如何计算Spark结构化流中的滞后差异? [英] How to calculate lag difference in Spark Structured Streaming?

问题描述

我正在编写一个Spark结构化流程序.我需要创建一个带有滞后差的附加列.

I am writing a Spark Structured Streaming program. I need to create an additional column with the lag difference.

为重现我的问题，我提供了代码段.此代码使用存储在data文件夹中的data.json文件:

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}
]

代码:

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()

我收到此错误:

pyspark.sql.utils.AnalysisException:非基于时间的窗口不是 在流式数据帧/数据集上受支持;; \ nWindow [lag(timestamp#71L，1，null)windowspecdefinition(host_id#68， timestamp#71L ASC首先为空，行在1个前置字符和1个之间 PRECEDING)为prev_timestamp#129L]

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]

推荐答案

pyspark.sql.utils.AnalysisException:流数据帧/数据集不支持基于非时间的窗口

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

表示您的窗口应基于timestamp列.因此，您每秒都有一个数据点，并且创建一个30s窗口，且stride为10s，结果窗口将创建一个新的window列，其中包含start和end列其中将包含时差为30s的时间戳.

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.

您应该以这种方式使用窗口:

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'))

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