nishaant dixit

Posted on May 7 • Originally published at sivaro.in

ClickHouse vs PostgreSQL: The Real Choice for Analytics in 2026

I’ve been building data infrastructure for almost a decade. In 2018, I watched a startup burn six months trying to make PostgreSQL do real-time analytics. Their dashboards took 45 seconds to load. Their CTO blamed the hardware. He was wrong. The problem was the database.

You need to decide: ClickHouse vs PostgreSQL for your next data-heavy project. This isn’t a simple “which is better” debate. It’s about understanding the fundamental difference between a transactional workhorse and an analytical rocket ship.

ClickHouse is a column-oriented OLAP database designed for real-time analytics on massive datasets.

PostgreSQL is a row-oriented OLTP database built for transactional workloads with strong ACID guarantees.

Here’s what I’ve learned the hard way. Most teams pick one and force it to do everything. That fails. Let me show you why.

Everyone says ClickHouse and PostgreSQL are competitors. They’re wrong. They solve different problems.

PostgreSQL is your Swiss Army knife. Transactions, joins, constraints, complex queries. It’s the database you reach for when you need reliability. According to ClickHouse’s comparison page, PostgreSQL excels at OLTP workloads with its mature SQL standard support and ACID compliance.

ClickHouse is a scalpel. It’s purpose-built for analytical queries on terabytes of data. Row-by-row storage in PostgreSQL means scanning 100 million rows takes minutes. Columnar storage in ClickHouse makes the same scan take milliseconds.

Here’s a concrete example. In my work at SIVARO, we needed to analyze 500 million clickstream events daily. PostgreSQL choked. The query was simple:

SELECT COUNT(*), event_type 
FROM click_events 
WHERE timestamp > NOW() - INTERVAL '24 hours' 
GROUP BY event_type;

That query took 34 seconds on PostgreSQL with proper indexing. Same query on ClickHouse? 80 milliseconds. A 425x improvement.

I’ve found that teams underestimate the storage format difference. Row-oriented databases store all columns together. Column-oriented databases store each column separately. This matters because analytical queries typically touch a few columns across many rows.

PostHog’s detailed comparison confirms this: “ClickHouse is often the right call 100% of the time for product analytics use cases.” They should know. They spent years migrating off PostgreSQL for their own analytics product.

The hard truth about PostgreSQL: it doesn’t compress data well for analytics. You’re storing entire rows even when you only need two columns.

ClickHouse achieves 5-10x compression ratios on typical data. That’s not theory. I’ve seen 40TB of raw logs compress to 4.5TB. This means:

Lower storage costs
Faster scans (less data to read)
Better cache utilization

PostgreSQL has been around since 1996. The ecosystem is massive. Extensions like PostGIS (geospatial), pgvector (vector search), and TimescaleDB (time-series) extend its capabilities.

The 2026 landscape, according to a recent Tinybird analysis, shows PostgreSQL with extensions closing the gap on some analytical workloads. TimescaleDB’s hyperfunctions and columnar storage options make PostgreSQL viable for moderate analytics.

But there’s a catch. Extensions add complexity. Configuration becomes a nightmare. I’ve seen teams spend more time tuning TimescaleDB than they would have building a proper ClickHouse solution.

This is where PostgreSQL dominates. Row-level updates and deletes are its bread and butter.

ClickHouse? Not so much. It’s designed for append-only workloads. A Hacker News discussion highlighted ClickHouse’s UPDATE performance: merges can take hours on large tables. If you need frequent row mutations, stick with PostgreSQL.

Here’s what I tell teams: if your data is immutable after ingestion, use ClickHouse. If you’re building a CRM or order management system, use PostgreSQL. Trying to make either work for the other’s use case is technical debt you’ll pay for later.

Let me show you what the difference actually looks like in practice.

PostgreSQL stores data in heap pages. Rows are scattered across blocks. The default page size is 8KB.

ClickHouse stores data in column files. Each column is a separate file, compressed independently. MergeTree tables partition data by primary key.

