I have two tables; sales and customers. The main table to be queried is sales, but sometimes we will want to get data about the particulae customer and get details about him, so we will have to join customers on the sales table. Therefore, the main filter column will be USER_ID for the table with users. I wanted to partition the table based on the user_id, but Databricks creates a partition file for each of the userids. What I want to do is to divide the table into files that have multiple userids inside them saved consecutively, so for example partition1 will have users with ID 1-1000, partition2 will have users with ID 1001-2000 and so on.
I have the same problem with partitioning by date, as it created a partition file for each day, but I would want to have it stored as for example a 5 day range.
Is there any way to store partitions on the range inside the column? And how to effect how many such partitions get created?
I’ve used df.write.partitionBy(‘column_name’).parquet(‘location’) so far and it created the problem described above.
You can customize the way partitions are created by specifying the partitioning scheme. Instead of using the default partitioning based on the distinct values in the column, you can define your own partitioning function that groups the data based on a range of values in the column.
Below is an example of how you can partition the sales table based on ranges of USER_ID:
from pyspark.sql.functions import *
def user_id_range_partition(user_id):
return floor(user_id / 1000)
sales.write.partitionBy(user_id_range_partition('USER_ID')).parquet('location')
Here, user_id_range_partition function takes a USER_ID value and returns the floor division of the value by 1000, which groups the USER_IDs into ranges of 1000. For example, USER_IDs 1-1000 will be in partition 0, USER_IDs 1001-2000 will be in partition 1, and so on.
Similarly, the same can be done for dates as well -
from pyspark.sql.functions import *
# Define the partitioning function that groups dates into ranges of 5 days
def date_range_partition(date_col):
start_date = to_date(lit('2022-01-01')) # define your own start date
days_since_start = floor((date_col - start_date).cast('int') / 5) * 5
return date_add(start_date, days_since_start)
# Partition the sales table based on date_range_partition function
sales.withColumn('sale_date_range', date_range_partition('SALE_DATE')).write.partitionBy('sale_date_range').parquet('location')
Additionally, you can make use of bucketBy as well. The way it works is it distributes data into a fixed number of buckets based on the hash value of the specified column. This can be useful for evenly distributing data among a fixed number of files, while still allowing for efficient filtering based on the column used for bucketing. For example - you could use bucketBy to distribute sales data among 10 buckets based on the USER_ID column -
from pyspark.sql.functions import floor
# Define the number of buckets and the bucketing column
num_buckets = 10
bucket_column = 'USER_ID'
# Define the bucketing function that hashes USER_ID into one of 10 buckets
def bucket_user_id(user_id):
return user_id % num_buckets
# Bucket the sales table based on the bucket_user_id function and the bucket_column
sales.write.bucketBy(num_buckets, bucket_column, {'numBuckets': num_buckets}).parquet('location')