Apache Spark Streaming - Stream Processing That Actually Works (Mostly)

So you're considering Spark Streaming. Smart - it's one of the few tools that actually delivers on the "unified batch and streaming" promise. But before you dive in, let's talk about what you're really signing up for.

Spark Streaming is Apache's attempt to make you not choose between batch and stream processing. Write your data logic once, run it on both. Sounds great on paper - reality is more complex.

Nobody mentions: it'll eat more memory than you expect, throw OutOfMemoryErrors when you least expect it, and you'll spend your first month figuring out why your "real-time" job takes 30 seconds to process 1 second of data.

DStreams: The Legacy Disaster

DStreams was Spark's first attempt at streaming. It worked, sort of, but had fundamental issues. Apache basically said "fuck it, we're not fixing this anymore" and moved to Structured Streaming. If you're still using DStreams in 2025, you're doing it wrong.

The main problem: DStreams pretended streaming was just tiny batch jobs. This worked until your data arrived out of order, duplicated, or faster than you could process it. Then you'd get memory leaks, inconsistent results, and a lot of 3am debugging sessions.

Structured Streaming: The Do-Over That Works

Structured Streaming is Apache's second attempt, and it's actually pretty good. Built on Spark SQL (which is solid), it treats streams as "unbounded tables" - fancy words for "SQL queries on moving data."

Key improvements over DStreams:

• Exactly-once processing that actually works (most of the time)
• Schema evolution so you don't break everything when someone adds a field
• Watermarking to handle late data without filling up all your RAM
• Better error messages that occasionally help you fix things

But here's the catch: migrating from DStreams to Structured Streaming isn't just changing APIs. The mental model is completely different. Plan for a full rewrite.

Real-Time Mode: Marketing vs Reality

Databricks launched Real-Time Mode in August 2025, claiming "single-digit millisecond latencies." Their demo videos look great. Production reality will be different.

In production? You'll get 10-100ms latency if you're lucky. Still pretty good, but not the marketing numbers. Reality check: if you need actual sub-millisecond latency, use something else.

Memory: The Hidden Cost

Here's what the docs don't emphasize enough: Spark Streaming is hungry. Really hungry. Budget for 3-5x your data size in memory, plus overhead for Spark's internal structures.

Common memory killers:

• State that grows forever because you forgot watermarking
• Small files problem writing thousands of tiny Parquet files
• GC pauses that make your streaming job stutter like a 1990s video
• Driver memory issues when collecting too much data

Spark 4.0: Actually Good Improvements

Apache Spark 4.0 dropped in May 2025 with some solid streaming improvements. The Arbitrary State API v2 is genuinely useful - better state debugging and schema evolution.

Performance improvements are real too - I've seen noticeable latency reductions in production workloads with proper tuning. Your mileage will vary based on your specific use case, but the optimizations actually help.

Production Reality Check

Companies like Netflix use Spark Streaming successfully, but they have teams of engineers tuning it. For the rest of us:

• Expect 2-3 months of tuning before production-ready
• Production considerations are extensive
• Memory issues will bite you repeatedly
• Best practices exist but learning them takes time

Bottom line: Spark Streaming works well when you accept its complexity and resource requirements. It's not the easiest streaming engine, but it handles scale better than most alternatives and integrates with the broader Spark ecosystem. Just don't expect it to be simple.

The dirty truth? Most teams underestimate the operational overhead by 3-5x. You'll need dedicated engineers who understand distributed systems, JVM tuning, and can debug Catalyst query plans. But if you have that expertise and the infrastructure budget, Spark Streaming can handle production workloads that would break simpler tools.

But wait - should you even use Spark Streaming? That depends entirely on your alternatives and specific use case. Here's how it actually stacks up against other streaming platforms.

Spark Streaming's architecture is clever but complex. Here's what actually happens when you deploy this thing in production.

