How to Choose ClickHouse over ParadeDB in PostgreSQL

Galaxy Glossary

Why should I use ClickHouse over ParadeDB?

Guidance on when ClickHouse outperforms ParadeDB for analytic workloads

Welcome to the Galaxy, Guardian!
You'll be receiving a confirmation email

Follow us on twitter :)

Oops! Something went wrong while submitting the form.

Description

Example H2

Example H3

Why pick ClickHouse instead of ParadeDB?

Choose ClickHouse when ultra-fast, columnar analytics on terabytes of immutable data is your top priority. Its vectorized execution, compression, and distributed MergeTree engine deliver millisecond queries that ParadeDB’s row-store extension cannot match at scale.

When does ParadeDB remain adequate?

Stick with ParadeDB for smaller datasets (<100 GB), transactional joins, or when you need Postgres extensions, roles, and ACID semantics in one cluster.It shines for hybrid transactional/analytical processing where latency <1 s is acceptable.

What workloads favor ClickHouse?

High-cardinality event logs, real-time dashboards, and funnel analysis run faster because ClickHouse stores columns separately, skips unnecessary blocks, and parallelizes scans across shards.

How do ingestion patterns differ?

Batch-insert millions of rows per second in ClickHouse using INSERT … VALUES or INSERT … SELECT.ParadeDB inherits Postgres’s slower single-row INSERT but supports COPY for bulk loads; performance still lags columnar storage.

Can I join Postgres data with ClickHouse?

Yes. Use clickhouse-fdw in Postgres or the Postgres table engine in ClickHouse to query across systems. This hybrid pattern keeps hot OLTP data in ParadeDB while offloading heavy reads to ClickHouse.

Best practices for migration

Start with one analytic table. Mirror writes to ParadeDB and ClickHouse via logical replication or Kafka.Validate query parity, then cut over read traffic once latency goals are met.

Common mistakes and fixes

Ignoring sort keys. Without ORDER BY in MergeTree, ClickHouse can’t skip data. Always define a compound key (e.g., (customer_id, order_date)).

Using default compression. ZSTD usually halves disk versus LZ4; set SET compression_codec_zstd before creating tables.

ClickHouse syntax vs ParadeDB

-- ClickHouse analytic table CREATE TABLE Orders_MV ENGINE = MergeTree ORDER BY (customer_id, order_date) AS SELECT id, customer_id, order_date, total_amount FROM Orders;. -- ParadeDB GIN index on JSONB CREATE INDEX idx_orders_total ON Orders USING GIN ((data->'total_amount'));

FAQ

Is ClickHouse fully ACID?

No. It provides per-part atomicity but lacks multi-statement ACID. Use ParadeDB or Postgres for strict transactions.

Can ParadeDB match ClickHouse compression?

ParadeDB relies on TOAST and extensions; compression ratios rarely exceed 3-4×, whereas ClickHouse routinely achieves 10-15×.

Do I need separate infrastructure?

Usually yes. ClickHouse nodes are optimized for SSDs and memory bandwidth.Running it side-by-side with Postgres avoids resource contention.

Why How to Choose ClickHouse over ParadeDB in PostgreSQL is important

How to Choose ClickHouse over ParadeDB in PostgreSQL Example Usage


-- Funnel analysis: orders per customer per month in ClickHouse
SELECT customer_id,
       toStartOfMonth(order_date)  AS month,
       count() AS orders,
       sum(total_amount) AS revenue
FROM Orders
GROUP BY customer_id, month
ORDER BY revenue DESC
LIMIT 20;

How to Choose ClickHouse over ParadeDB in PostgreSQL Syntax


ClickHouse
CREATE TABLE <name> (
    id UInt64,
    customer_id UInt64,
    order_date DateTime,
    total_amount Decimal(12,2)
) ENGINE = MergeTree
ORDER BY (customer_id, order_date)
PARTITION BY toYYYYMM(order_date)
SETTINGS index_granularity = 8192;

ParadeDB (Postgres extension)
CREATE EXTENSION IF NOT EXISTS paradedb;

-- Vector search example on product embeddings
ALTER TABLE Products ADD COLUMN embedding vector(768);
CREATE INDEX idx_products_embedding ON Products USING ivfflat (embedding vector_l2_ops) WITH (lists = 100);

-- Standard Postgres SELECT still works
SELECT name, price FROM Products ORDER BY price DESC LIMIT 10;

Common Mistakes

Overlooking appropriate ORDER BY keys in ClickHouse; fix by matching keys to your most common filtering pattern so data skipping works.
Migrating all tables to ClickHouse; fix by moving only read-heavy, append-only tables and keeping transactional ones in ParadeDB.