Discover the Latest Features in Apache Spark 4.0

Apache Spark 4.0 is a major update, bringing new APIs, performance improvements, and a more modular design. Here's a summary of what's new and why it matters. In this post, we’ll look at the most exciting new features in Apache Spark 4.0, what they mean for developers and data engineers, and how they prepare Spark for the future.

1. Spark Connect: A New Client-Server Protocol

Spark Connect, introduced in version 3.4, offers a client-server architecture that enables remote connectivity to Spark clusters using the DataFrame API, allowing it to be embedded in modern data applications, IDEs, notebooks, and programming languages. Check out more details at https://practical-software.com/how-spark-connect-enhances-the-future-of-apache-spark-connectivity

Spark Connect has seen significant advancements in Spark 4.0, aiming to achieve a near-parity with "Spark Classic" and enhance its capabilities as a decoupled client-server architecture. Here's a breakdown of what's new for Spark Connect in Spark 4.0:

• Enhanced API Coverage and Compatibility: A major focus has been on expanding the API coverage for Spark Connect to bring it very close to the full functionality of traditional Spark applications, making it much smoother to migrate existing applications to Spark Connect. Switching between using Spark Classic and Spark Connect is now more seamless due to improved compatibility between their Python and Scala APIs. Spark ML functionalities are now supported over Spark Connect, allowing users to leverage Spark's machine learning capabilities remotely.

• Multi-Language Support: Beyond the existing Python and Scala clients, Spark 4.0 introduces new, community-supported Spark Connect clients for Go, Swift, and Rust, significantly broadening the range of languages developers can use to interact with Spark clusters. This expanded language support allows developers to utilize Spark in their preferred language, even outside the JVM ecosystem, via the Connect API.

The spark.api.mode configuration in Apache Spark determines whether an application runs in Spark Classic or Spark Connect mode. Setting it to connect enables Spark Connect, which allows client applications to interact with a remote Spark server. This example demonstrates how to configure spark.api.mode in a PySpark application:

from pyspark.sql import SparkSession

spark = SparkSession.builder \
    .appName("SparkConnectExample") \
    .config("spark.api.mode", "connect") \
    .master("spark://your_spark_master_url") \
    .getOrCreate()

# Your Spark code here
data = spark.read.csv("your_data.csv")
data.show()

spark.stop()

2. Performance and Catalyst Improvements

Spark 4.0 continues to push boundaries in query optimization and execution, which reduced the chances of OOM by providing

• Faster joins and shuffle operations.

• Improved adaptive query execution (AQE).

• Better codegen for complex queries, reducing JVM overhead.

3. Python UDF Performance Improvements

Python is the most popular language for Spark, but Python UDFs have been a performance bottleneck for a while. In Spark 4.0, there were major improvements that resulted in a significant speedup for PySpark workloads using UDFs in large pipelines.

• Vectorized UDFs Using Apache Arrow has broader support

  • Traditional UDFs in Spark process data row-by-row, requiring each row to be serialized and sent from the JVM (Spark engine) to the Python process. The Python UDF then executes on one row, and the results are sent back to the JVM. This process incurs significant overhead due to per-row communication and serialization.

  • With Apache Arrow, Spark can batch rows into a columnar format (Arrow Tables), send entire batches between the JVM and Python at once, and process them using Pandas UDFs (also known as vectorized UDFs). Instead of processing row-by-row, you handle entire Pandas Series or DataFrames in your UDF, which is much faster!

• Better Python-JVM serialization.

• Enhanced error reporting for PySpark.

4. Ecosystem Modernization and Cleanup

• Dropped support for legacy Hive features (e.g. HiveContext, Hive Metastore Dialects and Hive SerDe Support).

• Streamlined dependency management gives up the monolithic JARs and introduces a more modular packaging system, allowing you to include only the components you need.

This means cleaner code bases, smaller builds, and less dependency hell.

Conclusion

Apache Spark 4.0 is not just focused on performance; it’s about adapting Spark for today's cloud-based, data-driven world. Whether you're creating a streaming ETL pipeline, an ML workflow, or a large analytics dashboard, these updates aim to make it faster, more adaptable, and ready for the future.

Already using Spark 4.0? Share your thoughts and benchmarks in the comments!