Micro-Batches: The Good and The Ugly

Micro-batches sound cool - chop streams into tiny batches, process them like normal Spark jobs. Works great until:

• Your data arrives faster than you can process it (backpressure hell)
• A batch fails and you're recomputing 3 hours of data from Kafka
• Latency requirements mean batch sizes so small that overhead kills performance
• Memory usage spikes during batch processing and triggers OOM errors

Structured Streaming: Better, Still Painful

Structured Streaming treats streams as "unbounded tables" - actually a pretty nice abstraction. Problem is debugging it. When something goes wrong, you're digging through Catalyst optimizer logs trying to figure out why your simple query is doing 47 joins.

The good parts:

• SQL queries work on streaming data (legitimately useful)
• Schema evolution doesn't break everything (mostly)
• Watermarking handles late data without eating all your memory
• Error messages occasionally help you fix things

The pain points:

• Performance testing requires specialized knowledge
• State management is powerful but complex
• Memory requirements are often 3-5x higher than expected

Real-Time Mode: Marketing vs Reality

Databricks claims single-digit millisecond latencies with Real-Time Mode. Their demo setup looks perfect. Production won't be.

In production? You'll get 10-100ms if everything goes right. Still good, but not the marketing numbers. If you need actual sub-millisecond latency, you're using the wrong tool.

Memory: The Expensive Truth

Nobody tells you about memory requirements. Spark Streaming is hungry. Really hungry.

Minimum memory guidelines

• Dev work needs 4GB minimum (2GB is painful)
• Production? Plan for stupid amounts of memory - 16GB+ per executor, sometimes way more
• Memory overhead adds another 10-15% on top

Memory killers in production

• State that just keeps growing because you fucked up watermarking
• Driver memory issues when collecting large datasets
• Small files problem creating thousands of tiny files that kill performance
• GC pauses that make your stream stutter like a 1990s video

Garbage Collection: The Performance Killer

GC tuning is critical but painful. Most teams end up using G1GC which handles large heaps better than the default collectors.

Common GC disasters:

• Full GC pauses that stop your stream for 30+ seconds
• Memory leaks that slowly consume all available heap
• Default GC settings that work fine for batch but kill streaming performance

State Management: Where Dreams Go to Die

State in streaming is hard. Spark's Arbitrary State API v2 is powerful but:

• Uses RocksDB which is fast but eats disk space like candy
• Checkpointing can take forever on large state
• State schema evolution is "supported" but good luck migrating terabytes of state
• Debugging state issues requires reading the source code

Performance: The Real Numbers

Vendor performance claims look great on paper, but in the real world:

• Memory requirements are 3-5x higher than the documentation suggests
• GC tuning takes weeks to get right
• Network partitions turn "exactly-once" into "at-least-once real quick"
• "Millions of events per second" assumes perfect conditions you won't have

Deployment Patterns: Choose Your Pain

Most teams deploy using one of these patterns:

• Lambda architecture: Batch and streaming separately (operational nightmare)
• Kappa architecture: Streaming-only (good luck with historical queries)
• Lakehouse: The new hotness (when it works)
• "Just make it work": What most teams actually do

The reality is that production deployment requires specialized knowledge, dedicated tuning time, and enough memory to run a small country. But when it works, it actually handles scale pretty well.

Here's the honest assessment: Spark Streaming is enterprise software disguised as an open-source project. It has enterprise-level complexity, enterprise-level resource requirements, and enterprise-level operational overhead. If you're a startup trying to process a few thousand events per second, use literally anything else. If you're Netflix processing billions of events and already have a team of Spark experts, it's probably the right choice.

The sweet spot? Mid-to-large companies that already have Spark infrastructure, dedicated platform engineers, and budget for both the hardware and the learning curve. For everyone else, the juice probably isn't worth the squeeze.

Ready to battle the complexity?

Let's get practical. Here are the real problems you'll face and the solutions that actually work when everything goes sideways at 3am.