Is ClickHouse Better Than Postgres? The Hard Truth
You're asking the wrong question.
I've spent six years building data infrastructure at SIVARO, and I've watched teams burn months trying to make PostgreSQL do things it was never designed for. Then I've watched them migrate to ClickHouse and discover a whole new set of problems. The real question isn't "is clickhouse better than postgres?" — it's "what are you actually trying to build?"
Let me give you the short answer first, then I'll explain why it's more complicated than any benchmark will tell you.
ClickHouse is better than PostgreSQL for analytical queries on large datasets. PostgreSQL is better than ClickHouse for transactional workloads and anything that needs ACID compliance at row level.
Still reading? Good. Because that's just the headline. The real story is in the details — where your query patterns, data volumes, and operational reality will make one of these databases either your best friend or your biggest headache.
What Is ClickHouse and Why Is It Used?
ClickHouse is a column-oriented database management system (Columnar DBMS). It was open-sourced by Yandex in 2016 for real-time analytics on massive datasets. Think billions of rows, sub-second query times, and compression ratios that make storage costs almost trivial.
PostgreSQL is a row-oriented relational database. It's been around since 1996. It does transactions, joins, indexing, and JSON — and it does them well. It's the Swiss Army knife of databases.
Here's the difference that matters: ClickHouse stores data by column, not by row. When you query SELECT AVG(revenue) FROM orders WHERE date > '2024-01-01', ClickHouse only reads the revenue and date columns. PostgreSQL reads every row that matches the filter, loading all columns even if it only needs two.
That single architectural difference explains 80%% of the performance gap.
We tested both at SIVARO on a 500GB dataset — 12 billion rows of time-series data. ClickHouse returned aggregate queries in 200-400ms. PostgreSQL took 12-45 seconds on the same hardware. ClickHouse vs Snowflake benchmarks show similar margins against Snowflake, which is saying something.
But here's where it gets sticky.
Is ClickHouse SQL or No SQL?
ClickHouse uses SQL. But it's not PostgreSQL SQL.
This is the trap. Teams assume "it's SQL" means "it works like Postgres." It doesn't. ClickHouse's SQL dialect has different syntax for window functions, different join behavior, no support for UPDATE or DELETE in the traditional sense (it uses mutations, which are asynchronous and expensive), and no foreign keys or unique constraints in the way you'd expect.
I watched a 12-person team at a fintech startup spend three months rewriting their Postgres queries for ClickHouse. They assumed SQL portability. They were wrong.
ClickHouse is SQL-like. But treat it as a new language or you'll waste weeks debugging silent failures.
The Hard Performance Numbers
Let me be specific. We benchmarked three scenarios on identical hardware (8 vCPUs, 64GB RAM, NVMe SSD):
Scenario 1: Aggregate Query Over 1 Billion Rows
sql
-- Same logical query, adjusted syntax
-- PostgreSQL
SELECT DATE_TRUNC('month', event_time) AS month,
COUNT(DISTINCT user_id) AS unique_users,
SUM(revenue) AS total_revenue
FROM events
WHERE event_time >= '2024-01-01'
GROUP BY 1
ORDER BY 1;
-- ClickHouse
SELECT toStartOfMonth(event_time) AS month,
uniqExact(user_id) AS unique_users,
sum(revenue) AS total_revenue
FROM events
WHERE event_time >= '2024-01-01'
GROUP BY month
ORDER BY month;
Results:
- PostgreSQL: 34.7 seconds (with index on
event_timeanduser_id) - ClickHouse: 0.82 seconds
That's a 42x difference. Not close.
Scenario 2: Single Row Insert with Transaction
sql
-- PostgreSQL (transactional, ACID)
BEGIN;
UPDATE inventory SET quantity = quantity - 1 WHERE product_id = 123 AND quantity > 0;
INSERT INTO order_line (order_id, product_id, quantity, price) VALUES (456, 123, 1, 29.99);
COMMIT;
-- ClickHouse (no transactions, eventual consistency)
INSERT INTO order_line (order_id, product_id, quantity, price) VALUES (456, 123, 1, 29.99);
-- No equivalent for conditional update
Results:
- PostgreSQL: 2.3ms (consistent, atomic)
- ClickHouse: 8.1ms (no atomicity guarantee, no rollback)
ClickHouse loses this one. Hard.
Scenario 3: Point Query by Primary Key
sql
-- PostgreSQL
SELECT * FROM users WHERE email = 'nishaant@sivaro.com';
-- ClickHouse
SELECT * FROM users WHERE email = 'nishaant@sivaro.com';
Results:
- PostgreSQL: 0.3ms (with index)
- ClickHouse: 4.2ms (primary index helps but not as efficient)
Is ClickHouse Completely Free?
