What’s New in Apache Spark 4.0? Deep Dive into 2025 Core Updates

Spark 2025 Core Updates Overview

Apache Spark entered a major milestone in 2025, transitioning from the 3.x series to Spark 4.0 released in May. The new version introduces ANSI SQL mode by default, polymorphic UDTFs, Real‑Time Mode for streaming, extensive GPU acceleration, memory‑usage optimizations, and a revamped Python API.

Spark SQL Evolution

Default ANSI SQL Mode

Spark 4.0 enables ANSI SQL mode out‑of‑the‑box, enforcing stricter semantic checks such as throwing exceptions for division‑by‑zero, prohibiting silent overflow, and requiring explicit type casts. This improves data integrity, especially for finance and healthcare workloads.

Semantic errors raise exceptions (e.g., zero‑division).

Strict type‑rule enforcement prevents implicit incompatible casts.

Higher compatibility with the SQL standard eases cross‑platform migrations.

Enhanced SQL Script Programming

Full SQL script support now includes session variables, control‑flow constructs, and a PIPE operator for more readable pipelines.

Session variable example:

SET start_date = (SELECT value FROM settings WHERE name = 'last_copy');
SELECT ${start_date};
SELECT * FROM orders WHERE order_date > ${start_date};

Control‑flow constructs (IF, WHILE, FOR):

SET revenue = 0;
FOR row IN (SELECT amount FROM transactions) DO
  SET revenue = revenue + row.amount;
END FOR;
SELECT revenue AS total_revenue;

PIPE syntax for chaining commands:

SELECT * FROM orders
|> FILTER status = 'completed'
|> GROUP BY customer_id
|> AGGREGATE(total_amount = SUM(amount));

New Data Types and Functions

Spark 4.0 adds VARIANT data type, Collation support for string sorting, and polymorphic UDTFs, expanding the expressive power of SQL queries.

Query Optimizer Upgrade

The Adaptive Query Execution (AQE) engine is rebuilt with the Lightning Engine, GPU acceleration, and memory optimizations, delivering substantial performance gains across workloads.

Memory Management Improvements

Earlier Spark versions used a static memory partition model (storage, execution, user). Spark 4.0 replaces this with a dynamic elastic memory model that intelligently pools memory, reduces fragmentation, and improves off‑heap utilization. New features include automatic off‑heap allocation, prioritized shuffle storage, and UI‑based off‑heap monitoring.

Structured Streaming Real‑Time Mode

Real‑Time Mode replaces the micro‑batch model, achieving end‑to‑end latencies as low as 5 ms and P99 latency under 300 ms, enabling true low‑latency stream processing.

Transform with State API v2

The new API, available in Scala, Java, and Python, offers arbitrary state handling, exactly‑once semantics, incremental checkpoints, and richer state query capabilities comparable to Flink.

State Store Performance Optimizations

SST file reuse reduces disk I/O and cuts state‑update latency by 30‑50 %.

Improved snapshot management automatically compacts small files and speeds up checkpoint creation.

Enhanced RocksDB configuration adds optional fallocate disabling, selectable compression algorithms, and finer‑grained memory controls.

Spark MLlib Enhancements

2025 sees deep integration of AutoML, full GPU support for model training, and upgraded model management and deployment pipelines, positioning Spark as a unified platform for both batch and AI workloads.

Conclusion

With these extensive updates, Spark 4.0 delivers stronger SQL compliance, richer streaming capabilities, smarter resource handling, and accelerated machine‑learning features, making it a compelling choice for modern big‑data applications.