Error: Spark driver stopped unexpectedly due to memory

Question

I have the below code where I need to reuse the flag from the previous day. So I am running the loop. I can't use the offset here as once I know the flag from the previous day then only I can use it for today. So, this loop runs 1000 times and after this whenever I try to do some operations on 'data_wt_flag1', it takes too much time, and after a while that results in a "spark driver stopped unexpectedly" error. I believe it is due to a memory issue. Is there a better way to write this logic? As I mentioned I can't use offset.

DateList=data.select("Date").distinct().orderBy('AsOfDate').rdd.flatMap(lambda x: x).collect()
Flag_list=[]

data_wt_flag1=spark.createDataFrame(data = [],schema = StructType([]))

for daily_date in DateList:
  print(daily_date)  
  Temp_data_daily=data.filter(col("Date").isin(daily_date))
  Temp_data_daily=Temp_data_daily.withColumn('lag_1',when(col("identifier").isin(Flag_list),1).otherwise(0))
    
  Temp_data_daily=Temp_data_daily.withColumn("condition_1", when(((col('col_1')==1) & ((col('col_2')==1) | (col('lag_1')==1))),1).otherwise(0))
  Flag_list=Temp_data_daily.filter(col('condition_1')==1).select("identifier").distinct().rdd.flatMap(lambda x: x).collect() 
  data_wt_flag1=data_wt_flag1.unionByName(Temp_data_daily,allowMissingColumns=True)

Logic of the code in Word:

If (col_1==1 and (col_2==1 or yesterday(condition_1)==1)) then today(condition_1)=1 otherwise 0.

So for the first date in the data, yesterday(condition_1) will be 0 for all identifiers so I am passing the null flag_list initially in a loop and then it will keep changing in every iteration and will be used to flag the identifier in the next iteration thus creating the lag_condition_1

Below is the sample data. I have only shown the columns which are required.

Identifier	Date	col_1	col_2
ABC	2023-08-20	1	1
GHI	2023-08-20	0	0
ABC	2023-08-21	1	0
GHI	2023-08-21	1	0
ABC	2023-08-22	1	0
GHI	2023-08-22	1	0
ABC	2023-08-23	1	0
GHI	2023-08-23	0	0

Below Table show the desired output.

Identifier	Date	col_1	col_2	lag_condition_1	condition_1
ABC	2023-08-20	1	1	0	1
GHI	2023-08-20	0	0	0	0
ABC	2023-08-21	1	0	1	1
GHI	2023-08-21	1	0	0	0
ABC	2023-08-22	1	0	1	1
GHI	2023-08-22	1	0	0	0
ABC	2023-08-23	1	0	1	1
GHI	2023-08-23	0	0	0	0

Here, for the first date, all lag_1 are zeros as I am passing an empty list. Then for the second date, we will have lag_1=1 for ABC and MNO as it has condition_1=1 in the previous date.

Answer 1

Yeah, a window function is your go-to:

import org.apache.spark.sql.{Row, functions => F}
import org.apache.spark.sql.expressions.Window
import org.apache.spark.sql.types.{DateType, StructType, StructField, StringType, IntegerType}

// Sample data
val data = Seq(
    ("ABC", "2023-08-20", 1),
    ("DEF", "2023-08-20", 0),
    ("GHI", "2023-08-20", 0),
    ("MNO", "2023-08-20", 1),
    ("XYZ", "2023-08-20", 0),
    ("ABC", "2023-08-21", 0),
    ("DEF", "2023-08-21", 1),
    ("GHI", "2023-08-21", 0),
    ("MNO", "2023-08-21", 0),
    ("XYZ", "2023-08-21", 0),
)

// Optional: define the schema
val schema = StructType(Seq(
  StructField("Identifier", StringType, nullable = false),
  StructField("Date", DateType, nullable = false),
  StructField("col_1", IntegerType, nullable = false)
))

// Create RDD of Rows
val rowsRDD = spark.sparkContext.parallelize(data.map { case (id, date, col1) => Row(id, java.sql.Date.valueOf(date), col1) })

// Create DataFrame
var df = spark.createDataFrame(rowsRDD, schema)

// Define operations on your data
val windowSpec = Window.partitionBy("Identifier").orderBy("Date")

df = df.withColumn("lag_1", F.lag("col_1", 1, 0).over(windowSpec))
df = df.withColumn(
  "condition_1",
  F.when(F.col("col_1") === 1 || F.col("lag_1") === 1, 1).otherwise(0)
)

df = df.orderBy("Date", "Identifier")

// Perform an action only once (at the end of your program) to collect results
val result = df.collect()  // Or do df.show() to print out the dataframe

I'm oversimplifying but as a rule of thumb, when you're calling methods of a Spark DataFrame (e.g. .orderBy()) or using stuff from Spark library (e.g. window functions like lag), you're utilising Spark as a distributed data processing tool (as it should be used). And after all the data operations are done, you collect results with collect() or show() to have the final result sent to your driver. As opposed to calling collect() in a loop to fetch intermediate results into an array (Flag_list in your snippet) and then iterating it on your driver for additional processing -- this is usually a sign of a bad practice.