Spark 4 highlights: a practical Spark & Data Eng guide

Introduction

We're on day 79 of our 100-day Apache Spark and data engineering journey, and it's time to look at what's changed in the ecosystem. Apache Spark has evolved significantly in recent years, and understanding these shifts matters because they directly affect how we build pipelines, handle data quality, and collaborate with teams.

In this article, we'll cover the key Spark 4 highlights: a practical Spark and Data Eng guide that will reshape how you write transformations, deploy applications, and manage stateful streaming workloads. Whether you're maintaining legacy Spark 2.x clusters or planning greenfield projects, these changes offer real productivity gains and sometimes critical behavioral shifts you need to know about.

ANSI SQL Mode by Default

One of the most impactful changes in Spark 4 is the shift to ANSI SQL mode as the default behavior. In earlier Spark versions, type coercion and NULL handling followed Spark's own conventions. Now, by default, Spark aligns with the ANSI SQL standard for operations like type casting, string comparisons, and arithmetic with NULLs.

This matters because it affects existing queries. Consider a scenario where your team has built ETL pipelines that rely on implicit type casting. A query that worked in Spark 3.x might behave differently or fail in Spark 4 if it depends on Spark's lenient casting rules.

Here's a practical example:

from pyspark.sql import SparkSession

spark = SparkSession.builder.appName("ANSI_Example").getOrCreate()

# In Spark 3.x, this might succeed with implicit casting
# In Spark 4, with ANSI SQL mode on, this raises an error
df = spark.createDataFrame([("123abc",)], ["value"])

# This will fail in Spark 4 unless you handle the cast explicitly
result = df.select(df.value.cast("int"))
# result.show()  # Throws an exception in ANSI mode

The fix is to use explicit casting and error handling:

from pyspark.sql.functions import try_cast

# Safe approach: try_cast returns NULL for invalid conversions
result = df.select(try_cast(df.value, "int").alias("value_int"))
result.show()
# +----------+
# |value_int |
# +----------+
# |null      |
# +----------+

You can disable ANSI mode during migration, but we recommend gradually migrating your codebase. Set the config in your SparkSession:

spark = SparkSession.builder \
    .appName("MyApp") \
    .config("spark.sql.ansi.enabled", "false") \
    .getOrCreate()

The long-term benefit is clearer, more portable SQL code. ANSI compliance makes it easier to migrate queries to other SQL engines and reduces surprises when teammates familiar with standard SQL join your projects.

Improved Python APIs

Spark 4 brings significant enhancements to the Python API, particularly around expression composition and DataFrame operations. The Spark team has worked to make Python feel like a first-class citizen, not just a wrapper around Scala.

One notable improvement is the enhanced support for complex expressions and the introduction of SQL expressions as first-class constructs in PySpark. This means you can now write more complex transformations without dropping into raw SQL strings.

Here's a practical scenario: building a complex aggregation pipeline for a metrics table in a SaaS product:

from pyspark.sql import functions as F
from pyspark.sql.window import Window

# Simulating event data: user_id, event_type, value, timestamp
events_df = spark.createDataFrame([
    (1, "purchase", 100.0, "2024-01-01 10:00:00"),
    (1, "click", 0.0, "2024-01-01 10:15:00"),
    (2, "purchase", 50.0, "2024-01-01 11:00:00"),
    (1, "purchase", 200.0, "2024-01-01 11:30:00"),
], ["user_id", "event_type", "value", "event_time"])

# Improved API: cleaner chaining and composition
window_spec = Window.partitionBy("user_id").orderBy("event_time")

result = events_df \
    .filter(F.col("event_type") == "purchase") \
    .withColumn("running_total", F.sum("value").over(window_spec)) \
    .withColumn("purchase_rank", F.row_number().over(window_spec.orderBy(F.desc("value")))) \
    .select("user_id", "value", "running_total", "purchase_rank")

result.show()
# +-------+-------+-------------+-------------+
# |user_id|value  |running_total|purchase_rank|
# +-------+-------+-------------+-------------+
# |1      |100.0  |100.0        |2            |
# |1      |200.0  |300.0        |1            |
# |2      |50.0   |50.0         |1            |
# +-------+-------+-------------+-------------+

Another improvement is better support for pandas interoperability and Arrow optimization. If you're using pandas UDFs (which we recommend over Python row-at-a-time UDFs), Spark 4 provides better performance through improved batching:

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

# Pandas UDF: processes batches efficiently
@pandas_udf("double")
def calculate_discount(value: pd.Series) -> pd.Series:
    return value * 0.9

# Better performance in Spark 4: improved batching, Arrow serialization
result = events_df.withColumn("discounted", calculate_discount(F.col("value")))
result.show()

These improvements reduce the performance penalty of Python-based workloads and make the API more expressive. You'll find yourself dropping into raw SQL strings less often.

