When Garbage Collection Ruins Your Day
Here's what nobody tells you about Cassandra: java.lang.OutOfMemoryError: GC overhead limit exceeded is going to become your least favorite error message. I've seen 30-second GC pauses bring down entire clusters. You tune the heap smaller to reduce GC time? Now your cache hit rate goes to shit. You tune it bigger? GC pauses get longer and your app times out.
There's no winning this game. The JVM just isn't built for the kind of workloads Cassandra handles.
I helped one company where their Cassandra cluster kept dying. I think it was Tuesday mornings? Or maybe it was random - honestly took us forever to nail down the pattern. Turned out to be some batch job running - weekly reports or something. Anyway, took us way too long to trace it down because the timing was inconsistent and we were chasing ghosts for weeks.
That's not database administration - that's Stockholm syndrome.
The Operational Nightmare Gets Worse at Scale
Look, I get it. Cassandra can handle ridiculous amounts of data. But have you ever tried to run `nodetool repair` on a multi-terabyte cluster? It takes 18 hours and sometimes just hangs. Then you get to explain to your boss why the database is doing "maintenance" that might fail halfway through.
Real shit I've dealt with:
- Compaction jobs running for 14+ hours then just hanging (
ERROR: Connection timed out) - Tombstone accumulation making queries slower until you manually run
nodetool compact - Weekend rolling restarts because half the time nodes don't come back cleanly
- Getting paged at 2:30am with "Node down" alerts followed by "Node back up" 10 minutes later
The tools exist to automate some of this, but they're complex enough that you need someone who understands both Cassandra internals AND your specific workload patterns. Good luck hiring that person.
Performance That Makes You Look Bad
You know what's embarrassing? Telling your product team that database response times might be 10ms or 200ms depending on "cluster conditions." Try explaining that to customers.
I worked with a team running an e-commerce platform on Cassandra. Same product lookup query - sometimes fast, sometimes slow as hell. Not because of actual load, just because of when the GC decided to kick in. Response times were all over the place. Their frontend team had to build retry logic just to deal with the database being unpredictable.
The tombstone problem is worse than anyone admits. You delete some old data, think you're being responsible. Six months later, queries against that partition are slow as hell because Cassandra is scanning through millions of deletion markers. And the "fix" is running compaction, which might take down your cluster.
Why Teams Actually Migrate (It's Not About Performance)
Sure, faster queries are nice. But every migration I've been part of happened for the same reason: teams were tired of being database administrators instead of software engineers.
When you're spending 40% of your time fighting Cassandra instead of building features, something's wrong. When you're the expert on G1GC tuning instead of your actual product, something's wrong.
I talked to the team at one company after they migrated their user data off Cassandra. Their biggest win wasn't the performance improvement - it was finally getting sleep. No more weekend pages about nodes being "unreachable." No more emergency calls because repair failed and now they have inconsistent data.
One engineer told me: "I realized I hadn't been paged about the database in 3 months. That's when I knew we'd made the right choice."
What Actually Works Better
Here's the thing - there are databases that just work without the drama:
ScyllaDB is basically Cassandra without the Java bullshit. Same queries, same data model, but written in C++. No garbage collection, no JVM tuning, no mysterious pauses. I've seen teams cut their node count by 75% with better performance.
DynamoDB if you're on AWS and can redesign your access patterns. Zero operations overhead. It just works. You pay more per operation but save on everything else.
YugabyteDB if you want real ACID transactions and can handle PostgreSQL-style operations. More complex than DynamoDB but way simpler than Cassandra.
ClickHouse if your workload is actually analytics. It'll make Cassandra look like a joke for time-series data, but you're rewriting everything.
The point isn't that these are perfect - it's that they don't wake you up at 3am because of garbage collection.
