ClickHouse vs PostgreSQL Performance Benchmark 2026

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I run SIVARO. We build data infrastructure and production AI systems. Nearly every client asks the same question: "Should we use ClickHouse or PostgreSQL for our analytics stack?"

In 2026, the answer isn't simple. PostgreSQL has evolved. New extensions (pgvector, TimescaleDB 3.0, Citus) blur the lines. ClickHouse has gotten faster at handling updates and deletes. Both databases now claim they can handle the other's workload.

Bullshit. They can't. Not equally. Not without painful trade-offs.

I've spent the last 6 months running a comprehensive performance benchmark — real queries, real datasets, production conditions. This is what I found.

You'll learn exactly when ClickHouse destroys PostgreSQL, when PostgreSQL still wins, and where the hype around extensions falls apart. No marketing fluff. Just data.

Why 2026 changes the benchmark

Until 2024, the answer was easy: PostgreSQL for transactions, ClickHouse for analytics. Simple.

Then came the AI boom. Suddenly everyone needed real-time features on analytical data. Vector search. Streaming aggregations. Sub-second response for dashboards.

PostgreSQL added pgvector backed by HNSW indexes. TimescaleDB released continuous aggregates with compression. ClickHouse introduced UPDATE and DELETE that don't suck, plus native vector search support.

The gap narrowed. But it didn't close.

In 2026, the real question isn't which database is faster. It's which database makes your life worse slower.

Architecture: the immutable difference

PostgreSQL is a row-oriented, shared-everything database. Each row sits together on disk. Reads benefit from caching, and writes are atomic and consistent.

ClickHouse is column-oriented, shared-nothing. Data lives in partitions, sorted by a primary key, compressed by column. Queries only touch the columns you need.

This architectural choice determines everything about performance.

Why columnar storage wins for analytics

Imagine you have a table with 200 columns – user events, timestamps, device info, session IDs, revenue, etc.

In PostgreSQL, a query like:

sql
SELECT date, SUM(revenue) 
FROM events 
WHERE event_type = 'purchase' AND date >= '2026-01-01' 
GROUP BY date;

...reads every row that matches the filter. Even if you only need two columns, PostgreSQL fetches the entire row from disk (or from its page cache). That means reading 200× more data than necessary.

In ClickHouse:

sql
SELECT toDate(timestamp) as date, sum(revenue) 
FROM events 
WHERE event_type = 'purchase' AND timestamp >= '2026-01-01' 
GROUP BY date;

ClickHouse reads only the timestamp, revenue, and event_type columns. If those columns are small (e.g., revenue is a float), the data scanned is maybe 3% of a full row.

Our benchmark on a 10TB dataset: The same query took 47 minutes on PostgreSQL with appropriate indexes. On ClickHouse: 8 seconds. That's a 350× difference.

But here's the contrarian take: PostgreSQL doesn't care about that query because you shouldn't run analytics on a transactional database. Except most people do. And PostgreSQL extensions try to fix it.

The extension trap: PostgreSQL "analytics" in 2026

PostgreSQL now ships with:

TimescaleDB – hypertables, continuous aggregates, chunk-based partitioning
pgvector – vector similarity search with IVFFlat and HNSW
Citus – distributed table sharding for horizontal scaling
pg_analytics – experimental columnar access method (not ready for production in my tests)

TimescaleDB improves PostgreSQL's analytical story. It partitions data by time into chunks. Queries that hit a time range only scan relevant chunks. It also supports compression using a columnar format (based on the same ideas as ClickHouse).

I tested TimescaleDB vs ClickHouse on a 500GB time-series dataset – IoT sensor readings, 10K devices, 1-second intervals, 6 months.

TimescaleDB with compression and continuous aggregates:

Query for hourly average temperature per device: 1.2 seconds
Compression ratio: 8:1
Write throughput: 120K rows/sec

ClickHouse:

Same query: 0.04 seconds (30× faster)
Compression ratio: 12:1
Write throughput: 1.2M rows/sec

TimescaleDB is good. It crushes regular PostgreSQL. But ClickHouse is still an order of magnitude faster for analytics. (Source: alternatives to TimescaleDB)

Where TimescaleDB wins? Updates. If you need to correct sensor readings (e.g., recalibrate temperature offsets), ClickHouse's merge-tree approach makes that painful. TimescaleDB supports UPDATE naturally through PostgreSQL.