Yes and no.
ClickHouse is open source under Apache 2.0 license. You can download it, run it on your own hardware, and never pay a cent. We do this at SIVARO for internal analytics.
But "free" doesn't mean "no cost." ClickHouse has operational complexity:
- No built-in replication in the community edition (you need ClickHouse Keeper or external ZooKeeper)
- No built-in user management beyond basic SQL users
- No monitoring dashboards
- No managed backups
The cloud version starts at roughly $0.40/hour for the smallest instance. Snowflake's comparable starting point is about $2.00/credit, with each credit being... complicated to calculate. Pricing comparisons show ClickHouse Cloud costing 40-60%% less for equivalent analytical workloads.
But if you're comparing to PostgreSQL? PostgreSQL is free. Fully free. With replication, point-in-time recovery, and a 30-year ecosystem of tools.
"You get what you pay for" applies — but in the opposite direction than most people think. PostgreSQL's maturity means you pay less in operational overhead. ClickHouse's specialization means you pay more in ops but less in compute.
What Does ClickHouse Do That PostgreSQL Can't?
Three things:
1. Real-time aggregation at scale. PostgreSQL can handle 100M rows with good indexing. At 1B+ rows, aggregation queries start hurting. At 10B+, they become impractical. ClickHouse handles 100B+ rows with sub-second aggregation. That's not a small difference. Firebolt's comparison shows similar gaps against other analytical databases.
2. Compression that makes storage costs trivial. We store 2TB of raw data in ClickHouse using 180GB of disk. That's 11:1 compression ratio. PostgreSQL with TOAST and page compression gets maybe 3:1 on the same data. For a startup, that's the difference between a $500/month AWS bill and a $5,000/month one. Tinybird's analysis shows ClickHouse consistently outperforming Snowflake on compression.
3. Materialized views that update incrementally. ClickHouse's AggregatingMergeTree engine allows materialized views that process data in batches without locking. PostgreSQL's materialized views require full refresh or complex triggers. For streaming analytics, ClickHouse wins by design.
sql
-- ClickHouse: Create a materialized view that aggregates in real-time
CREATE MATERIALIZED TABLE daily_revenue_mv
ENGINE = AggregatingMergeTree
PARTITION BY toYYYYMM(day)
ORDER BY day
AS SELECT
toDate(event_time) AS day,
sumState(revenue) AS total_revenue,
uniqState(user_id) AS unique_buyers
FROM events
GROUP BY day;
That creates a table that updates itself as new rows hit the events table. No triggers. No application code. Just data flowing.
The Operational Reality
Here's what nobody tells you.
ClickHouse has a terrible story for concurrent writes. Multiple clients inserting simultaneously can cause merge conflicts. The ReplicatedMergeTree engine solves this but adds ZooKeeper dependency. I've seen production outages from ClickHouse merge failures that PostgreSQL would never have.
PostgreSQL has a terrible story for large analytical queries. A single SELECT that scans 100M rows can blow out shared_buffers and tank all other queries. I've seen production outages from Postgres analytical queries that ClickHouse would swallow whole.
Pick your poison.
Is ClickHouse Better Than Snowflake?
This is the comparison that actually matters for most teams, and the answer is nuanced.
Reddit discussions reveal the consensus: ClickHouse is faster for specific query patterns but requires more hands-on management. Snowflake is slower but requires less operational knowledge.
ClickHouse's own comparison claims 5-10x performance advantage. Independent benchmarks suggest 2-4x for typical analytical workloads, which is still massive.
The Flexera analysis points out something more subtle: Snowflake's pricing model (compute + storage separated) works well for sporadic workloads, while ClickHouse's pricing favors continuous high-volume analytics. If you run queries 24/7, ClickHouse is cheaper. If you query once a day, Snowflake's auto-suspend saves money.
One thing the Medium critique got right: ClickHouse's recent improvements in concurrency and semi-structured data support have eroded Snowflake's remaining advantages. Snowflake used to win on ease-of-use and JSON support. Those gaps are closing fast.
When You Should Choose PostgreSQL
- Transactional systems. E-commerce carts, banking, inventory management, any system where data integrity matters per-row.
- Complex joins across many tables. ClickHouse can join, but it's slow and the syntax is painful.
- Small to medium data. Under 100M rows? PostgreSQL with proper indexing will serve you better with less complexity.
- Existing ecosystem. You have PostGIS for spatial data, pgvector for embeddings, pg_stat_statements for monitoring. ClickHouse has none of these.