Spark Connect Maturity

Spark Connect is now production-ready in Spark 4, and this changes how we think about Spark deployment. Spark Connect decouples the Spark client from the cluster, using a gRPC protocol for communication. In practice, this means:

• You can run Spark code from a Jupyter notebook or Python script without installing the full Spark runtime locally
• Teams can use different languages (Python, Scala, R) on the same remote cluster without compatibility headaches
• Development environments are lighter and deployments are more reproducible

Here's how Spark Connect works in practice. Instead of installing Spark 4.0 locally, you point to a remote Spark Connect server:

from pyspark.sql import SparkSession

# Connect to a remote Spark cluster running Spark Connect
spark = SparkSession.builder \
    .remote("sc://spark-cluster.example.com:15002") \
    .appName("RemoteApp") \
    .getOrCreate()

# Your code is the same; execution happens on the cluster
df = spark.read.parquet("s3://my-bucket/data/")
result = df.groupBy("category").count()
result.show()

This is particularly valuable for teams running multiple Spark clusters. A data engineer can prototype a transformation on a development cluster via Spark Connect from their laptop, then deploy the same code to production without worrying about version mismatches or local environment setup.

The Spark Connect server runs alongside your Spark cluster and handles the session lifecycle. Existing Spark configurations and security (Kerberos, OAuth) work as before. Spark Connect also provides better job cancellation and session isolation, which is useful in shared team environments.

One caveat: not all Spark features are available over Spark Connect yet. Custom RDD operations and certain low-level APIs aren't supported. For most DataFrames and SQL workloads, though, you're safe.

Better Streaming State Management

Streaming state management has always been tricky in Spark. Structured Streaming now offers improved APIs and guarantees for stateful operations, making long-running streaming jobs more reliable.

Consider a real-world scenario: tracking user sessions in an e-commerce platform. Users generate events (page views, clicks, purchases), and we need to group them into sessions with a 30-minute inactivity timeout.

In Spark 3.x, this required custom state management and RDD operations. Spark 4 introduces native session windowing:

from pyspark.sql import functions as F
from pyspark.sql.types import StructType, StructField, StringType, LongType

# Event schema: user_id, event_type, event_time
events_schema = StructType([
    StructField("user_id", StringType()),
    StructField("event_type", StringType()),
    StructField("event_time", LongType())
])

# Simulating streaming source (Kafka, Kinesis, etc.)
streaming_df = spark.readStream \
    .schema(events_schema) \
    .json("/tmp/events/")

# Session windowing: groups events with 30-minute gaps into sessions
result = streaming_df \
    .withColumn("event_ts", F.from_unixtime(F.col("event_time"))) \
    .groupBy(
        F.col("user_id"),
        F.session_window(F.col("event_ts"), "30 minutes").alias("session")
    ) \
    .agg(
        F.collect_list("event_type").alias("events"),
        F.min("event_time").alias("session_start"),
        F.max("event_time").alias("session_end")
    ) \
    .writeStream \
    .outputMode("update") \
    .format("console") \
    .start()

Another improvement is enhanced state store management. The state store is where Spark keeps track of aggregated data for streaming operations (like running counts or joins). Spark 4 offers better control over state cleanup and retention, reducing memory pressure on long-running jobs:

# Improved state management with TTL
result = streaming_df \
    .groupBy(F.window(F.col("event_time"), "5 minutes"), "user_id") \
    .agg(F.count("*").alias("events")) \
    .writeStream \
    .outputMode("update") \
    .option("stateStoreMinBatchesToRetain", "2") \
    .format("kafka") \
    .option("kafka.bootstrap.servers", "localhost:9092") \
    .option("topic", "sessions") \
    .start()

These improvements make streaming pipelines more predictable and easier to tune for production. State memory usage is now visible in Spark UI metrics, helping you identify and fix memory leaks in stateful operations.

Common Mistakes

We've seen teams run into predictable pitfalls when upgrading to Spark 4:

• Not testing ANSI mode changes: Upgrade to Spark 4, disable ANSI mode, run tests, then gradually enable ANSI and fix queries. Don't try to flip the switch all at once on production.
• Assuming Spark Connect is a drop-in replacement: It's not. Test your full workload with Spark Connect in development. Some edge cases around RDD operations, schema inference, and broadcasting won't work the same way.
• Ignoring state store metrics: New streaming state management features are powerful, but they require monitoring. Watch your state store size and GC logs in production. Misconfigured TTL can lead to unbounded state growth.
• Mixing pandas UDFs with Row-based operations**: In Spark 4, pandas UDFs are the way to go for performance. If you're still using `@udf` decorators with Python functions that operate row-by-row, you're leaving performance on the table.

Key Takeaways

• ANSI SQL mode is now default in Spark 4; audit and migrate your SQL code before upgrading, using try_cast for defensive type conversions.
• Python APIs in Spark 4 are significantly improved; favor pandas UDFs and modern DataFrame API over row-at-a-time operations and SQL strings.
• Spark Connect is production-ready and decouples clients from clusters; it's ideal for team development workflows but not suitable for all edge cases (test first).
• Streaming state management is more robust; use native session windowing for user session logic and monitor state store metrics to prevent memory issues in long-running jobs.
• Plan your upgrade in stages: disable ANSI initially, test, then gradually enable it while fixing queries; this reduces risk and gives your team time to adapt.