How to ClickHouse Use Cases in PostgreSQL

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What are the main ClickHouse use cases and how do I implement them?

ClickHouse excels at ultra-fast analytical queries, real-time dashboards, and high-volume event storage.

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Description

Example H2

Example H3

What problems does ClickHouse solve?

ClickHouse is column-oriented, so it scans only needed columns, delivering sub-second analytics on billions of rows. Its compression and vectorized execution make cost per query low, perfect for real-time dashboards and user-facing analytics.

When should I pick ClickHouse over PostgreSQL?

Choose ClickHouse when you require interactive reports on >100M rows, streaming inserts at >100K rows/s, or roll-ups on time-series data.PostgreSQL stays ideal for OLTP, constraints, and complex joins on smaller datasets.

How do I model ecommerce data in ClickHouse?

Create denormalized tables or materialized views that group frequent joins up front.Insert events into OrderItems and aggregate into daily summaries to keep queries fast.

Example: Orders fact table

CREATE TABLE OrdersFact (order_id UInt64, customer_id UInt64, order_date DateTime, total_amount Decimal(12,2)) ENGINE = MergeTree PARTITION BY toYYYYMM(order_date) ORDER BY (order_date, order_id);

How to query recent revenue quickly?

ClickHouse’s SAMPLE and approximate functions return answers in milliseconds.Use them in dashboards that refresh every few seconds.

Query last 24 h revenue

SELECT sum(total_amount) AS revenue FROM OrdersFact WHERE order_date > now() - INTERVAL 1 day;

Can ClickHouse power customer-level drilldowns?

Yes. Pre-aggregate by customer in a materialized view.The base table remains insert-only, while the view keeps rolled-up stats for instantaneous filtering.

Materialized view syntax

CREATE MATERIALIZED VIEW CustomerSales ENGINE = AggregatingMergeTree() PARTITION BY customer_id ORDER BY customer_id AS SELECT customer_id, sumState(total_amount) AS amt_state FROM OrdersFact GROUP BY customer_id;

Best practices for ClickHouse use cases

Partition by time, order by high-cardinality keys, batch inserts at 10 k+ rows, and avoid small parts by using INSERT … SELECT for backfills.

What are typical ClickHouse integrations?

Use Kafka or RabbitMQ for streaming ingests, Grafana or Superset for visualization, and keep PostgreSQL as transactional source while replicating to ClickHouse.

Why How to ClickHouse Use Cases in PostgreSQL is important

How to ClickHouse Use Cases in PostgreSQL Example Usage


-- How many products did each customer buy last month?
SELECT c.name,
       sum(oi.quantity) AS items_purchased,
       sum(p.price * oi.quantity) AS spend
FROM OrderItems AS oi
JOIN OrdersFact AS o  ON o.order_id = oi.order_id
JOIN Customers   AS c  ON c.id = o.customer_id
JOIN Products    AS p  ON p.id = oi.product_id
WHERE o.order_date >= toStartOfMonth(now()) - INTERVAL 1 month
GROUP BY c.name
ORDER BY spend DESC
LIMIT 10;

How to ClickHouse Use Cases in PostgreSQL Syntax


-- Create denormalized OrdersFact for analytics
CREATE TABLE OrdersFact (
    order_id       UInt64,
    customer_id    UInt64,
    order_date     DateTime,
    total_amount   Decimal(12,2)
) ENGINE = MergeTree
PARTITION BY toYYYYMM(order_date)
ORDER BY (order_date, order_id);

-- Real-time customer sales materialized view
CREATE MATERIALIZED VIEW CustomerSales
ENGINE = AggregatingMergeTree()
PARTITION BY customer_id
ORDER BY customer_id AS
SELECT customer_id,
       sumState(total_amount) AS amt_state
FROM OrdersFact
GROUP BY customer_id;

-- Query revenue last 30 days
SELECT sum(total_amount) AS revenue
FROM OrdersFact
WHERE order_date > now() - INTERVAL 30 day;

Common Mistakes

Ignoring proper partitioning: defaulting to ORDER BY id slows range queries. Always partition by time and ORDER BY frequently filtered columns to keep read latency low.
Writing single-row inserts: ClickHouse is optimized for batches; inserting one row per HTTP call creates small parts and degrades performance. Batch 10 k+ rows or use Kafka.