Apache Spark - Repartitioning 101
Published: 2023-05-06 12:03:48

## What is Repartitioning?

Repartitioning in Spark is the process of redistributing the data across different partitions in a Spark RDD (Resilient Distributed Dataset) or DataFrame. In simpler terms, it is the process of changing the number of partitions that a dataset is divided into. Repartitioning can be useful for improving performance in Spark applications by controlling the distribution of data across the cluster and ensuring that data is evenly distributed across partitions. 

Repartitioning can be done in two ways in Spark: 


1. Coalesce: Coalescing is the process of reducing the number of partitions in an RDD or DataFrame. It combines existing partitions to create larger partitions and is a more efficient way of reducing the number of partitions than using repartition. Coalesce should be used when the data size is reduced significantly as compared to the number of partitions.
2. Repartition: Repartitioning is the process of increasing or decreasing the number of partitions in an RDD or DataFrame. It is a more expensive operation than coalesce as it shuffles data across the network, but it can be used to increase parallelism and optimize the performance of Spark applications. Repartitioning should be used when the data size is increased significantly as compared to the number of partitions.



## Use-cases where repartitioning is used & examples

Some common use-cases for using repartitioning in Spark include:


### Data skew

Repartitioning can be used to address data skew issues, where some partitions have a significantly larger amount of data compared to others. By repartitioning the data and redistributing it evenly across partitions, the workload can be balanced, and processing time can be improved.

#### Example

Suppose you have a dataset of customer transactions, and some customers have many more transactions than others, causing data skew. You want to perform some aggregations on the data, such as summing the total transaction amount for each customer.

Without repartitioning, the data may be unevenly distributed across partitions, leading to some partitions having significantly more data than others. This can result in some partitions taking much longer to process than others, leading to slow overall performance.

To address this data skew, you can repartition the data based on the customer ID column so that each partition contains a more even distribution of customers. This can be done using the `repartition()` method in Spark:


```
val transactions = spark.read.csv("customer_transactions.csv")
val transactionsByCustomer = transactions.groupBy("customer_id").sum("transaction_amount")
val evenlyDistributedData = transactionsByCustomer.repartition(10, "customer_id")
```


In this example, we are repartitioning the data into 10 partitions based on the `customer_id` column to evenly distribute the workload across nodes. By doing this, we can improve performance and avoid data skew issues.


### Join operations

When performing join operations on large datasets, repartitioning can be used to co-partition the data before the join operation. This can improve performance by minimizing data shuffling during the join.

#### Example

Suppose you have two large DataFrames, `df1` and `df2`, that you want to join on a common column `id`. However, the data in both DataFrames is not evenly distributed among partitions, and some partitions may have a significantly larger amount of data than others, causing data skew. To address this, you can use repartitioning to co-partition the data before the join.

First, you can repartition both DataFrames based on the `id` column:


```
df1 = df1.repartition("id")
df2 = df2.repartition("id")
```


This will ensure that both DataFrames are partitioned based on the same column and that data with the same `id` values are stored in the same partitions across both DataFrames.

Then, you can perform the join operation on the repartitioned DataFrames:

```
joined_df = df1.join(df2, "id")
```


Since the data is co-partitioned, the join operation will be more efficient, as data shuffling during the join will be minimized.


### Filtering and sorting

Repartitioning can also be used to optimize filtering and sorting operations. By partitioning the data based on the filter or sort criteria, Spark can perform these operations in parallel across multiple partitions, leading to faster processing times.

#### Example

Suppose we have a large dataset of online customer transactions, with columns such as transaction_id, customer_id, transaction_date, and transaction_amount. We want to filter the dataset to only include transactions made by customers in a particular city and sort the remaining data by transaction date.

To optimize this operation, we can first partition the data by the customer's city using the `repartition()` method:

```
transactions = spark.read.format("csv").load("transactions.csv")

# Repartition the data based on the customer's city
transactions = transactions.repartition("customer_city")

# Filter transactions made by customers in a particular city
city_transactions = transactions.filter("customer_city = 'New York'")

# Sort the remaining data by transaction date
sorted_transactions = city_transactions.sort("transaction_date")
```


By repartitioning the data based on the customer's city, Spark can parallelize the filtering and sorting operations across multiple partitions, improving performance and reducing data shuffling.


### Writing data to disk

Repartitioning can be used to optimize writing data to disk. By reducing the number of partitions and increasing their size, the amount of overhead incurred during writing can be reduced, resulting in faster write times.

#### Example

Suppose we have a large dataset with millions of records that we want to write to disk in CSV format. Initially, the data is partitioned into 100 partitions, each containing 100,000 records.

However, we find that writing the data to disk is taking longer than expected due to the high number of small partitions. To optimize the write operation, we can repartition the data into fewer partitions with a larger size.

We can use the following code to repartition the data:


```
data = data.repartition(10)
```


This will create 10 partitions, each containing 1 million records. By reducing the number of partitions and increasing their size, we can minimize the overhead incurred during writing and improve write performance.

We can then write the data to disk using the `write.csv()` function:


```
data.write.csv("output.csv")
```


This will write the data to a single CSV file with a smaller overhead compared to writing multiple files for each partition.


### Load balancing

Repartitioning can be used to balance the workload across multiple nodes in a cluster. By evenly distributing the data across partitions, the workload can be distributed evenly, leading to more efficient use of cluster resources.

#### Example

Suppose we have a large dataset of customer orders and we want to perform some processing on it in parallel using a cluster of Spark nodes. However, the data is not evenly distributed across the partitions, with some partitions having significantly more data than others. This can result in some nodes being underutilized while others are overloaded.

To address this, we can use repartitioning to evenly distribute the data across partitions. For example, we can use the `repartition` method to increase the number of partitions and redistribute the data:


```
orders = spark.read.csv("orders.csv", header=True)

# Check the current partitioning
print("Number of partitions before repartitioning:", orders.rdd.getNumPartitions())

# Repartition the data to evenly distribute it across partitions
orders = orders.repartition(100)

# Check the new partitioning
print("Number of partitions after repartitioning:", orders.rdd.getNumPartitions())
```


In this example, we start with a DataFrame `orders` that has a default partitioning based on the number of available cores in the Spark cluster. We then use `repartition` to increase the number of partitions to 100 and redistribute the data evenly across them. This can help balance the workload across the cluster and improve performance by ensuring that each node has a similar amount of data to process.