How to Choose ParadeDB Over ClickHouse in PostgreSQL

Why should I pick ParadeDB instead of ClickHouse for analytics?

ParadeDB extends PostgreSQL with columnar storage and vector search, giving ClickHouse-like analytics without leaving the Postgres ecosystem.

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Description

When does ParadeDB beat ClickHouse?

Choose ParadeDB when you need fast analytics but also require PostgreSQL’s ACID transactions, rich joins, and extension ecosystem. ParadeDB’s columnar engine and vector index bring OLAP performance to the same cluster that runs your OLTP workload, eliminating data duplication and ETL.

How does ParadeDB simplify operations?

Because ParadeDB is a Postgres extension, you reuse the backup, HA, and monitoring tools you already trust. In contrast, ClickHouse introduces a new cluster, new ACLs, and separate observability pipelines.

What performance gains can you expect?

Columnar projections compress Orders and OrderItems up to 8×, scan data 3-5× faster, and push down filters automatically. Vector search on product embeddings delivers <50 ms ANN queries without external services.

Is SQL fully compatible?

Yes. ParadeDB accepts standard Postgres syntax, so CTEs, window functions, and JSON operators work out of the box. ClickHouse supports its own SQL dialect, which may require rewrites.

Practical paradeDB setup steps

1) Install the extension. 2) Create columnar projections on large tables.
3) Point BI tools or Galaxy to the same Postgres endpoint—no migration.

Example: speed up daily revenue query

With a projection on Orders, a dashboard query that once scanned 10 M rows now reads a 50 MB compressed segment, returning in milliseconds.

Best practices for ParadeDB

• Create projections only on hot analytical paths.
• Use partition_by for time-series tables.
• Keep work_mem ≥128 MB during initial projection build.
• Monitor pg_stat_all_tables to spot row vs. columnar usage.

Common use cases

• Mixed OLTP/OLAP ecommerce workloads.
• AI product search with pgvector.
• Real-time dashboards without a data warehouse.

Why How to Choose ParadeDB Over ClickHouse in PostgreSQL is important

How to Choose ParadeDB Over ClickHouse in PostgreSQL Example Usage


-- Find top 5 customers by revenue in 2024 using ParadeDB projection
SELECT c.id,
       c.name,
       SUM(o.total_amount) AS revenue_2024
FROM   Customers   c
JOIN   orders_projection o ON o.customer_id = c.id
WHERE  o.order_date >= '2024-01-01'
GROUP  BY c.id, c.name
ORDER  BY revenue_2024 DESC
LIMIT  5;

How to Choose ParadeDB Over ClickHouse in PostgreSQL Syntax


-- Install ParadeDB
after connecting as superuser:
CREATE EXTENSION IF NOT EXISTS paradedb;

-- Create a columnar projection on Orders
SELECT paradedb.create_projection(
    'orders_projection',
    $$
        SELECT id, customer_id, order_date, total_amount
        FROM Orders
    $$,
    partition_by := 'order_date'
);

-- Query combines row & columnar tables
SELECT c.name,
       SUM(o.total_amount) AS lifetime_value
FROM   Customers c
JOIN   orders_projection o ON o.customer_id = c.id
GROUP  BY c.name
ORDER  BY lifetime_value DESC;

Common Mistakes

Building projections on every table. This bloats storage and slows writes. Fix it by targeting only large, read-heavy tables like Orders and OrderItems.
Assuming projections auto-refresh. ParadeDB needs REFRESH MATERIALIZED PROJECTION or trigger‐based updates. Schedule refreshes or enable incremental updates.