ClickHouse - Analytics Database That Actually Works

Analytical queries on regular databases are slow as shit. Last Tuesday I watched PostgreSQL churn through some 50-million-row aggregation for like 10 minutes while ClickHouse did the same thing in 3 seconds. This happens constantly.

Column Storage Finally Makes Sense

Here's why columnar storage matters: when you're summing revenue by month, you don't give a fuck about customer names or addresses. ClickHouse only reads the columns you need. PostgreSQL reads entire rows even when you only want one column, which is why your analytics queries time out and analysts complain all day. This architectural difference is what makes ClickHouse significantly faster for analytical workloads.

I learned this debugging a production dashboard that kept dying every morning. Simple query - just summing orders by date - but PostgreSQL was reading all 47 columns for 100 million rows. Took forever. Moved it to ClickHouse and that 5-minute query became a 2-second query. Analysts finally shut up about slow dashboards.

Performance Numbers That Matter

Yeah, ClickHouse is faster. How much depends on your specific clusterfuck of data:

• Simple aggregations: Usually 10-50x faster
• Complex analytics: 100x+ speedups happen a lot
• Single-row lookups: PostgreSQL might actually win here

That "4,000x faster" marketing bullshit comes from very specific benchmark conditions with 600 million rows and perfect conditions. Real world is messier, but even 10x faster matters when your current queries take 30 minutes.

Stuff I've actually measured in production:

• COUNT(*) with GROUP BY on maybe 100M rows: PostgreSQL took like 8 or 9 minutes, ClickHouse did it in 12 seconds
• Some complex JOIN aggregation: PostgreSQL timed out after 30+ minutes, ClickHouse finished in 45 seconds
• Time-series analytics on around 1 billion events: PostgreSQL just gave up, ClickHouse took 3 minutes

When ClickHouse Will Fuck You Over

Don't use ClickHouse for:

• User auth systems - UPDATE/DELETE operations were garbage until version 25.7
• Shopping carts - No real transactions means you'll corrupt data eventually
• Comment systems - Small frequent writes will kill performance due to part optimization overhead
• Any normal CRUD app - Just use PostgreSQL, don't be clever

ClickHouse is for analytics, not applications. I've watched teams try to use it as a regular database. It's always a disaster. The use cases documentation makes this clear but nobody reads it.

Memory Usage Will Kill Your Budget

ClickHouse eats RAM like crazy. A query processing 1GB of data might use 4GB of RAM during execution. Size your servers with way more RAM than you think you need or get ready for OOM kills at 3am when the overnight jobs run. The memory settings documentation is essential reading, and you'll want to monitor system.query_log for memory-hungry queries.

Row vs Column storage: Why ClickHouse reads only the columns you need instead of entire rows

SQL Compatibility (They Finally Fixed the Worst Part)

ClickHouse SQL was "mostly" standard until you needed UPDATE or DELETE - then you were fucked. That pain is finally over. ClickHouse 25.7 (July 2025) added normal SQL UPDATE and DELETE statements that are up to 1,000× faster than the old mutation garbage.

No more waiting hours for ALTER TABLE ... UPDATE bullshit. Now you just write normal SQL:

UPDATE orders SET discount = 0.2 WHERE quantity >= 40;
DELETE FROM orders WHERE order_id = 1001;

They work instantly using "patch parts" - basically ClickHouse inserts tiny update patches that get applied during background merges. Smart engineering that finally makes ClickHouse feel like a real database for updates.

DELETE operations now work by creating patch parts that mark rows as deleted during background merges

What works without pain:

• Basic SELECT, GROUP BY, window functions
• JOINs (though they'll eat all your RAM)
• Most aggregation functions you actually need

What will bite you in the ass:

• UPDATE/DELETE operations are experimental garbage in older versions
• String functions have different names than PostgreSQL for no good reason
• JSON parsing works but the syntax is weird as hell

Storage Engines You Actually Need

Forget the marketing bullshit about 15 different MergeTree variants. Here's what you'll actually use:

MergeTree: The only engine you need 90% of the time. Handles sorting, partitioning, and compression without you having to think about it.

ReplacingMergeTree: Use when you need deduplication, but it's not real-time. The dedup happens during merges, which can take hours. Learned this the hard way when duplicate rows kept showing up in reports.

SummingMergeTree: Pre-aggregation sounds amazing until you need to debug pre-aggregated data and want to die. Only use if you desperately need the performance boost.

Skip everything else unless you have very specific requirements. More complexity just means more shit breaks at 2am.

Clustering Reality (Expensive as Hell)

ClickHouse clustering gets expensive fast. Each shard needs its own replica, so a 3-shard, 2-replica cluster means 6 servers minimum. The replication documentation explains this but undersells the cost impact.

Found this out when our "simple" 3-node cluster became a 6-node cluster after we realized we needed replication for production. Cloud hosting costs doubled overnight. Management was not happy.

The distributed table syntax is confusing as shit:

CREATE TABLE users_distributed AS users 
ENGINE = Distributed('cluster', 'default', 'users', rand())

That rand() at the end distributes data randomly across shards. Use cityHash64(user_id) if you want consistent sharding by user.

Memory Management (This Will Ruin Your Week)

Memory horror stories from production:

• Query scanning 50GB data ate 240GB RAM on some big AWS instance
• JOIN between 100M and 200M row tables consumed 400GB RAM, killed the server
• Aggregation on high-cardinality column (user_id) OOM killed our 16GB server at 3am
• GROUP BY with arrayJoin() multiplied memory usage by 50x, nobody warned me about this shit

ClickHouse uses all available RAM and then begs for more. A 100GB query might use 400GB of RAM during execution. The memory management is aggressive as hell and the settings documentation doesn't warn you enough about this. Read the performance tuning guide and monitor system.query_log religiously.

ClickHouse 25.7 optimized COUNT() aggregations by storing counts directly in hash tables, making them 20-30% faster

Lessons learned from getting paged at 3am:

• Size servers with way more RAM than you think you need, like 4x your largest query
• Set max_memory_usage per query or get ready for OOM kills during peak hours
• Monitor memory usage constantly - queries can explode for no apparent reason

Integration Pain Points (They All Suck)

Kafka: The Kafka connector works but error handling is garbage. One malformed message stops the entire consumer. Found this out during a production incident when some upstream service sent bad JSON and our ingestion died for 4 hours. The streaming ingestion guide has some workarounds, but you'll still need robust error handling patterns.

dbt: The dbt adapter exists but it's nowhere near as solid as PostgreSQL. Expect weird shit with incremental models. Check the integration documentation before committing to this approach.

Grafana: Works fine for dashboards but the query builder doesn't understand ClickHouse's SQL dialect. You'll end up writing raw SQL for anything remotely complex. The visualization guide helps with the basics.