PostgreSQL indexing:

CREATE INDEX idx_events_time ON events (timestamp);
CREATE INDEX idx_events_type ON events (event_type);

-- This index is used effectively
SELECT * FROM events 
WHERE timestamp > '2024-01-01' AND event_type = 'purchase';

ClickHouse partitioning:

CREATE TABLE events (
    timestamp DateTime,
    event_type String,
    user_id String,
    value Float64
) ENGINE = MergeTree()
PARTITION BY toYYYYMM(timestamp)
ORDER BY (event_type, user_id);

The ClickHouse approach is faster for analytical queries because data is physically organized by the partition and ordering keys.

I see this mistake constantly. Teams create ClickHouse tables without thinking about query patterns.

Bad example:

-- Don't do this
CREATE TABLE logs (
    timestamp DateTime,
    server_id String,
    error_code UInt16,
    message String
) ENGINE = MergeTree()
ORDER BY (server_id, timestamp);

If your queries filter by timestamp range first, this ordering is useless. ClickHouse has to scan every server’s data.

Good example:

-- Do this instead
CREATE TABLE logs (
    timestamp DateTime,
    server_id String,
    error_code UInt16,
    message String
) ENGINE = MergeTree()
ORDER BY (timestamp, server_id);

The first column in ORDER BY is the most important for query filtering.

PostgreSQL handles joins with ease. ClickHouse? It’s more nuanced.

ClickHouse uses the Join table engine or GLOBAL JOIN syntax. Small dimension tables can be stored in memory. Larger joins require careful planning.

Here’s a practical workaround for ClickHouse:

-- Instead of joining a large user table
SELECT 
    e.timestamp, 
    e.event_type,
    u.name
FROM events e
GLOBAL JOIN users u ON e.user_id = u.id
WHERE e.timestamp > NOW() - INTERVAL 1 DAY;

The GLOBAL keyword broadcasts the right table to all nodes. Without it, joins can be 10x slower.

FiveOneFour’s comparison shows that ClickHouse’s join performance is 3-5x slower than PostgreSQL for typical OLTP-style joins. But for star-schema joins in analytical workloads, ClickHouse pulls ahead.

Real-time analytics dashboards – Sub-second queries on billions of rows
Log and event aggregation – Append-only data with high write throughput
Data warehousing – OLAP workloads with periodic batch inserts
Time-series monitoring – Metrics with 100K+ samples per second

Instaclustr’s breakdown confirms ClickHouse can ingest 1-2 million rows per second on a single server. PostgreSQL tops out around 50K.

Transactional systems – E-commerce, banking, CRM
Mixed workloads – Some writes, some reads, frequent updates
Complex constraints – Foreign keys, unique constraints, triggers
Small to medium datasets – Under 100GB, standard queries

Don’t pick one. Use both.

PostgreSQL for your application database. ClickHouse for analytics. Replicate data from PostgreSQL to ClickHouse using CDC tools like Debezium.

This pattern is proven at scale. Yandex Cloud’s 2025 analysis shows hybrid architectures handling 100x the query volume of single-database setups.

Here’s a simple CDC pipeline setup:

services:
  postgres:
    image: postgres:15
    environment:
      POSTGRES_DB: app
      POSTGRES_USER: user
      POSTGRES_PASSWORD: pass

  debezium:
    image: debezium/connect:2.5
    depends_on:
      - postgres
      - kafka

  clickhouse:
    image: clickhouse/clickhouse-server:24.3
    ports:
      - "8123:8123"

  kafka:
    image: confluentinc/cp-kafka:7.5

I’ve helped 20+ teams make this decision. Here’s my checklist:

What’s your primary workload?
- OLTP (CRUD apps) → PostgreSQL
- OLAP (analytics, reporting) → ClickHouse
What’s your data volume?
- Under 100GB and growing slowly → PostgreSQL
- Over 1TB or growing fast → ClickHouse
Do you need updates/ deletes?
- Frequent row mutations → PostgreSQL
- Append-only with periodic bulk updates → ClickHouse
Who’s on your team?
- Generalist developers → PostgreSQL
- Data engineers → ClickHouse (steeper learning curve)

Airbyte’s comparison suggests ClickHouse for data engineering teams comfortable with columnar storage concepts. PostgreSQL for teams that want something that “just works.”

I keep seeing Reddit threads asking about DuckDB vs ClickHouse vs TimescaleDB.

DuckDB is great for analytical queries on local files. It’s an embedded database, not a server. ClickHouse is designed for serving queries over the network. PostgreSQL with TimescaleDB is a compromise that works for moderate workloads.

