This article was originally published at sivaro.in

ClickHouse vs PostgreSQL: Which Is Faster in 2026?

Slug: clickhouse-vs-postgresql-which-is-faster-2026

Last month a fintech founder called me at 11pm. Their Postgres box was choking on 400 million rows of transaction logs, dashboards took 40 seconds to load, and the CFO was asking why the numbers didn't match. They'd already spent two weeks evaluating managed warehouses. Nobody had told them the answer was sitting right next to them the whole time.

I've been building data infrastructure since 2018. SIVARO runs systems pushing 200K events/sec through both engines. I've migrated a dozen companies off Postgres analytics into ClickHouse, and I've told just as many to stay put.

So let's settle clickhouse vs postgresql which is faster 2026 with actual numbers, not vendor blog posts.

You'll learn where each engine wins, when migration is a mistake, what replication actually costs you, and how to pick without regret.

The Short Answer Nobody Gives You

ClickHouse is dramatically faster for analytical queries. That's not controversial in 2026. We routinely see 50-500x improvements on aggregation-heavy workloads — a 12-second Postgres GROUP BY across 2 billion rows becomes 40 milliseconds in ClickHouse. I've watched that exact gap on client hardware.

PostgreSQL is faster for transactions, joins across normalized schemas, and anything involving frequent updates to single rows. It's also the better default when you're under ~50-100M rows with moderate concurrency.

Most people think "faster database" is a single axis. It isn't. You're comparing a row store optimized for correctness and concurrency against a columnar store optimized for scanning and compression. They're solving different physics problems.

Why ClickHouse Crushes Analytics (The Actual Mechanics)

ClickHouse stores data by column, not by row. When you run SELECT avg(amount) FROM transactions WHERE created_at > '2026-01-01', it reads only two columns off disk instead of reconstructing entire rows. On a wide table with 40 columns, that's roughly a 20x reduction in I/O before compression even enters the picture.

Then compression hits. ClickHouse's default codecs (LZ4) and specialized ones (Delta, DoubleDelta, Gorilla) routinely get 8-15x compression on time-series data. Postgres TOAST gets you maybe 2-3x on the same data, and only for large values.

Vectorized execution is the third piece. ClickHouse processes data in batches of thousands of rows using SIMD instructions. Postgres processes row-by-row through its executor. This is why ClickHouse benchmarks in ClickBench show 100x+ gaps on scan-heavy queries.

Here's a real query from a client's system — sessionization across 800M events:

-- ClickHouse: ~380ms cold, ~90ms warm
SELECT
    user_id,
    windowFunnel(3600)(
        toDateTime(timestamp),
        event = 'page_view',
        event = 'add_to_cart',
        event = 'checkout'
    ) AS funnel_stage
FROM events
WHERE timestamp >= now() - INTERVAL 30 DAY
GROUP BY user_id
HAVING funnel_stage = 3

The Postgres equivalent required three CTEs with window functions and took 47 seconds. We rewrote it in ClickHouse and the analytics team stopped complaining about "slow dashboards."

But here's the trade-off nobody mentions in benchmark posts: ClickHouse struggles with frequent single-row updates. It was designed for append-heavy workloads. Mutations (ALTER TABLE ... UPDATE) are asynchronous, expensive, and should be rare.

Where PostgreSQL Still Wins (And Why You Should Care)

Postgres handles concurrent writes like a champ. MVCC, proper row-level locking, real transactions, foreign keys that actually enforce. ClickHouse has transactions only within a single table and partition (2026 reality — check the ClickHouse docs before betting your ledger on it).

I had a client in 2024 — an inventory system — who insisted on moving everything to ClickHouse because "Postgres was slow." Two months later their stock counts were drifted, because the write path assumed ACID semantics ClickHouse doesn't provide. We moved the transactional core back.

Rule of thumb I give everyone: if your workload is 80%+ reads with heavy scans and aggregations, ClickHouse wins. If you have a mix of reads, writes, updates, and small transactional queries, Postgres wins. It's that simple.

Postgres also has a mature ecosystem. pgvector for embeddings, PostGIS for geo, a decade of extensions, every ORM supports it, and your team already knows it. ClickHouse is catching up — it has vector search now — but the depth isn't there yet.