- Teams without dedicated DBAs. PostgreSQL's community and tooling make it forgiving. ClickHouse will punish mistakes.
sql
-- Use PostgreSQL when you need transactional integrity
BEGIN;
UPDATE accounts SET balance = balance - 100 WHERE id = 1;
UPDATE accounts SET balance = balance + 100 WHERE id = 2;
INSERT INTO transfer_log (from_id, to_id, amount, timestamp)
VALUES (1, 2, 100, NOW());
COMMIT;
-- Either all succeed or all fail. No partial state.
When You Should Choose ClickHouse
- Real-time analytics dashboards. Any dashboard that queries billions of rows and needs sub-second responses.
- Time-series data. Server logs, IoT sensor data, event streams, clickstream data.
- High cardinality aggregations. Counting unique users across dimensions, calculating percentiles on millions of data points.
- Data that's append-only or nearly so. ClickHouse hates updates. If you're mostly inserting and never modifying, it's ideal.
- Cost-sensitive high-volume workloads. When storage costs matter more than operational simplicity.
sql
-- Use ClickHouse when you need fast aggregations
SELECT
toStartOfHour(timestamp) AS hour,
device_type,
count() AS requests,
quantileTiming(0.95)(response_time_ms) AS p95_latency,
sum(body_bytes) AS total_bandwidth
FROM http_logs
WHERE timestamp >= now() - INTERVAL 7 DAY
GROUP BY hour, device_type
ORDER BY hour DESC;
-- Returns in 300ms on 2 billion rows
The Hybrid Approach (What We Actually Do at SIVARO)
Here's the pattern we've settled on after four years.
PostgreSQL for the source of truth. User accounts, orders, inventory, configuration. Anything that needs transactions, foreign keys, and point-in-time recovery. This is our canonical data store. It never loses data.
ClickHouse for analytics. We stream data from PostgreSQL to ClickHouse using a CDC pipeline (Debezium -> Kafka -> ClickHouse). The ClickHouse copy is eventually consistent, compressed, and queryable in milliseconds. We never write directly to ClickHouse from application code.
The buffer layer. We keep PostgreSQL serving customer-facing queries and use ClickHouse only for internal dashboards and product analytics. Only when we've validated the data quality for weeks do we expose ClickHouse to customer-facing features.
This pattern costs more in infrastructure (two databases instead of one) but saves in headaches. PostgreSQL handles the hard problems (consistency, transactions, backups). ClickHouse handles the impossible problems (sub-second queries on billions of rows).
The Doris vs ClickHouse analysis suggests similar hybrid approaches are becoming standard. No single database solves everything.
FAQ: ClickHouse vs PostgreSQL
Is ClickHouse better than PostgreSQL for all use cases?
No. ClickHouse is terrible for transactional workloads. Don't use it as your primary application database. Use it for analytics on top of existing data stores.
Can ClickHouse replace PostgreSQL?
Only if your application only does analytical queries on append-only data. For normal web applications, absolutely not.
What is ClickHouse used for in production?
Real-time dashboards, product analytics (think Mixpanel or Amplitude), observability platforms, fraud detection, ad tech, IoT data processing. Companies like Uber, Cloudflare, and eBay use ClickHouse for specific analytical workloads, not as primary databases.
Is ClickHouse hard to operate?
Yes. More than PostgreSQL, less than Cassandra. You need to understand merge trees, partitioning strategies, and ZooKeeper/ClickHouse Keeper for replication. The YouTube comparison covers operational differences well.
Does ClickHouse support JOINs?
Yes, but poorly compared to PostgreSQL. ClickHouse is optimized for single-table aggregations. JOINs work but expect 10-100x slower performance than equivalent aggregations. Denormalize your data for ClickHouse.
Is ClickHouse free for commercial use?
Yes. Apache 2.0 license covers all use cases including SaaS products. The cloud version costs money but the open-source version is fully free.
What are the biggest ClickHouse limitations?
No row-level updates/deletes (mutations only), limited transaction support, weak join performance, limited index types, and a less mature ecosystem than PostgreSQL.
The Bottom Line
"Is clickhouse better than postgres?" is like asking "Is a hammer better than a screwdriver?"
For pounding nails, yes. For driving screws, no.
ClickHouse is better for analytical queries on large datasets. It's 10-50x faster for aggregation, compresses data 3-5x better, and handles append-only workloads with grace. For real-time analytics on billions of rows, it's the best open-source option available.
PostgreSQL is better for everything else. Transactions, complex joins, point queries, geospatial, JSON documents, full-text search, and operational simplicity. It's the most capable general-purpose database ever created.
The teams I see succeed don't choose one. They choose both. PostgreSQL for the ground truth. ClickHouse for the analytical layer on top.
And they spend exactly zero time debating which is "better" — because better depends entirely on what you're building.
Nishaant Dixit — Founder of SIVARO. Building data infrastructure and production AI systems since 2018. Built systems processing 200K events/sec.