What’s New in Apache Spark 4.0.0: SQL, Connect, Python & Streaming

Introduction to Apache Spark 4.0.0

Apache Spark 4.0.0 represents a landmark update to one of the industry’s most widely adopted big-data and analytics engines. Released in late May 2025, this version delivers major enhancements across Spark Connect, SQL, Python, and Structured Streaming—while maintaining backward compatibility so existing workloads continue to run without modification. Whether you’re managing petabytes of data in the cloud or experimenting in a local notebook, Spark 4.0.0 brings new capabilities that boost performance, developer productivity, and compliance with ANSI-style SQL semantics.

What’s Changed Under the Hood

At its core, Spark 4.0.0 builds on prior Spark 3.x foundations but shifts several key defaults and subsystems to more modern, standards-compliant behavior. One of the most significant behind-the-scenes updates is that ANSI SQL mode is enabled by default—meaning common errors (divide-by-zero, overflow, invalid casts) now fail fast instead of silently producing nulls or truncated values. This change improves data integrity and paves the way for easier migration of workloads from traditional data warehouses.

Spark Connect—a client-server architecture introduced in Spark 3.5—matures dramatically in 4.0.0, achieving near feature parity with classic Spark execution. Now you can write a Spark Connect application in Python or Scala with the same SQL, DataFrame, ML, and Graph APIs you would on a Spark cluster. In addition, a new lightweight PySpark client (pyspark-client) is now packaged at only 1.5 MB, speeding up containerized deployments and lowering the barrier to entry for Python-only workloads.

Spark Connect and Client‐Server Enhancements

Spark Connect Feature Parity

• In previous Spark 3.x releases, Spark Connect lacked certain SQL functions, ML APIs, or streaming interfaces. Spark 4.0.0 closes those gaps—almost every SQL, DataFrame, MLlib, and Structured Streaming API now works in Connect mode exactly as in classic mode. In practical terms, this means you can point a Spark driver to a remote Spark Connect server and run the same PySpark or Scala code you would locally.

New Client Implementations (Go, Swift, Rust)

• Beyond Python and Scala, Spark 4.0.0 introduces community-supported Connect clients for Go, Swift, and Rust. If your organization builds microservices in Go or mobile prototypes in Swift, you can now submit Spark jobs natively from those environments—no JNI or cross-compilation required. ([databricks.com][2])

Switching Modes with spark.api.mode

• To simplify gradual migration, Spark 4.0.0 adds a new configuration key, spark.api.mode, which lets you toggle between “classic” and “connect” execution at runtime. Teams can test Connect mode on small clusters before rolling out to production, ensuring compatibility without rewriting session setup or query code.

SQL Language Enhancements

ANSI SQL Mode by Default

• Spark 4.0.0 flips the switch on ANSI SQL mode, enforcing standard semantics on operations that previously resulted in silent fails or implicit type coercion. For example, dividing an integer by zero will now throw a runtime exception rather than returning null. Similarly, numeric overflows and invalid date casts produce explicit errors. This stricter behavior boosts data quality and accelerates bug discovery during ETL development.

SQL UDFs (User-Defined Functions)

• While Spark has long supported UDFs in Scala or Python, version 4.0.0 introduces native SQL UDFs that you can define directly in DDL. These SQL UDFs compile under the query optimizer, yielding better performance than interpreted Python or Java functions. Teams can now register reusable SQL macros—such as custom string-parsing logic or business-specific calculations—without stashing code in external JARs or Python wheels.

PIPE Syntax (|>)

• Borrowing from functional languages, Spark 4.0.0 adds a new PIPE operator (|>) in SQL. Instead of nesting subqueries, you can chain transformations in a linear, readable fashion:

  SELECT * FROM my_table |> FILTER(col1 > 100) |> SELECT(col1, col2, my_udf(col3)) |> ORDER BY col2 DESC

  This “pipelined” style reads left-to-right, making complex SQL easier to follow and maintain.

Collation Support for String Comparisons

• Spark now supports language- and accent-aware collation rules for string ordering and equality checks. For global applications, this ability to choose Unicode case-insensitive or accent-insensitive collations (e.g., Unicode_CI_AI) ensures that queries involving multilingual text behave correctly across different locales.

Session Variables & Parameter Markers

• New session-level variables let you store temporary state in a SQL session without resorting to external key-value tables. Combined with named (:var) and unnamed (?) parameter markers, you can now write parameterized queries that prevent SQL injection and simplify dynamic query generation. This is especially helpful in business intelligence dashboards or web apps that pass user input into Spark SQL.

SQL Scripting (Multi-Statement Workflows)

• Spark 4.0.0 introduces SQL scripting, which allows you to run multi-statement pipelines—complete with local variables and conditional logic—entirely in SQL. For example:

  DECLARE threshold INT DEFAULT 1000; CREATE TEMP VIEW filtered AS SELECT * FROM events WHERE count > threshold; INSERT INTO summary_table SELECT date, COUNT(*) FROM filtered GROUP BY date;

  Previously, such control logic often required separate driver code or stored procedures outside Spark. Now, you can move entire ETL jobs into pure SQL scripts, improving maintainability and lowering context switching.

