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Spark: Data Skew

Data skew in Spark occurs when one or a few partitions have much more data than others. It usually happens during shuffling operations (like .join or .agg) when a disproportionate amount of data gets assigned to certain keys.

Skewed data can lead to a few tasks taking much longer to run than others, resulting in inefficient resource utilization and increased overall processing time.

Consequences of Data Skew:

  • Performance Bottleneck: Tasks dealing with larger partitions take disproportionately longer to complete, causing other tasks and resources to idle.

  • Out of Memory (OOM) Errors: Excessive data in certain tasks can lead to memory overflow, causing OOM errors and task failures.

Handling Data Skew

Salting Keys

Salting involves adding a random value to the join key, which helps distribute the data more evenly across the partitions.

Note

Use salting in join operations where you have a skewed key. By appending a random value to the key, you can prevent a large number of values from being mapped to the same key.

Example

If joining on a highly skewed customer ID, append a random number (like customer ID + “_1”, customer ID + “_2”, etc.) to distribute the load.

Quote

Problem: A significant data skew is present with customer_id = 123.

Original Datasets:

| order_id | customer_id | amount |
+----------+-------------+--------+
| O1       | 123         | 150    |
| O2       | 123         | 200    |
| O3       | 456         | 50     |
| O4       | 789         | 100    |
| O5       | 123         | 300    |

| customer_id | name      |
+-------------+-----------+
| 123         | John Doe  |
| 456         | Tom Smith |
| 789         | Bob Brown |

Applying Salting Technique:

  1. Modify customer_id by appending a random number

    | order_id | customer_id_salted | amount |
+----------+--------------------+--------+
| O1       | 123_1              | 150    |
| O2       | 123_2              | 200    |
| O3       | 456_1              | 50     |
| O4       | 789_1              | 100    |
| O5       | 123_1              | 300    |

    | customer_id_salted | name      |
+--------------------+-----------+
| 123_1              | John Doe  |
| 123_2              | John Doe  |
| 456_1              | Tom Smith |
| 789_1              | Bob Brown |

  2. Perform the Join Operation

    After joining ordersDF with customersDF on customer_id_salted, the skew towards customer_id = 123 is reduced as the orders for this customer are now distributed across two keys (123_1 and 123_2).

  3. Remove the Salted Component

    After joining, you can remove the appended random number to get the original customer_id.

Increasing Parallelism

Increasing the level of parallelism can help distribute skewed data across more partitions.

Note

Adjust the spark.default.parallelism and spark.sql.shuffle.partitions properties to increase the number of partitions.

Example

For a large skewed dataset, increasing the shuffle partitions can help distribute data more evenly across the cluster.

Quote

Problem: A significant data skew is present in the sales dataset, particularly for a few region IDs, causing uneven load distribution.

Original Datasets:

| sale_id | region_id | amount |
+---------+-----------+--------+
| S1      | R1        | 500    |
| S2      | R1        | 450    |
| S3      | R2        | 300    |
| S4      | R3        | 200    |
| S5      | R1        | 550    |

| region_id | name  |
+-----------+-------+
| 1         | North |
| 2         | South |
| 3         | East  |

Increasing Parallelism:

  1. Adjust Spark Configurations

    Increase the number of partitions by adjusting spark.sql.shuffle.partitions. Suppose we increase it from the default value to a higher number, say 50.

  2. Perform the Join Operation

    After adjusting the number of partitions, perform the join operation. The sales data will now be distributed across more partitions, reducing the load on individual tasks.

Effect of Increased Parallelism:

  • By increasing the number of partitions, the data for the skewed key (region_id = R1) is spread across more tasks, preventing any single task from being overloaded.
  • This results in more uniform execution times across tasks and better resource utilization, improving overall performance.

Increasing parallelism is a straightforward way to mitigate the impact of data skew in Spark applications. It does not alter the data itself but optimizes how data is distributed and processed across the cluster. This strategy is particularly effective when dealing with large datasets with skewed distributions, as it can lead to more balanced workload distribution and prevent bottlenecks in specific tasks.

Broadcast Join for Skewed Keys

For a skewed join where one side of the join is much smaller than the other, broadcasting the smaller table can prevent skew.

Note

Force a broadcast join for the smaller DataFrame to avoid shuffling the larger DataFrame.

Example

In a join between a large transactions table and a small customers table, broadcasting the customers table can avoid data skew.

Quote

Problem: A significant data skew is present in the product sales dataset for a few customer IDs, leading to inefficient processing during the join operation.

Original Datasets:

| sale_id | customer_id | amount |
+---------+-------------+--------+
| S1      | C123        | 600    |
| S2      | C123        | 450    |
| S3      | C456        | 150    |
| S4      | C789        | 200    |
| S5      | C123        | 250    |

| customer_id | name      |
+-------------+-----------+
| C123        | John Doe  |
| C456        | Tom Smith |
| C789        | Bob Brown |

Using Broadcast Join:

  1. Identify the Smaller DataFrame

    The customersDF is identified as the smaller DataFrame suitable for broadcasting.

