How to Choose ClickHouse over Oracle in PostgreSQL

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Why should I use ClickHouse instead of Oracle for analytics?

Explains when and why to select ClickHouse instead of Oracle for high-speed analytic workloads.

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Description

Table of Contents

Why choose ClickHouse over Oracle for analytic workloads?

ClickHouse’s column-oriented engine scans only requested columns, cutting disk I/O and returning aggregations on billions of rows in milliseconds—ideal for ecommerce dashboards.

Oracle’s row storage suits OLTP but slows complex GROUP BY operations on large fact tables.If 90 % of your workload is reads, ClickHouse’s compression and vectorized execution deliver lower latency and smaller storage bills.

Does ClickHouse reduce infrastructure cost?

ClickHouse is open-source under Apache 2.0 and runs on commodity hardware. License fees and high-end SANs required for Oracle RAC disappear, trimming total cost of ownership.

How does ClickHouse simplify horizontal scaling?

Shards and replicas are defined in config files; nodes auto-discover peers via ZooKeeper.Adding capacity means provisioning a server and updating configs—no expensive Oracle partitioning or RAC setup.

How to create an ecommerce analytics table in ClickHouse?

Use the MergeTree family.Define ORDER BY keys that match your filter patterns.

CREATE TABLE Orders (
id UInt64,
customer_id UInt64,
order_date DateTime,
total_amount Decimal(10,2)
) ENGINE = MergeTree
PARTITION BY toYYYYMM(order_date)
ORDER BY (order_date, id);

What is the basic query syntax compared to Oracle?

ClickHouse supports ANSI SQL plus FORMAT clauses for streaming results.

SELECT
toDate(order_date) AS day,
sum(total_amount) AS daily_sales
FROM Orders
GROUP BY day
ORDER BY day
FORMAT JSONEachRow;

How to migrate data from Oracle to ClickHouse?

Export rows with Oracle Data Pump, then pipe CSVs into ClickHouse:

$ expdp user/pass tables=ORDERS directory=DATA_PUMP_DIR dumpfile=orders.dmp
$ impdp ...

transform=segment_attributes:n
$ cat orders.csv | clickhouse-client \
--query="INSERT INTO Orders FORMAT CSV"

For continuous sync, stream Oracle redo logs into Kafka and ingest with clickhouse-client --query="INSERT INTO Orders FORMAT JSONEachRow".

Best practices for adopting ClickHouse

Index only what you filter

MergeTree indexes rely on ORDER BY keys; keep composite keys small and high-cardinality.

Leverage materialized views

Pre-aggregate heavy reports to serve dashboards instantly without recomputing billion-row joins.

Common mistakes and fixes

Loading wide CSVs without explicit types

Blind inserts force Nullable(String) columns and slow casts.Always create tables before loading.

Ignoring the Distributed engine

Querying local shards yields partial results. Use a Distributed table or clusterAllReplicas() for global queries.

Conclusion

Choose ClickHouse over Oracle when your ecommerce platform needs sub-second analytics on terabytes of data without paying hefty license fees. Proper schema design, ordered partitions, and distributed queries unlock linear scalability.

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Why How to Choose ClickHouse over Oracle in PostgreSQL is important

How to Choose ClickHouse over Oracle in PostgreSQL Example Usage


-- Daily revenue per product in ClickHouse
SELECT
    p.name,
    toDate(o.order_date) AS day,
    sum(oi.quantity * p.price) AS revenue
FROM OrderItems oi
JOIN Orders o ON oi.order_id = o.id
JOIN Products p ON oi.product_id = p.id
GROUP BY p.name, day
ORDER BY day, revenue DESC
FORMAT JSONEachRow;

How to Choose ClickHouse over Oracle in PostgreSQL Syntax


CREATE TABLE table_name (
    column_name1 data_type [DEFAULT expr] [CODEC(codec)],
    column_name2 data_type ...
)
ENGINE = {MergeTree | ReplacingMergeTree | AggregatingMergeTree}
PARTITION BY expr
ORDER BY expr
[PRIMARY KEY expr]
[SAMPLE BY expr];

INSERT INTO table_name [(column_list)] FORMAT {CSV | JSONEachRow | TabSeparated} < data_stream;

SELECT columns
FROM table_name
[WHERE condition]
[GROUP BY expr]
[ORDER BY expr]
[LIMIT n]
FORMAT {JSON | JSONEachRow | CSV};

Common Mistakes

Frequently Asked Questions (FAQs)

Does ClickHouse support ACID transactions like Oracle?

No. ClickHouse provides atomic inserts at the part level but lacks multi-statement transactions. Use it for analytics, not OLTP.

Can I run ClickHouse on the same hardware as Oracle?

Yes, but isolate disk volumes. ClickHouse benefits from fast NVMe or SSDs; mixing with Oracle’s row-store I/O can degrade both systems.

How do I secure ClickHouse?

Enable TLS, create users with limited quotas, and integrate LDAP or Kerberos. Network-level firewalls and VPNs further protect the cluster.

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