Spark Structured streaming - 使用模式从文件中读取时间戳

Question

我正在从事结构化流媒体工作。

我从文件中读取的数据包含时间戳（以毫秒为单位）、deviceId 和该设备报告的值。多个设备报告数据。

我正在尝试编写一个作业，将所有设备发送的值汇总（求和）为 1 分钟的 windows。

我遇到的问题是时间戳。

当我尝试将“timestamp”解析为 Long 时，window 函数抱怨它需要“timestamp type”。当我尝试按照下面的代码片段解析为 TimestampType 时，我得到了 .MatchError 异常（完整的异常可以在下面看到）并且我正在努力弄清楚为什么以及处理它的正确方法是什么

// Create schema
StructType readSchema = new StructType().add("value" , "integer")
                                        .add("deviceId", "long")
                                        .add("timestamp", new TimestampType());

// Read data from file
Dataset<Row> inputDataFrame = sparkSession.readStream()
                                          .schema(readSchema)
                                          .parquet(path);

Dataset<Row> aggregations = inputDataFrame.groupBy(window(inputDataFrame.col("timestamp"), "1 minutes"),
                                                  inputDataFrame.col("deviceId"))
                                          .agg(sum("value"));

异常：

 org.apache.spark.sql.types.TimestampType@3eeac696 (of class org.apache.spark.sql.types.TimestampType)
scala.MatchError: org.apache.spark.sql.types.TimestampType@3eeac696 (of class org.apache.spark.sql.types.TimestampType)
    at org.apache.spark.sql.catalyst.encoders.RowEncoder$.externalDataTypeFor(RowEncoder.scala:215)
    at org.apache.spark.sql.catalyst.encoders.RowEncoder$.externalDataTypeForInput(RowEncoder.scala:212)
    at org.apache.spark.sql.catalyst.expressions.objects.ValidateExternalType.<init>(objects.scala:1692)
    at org.apache.spark.sql.catalyst.encoders.RowEncoder$.$anonfun$serializerFor(RowEncoder.scala:175)
    at scala.collection.TraversableLike.$anonfun$flatMap(TraversableLike.scala:245)
    at scala.collection.IndexedSeqOptimized.foreach(IndexedSeqOptimized.scala:36)
    at scala.collection.IndexedSeqOptimized.foreach$(IndexedSeqOptimized.scala:33)
    at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:198)
    at scala.collection.TraversableLike.flatMap(TraversableLike.scala:245)
    at scala.collection.TraversableLike.flatMap$(TraversableLike.scala:242)
    at scala.collection.mutable.ArrayOps$ofRef.flatMap(ArrayOps.scala:198)
    at org.apache.spark.sql.catalyst.encoders.RowEncoder$.serializerFor(RowEncoder.scala:171)
    at org.apache.spark.sql.catalyst.encoders.RowEncoder$.apply(RowEncoder.scala:66)
    at org.apache.spark.sql.Dataset$.$anonfun$ofRows(Dataset.scala:92)
    at org.apache.spark.sql.SparkSession.withActive(SparkSession.scala:763)
    at org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:89)
    at org.apache.spark.sql.streaming.DataStreamReader.load(DataStreamReader.scala:232)
    at org.apache.spark.sql.streaming.DataStreamReader.load(DataStreamReader.scala:242)
    at org.apache.spark.sql.streaming.DataStreamReader.parquet(DataStreamReader.scala:450)

Answer 1

通常，当您的时间戳以 long 形式存储在 milis 中时，您可以将其转换为 timestamp 类型，如下所示：

// Create schema and keep column 'timestamp' as long
StructType readSchema = new StructType()
    .add("value", "integer")
    .add("deviceId", "long")
    .add("timestamp", "long");

// Read data from file
Dataset<Row> inputDataFrame = sparkSession.readStream()
                                          .schema(readSchema)
                                          .parquet(path);

// convert timestamp column into a proper timestamp type
Dataset<Row> df1 = inputDataFrame.withColumn("new_timestamp", expr("timestamp/1000").cast(DataTypes.TimestampType));

df1.show(false)

+-----+--------+-------------+-----------------------+
|value|deviceId|timestamp    |new_timestamp          |
+-----+--------+-------------+-----------------------+
|1    |1337    |1618836775397|2021-04-19 14:52:55.397|
+-----+--------+-------------+-----------------------+

df1.printSchema();

root
 |-- value: integer (nullable = true)
 |-- deviceId: long (nullable = true)
 |-- timestamp: long (nullable = true)
 |-- new_timestamp: timestamp (nullable = true)

Spark Structured streaming - 使用模式从文件中读取时间戳

Spark Structured streaming - reading timestamp from file using schema

apache-spark

spark-structured-streaming