  2. Perform the Broadcast Join

    Explicitly broadcast the customersDF during the join operation with salesDF.

    from pyspark.sql.functions import broadcast

joinedDF = (
    salesDF.join(
        broadcast(customersDF),
        on=(salesDF.customer_id == customersDF.customer_id)
    )
)

Effect of Broadcast Join:

  • By broadcasting the smaller customersDF, Spark sends this DataFrame to each node only once, significantly reducing the shuffle needed for the join.
  • The skew in salesDF is less impactful, as the larger dataset does not need to be shuffled. This results in a more efficient join operation, especially when the skew is present in the larger DataFrame.

Utilizing a Broadcast Join is particularly effective in scenarios where one of the DataFrames involved in the join is significantly smaller and can fit into the memory of each worker node. This technique is a powerful tool to combat the challenges posed by data skew, as it avoids the expensive operation of shuffling the larger skewed DataFrame, leading to more efficient and faster join operations in Spark.

Filtering and Splitting Skewed Keys

Identify skewed keys and process them separately from the rest of the data.

Note

Filter out the skewed keys, process them separately, and then union the result with the rest of the processed data.

Example

If a particular customer ID is responsible for most of the data, filter out this ID, perform necessary operations, and then union the result with the operations performed on the rest of the data.

Quote

Problem: In an e-commerce order dataset, there is significant skew due to a few customers placing an unusually high number of orders, resulting in inefficient processing during join operations.

Original Datasets:

| order_id | customer_id | amount |
+----------+-------------+--------+
| O1       | 123         | 150    |
| O2       | 123         | 200    |
| O3       | 456         | 50     |
| O4       | 789         | 100    |
| O5       | 123         | 300    |

| customer_id | name      |
+-------------+-----------+
| 123         | John Doe  |
| 456         | Tom Smith |
| 789         | Bob Brown |

Applying Filtering and Splitting Skewed Keys:

  1. Identify the Skewed Key

    Detect the skewed key (e.g., customer_id = 123) causing the skew.

  2. Split and Process the Data

  3. Filter out the skewed key from the main DataFrame and process these records separately.

  4. Perform join operations on the non-skewed part and the skewed part independently.
  5. Union the results to get the final output.

Effect of Filtering and Splitting Skewed Keys:

  • By separating the skewed data (customer_id = C100), the join operation is more balanced and efficient.
  • The skewed part can be processed using specialized strategies, such as increasing parallelism or using broadcast joins, to manage the heavier load.
  • This approach leads to better performance and resource utilization during the join operation.

Filtering and splitting skewed keys is an effective strategy to address data skew in Spark. It involves identifying the skew-causing keys, processing the skewed and non-skewed data separately, and then combining the results. This method not only alleviates the impact of skew on performance but also offers the flexibility to apply different optimization techniques to the skewed subset of the data. It’s particularly useful in scenarios where a small subset of keys disproportionately affects the performance of join operations.

Custom Partitioning

Use custom partitioning logic to distribute data more evenly across partitions.

Note

Implement a custom partitioner that can distribute data more uniformly, even when dealing with skewed keys.

Example

Create a partitioner that assigns data to partitions based on a custom logic that accounts for the skewness in the key distribution.

Quote

Problem: In a large transaction dataset, there is significant skew due to a few customers having a disproportionately high number of transactions, leading to inefficient processing during join operations.

| transaction_id | customer_id | amount |
+----------------+-------------+--------+
| T1             | 123         | 150    |
| T2             | 123         | 200    |
| T3             | 456         | 50     |
| T4             | 789         | 100    |
| T5             | 123         | 300    |

| customer_id | name    |
+-------------+---------+
| 123         | Alice   |
| 456         | Bob     |
| 789         | Charlie |

Applying Custom Partitioning:

  1. Define Custom Partitioning Logic

    Create a custom partitioner that distributes the data based on some logic that accounts for the skew. For example, you might create partitions based on a hash of the customer_id or use some domain knowledge to distribute the data more evenly.

  2. Apply Custom Partitioning to DataFrames

    Apply this custom partitioning to both transactionsDF and customerDF before the join operation. This ensures that the data for each customer is distributed across multiple partitions if needed.

Effect of Custom Partitioning:

  • By using custom partitioning, the transactions for the heavily skewed customer_id (e.g., C001) are distributed across multiple partitions.
  • This results in a more even distribution of the workload across the cluster, preventing individual tasks from becoming bottlenecks due to processing a large portion of skewed data.
  • Custom partitioning leads to better parallelism and more efficient utilization of cluster resources during the join operation.

Custom partitioning in Spark is a powerful technique to address data skew, especially in large-scale join operations. By distributing data more evenly across partitions based on a custom-defined logic, it mitigates the impact of skew and ensures more balanced processing. This approach requires an understanding of the data distribution and might involve some experimentation to identify the most effective partitioning strategy. However, when properly implemented, custom partitioning can significantly enhance the performance and efficiency of Spark applications dealing with skewed datasets.

References