How to calculate lag difference in Spark Structured Streaming?
I am writing a Spark Structured Streaming program. I need to create an additional column with the lag difference.
To reproduce my issue, I provide the code snippet. This code consumes data.json file stored in data folder:
[
{"id": 77,"type": "person","timestamp": 1532609003},
{"id": 77,"type": "person","timestamp": 1532609005},
{"id": 78,"type": "crane","timestamp": 1532609005}
]
Code:
from pyspark.sql import SparkSession
import pyspark.sql.functions as func
from pyspark.sql.window import Window
from pyspark.sql.types import *
spark = SparkSession
.builder
.appName("Test")
.master("local[2]")
.getOrCreate()
schema = StructType([
StructField("id", IntegerType()),
StructField("type", StringType()),
StructField("timestamp", LongType())
])
ds = spark
.readStream
.format("json")
.schema(schema)
.load("data/")
diff_window = Window.partitionBy("id").orderBy("timestamp")
ds = ds.withColumn("prev_timestamp", func.lag(ds.timestamp).over(diff_window))
query = ds
.writeStream
.format('console')
.start()
query.awaitTermination()
I get this error:
pyspark.sql.utils.AnalysisException: u'Non-time-based windows are not
supported on streaming DataFrames/Datasets;;nWindow
[lag(timestamp#71L, 1, null) windowspecdefinition(host_id#68,
timestamp#71L ASC NULLS FIRST, ROWS BETWEEN 1 PRECEDING AND 1
PRECEDING) AS prev_timestamp#129L]
apache-spark pyspark apache-spark-sql spark-structured-streaming
asked Nov 23 '18 at 16:58
MozimakiMozimaki
474
add a comment |
I am writing a Spark Structured Streaming program. I need to create an additional column with the lag difference.
To reproduce my issue, I provide the code snippet. This code consumes data.json file stored in data folder:
[
{"id": 77,"type": "person","timestamp": 1532609003},
{"id": 77,"type": "person","timestamp": 1532609005},
{"id": 78,"type": "crane","timestamp": 1532609005}
]
Code:
from pyspark.sql import SparkSession
import pyspark.sql.functions as func
from pyspark.sql.window import Window
from pyspark.sql.types import *
spark = SparkSession
.builder
.appName("Test")
.master("local[2]")
.getOrCreate()
schema = StructType([
StructField("id", IntegerType()),
StructField("type", StringType()),
StructField("timestamp", LongType())
])
ds = spark
.readStream
.format("json")
.schema(schema)
.load("data/")
diff_window = Window.partitionBy("id").orderBy("timestamp")
ds = ds.withColumn("prev_timestamp", func.lag(ds.timestamp).over(diff_window))
query = ds
.writeStream
.format('console')
.start()
query.awaitTermination()
I get this error:
pyspark.sql.utils.AnalysisException: u'Non-time-based windows are not
supported on streaming DataFrames/Datasets;;nWindow
[lag(timestamp#71L, 1, null) windowspecdefinition(host_id#68,
timestamp#71L ASC NULLS FIRST, ROWS BETWEEN 1 PRECEDING AND 1
PRECEDING) AS prev_timestamp#129L]
apache-spark pyspark apache-spark-sql spark-structured-streaming
asked Nov 23 '18 at 16:58
MozimakiMozimaki
474
add a comment |
I am writing a Spark Structured Streaming program. I need to create an additional column with the lag difference.
To reproduce my issue, I provide the code snippet. This code consumes data.json file stored in data folder:
[
{"id": 77,"type": "person","timestamp": 1532609003},
{"id": 77,"type": "person","timestamp": 1532609005},
{"id": 78,"type": "crane","timestamp": 1532609005}
]
Code:
from pyspark.sql import SparkSession
import pyspark.sql.functions as func
from pyspark.sql.window import Window
from pyspark.sql.types import *
spark = SparkSession
.builder
.appName("Test")
.master("local[2]")
.getOrCreate()
schema = StructType([
StructField("id", IntegerType()),
StructField("type", StringType()),
StructField("timestamp", LongType())
])
ds = spark
.readStream
.format("json")
.schema(schema)
.load("data/")
diff_window = Window.partitionBy("id").orderBy("timestamp")
ds = ds.withColumn("prev_timestamp", func.lag(ds.timestamp).over(diff_window))
query = ds
.writeStream
.format('console')
.start()
query.awaitTermination()
I get this error:
pyspark.sql.utils.AnalysisException: u'Non-time-based windows are not
supported on streaming DataFrames/Datasets;;nWindow
[lag(timestamp#71L, 1, null) windowspecdefinition(host_id#68,
timestamp#71L ASC NULLS FIRST, ROWS BETWEEN 1 PRECEDING AND 1
PRECEDING) AS prev_timestamp#129L]
apache-spark pyspark apache-spark-sql spark-structured-streaming
asked Nov 23 '18 at 16:58
MozimakiMozimaki
474
I am writing a Spark Structured Streaming program. I need to create an additional column with the lag difference.
To reproduce my issue, I provide the code snippet. This code consumes data.json file stored in data folder:
[
{"id": 77,"type": "person","timestamp": 1532609003},
{"id": 77,"type": "person","timestamp": 1532609005},
{"id": 78,"type": "crane","timestamp": 1532609005}
]
Code:
from pyspark.sql import SparkSession
import pyspark.sql.functions as func
from pyspark.sql.window import Window
from pyspark.sql.types import *
spark = SparkSession
.builder
.appName("Test")
.master("local[2]")
.getOrCreate()
schema = StructType([
StructField("id", IntegerType()),
StructField("type", StringType()),
StructField("timestamp", LongType())
])
ds = spark
.readStream
.format("json")
.schema(schema)
.load("data/")
diff_window = Window.partitionBy("id").orderBy("timestamp")
ds = ds.withColumn("prev_timestamp", func.lag(ds.timestamp).over(diff_window))
query = ds
.writeStream
.format('console')
.start()
query.awaitTermination()
I get this error:
pyspark.sql.utils.AnalysisException: u'Non-time-based windows are not
supported on streaming DataFrames/Datasets;;nWindow
[lag(timestamp#71L, 1, null) windowspecdefinition(host_id#68,
timestamp#71L ASC NULLS FIRST, ROWS BETWEEN 1 PRECEDING AND 1
PRECEDING) AS prev_timestamp#129L]
apache-spark pyspark apache-spark-sql spark-structured-streaming
apache-spark pyspark apache-spark-sql spark-structured-streaming
asked Nov 23 '18 at 16:58
MozimakiMozimaki
474
asked Nov 23 '18 at 16:58
MozimakiMozimaki
474
edited Nov 23 '18 at 18:32
Mozimaki
asked Nov 23 '18 at 16:58
MozimakiMozimaki
474
asked Nov 23 '18 at 16:58
MozimakiMozimaki
474
asked Nov 23 '18 at 16:58
MozimakiMozimaki
474
474
add a comment |
add a comment |
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