How to Choose ClickHouse over Snowflake in PostgreSQL

Galaxy Glossary

Why should I use ClickHouse instead of Snowflake?

ClickHouse delivers sub-second OLAP queries at lower cost than Snowflake when workloads are CPU-bound, real-time, and self-hosted.

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Description

Why pick ClickHouse instead of Snowflake?

ClickHouse is a column-oriented OLAP database that executes analytic SQL 10–100× faster than Snowflake on CPU-bound queries because data is stored in compressed columns and processed with vectorized execution.

Snowflake excels at elastic storage and zero-ops.When workloads require milliseconds of latency, real-time ingestion, or self-managed costs, ClickHouse often wins.

When does ClickHouse outperform Snowflake?

• Dashboards refreshing every few seconds.
• High-cardinality funnel or retention analysis.
• Event streams ingested at >1 M rows/s.
• Budgets that favor open-source over SaaS.

Does ClickHouse handle petabyte-scale analytics?

Yes. Sharding and ReplicaSets let clusters scale linearly.MergedTree engines compact data automatically, keeping read paths short even at PB scale.

How does ClickHouse architecture differ?

ClickHouse stores data on local disks and pushes computation to each shard. Snowflake stores data in cloud object storage and pulls it into transient compute clusters.

How does pricing compare?

ClickHouse Cloud bills per vCPU and compressed GB; self-hosted clusters incur only infra costs.Snowflake bills per warehouse-second and uncompressed storage, which grows faster on wide tables.

How to migrate analytic workloads from Snowflake to ClickHouse?

1. Export tables to Parquet.
2. Create equivalent schemas in ClickHouse.
3. Use clickhouse-client --query or clickhouse-copier to ingest files.
4.Rewrite queries using ClickHouse functions (anyHeavy, quantileTDigest, etc.).

Best practices for running ClickHouse in production?

• Partition by day and order by frequently-filtered columns.
• Keep replica lag <5 s with replication_alter_partitions_sync.
• Monitor System.Merges and System.Mutations.
• Use MaterializedView for roll-ups.

Practical ClickHouse code examples

-- Real-time revenue dashboard afterSalesView := CREATE MATERIALIZED VIEW Orders_mv ENGINE = SummingMergeTree PARTITION BY toYYYYMM(order_date) ORDER BY (order_date) AS SELECT order_date, sum(total_amount) AS daily_revenue FROM Orders GROUP BY order_date;. -- Sub-second product leaderboard SELECT product_id, sum(quantity) AS units FROM OrderItems GROUP BY product_id ORDER BY units DESC LIMIT 20;

Why How to Choose ClickHouse over Snowflake in PostgreSQL is important

How to Choose ClickHouse over Snowflake in PostgreSQL Example Usage


-- Compare repeat customers within 7 days in ClickHouse
SELECT c.id, countDistinct(o.id) AS orders_7d
FROM Customers AS c
JOIN Orders AS o ON o.customer_id = c.id
WHERE o.order_date >= today() - 7
GROUP BY c.id
ORDER BY orders_7d DESC
LIMIT 10;

How to Choose ClickHouse over Snowflake in PostgreSQL Syntax


-- ClickHouse schema for e-commerce demo
CREATE TABLE Customers 
(
    id UInt32,
    name String,
    email String,
    created_at DateTime
) ENGINE = MergeTree
PARTITION BY toYYYYMM(created_at)
ORDER BY id;

CREATE TABLE Orders
(
    id UInt32,
    customer_id UInt32,
    order_date Date,
    total_amount Decimal(10,2)
) ENGINE = MergeTree
PARTITION BY toYYYYMM(order_date)
ORDER BY (customer_id, order_date);

CREATE TABLE Products
(
    id UInt32,
    name String,
    price Decimal(10,2),
    stock UInt32
) ENGINE = MergeTree
ORDER BY id;

CREATE TABLE OrderItems
(
    id UInt32,
    order_id UInt32,
    product_id UInt32,
    quantity UInt32
) ENGINE = MergeTree
ORDER BY (order_id, product_id);

-- Snowflake equivalent for comparison
CREATE OR REPLACE TABLE Customers (
    id INT,
    name STRING,
    email STRING,
    created_at TIMESTAMP
);
-- Warehouses must be sized to support ingestion

Common Mistakes

Using ClickHouse for high-concurrency, small transactional queries. ClickHouse’s columnar storage makes single-row reads slow. Keep OLTP in PostgreSQL or MySQL and sync analytics data into ClickHouse.
Ignoring partitioning and ordering keys. A wrong key forces ClickHouse to scan all parts, destroying performance. Always partition by time and order by the most selective filters.

Frequently Asked Questions (FAQs)

Is ClickHouse harder to manage than Snowflake?

Self-hosting ClickHouse requires node monitoring and backups, but ClickHouse Cloud and Kubernetes operators reduce ops overhead to a few hours per month.

Can ClickHouse run on AWS, GCP, and Azure?

Yes. Official containers and the ClickHouse Cloud service support all major clouds, letting teams avoid vendor lock-in.

How quickly can I migrate from Snowflake?

Small datasets (<5 TB) move in days with Parquet exports. Larger datasets need phased backfills plus Change Data Capture for continuous sync.

Want to learn about other SQL terms?

dbt tests

dbt tests are declarative assertions in dbt that automatically validate data quality during model builds.

dbt utils

dbt utils is a community-maintained dbt package that adds time-saving utility macros for analytics engineers, such as generating surrogate keys, safely unioning models, and automating date spines.

dbt Tutorial Glossary: Build, Test & Deploy SQL Models

dbt is an open-source framework that lets data teams transform raw warehouse tables into tested, documented, production-ready datasets using version-controlled SQL.

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