS window#11-T10000ms, value#0]
                        +- *(1) Filter isnotnull(timestamp#1-T10000ms)
                           +- EventTimeWatermark timestamp#1: timestamp, interval 10 seconds
                              +- LocalTableScan <empty>, [timestamp#1, value#0]
{code}
 

 you also can debug in statefulOperators.scala  
{code:java}
  protected def removeKeysOlderThanWatermark(
      storeManager: StreamingAggregationStateManager,
      store: StateStore): Unit = {
    if (watermarkPredicateForKeys.nonEmpty) {
      storeManager.keys(store).foreach { keyRow =>
        if (watermarkPredicateForKeys.get.eval(keyRow)) {
          storeManager.remove(store, keyRow)  //this line
        }
      }
    }
  }
}

{code}
you will find java does not remove old state.

 I think java should write like this
{code:java}
        SparkSession spark = SparkSession.builder().appName("test").master("local[*]")
                .config("spark.sql.shuffle.partitions", 1)
                .getOrCreate();        Dataset<Row> lines = spark.readStream().format("socket")
                .option("host", "skynet")
                .option("includeTimestamp",true)
                .option("port", 8888).load();
        Dataset<Row> words = lines.select("timestamp", "value");
        Dataset<Row> wordsWatermark = words.withWatermark("timestamp", "10 seconds");
        Dataset<Row> count = wordsWatermark
                .groupBy(functions.window(wordsWatermark.col("timestamp"), "10 seconds", "10 seconds")
                        , wordsWatermark.col("value")).count();
        StreamingQuery start = count.writeStream()
                .outputMode("update")
                .format("console").start();
        start.awaitTermination();    }
{code}