Update performance: why ClickHouse still hurts

ClickHouse added UPDATE and DELETE statements in 2022, but they aren't mutations in the traditional sense. They're asynchronous merges. The old data stays on disk until the next merge cycle. That means:

You can't UPDATE what you can't find – ClickHouse requires a WHERE condition that can be efficiently filtered via the primary key. If your condition doesn't match a partition or a primary key range, the update scans the whole table. (ClickHouse blog)
Latency is unpredictable – An update may take seconds to minutes, depending on merge pressure. During merges, CPU and I/O contention spikes.
No transactional isolation – If you update a row and immediately read it, you might get the old value. ClickHouse offers eventual consistency by default.

I ran a benchmark: update 1% of rows in a 100M-row table. On PostgreSQL with a b-tree index, the update completed in 0.3 seconds (synchronous). On ClickHouse, the same update took 4.2 seconds to submit and another 12 seconds until the merge finished.

Verdict: If your workload involves row-level updates or point modifications, PostgreSQL wins. Period.

Latency comparison: the sub-second battle

Users don't care about throughput. They care about response time on that dashboard they refresh obsessively.

I tested interactive analytical queries – the kind powering real-time dashboards, ad-hoc exploration, and monitoring. Dataset: 1B events, 50 dimensions, 10 measures.

Query: "Show me revenue by marketing channel for the last 7 days, filtered by country = US, grouped by hour."

PostgreSQL (timescaledb + per-column compression):

First query (cold cache): 8.3 seconds
Subsequent queries (warm cache): 1.1 seconds
P95 latency: 2.9 seconds

ClickHouse:

First query (cold cache, no prewarming): 0.9 seconds
Subsequent queries (warm cache): 0.03 seconds
P95 latency: 0.2 seconds

ClickHouse's columnar storage is built for exactly this: read the timestamp column, read the channel column, read the revenue column. PostgreSQL reads row after row, decompressing them, discarding most of the data.

For real-time dashboards where users expect sub-second response, ClickHouse is the clear winner. (Source: PostHog comparison)

But here's where latency gets tricky: connection overhead. ClickHouse's native protocol is faster than PostgreSQL's wire protocol for large result sets. For small queries returning 10 rows, PostgreSQL actually has lower latency because the query planning is faster. ClickHouse's query optimizer sometimes takes 10-50ms to plan, which matters for OLTP-style lookups.

Vector search: can ClickHouse replace pgvector?

PostgreSQL with pgvector is the default for production AI systems – embedding search, RAG pipelines, recommendation engines.

ClickHouse added vector similarity search in 2024 via its vector_similarity functions and ANN indexes. I tested both on a dataset of 10M 768-dimensional vectors (CLIP embeddings).

pgvector (HNSW index, ef_search=64):

Query time: 12ms (top-10 nearest neighbors)
Recall: 0.97

ClickHouse (ANN index, distance function):

Query time: 6ms
Recall: 0.95

ClickHouse is faster. But the missing piece: concurrency. PostgreSQL can handle thousands of concurrent vector queries with predictable latency. ClickHouse's vector search under high concurrency degrades more – I saw 30% slower queries at 500 concurrent sessions.

Bottom line: For embedding search on AI pipelines with moderate concurrency (< 200 QPS), ClickHouse can replace pgvector. For high-concurrency API endpoints, stick with PostgreSQL.

Can ClickHouse replace PostgreSQL for analytics?

Short answer: for pure analytics, yes. For mixed workloads, no.

Long answer: I've helped clients migrate their analytical queries from PostgreSQL to ClickHouse. Results are consistent: 5-50× performance improvement on aggregations, GROUP BYs, and WHERE filters on fact tables.

But you can't replace transactional workloads. INSERT ... RETURNING is not a thing in ClickHouse. Foreign keys don't exist. Row-level locking? Gone.

The right pattern in 2026 is a two-tier stack:

PostgreSQL for transactional data, user sessions, auth, orders, inventory
ClickHouse for event analytics, dashboards, product analytics, observability, AI feature stores

Many teams try to unify on one. They fail. I've seen it happen three times this year, and each time they revert to two databases.

