Spark Repartition() vs Coalesce()
Spark repartition() vs coalesce() – repartition() is used to increase or decrease the RDD, DataFrame, Dataset partitions whereas the coalesce() is used to only decrease the number of partitions in an efficient way.
import pyspark
from pyspark.sql import SparkSession
spark = SparkSession.builder.master("local[1]") \
.appName('SplitFile') \
.getOrCreate()
readDF = spark.read.format("csv").option("header", True).option("delimiter", "|").load(
r"C:\Users\ankus\PycharmProjects\pythonProject2\venv\resources\empdata.csv")
print(readDF.count())
print(readDF.rdd.getNumPartitions())
readDF = readDF.repartition(10)
print(readDF.rdd.getNumPartitions())
readDF.printSchema()
readDF.repartition(4,'gender').write.csv(r'C:\Users\ankus\PycharmProjects\pythonProject2\venv\Output','overwrite')
#readDF.coalesce(10).write.save(r'C:\Users\ankus\PycharmProjects\pythonProject2\venv\Output','csv','overwrite',None)
Example 2
import pyspark
from pyspark.sql import SparkSession
spark = SparkSession.builder.master("local[1]") \
.appName('SplitFile') \
.getOrCreate()
rdd = spark.sparkContext.parallelize(range(0,20),6)
print('no of partition is ', rdd.getNumPartitions())
rdd.saveAsTextFile(r'C:\Users\ankus\PycharmProjects\pythonProject2\venv\Output2',)
rdd1.saveAsTextFile("/tmp/partition")
//Writes 6 part files, one for each partition
Partition 1 : 0 1 2
Partition 2 : 3 4 5
Partition 3 : 6 7 8 9
Partition 4 : 10 11 12
Partition 5 : 13 14 15
Partition 6 : 16 17 18 19
Rdd Coalesce
rdd3 = rdd1.coalesce(4)
printl("Repartition size : "+rdd3.partitions.size)
rdd3.saveAsTextFile("/tmp/coalesce")
Partition 1 : 0 1 2
Partition 2 : 3 4 5 6 7 8 9
Partition 4 : 10 11 12
Partition 5 : 13 14 15 16 17 18 19
Rdd Repartition
df2 = df.repartition(6) printl(df2.rdd.partitions.length) Partition 1 : 14 1 5 Partition 2 : 4 16 15 Partition 3 : 8 3 18 Partition 4 : 12 2 19 Partition 5 : 6 17 7 0 Partition 6 : 9 10 11 13
