How to Use PostgreSQL over ClickHouse

Why should I choose PostgreSQL over ClickHouse?

PostgreSQL provides ACID transactions, flexible indexing, and broad SQL features, making it better than ClickHouse for OLTP and hybrid analytics workloads.

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Description

Why pick PostgreSQL instead of ClickHouse for mixed workloads?

PostgreSQL supports full ACID transactions, row-level locking, and multi-version concurrency control (MVCC). These features guarantee consistency when your application mixes inserts, updates, and reads—capabilities ClickHouse sacrifices for analytical speed.

How does PostgreSQL handle complex queries more flexibly?

PostgreSQL implements a complete SQL:2023 feature set—window functions, CTEs, recursive queries, JSONB, and PostGIS.ClickHouse focuses on aggregate queries and lacks robust UPDATE/DELETE, making PostgreSQL the safer choice for evolving business logic.

When is PostgreSQL’s extensibility a decisive factor?

Extensions like "pgvector" for embeddings, "timescaledb" for time-series, and FDWs that link to ClickHouse let you add new capabilities without migrating data. This modularity keeps your architecture simple and future-proof.

What performance tactics narrow the OLAP gap?

Partitioning, BRIN indexes, and parallel query execution let PostgreSQL scan large tables efficiently.While ClickHouse may still win pure OLAP benchmarks, these features significantly close the gap for most teams.

How do ecosystem tools strengthen PostgreSQL’s case?

Replicating to replicas, logical decoding for CDC, and built-in backup tooling provide operational maturity. Libraries, ORMs, and managed services (AWS RDS, Google Cloud SQL) reduce total cost of ownership.

Best practice: combine both with FDWs

Use PostgreSQL as your single transactional source of truth, then push aggregated or historic data to ClickHouse via the clickhouse_fdw extension.Analytics teams query ClickHouse, while applications stay on PostgreSQL.

Why How to Use PostgreSQL over ClickHouse is important

How to Use PostgreSQL over ClickHouse Example Usage


-- Identify customers who placed >3 orders this month
WITH MonthlyOrders AS (
    SELECT customer_id,
           COUNT(*) AS order_count
    FROM   Orders
    WHERE  order_date >= date_trunc('month', CURRENT_DATE)
    GROUP  BY customer_id
)
SELECT c.id,
       c.name,
       c.email
FROM   Customers c
JOIN   MonthlyOrders m ON m.customer_id = c.id
WHERE  m.order_count > 3
ORDER  BY m.order_count DESC;

How to Use PostgreSQL over ClickHouse Syntax


-- Creating a transactional Orders table
CREATE TABLE Orders (
    id            SERIAL PRIMARY KEY,
    customer_id   INT NOT NULL,
    order_date    TIMESTAMP NOT NULL DEFAULT NOW(),
    total_amount  NUMERIC(12,2) NOT NULL
);

-- Speeding up analytics with partitioning
ALTER TABLE Orders PARTITION BY RANGE (order_date);

-- Upserting product stock atomically
INSERT INTO Products (id, name, price, stock)
VALUES (101, 'USB-C Cable', 9.99, 50)
ON CONFLICT (id)
DO UPDATE SET stock = Products.stock + EXCLUDED.stock;

-- Parallel aggregations
SET max_parallel_workers_per_gather = 4;
SELECT date_trunc('day', order_date) AS day,
       SUM(total_amount)              AS revenue
FROM   Orders
GROUP  BY 1
ORDER  BY 1;

Common Mistakes

Assuming ClickHouse is always faster: ClickHouse shines in columnar scans, but write-heavy or point-lookup workloads slow dramatically. Benchmark your own workload before migrating.
Ignoring extension power: Teams discard PostgreSQL for niche features that actually exist as extensions (e.g., timeseries, vector). Search pgxn.org before redesigning your stack.