Benchmark Reality Check: ClickHouse vs PostgreSQL Which Is Faster 2026

Benchmarks lie in isolation. Here's what I actually see on client hardware in 2026, all on comparable cloud instances (32 vCPU, 128GB RAM, NVMe):

Workload	PostgreSQL 17	ClickHouse 25.x	Winner
Point lookup by PK	0.4ms	8ms	Postgres
Aggregate 1B rows	18s	120ms	ClickHouse
Insert 10K rows/sec	14K/sec	400K/sec (batched)	ClickHouse
Single-row update	0.3ms	40ms+ async	Postgres
Multi-table JOIN (normalized)	200ms	900ms	Postgres
Wide scan (100 cols, 500M rows)	42s	0.4s	ClickHouse

Notice the pattern. You get a hundred-fold win in one column, and you lose in another. There's no free lunch.

The interesting 2026 wrinkle: Postgres 17's parallel query improvements and the new pg_columnar extension (still experimental) have closed some gap on mid-size analytical workloads. If you're under 100M rows, Postgres often delivers "good enough" analytics and you don't need a second system.

I'd honestly tell 6 out of 10 clients asking about migration to stay on Postgres. The operational cost of running two systems usually exceeds the query speedup for smaller companies.

When You Actually Need to Migrate (And How)

The migration question has two parts: should you and how.

Should you:

• Query latency >5 seconds on dashboards your users refresh daily
• Data volume >500M rows and growing >20%/month
• Analytical workload is 80%+ of total query load
• You're spending more on Postgres vertical scaling than a ClickHouse cluster would cost
• Your team has appetite for a second operational system

How:

The postgresql to clickhouse data migration tool landscape in 2026 is genuine. Three options I actually deploy:

PeerDB (now part of ClickHouse Inc.) handles CDC from Postgres logical replication. It's the cleanest path for ongoing sync. Here's a config:

# peerdb flow config
source:
  type: postgres
  host: prod-db.internal
  publication: peerdb_publication
destination:
  type: clickhouse
  host: analytics.internal
  database: warehouse
sync:
  mode: cdc
  initial_snapshot: true

ClickHouse's native PostgreSQL table function works for one-shot loads under ~50M rows:

INSERT INTO events_from_pg
SELECT * FROM postgresql(
  'host=prod-db.internal port=5432 dbname=app',
  'events',
  'readonly_user',
  'password'
)
WHERE created_at >= '2026-01-01';

Custom dual-write with an outbox pattern. Highest effort, most control. I only recommend it when PeerDB's conflict resolution doesn't fit your schema. Roughly 15% of my migrations go this route.

For Airbyte fans: it works, but the sync latency and throughput caps make it a poor fit above 10M rows/day. Use it for one-time historical loads, not ongoing replication.

ClickHouse vs PostgreSQL Replication: Different Beasts Entirely

This is where people get confused. Both call it "replication," but it means completely different things.

Postgres replication copies the entire write-ahead log (WAL). Every insert, update, delete — byte-for-byte. It gives you a physical or logical replica that can accept read traffic or stand ready for failover. Latency is milliseconds. This is production-grade HA.

ClickHouse replication copies MergeTree parts, not rows. ReplicatedMergeTree ensures the same data ends up on every replica eventually. It's designed for horizontal scaling of reads and durability, not for HA failover in the strict sense. A single-node Postgres is more HA-ready than a three-node ClickHouse cluster if you don't architect it carefully.

For hybrid setups — Postgres as the source of truth, ClickHouse as the analytical mirror — you need logical replication out of Postgres. That means wal_level = logical and a replication slot:

-- on Postgres source
ALTER SYSTEM SET wal_level = logical;
SELECT pg_create_logical_replication_slot('clickhouse_slot', 'pgoutput');

-- monitor replication lag (this is the metric that matters)
SELECT
    slot_name,
    pg_wal_lsn_diff(pg_current_wal_lsn(), confirmed_flush_lsn) AS lag_bytes
FROM pg_replication_slots;

Watch lag_bytes like a hawk. I've seen it hit 5GB during batch jobs, which means your ClickHouse replica is 20 minutes behind reality. Users notice.

The ClickHouse docs on database replication are worth reading twice before you architect anything.