Data Integrity & Developer Productivity

VARIANT Data Type

• Dealing with semi-structured JSON or nested maps becomes simpler with the new VARIANT data type. A VARIANT column can store arbitrary JSON objects, arrays, or mixed types, while still allowing you to query nested fields using standard dot notation. This flexibility streamlines ingestion of varied data feeds—such as clickstream logs or IoT telemetry—without predefining a rigid schema.

Structured Logging (JSON-Formatted Logs)

• Debugging distributed workloads improves thanks to the new structured logging framework. By enabling spark.log.structuredLogging.enabled=true, Spark emits every log line in JSON format, including fields like timestamp, log level, thread ID, and full Mapped Diagnostic Context (MDC). This format integrates seamlessly with modern observability tools—e.g., ELK, Splunk, or Databricks’ native log ingestion—so you can filter, search, and visualize logs with ease.

Java 21 Support & Compatibility

• Apache Spark 4.0.0 adds official support for Java 21, allowing users to run Spark jobs on the latest LTS JVM. In addition, Spark continues to maintain binary compatibility for Java clients, ensuring that existing JARs built on Java 8 or 11 still execute correctly.

Python API Advances

Lightweight PySpark-Client (1.5 MB)

• One of the most talked-about additions is the new PySpark client, packaged as a mere 1.5 MB wheel. Instead of bundling the entire Spark runtime, this client communicates over Spark Connect, dramatically reducing container image sizes for Python-only workloads. Data scientists can now spin up a PySpark notebook container in seconds—making it easier to run interactive analytics on cloud platforms with minimal cold-start times.

Native Plotting on DataFrames

• Spark 4.0.0 adds built-in plotting methods on PySpark DataFrames. You can now call .plot() directly on a DataFrame—Spark will sample or aggregate data as needed and render charts via Plotly under the hood. Common plot types (histogram, scatter, line) are available out of the box. This feature removes the extra step of converting to pandas just to visualize data, so exploratory data analysis feels more seamless:

  df.filter(col("age") > 18).select("age").plot.hist(bins=20, title="Age Distribution")

  Behind the scenes, Spark handles data sampling or shuffling to a single node to render a responsive chart in a notebook.

Python Data Source API

• With Spark 4.0.0, you can write new file format or database connectors entirely in Python—no need for a Java/Scala “shim.” The new Python Data Source API exposes reader and writer interfaces that plug into Spark’s DataSourceV2 framework. If your organization needs to ingest a niche data store (for example, a custom REST API or legacy system) and Python is your team’s lingua franca, you can build and register a connector in pure Python.

Structured Streaming Enhancements

Arbitrary Stateful Processing v2 (transformWithState)

• Spark 4.0.0 introduces transformWithState, a new operator that unifies stateful streaming logic under a richer API. Key features include object-oriented state definitions, composite types, timer support, TTL (time-to-live) for state entries, and built-in schema evolution for state metadata. Checkpointing and rebalancing are improved, making complex streaming pipelines—such as session window tracking or multi-stage aggregations—more robust and easier to maintain.

State Data Source—Reader

• A brand-new “state store” data source lets you query live streaming state as a tabular DataFrame. Rather than digging into low-level state APIs, you can now do something like:

  val stateDF = spark .readStream .format("stateStore") .option("checkpointLocation", "/tmp/checkpoints") .load()

  This “State as a Table” approach gives per-record visibility into exactly how state evolves over time—ideal for debugging tricky stream processing jobs or auditing counters and session windows.

Improved State Store Performance & Logging

• Under the covers, Spark 4.0.0 optimizes the Static Sorted Table (SST) reuse in the state store, reducing disk I/O and improving overall throughput. The checkpoint format has been revamped for faster snapshots and smaller metadata footprints. Additionally, error classification and structured logging give more clarity when state store failures occur—helping you identify corrupt partitions or out-of-memory issues faster.

Additional Noteworthy Features

Spark on Kubernetes (K8s) Operator Enhancements

• Spark 4.0.0 introduces an improved Kubernetes operator that automatically manages the lifecycle of Spark driver and executor pods. New features include dynamic resource scaling, finer-grained pod affinity rules, and more robust handling of node failures. If you run Spark on a Kubernetes cluster, you’ll notice shorter restart times and more predictable resource utilization.

Delta Lake 4.0 Preview Compatibility

• Delta Lake 4.0 (in preview) is designed to run on Spark 4.0.0, introducing versioned table upgrades, new schema evolution flags, and performance optimizations for merge operations. While Delta Lake 4.0 is still in preview, many connectors (Databricks, AWS, Azure) are already shipping Spark 4 support so you can experiment with ACID-compliant data lakes on your Spark 4 cluster.

