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Filtering Spark Dataframe

By : user2174190
Date : October 18 2020, 08:10 PM
wish helps you Finally, i was able to resolve it.The problem was there was some corrupt data with not all fields present. Firstly, i tried is using pandas by reading the csv files in pandas as:
code :
pd_frame = pd.read_csv('imdb.csv', error_bad_lines=False)
imdb_data= spark.createDataFrame(pd_frame)
imdb_data = spark.read.csv('imdb.csv', header='true', mode='DROPMALFORMED')

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Spark efficiently filtering entries from big dataframe that exist in a small dataframe

By : David Zheng
Date : March 29 2020, 07:55 AM
it should still fix some issue AFAIK its all depends on the size of data you are handling and performance ,

Filtering spark dataframe options

By : pghege
Date : March 29 2020, 07:55 AM
I hope this helps . I am looking at two different dataframe filterings, and i cannot see the difference in what they do: , If we compare the plans, they are exactly the same:
code :
val df = sc.parallelize(Seq((1,"a",123),(2,"b",456))).toDF("col1","col2","col3")

scala> df.filter(df.col("col2").equalTo("b")).explain
== Physical Plan ==
TungstenProject [_1#0 AS col1#3,_2#1 AS col2#4,_3#2 AS col3#5]
 Filter (_2#1 = b)
  Scan PhysicalRDD[_1#0,_2#1,_3#2]

scala> df.filter(col("col2").equalTo("b")).explain
== Physical Plan ==
TungstenProject [_1#0 AS col1#3,_2#1 AS col2#4,_3#2 AS col3#5]
 Filter (_2#1 = b)
  Scan PhysicalRDD[_1#0,_2#1,_3#2]
scala> df.filter(df("col2").equalTo("b")).explain
== Physical Plan ==
TungstenProject [_1#0 AS col1#3,_2#1 AS col2#4,_3#2 AS col3#5]
 Filter (_2#1 = b)
  Scan PhysicalRDD[_1#0,_2#1,_3#2]

scala> df.filter(df("col2") === "b" ).explain
== Physical Plan ==
TungstenProject [_1#0 AS col1#3,_2#1 AS col2#4,_3#2 AS col3#5]
 Filter (_2#1 = b)
  Scan PhysicalRDD[_1#0,_2#1,_3#2]

Filtering a spark DataFrame down to updates only

By : user1201300
Date : March 29 2020, 07:55 AM
Hope this helps Suppose I have a DataFrame in Spark consisting of columns for id, date, and a number of properties (x, y, z, say). Unfortunately the DataFrame is very large. Fortunately, most of the records are "no-change" records, in which the id, x, y, and z are the same, and only the date changes. For example , You can easily use lag. Window
code :
val window = Window.partitionBy($"id").orderBy($"date".asc)
import org.apache.spark.sql.functions.{coalesce, lag, lit}

val keep = coalesce(lag($"x", 1).over(window) =!= $"x", lit(true))

df.withColumn("keep", keep).where($"keep").drop("keep").show

// +--------+---+---+
// |    date| id|  x|
// +--------+---+---+
// |20150101|  1|  1|
// |20150105|  1|  2|
// |20150107|  1|  1|
// +--------+---+---+

spark (Scala) dataframe filtering (FIR)

By : Cassio Couto
Date : March 29 2020, 07:55 AM
Hope that helps Spark 2.0+
In Spark 2.0 and later it is possible to use window function as a input for groupBy. It allows you to specify windowDuration, slideDuration and startTime (offset). It works only with TimestampType column but it is not that hard to find a workaround for that. In your case it will require some additional steps to correct for boundaries but general solution can expressed as shown below:

Spark 1.5.2: Filtering a dataframe in Scala

By : user5364514
Date : March 29 2020, 07:55 AM
it should still fix some issue As you can see in the Column's documentation, you can use the === method to compare column's values with Any type of variable.
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