The 2026 Cost Reality Nobody Predicts

At first I thought ClickHouse was always cheaper. Columnar compression, better scan performance — obvious win, right?

Turns out it's a pricing and ops problem more than a performance problem.

ClickHouse Cloud in 2026 runs around $0.22/vCPU-hour plus storage. A comparable Postgres on RDS with io2 storage is roughly comparable per compute hour, but Postgres needs more of it for the same analytical workload. On a workload doing 10,000 analytical queries per minute, we've measured a 4-6x cost reduction moving to ClickHouse after accounting for instance size.

But. And this is the "but" that kills budgets: ClickHouse needs someone who knows ClickHouse. Query optimization is different. Partition key choice determines your life for the next three years. Merge behavior under sustained writes has its own tuning. If your team has one strong Postgres person and nobody who's touched ClickHouse, you'll spend the first 6 months learning expensive lessons.

We charge clients $40-80K for a properly executed ClickHouse migration. That's realistic. If you're not planning to amortize that over 2+ years of analytical pain, don't do it.

A Decision Framework You Can Actually Use

Answer these five questions. Be honest.

1. Do queries take >5 seconds today and the business cares? If no, Postgres.
2. Is your data >500M rows or growing fast? If no, Postgres.
3. Is >70% of your query load analytical (scans, aggregations)? If no, Postgres.
4. Do you have (or can you hire) someone who'll own ClickHouse? If no, Postgres.
5. Will the query speedup pay back migration cost in under 24 months? If no, Postgres.

Five yesses means migrate. Three or fewer means stay. In between, run both — Postgres as OLTP, ClickHouse as analytical mirror — and don't overthink the optics of "two databases."

The right architecture for most companies past $10M ARR in 2026 is honestly both. Postgres for the transactional core. ClickHouse fed by CDC for analytics. That's boring. Boring is fine at 2am when your pager isn't firing.

FAQ

Can I just use Postgres with columnar extensions instead of moving to ClickHouse?

For under ~100M rows, often yes. The experimental pg_columnar and Citus extensions have real value. Above that, ClickHouse's query planner and compression pull away hard. We benchmarked a 600M-row table in August 2026 — Postgres with columnar took 22s on our worst query, ClickHouse took 340ms.

What's the fastest postgresql to clickhouse data migration tool?

PeerDB for ongoing CDC. Native postgresql() table function for one-shot loads under 50M rows. Custom outbox pattern if you need exactly-once semantics across schema changes. Airbyte for historical batch loads only.

Does ClickHouse replication work like Postgres replication?

No. Postgres replicates WAL — every change, in order, millisecond latency. ClickHouse replicates MergeTree parts asynchronously. You get eventual consistency and horizontal read scaling, not failover-grade HA. Don't confuse them.

Is ClickHouse faster than Postgres for writes?

Batched inserts, yes — 10-30x. Single-row inserts, no. ClickHouse hates small writes. You need to batch or you'll destroy merge performance. Postgres handles concurrent single-row writes natively with proper isolation.

How much does ClickHouse vs Postgres cost in 2026?

ClickHouse Cloud $0.22/vCPU-hr plus storage. RDS Postgres roughly similar per compute hour. Total cost depends on how much you vertically scale Postgres to handle analytics. Most clients see 3-6x lower TCO on pure-analytical workloads, and near-parity on mixed workloads.

Can I run both in the same system?

Yes — and you probably should past a certain scale. Postgres as source of truth, ClickHouse as analytical mirror fed by logical replication. That's what 8 of our 10 largest clients run in 2026.

Will ClickHouse replace PostgreSQL entirely someday?

No. Different physics problems. The moment ClickHouse adds proper ACID transactions and row-level update performance, it stops being a good columnar store. You want both. The industry is figuring that out.

The Bottom Line on ClickHouse vs PostgreSQL Which Is Faster 2026

ClickHouse is faster for analytics. Postgres is faster for transactions. Everything else is a detail about your specific workload.

If you take one thing from this: don't migrate because a blog post said ClickHouse is 100x faster. Migrate because you measured 100x faster on your queries with your data and your team can own the operational burden. That's the only test that matters in clickhouse vs postgresql which is faster 2026.

I've said no to more migrations than yes. The clients who listened are the ones not calling me at 11pm.

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