Machine Learning on Spark Connect

• Spark 4.0.0 extends MLlib support to Spark Connect. Now you can train and save models (e.g., LogisticRegression, RandomForestClassifier) through a Spark Connect session, then serve predictions from your Python or Go microservice. This decouples model training from serving infrastructure, enabling lighter weight deployment patterns.

Swift Client Implementation

• For iOS/macOS developers, the new Swift client lets you submit Spark jobs directly from a Swift application. Although early in adoption, the Swift client opens doors for mobile apps that want to orchestrate Spark pipelines in hybrid analytics architectures (e.g., a SwiftUI dashboard that triggers a Spark job and polls for status).

Upgrade and Compatibility Considerations

Upgrading from Spark 3.x to 4.0.0 is generally straightforward, but there are a few areas to watch:

• ANSI SQL Mode Impacts: Queries that previously assumed silent truncation or default nulls may now error out. Review any ETL SQL that relies on implicit type coercion and adjust gracefully (e.g., wrap expressions in TRY_CAST).
• Deprecated APIs Removed: Some older RDD‐based APIs that were marked for deprecation in Spark 3.5 have been removed in 4.0. If your code references methods like saveAsObjectFile, you’ll need to migrate to the DataFrame-based equivalent.
• Third-Party Libraries: Ensure that any Spark packages, connectors, or UDF libraries you depend on are updated to support Spark 4.0. Many major projects (e.g., Delta Lake, Iceberg, Hive) released compatible versions in May 2025, but check your vendor’s compatibility matrix.
• Java & Scala Versions: If you use Java 8 or Java 11, you can continue running without change. For Java 21 users, you’ll gain access to improved performance and new language features (records, sealed classes) at the cost of ensuring your cluster’s runtime images are updated.
• Kubernetes Operator Version: If you run Spark on K8s, upgrade your Spark operator to at least version 1.6.0 (or whichever release line is tagged for Spark 4.0.0). Running an older operator may cause scheduling failures or incompatibilities with driver pod specs.

How to Get Apache Spark 4.0.0

Spark 4.0.0 is fully open source under the Apache 2.0 license. You can download binary distributions or source code bundles from the official site:

• Download Page: https://spark.apache.org/downloads.html (choose “Spark 4.0.0” from the dropdown).
• Release Notes: https://spark.apache.org/releases/spark-release-4-0-0.html (detailed list of all JIRA tickets and feature descriptions).
• Source on GitHub: Tag v4.0.0 in the Apache Spark GitHub repository: https://github.com/apache/spark/releases/tag/v4.0.0

If you use a managed Spark service—such as Databricks, AWS EMR, or Google Dataproc—look for the runtime or AMI version that corresponds to Spark 4.0.0. For example, Databricks Runtime 17.0 is built on Spark 4.0.0 and available in the community edition or through enterprise subscriptions.

Why Upgrade? The Business Case

• Performance Gains: ANSI SQL mode and structured logging reduce silent data errors and speed up troubleshooting. Many reports and benchmarks show a 10–20% performance improvement on large SQL joins and streaming aggregations due to engine optimizations in Spark 4.0.0.
• Developer Productivity: Native Python plotting and pure-Python connector APIs shorten the feedback loop for data scientists and engineers. Teams no longer need separate BI tools just to visualize DataFrame results.
• Cloud-Native Deployments: The lightweight 1.5 MB PySpark client drastically shrinks container images—ideal for Kubernetes pod startup times and auto-scaling scenarios in the cloud.
• Future-Proof Architecture: Spark Connect’s maturity means you can decouple driver logic from cluster resources, simplifying microservice architectures and enabling polyglot Spark applications. This design is especially useful for organizations that want to centralize Spark cluster management or adopt serverless paradigms.
• Maintainability & Compliance: By defaulting to stricter SQL semantics and adding new data types (like VARIANT), Spark 4.0.0 aligns more closely with data governance requirements, making it easier to pass audits when handling sensitive or financial data.

Tecyfy Takeaway

Apache Spark 4.0.0 is a significant milestone—melding enterprise-grade SQL compliance, a fully mature Spark Connect architecture, and cutting-edge Python usability into a single release. If your data team relies on Spark for ETL, analytics, or ML workloads, upgrading to 4.0.0 will improve reliability, simplify development, and unlock new use cases (e.g., pure-Python connectors, interactive plotting).

To get started:

1. Download Spark 4.0.0 from the official Apache site or choose the equivalent runtime in your cloud provider.
2. Review the release notes for any API deprecations or configuration changes that may affect your jobs (https://spark.apache.org/releases/spark-release-4-0-0.html).
3. Test your existing workloads in a sandbox cluster, paying special attention to SQL queries that may now fail under ANSI mode.
4. Explore the new features—write a simple Spark Connect job in Python, experiment with SQL UDFs, or visualize your DataFrame with .plot() in a notebook.

With its focus on compatibility and developer experience, Spark 4.0.0 lays the foundation for the next wave of big-data innovation. Download it today, and see how the new features can accelerate your data pipelines and analytics workflows.