How to calculate lag difference in Spark Structured Streaming?

Question

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]

Answer 1

pyspark.sql.utils.AnalysisException: u'Non-time-based windows are not supported on streaming DataFrames/Datasets

Meaning that your window should be based on a timestamp column. So it you have a data point for each second, and you make a 30s window with a stride of 10s, your resultant window would create a new window column, with start and end columns which will contain timestamps with a difference of 30s.

You should use the window in this way:

words = words.withColumn('date_time', F.col('date_time').cast('timestamp'))

w = F.window('date_time', '30 seconds', '10 seconds')
words = words \
   .withWatermark('date_format', '1 minutes') \
   .groupBy(w).agg(F.mean('value'))