How to check specific partition data from Spark partitions in Pyspark - pyspark

Question

I have a created two dataframes in pyspark from my hive table as:
data1 = spark.sql("""
SELECT ID, MODEL_NUMBER, MODEL_YEAR ,COUNTRY_CODE
from MODEL_TABLE1 where COUNTRY_CODE in ('IND','CHN','USA','RUS','AUS')
""");
each country is having millions of unique ID in alphanumeric format.
data2 = spark.sql("""
SELECT ID,MODEL_NUMBER, MODEL_YEAR, COUNTRY_CODE
from MODEL_TABLE2 where COUNTRY_CODE in ('IND','CHN')
""");
I want to join both of these dataframe using pyspark on ID column.
How can we re-partition our data so that its get distributed uniformly across the partitions.
Can i use below to reparation my data?
newdf1 = data2.repartition(100, "ID")
newdf2 = data2.repartition(100, "ID")
what would be the best way for partitioning so that join work faster?

Answer 1

As far as I know your approach repartition providing an ID column is correct. Consider the following as proof of concept using spark_partition_id() to get the corrresponding partition id:
Create some dummy data
import pandas as pd
import numpy as np
from pyspark.sql.functions import spark_partition_id
def create_dummy_data():
data = np.vstack([np.random.randint(0, 5, size=10),
np.random.random(10)])
df = pd.DataFrame(data.T, columns=["id", "values"])
return spark.createDataFrame(df)
def show_partition_id(df):
"""Helper function to show partition."""
return df.select(*df.columns, spark_partition_id().alias("pid")).show()
df1 = create_dummy_data()
df2 = create_dummy_data()
Show partition id before repartioning
show_partition_id(df1)
+---+-------------------+---+
| id| values|pid|
+---+-------------------+---+
|1.0| 0.6051170383675885| 0|
|3.0| 0.4613520717857513| 0|
|0.0| 0.797734780966592| 1|
|2.0|0.35594664760134587| 1|
|2.0|0.08223203758144915| 2|
|0.0| 0.3112880092048709| 2|
|4.0| 0.2689639324292137| 3|
|1.0| 0.6466782159542134| 3|
|0.0| 0.8340472796153436| 3|
|4.0| 0.8054752411745659| 3|
+---+-------------------+---+
show_partition_id(df2)
+---+-------------------+---+
| id| values|pid|
+---+-------------------+---+
|4.0| 0.8950517294190533| 0|
|3.0| 0.4084717827425539| 0|
|3.0| 0.798146627431009| 1|
|4.0| 0.8039931522181247| 1|
|3.0| 0.732125135531736| 2|
|0.0| 0.536328329270619| 2|
|1.0|0.25952064363007576| 3|
|2.0| 0.1958334111199559| 3|
|0.0| 0.728098753644471| 3|
|0.0| 0.9825387111807906| 3|
+---+-------------------+---+
Show partition id after repartitioning
show_partition_id(df1.repartition(2, "id"))
+---+-------------------+---+
| id| values|pid|
+---+-------------------+---+
|1.0| 0.6051170383675885| 0|
|3.0| 0.4613520717857513| 0|
|4.0| 0.2689639324292137| 0|
|1.0| 0.6466782159542134| 0|
|4.0| 0.8054752411745659| 0|
|0.0| 0.797734780966592| 1|
|2.0|0.35594664760134587| 1|
|2.0|0.08223203758144915| 1|
|0.0| 0.3112880092048709| 1|
|0.0| 0.8340472796153436| 1|
+---+-------------------+---+
show_partition_id(df2.repartition(2, "id"))
+---+-------------------+---+
| id| values|pid|
+---+-------------------+---+
|4.0| 0.8950517294190533| 0|
|3.0| 0.4084717827425539| 0|
|3.0| 0.798146627431009| 0|
|4.0| 0.8039931522181247| 0|
|3.0| 0.732125135531736| 0|
|1.0|0.25952064363007576| 0|
|0.0| 0.536328329270619| 1|
|2.0| 0.1958334111199559| 1|
|0.0| 0.728098753644471| 1|
|0.0| 0.9825387111807906| 1|
+---+-------------------+---+
After repartitioning, ids 0 and 2 are located on the same partition and the rest is on the other partition.