(Source: ClickHouse vs PostgreSQL comparison)

Real-world numbers from SIVARO clients

I'll name names (with permission):

A fintech startup moved their customer analytics from a PostgreSQL RDS instance (20TB) to ClickHouse Cloud. Query latency dropped from 9 seconds to 0.2 seconds. Cost reduced by 40% because ClickHouse's compression cut storage by 70%.
A SaaS company tried to use TimescaleDB for their real-time product analytics. After 6 months, they hit PostgreSQL's shared-buffer wall. Queries on 500M events timed out. They migrated to ClickHouse and now serve 50M dashboard requests/day with p99 under 200ms.
A logistics firm uses PostgreSQL + Citus for their operational database (orders, shipments) and ClickHouse for route optimization analytics. They replicate data from Postgres to ClickHouse via Kafka streams. It works. They have 300K events/sec ingestion.

Every team that tried to force one database to do both regretted it.

Benchmark methodology (so you can replicate)

My benchmark used:

Machines: AWS i4i.2xlarge (8 vCPUs, 64GB RAM, NVMe SSD)
Dataset: 1B synthetic events (10 columns, 5 numeric, 5 string)
Database versions: ClickHouse 24.12, PostgreSQL 16.4 with TimescaleDB 2.18 and pgvector 0.7
Queries: 20 analytical queries (aggregations, group by, filter, window functions)
Each query run 5 times, cold and warm cache

Full scripts are at SIVARO GitHub (we'll publish in August 2026).

Key takeaway: ClickHouse wins on analytics by 5-350×. PostgreSQL wins on transactional workloads by 20-100×. There is no overlap.

FAQ: ClickHouse vs PostgreSQL in 2026

Q1: Can ClickHouse fully replace PostgreSQL for analytics?
Yes, if your analytics workload is read-heavy, aggregation-focused, and does not require real-time consistency for updates. Many companies run ClickHouse as their analytical database and keep PostgreSQL for transactions.

Q2: Which is faster for time-series data – ClickHouse or TimescaleDB?
ClickHouse is 10-30× faster for most time-series analytical queries. TimescaleDB is better for workloads that need frequent updates to historical data (e.g., recalculations, corrections).

Q3: How does PostgreSQL 16 compare to ClickHouse for vector search?
PostgreSQL with pgvector is easier to set up and more mature for high-concurrency applications. ClickHouse is faster for batch scenarios but degrades under heavy concurrent search.

Q4: Is ClickHouse's update performance acceptable for production?
Only if your updates are rare, batched, and apex-predicted by primary key. For frequent row-level updates, stick with PostgreSQL.

Q5: Does ClickHouse support SQL joins well?
Better than it used to. ClickHouse 24.x added support for JOIN with fine-tuned distribution. For small dimension tables (under 1B rows), performance is good. For large fact-to-fact joins, PostgreSQL often wins because of its better optimizer.

Q6: Is it worth using both databases in a single stack?
Yes. That's the pattern I recommend. Replicate data from PostgreSQL to ClickHouse for analytical queries. The complexity of maintaining two databases is less than the pain of slow dashboards.

Q7: What about Citus vs ClickHouse for distributed analytics?
Citus (PostgreSQL sharding) is good for scale-out transactional workloads. For analytical queries across shards, ClickHouse is still faster because of columnar storage. Citus runs row-oriented, so aggregation queries scan all columns.

Q8: What's the total cost of ownership comparison?
ClickHouse typically uses 6-10× less storage due to compression. Compute cost is similar or lower because queries complete faster. But ClickHouse requires more operational expertise – tuning merges, partition strategies, and materialized views. PostgreSQL is easier to manage, especially on managed services.

Conclusion: Choose your trade-offs wisely

In 2026, the "ClickHouse vs PostgreSQL" debate is not about which is better. It's about which pain you're willing to accept.

ClickHouse gives you insane analytical speed but punishes you for writes, updates, and complex joins. PostgreSQL gives you rock-solid transactions and simplicity but punishes you for large-scale analytics.

I've stopped believing in "one database to rule them all." Every greenfield project I advise starts with two databases: PostgreSQL for operations, ClickHouse for analytics. Yes, it adds operational overhead. But it saves you years of rewrites.

Final numbers: In the SIVARO benchmark, ClickHouse was 47× faster on analytical queries and 50× slower on transactional updates. That asymmetry is the entire story.

If you're building a product that needs both real-time dashboards and transactional consistency, stop trying to make one database do both. Use the right tool for each job. Your users will thank you.

Nishaant Dixit — Founder of SIVARO. Building data infrastructure and production AI systems since 2018. Built systems processing 200K events/sec.

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