How to Analyze ClickHouse Pricing in PostgreSQL

Galaxy Glossary

How do I calculate and reduce ClickHouse pricing?

ClickHouse pricing analysis means estimating storage, compute, and data-transfer costs by querying ClickHouse system tables or cloud billing exports.

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Description

What cost drivers shape ClickHouse pricing?

ClickHouse Cloud charges mainly on three dimensions: compressed storage (GB-month), compute units (vCPU-hour), and egress traffic (GB). Self-hosted clusters add hardware, network, and support costs. Understanding which driver dominates lets you focus optimization efforts where they actually save money.

How can I estimate storage cost with SQL?

Query system.parts to sum on-disk bytes for each table. Divide by 1 024³ to convert to GB, then multiply by the provider’s $/GB-month rate. Run the query on a schedule and store the results in a cost-tracking table so trends are visible.

Example: storage usage for ecommerce tables

The query in the next section scans only the ecommerce database and groups by table to show granular usage, helping you spot oversized fact tables such as OrderItems.

How do I translate vCPU-hours into dollars?

Cloud vendors expose per-hour pricing per service class. Multiply the number of CPU-seconds reported in system.query_log by the hourly rate / 3 600. Roll up by day to catch workload spikes early and downsize before the next billing cycle.

When should I use tiered storage?

Historical data queried rarely can live on cheaper object storage. Move partitions older than N months with ALTER TABLE … MOVE PARTITION. This keeps hot storage light and reduces $/GB on the bill without sacrificing availability.

Best practices for keeping ClickHouse bills low

Compress columns with the CODEC(ZSTD, LZ4) family, merge small parts regularly, and avoid SELECT *. Restrict materialized views to columns actually used in dashboards. Finally, delete raw JSON once it is parsed into typed columns.

Why How to Analyze ClickHouse Pricing in PostgreSQL is important

How to Analyze ClickHouse Pricing in PostgreSQL Example Usage


-- Find the most expensive customer orders and estimate their impact on storage
SELECT c.name,
       o.id AS order_id,
       o.total_amount,
       round(sum(oi.quantity * p.price), 2) AS line_total,
       round(sum(oi.quantity * p.price) / 1000 * 0.18, 4) AS est_storage_usd
FROM Customers  AS c
JOIN Orders     AS o  ON o.customer_id = c.id
JOIN OrderItems AS oi ON oi.order_id   = o.id
JOIN Products   AS p  ON p.id          = oi.product_id
GROUP BY c.name, o.id, o.total_amount
ORDER BY est_storage_usd DESC
LIMIT 10;

How to Analyze ClickHouse Pricing in PostgreSQL Syntax


-- Estimate compressed storage by table
SELECT
    table,
    round(sum(bytes_on_disk) / 1024^3, 2) AS storage_gb,
    round(sum(bytes_on_disk) / 1024^3 * 0.18, 2) AS est_monthly_storage_usd -- assuming $0.18/GB-month
FROM system.parts
WHERE database = 'ecommerce'
GROUP BY table
ORDER BY storage_gb DESC;

-- Track query CPU usage (place in a scheduled task)
INSERT INTO cost_cpu_daily
SELECT
    toDate(time)               AS usage_date,
    round(sum(query_duration_ms) / 1000 / 3600, 2) AS vcpu_hours,
    round(sum(query_duration_ms) / 1000 / 3600 * 0.044, 2) AS est_compute_usd -- $0.044/vCPU-hr
FROM system.query_log
WHERE type = 'QueryFinish'
  AND time >= today() - 1
GROUP BY usage_date;

Common Mistakes

Ignoring compressed size. Looking at raw row counts without checking <code>bytes_on_disk</code> underestimates storage by up to 10×. Always query system tables for compressed size.
Relying on monthly invoices. Waiting for the bill hides daily spikes and prevents timely scaling. Instead create scheduled cost-tracking tables and set alerts.

Frequently Asked Questions (FAQs)

Is ClickHouse Cloud cheaper than self-hosted?

For small to medium workloads, managed ClickHouse often costs less once you factor in maintenance hours. At petabyte scale, self-hosting with spot instances can be cheaper but needs ops expertise.

How often should I run cost-tracking queries?

Daily snapshots strike a balance between granularity and overhead. Hourly tracking is helpful during migrations or major traffic events.

Want to learn about other SQL terms?

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