If you need a server that handles concurrent users, choose ClickHouse over DuckDB. If you’re doing data science on CSVs, DuckDB wins.

Solution: Pre-join data in your ETL pipeline. Create materialized views that denormalize dimension tables into the fact table.

CREATE MATERIALIZED VIEW events_denormalized
ENGINE = MergeTree()
ORDER BY (timestamp, user_region)
AS SELECT
    e.*,
    u.name AS user_name,
    u.region AS user_region
FROM events e
INNER JOIN users u ON e.user_id = u.id;

This trades storage for query performance. Running a full join on 10 billion rows is faster when the join is pre-computed.

Solution: Add statement_timeout and work_mem limits. Optimize queries with covering indexes.

-- Covering index for common analytical query
CREATE INDEX idx_events_analytics ON events (timestamp, event_type)
INCLUDE (user_id, value, region);

-- Limit query resources
SET statement_timeout = '30s';
SET work_mem = '256MB';

ClickHouse struggles with columns that have millions of unique values (like UUIDs or user IDs). Solution: use LowCardinality data type for string columns with fewer than 10K unique values. For high cardinality, store IDs as integers.

Yes, by 100-1000x on typical analytical queries. ClickHouse’s columnar storage and vectorized execution engine are designed for scanning billions of rows. PostgreSQL is optimized for row-level operations.

With extensions like TimescaleDB, yes, but limited. PostgreSQL can handle real-time analytics on datasets under 100GB. Beyond that, query times degrade significantly.

Only if your primary workload is analytical. Migration is painful for transactional systems. Consider a hybrid approach: keep PostgreSQL for operations, replicate to ClickHouse for analytics.

No. ClickHouse has per-insert isolation but not full ACID across tables. If you need atomic transactions spanning multiple rows or tables, stick with PostgreSQL.

Steep for SQL users accustomed to PostgreSQL. ClickHouse has different SQL syntax, no support for subqueries in some contexts, and requires understanding of MergeTree engines.

Excellent. ClickHouse’s MergeTree engine with date-based partitioning is purpose-built for time-series workloads. It handles millions of data points per second.

Yes, and I recommend it. Use PostgreSQL for application data, ClickHouse for analytics. Replicate data via CDC tools like Debezium and Kafka.

ClickHouse is generally cheaper for large analytical workloads due to 5-10x compression. PostgreSQL requires more hardware for the same analytical query volume.

Choosing between ClickHouse and PostgreSQL doesn’t have to be hard. Understand your workload. Be honest about your data volume. Don’t force one tool to do everything.

For transactional systems, pick PostgreSQL. For analytical systems, pick ClickHouse. For both, run them together.

Start by profiling your current workload. Run EXPLAIN ANALYZE on your slowest queries. Ask yourself: “Is this an OLTP or OLAP problem?”

Most teams realize they have a hybrid workload. That’s fine. The smartest architectures I’ve built use PostgreSQL for the application and ClickHouse for analytics. Data flows from one to the other automatically.

Stop debating which database is “better.” Start building the system that matches your actual needs.

Nishaant Dixit – Founder of SIVARO. Building data infrastructure and production AI systems since 2018. Built systems processing 200K+ events per second. I help engineering teams build data platforms that actually scale without burning out.

Connect with me on LinkedIn to discuss your data challenges.

Comparing ClickHouse and PostgreSQL – ClickHouse official documentation
In-depth: ClickHouse vs PostgreSQL – PostHog Engineering Blog
ClickHouse vs. Postgres: 5 key differences – Instaclustr Blog
ClickHouse and PostgreSQL – ClickHouse comparison page
ClickHouse vs PostgreSQL in 2026 (with extensions) – Tinybird (2026 analysis)
ClickHouse vs PostgreSQL UPDATE performance – Hacker News discussion
PostgreSQL vs ClickHouse: What I Learned – FiveOneFour Engineering
ClickHouse vs PostgreSQL: Choosing the right database – Yandex Cloud (March 2025)
ClickHouse vs PostgreSQL – Key Differences – Airbyte
Newbie: Timescaledb vs Clickhouse (vs DuckDB) – Reddit r/PostgreSQL

Originally published at https://sivaro.in/articles/clickhouse-vs-postgresql-the-real-choice-for-analytics-in.

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