Apache Spark 4.0: What the Performance Improvements Actually Mean for Your Daily Work

Why Spark 4.0 means less time tuning and more time building

I've been running Spark jobs for the past few years, and honestly, some days it feels like I spend more time debugging, fixing, and optimizing than actually building data pipelines. You know the drill, that ETL job that should take 2 hours but somehow stretches to 5, or the dashboard query that times out right before the weekly business review.

When Spark 4.0 was announced, I'll admit I was skeptical. Another major version with promises of better performance? We've heard this before. But after looking at the features and the reported benchmarks and testing it on real workloads, there are some genuine improvements worth talking about.

The Reality Check: Performance Actually Matters

Let's be real for a minute. We all know that one job, the one that runs overnight and occasionally decides to take 8 hours instead of 4, throwing off the entire morning batch. Or the interactive query that works fine on sample data but crawls when you point it at the full dataset.

These aren't edge cases. They are our daily life.

Spark 4.0 tackles some of these everyday frustrations through improvements that actually matter to how we work, not just benchmark numbers in a blog post.

SQL Engine: Less Time Staring at Progress Bars

The SQL engine improvements in Spark 4.0 are probably where you'll notice the biggest difference day to day. The query planner got smarter about making decisions that used to require manual intervention.

Query Planning That Actually Makes Sense

You know those joins where you'd have to add broadcast hints because Spark couldn't figure out that a 100MB lookup table shouldn't be shuffled? The optimizer in 4.0 is better at making these decisions automatically. It's not magic, but it's noticeably better at avoiding obviously bad execution plans.

The SQL UDF optimization is particularly nice if you've been stuck maintaining legacy business logic in SQL functions. Instead of being treated as black boxes, these functions can now be optimized as part of the query plan, for example, this kind of business logic used to kill performance in Spark 3.5, the optimizer couldn't see inside the UDF to understand that it's just a simple case statement, so it treated every function call as an expensive black box operation that couldn't be optimized, pushed down, or parallelized efficiently:

-- 
CREATE FUNCTION apply_discount(price DOUBLE, customer_tier STRING) 
RETURNS DOUBLE
RETURN CASE 
  WHEN customer_tier = 'PREMIUM' THEN price * 0.85
  WHEN customer_tier = 'GOLD' THEN price * 0.90
  ELSE price * 0.95
END;

SELECT product_id, apply_discount(unit_price, tier) as final_price
FROM sales_data s JOIN customers c ON s.customer_id = c.id
WHERE sale_date >= current_date() - interval 30 days;

Semi-Structured Data That Doesn't Hurt

If you're dealing with JSON event data or logs, the new VARIANT data type helps avoid the repeated schema parsing that made these workloads painfully slow. I tested this with some clickstream processing jobs, and the improvement is noticeable, about 40% faster for typical JSON transformation patterns.

Adaptive Query Execution: Finally, Automatic Tuning That Works

AQE existed in Spark 3.x, but it felt like a feature that was "almost there." Spark 4.0's version actually feels production-ready.

Joins That Adapt to Reality

The dynamic join strategy selection is probably the most practically useful improvement. How many times have you written a query assuming tables were certain sizes, only to have the execution plan fall apart when the data distribution wasn't what you expected?

Spark 4.0 can switch join strategies mid-execution based on actual data sizes. That small dimension table that grew larger than expected? The engine can adapt without you having to rewrite the query with different hints.

Skew Handling That Actually Handles Skew

Data skew has been the source of so many "why is this one task taking forever?" debugging sessions. The improved automatic skew mitigation in 4.0 can split oversized partitions on the fly, which means fewer jobs that finish in 10 minutes except for that one straggler task that takes 2 hours. This query used to be a skew nightmare with certain customer distributions:

customer_metrics = (
    transactions_df
    .groupBy("customer_id")
    .agg(
        sum("amount").alias("total_spent"),
        count("*").alias("transaction_count")
    )
    .filter(col("total_spent") > 1000)
)
# Spark 4.0's AQE handles the skew automatically

PySpark: No More "Just Rewrite It in Scala"

I like Python (I am a physicist by education). Most of the data engineers I work with like Python. But we've all had to have that conversation about rewriting performance critical UDFs in Scala because the Python overhead was killing job performance.

Spark 4.0's Arrow integration makes this less of an issue. Python UDFs are genuinely faster, in some cases, dramatically so.

UDFs That Don't Make You Cringe

The Arrow optimization for Python UDFs eliminates a lot of the serialization overhead that made them slow. I tested some feature engineering pipelines that were heavy on custom transformations, and the speedup was significant enough to matter. This kind of complex transformation used to be painfully slow :

from pyspark.sql.functions import pandas_udf
from pyspark.sql.types import DoubleType
import pandas as pd

@pandas_udf(returnType=DoubleType())
def calculate_risk_score(amounts: pd.Series, frequencies: pd.Series, 
                        categories: pd.Series) -> pd.Series:
    # Complex business logic that can't be easily vectorized
    risk_scores = []
    for amount, freq, cat in zip(amounts, frequencies, categories):
        score = base_risk_calculation(amount, freq)
        score = adjust_for_category(score, cat)
        risk_scores.append(score)
    return pd.Series(risk_scores)

# This runs noticeably faster in Spark 4.0
df.withColumn("risk_score", calculate_risk_score("amount", "frequency", "category"))

Python Data Sources That Don't Suck

The new Python Data Source API means you can build custom connectors without the performance penalty. If you're pulling data from internal APIs or unusual data stores, you can now write the connector in Python and still get decent performance.

Spark Connect: Remote Development That Actually Works

One underrated improvement in Spark 4.0 is the maturation of Spark Connect. If you've ever tried to develop Spark applications remotely, running your IDE locally while connecting to a distant cluster, you know the pain of slow feedback loops and flaky connections. Spark Connect introduces a proper client-server architecture that makes remote development actually pleasant. I wrote about it in detail in a previous post. Check it out.

What the Numbers Actually Mean

The benchmark numbers are encouraging. I must say that I didn’t run full-blown benchmarks, so your mileage may vary:

• 30% faster TPC-DS queries

• 2x speedup for Python UDF workloads

• 25% better streaming throughput

More importantly, you might spend less time optimizing and more time building.

The Honest Assessment

Spark 4.0 isn't going to solve all your data engineering problems. You'll still need to think about data modeling, pipeline architecture, and resource planning. Bad code will still be bad code.

But the performance improvements are real and practical. Jobs that used to require manual tuning often work better out of the box. PySpark is finally fast enough that you don't have to apologize for using it. The adaptive optimizations handle a lot of edge cases automatically.

For teams dealing with growing data volumes, tight SLAs, or budget pressure, these improvements can make a meaningful difference in daily operations.

Should You Upgrade?

If you're running Spark 3.5 and performance is important to your work (and when isn't it?), Spark 4.0 is worth the upgrade effort. The performance gains are substantial enough to justify the testing and migration work.

The improvements feel like Spark maturing into a platform that requires less manual intervention and produces more predictable performance. That's valuable for anyone who's tired of being a full-time Spark tuning specialist.