Introduction

Apache Spark 3.0 introduced several exciting features aimed at improving performance and scalability for large-scale data processing. One of the standout features is Adaptive Query Execution (AQE), which brings dynamic query optimization to Spark. AQE enables Spark to adjust query plans during execution based on real-time statistics, allowing it to handle a wide range of workloads more efficiently.

In this blog post, we’ll explore how AQE works, the various optimization techniques it introduces, and how to use it to enhance your Spark jobs.

What is Adaptive Query Execution (AQE)?

Adaptive Query Execution (AQE) is a mechanism in Apache Spark that allows the execution engine to dynamically adjust the execution plan based on runtime statistics. The key concept behind AQE is that it can make real-time decisions to optimize the query plan, even after execution has started, based on actual data rather than static optimizations. This dynamic execution process helps Spark overcome limitations of traditional query planning.

AQE addresses several challenges in query optimization, including:

  • Skewed data: In large-scale data processing, some partitions may contain much more data than others, leading to slower processing. AQE detects and mitigates skew during execution.
  • Join strategies: AQE can dynamically choose the best join strategy (broadcast vs. shuffle) based on the size of data partitions.
  • Dynamic partition pruning: AQE can optimize the partition pruning process by analyzing the data during query execution, improving query performance for large datasets.

Key Features of AQE in Spark 3.0

Let’s break down some of the key features introduced by Adaptive Query Execution in Spark 3.0.

1. Dynamic Partition Pruning

Dynamic partition pruning allows Spark to dynamically prune partitions during the query execution phase, based on the data it is processing. This reduces the amount of data read and processed, significantly improving performance. Traditional partition pruning occurs at the planning stage, while AQE can prune partitions on the fly during execution.

For example, when joining two tables, AQE can detect which partitions of the right table need to be scanned based on the data from the left table. This results in less data being read and fewer shuffle operations.

2. Handling Data Skew with AQE

Data skew is a common problem in distributed data processing, where some partitions are much larger than others. This can cause certain tasks to run much longer, affecting the overall job performance. Spark 3.0 addresses this issue by introducing a mechanism to detect and handle skewed data dynamically.

When AQE detects skew, it can split the large partitions into smaller ones or use different strategies to handle the skewed data more efficiently. This helps in reducing the overall job time and improves resource utilization.

3. Dynamic Join Strategy Selection

In Spark 3.0, AQE allows for dynamic selection of the join strategy during query execution. It can choose between a broadcast join and a shuffle join based on the size of the data partitions at runtime.

  • Broadcast join is used when one side of the join is much smaller than the other. Spark will broadcast the smaller table to all workers to avoid expensive shuffle operations.
  • Shuffle join is used when both sides of the join are large, and broadcasting is inefficient. AQE automatically chooses the most efficient join strategy based on the actual partition sizes.

4. Repartitioning for Better Performance

AQE can also repartition the data dynamically during the execution of a query. This helps improve the parallelism and optimize the use of resources during the execution of the query, especially when working with skewed or unevenly distributed data.

For example, if the number of partitions created at the start of the query isn’t optimal, AQE can dynamically adjust it based on the actual data distribution, ensuring that tasks are evenly distributed across all available resources.

How to Enable AQE in Spark 3.0

AQE is disabled by default in Spark 3.0, but it can be easily enabled through configuration settings. To enable AQE, set the following Spark configuration options:

spark.sql.adaptive.enabled=true

You can also control various other AQE-specific configurations to fine-tune the feature, such as:

  • spark.sql.adaptive.shuffle.targetPostShuffleInputSize: Controls the target size of post-shuffle partitions.
  • spark.sql.adaptive.coalescePartitions.enabled: Enables or disables the automatic coalescing of partitions after a shuffle.
  • spark.sql.adaptive.join.enabled: Controls whether AQE can dynamically change join strategies.

Example of AQE in Action

Consider a scenario where you have a large dataset and are performing a join between two tables. Without AQE, Spark might select a shuffle join strategy upfront. However, if AQE is enabled, Spark will dynamically analyze the data and switch to a broadcast join if it detects that one of the tables is small enough.

Here’s an example configuration and code snippet demonstrating AQE in action:

spark.sql.adaptive.enabled=true
spark.sql.adaptive.shuffle.targetPostShuffleInputSize=64MB

from pyspark.sql import SparkSession

spark = SparkSession.builder \
.appName("AQE Example") \
.getOrCreate()

# Enabling AQE
spark.conf.set("spark.sql.adaptive.enabled", "true")

# Sample data for join
left_df = spark.read.json("left_table.json")
right_df = spark.read.json("right_table.json")

# Perform join operation with AQE enabled
result_df = left_df.join(right_df, "key")
result_df.show()

In this example, Spark will automatically decide whether to use a broadcast join or a shuffle join based on the data sizes.

Benefits of AQE

  • Improved query performance: AQE enables dynamic query optimization, which leads to faster query execution by making better decisions during runtime.
  • Reduced resource usage: By handling data skew and choosing optimal join strategies, AQE minimizes the computational overhead and reduces resource consumption.
  • Better scalability: AQE improves the scalability of Spark applications, particularly for large-scale data processing tasks that involve complex queries.

Conclusion

Apache Spark 3.0’s Adaptive Query Execution (AQE) is a powerful feature that provides dynamic query optimization. By enabling AQE, you can enhance the performance of your Spark jobs by intelligently handling data skew, adjusting join strategies, and optimizing partition pruning. These improvements make Spark more efficient for large-scale data processing and ensure better resource utilization.

To fully leverage AQE, ensure you enable it in your Spark configuration and fine-tune it based on your specific use cases. With AQE, Spark 3.0 takes a major step forward in query optimization, enabling faster and more efficient data processing at scale.