index in clickhouse Examples & 2025 Guide

Understanding how indexes work in ClickHouse is essential for high-performance analytics and large-scale data queries. ClickHouse is renowned for its blazing-fast OLAP capabilities—but only if you leverage the right index types. Misconfiguration or lack of knowledge leads to slow queries and wasted resources. AI2sql lets you generate optimal ClickHouse index SQL in seconds, taking the guesswork (and grunt work) out of advanced database performance.

1. What is index in ClickHouse?

In ClickHouse, an index is a data structure that helps the database engine skip irrelevant data parts during SELECT queries, dramatically improving performance for large tables. Unlike in traditional RDBMSs, indexes in ClickHouse are specialized and tightly linked to the MergeTree family of table engines.

• Primary Key: Defines the physical data order in partitions; it is a critical part of MergeTree tables.

• Data Skipping Indexes (sometimes just called 'secondary indexes'): Allow ClickHouse to skip reading parts of data by tracking min/max or other summary statistics.

2. How index in ClickHouse Works

ClickHouse stores data in parts, each of which maintains index information to help the engine quickly determine if a part contains relevant rows for a given query. Types include:

• Primary Key Index: Not an index in the traditional sense, but a way to organize and filter by key columns efficiently.

• Data Skipping Indexes: The minmax, set, bloom_filter, and ngrambf_v1 indexes all provide various mechanisms for segment elimination during query execution.

3. Key Features & Benefits

• Massive performance improvements for analytical queries on large data sets

• Highly customizable for time series, logs, distributed analytics, etc.

• No traditional overhead of UPDATE/DELETE operations, so indexing scales easily

• Native support for data skipping and filtering

4. Real-World Examples

Below are practical index in clickhouse examples for daily use:

A. Creating a MergeTree table with a primary key

CREATE TABLE events (
    event_date  Date,
    user_id     UInt64,
    event_type  String
)
ENGINE = MergeTree()
PARTITION BY toYYYYMM(event_date)
ORDER BY (user_id, event_type);

B. Adding a minmax index for efficient range queries

CREATE TABLE logs (
    log_time  DateTime,
    message   String
)
ENGINE = MergeTree()
ORDER BY log_time
SETTINGS index_granularity = 8192;

C. Creating a data skipping index (for text searches)

ALTER TABLE logs 
ADD INDEX idx_message message TYPE set(100) GRANULARITY 4;

Benchmark: Index Impact on Query Speed

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Conclusion

Setting up and optimizing indexes in ClickHouse is vital for high-speed, scalable query performance. Whether you use primary keys for ordered storage or specialized data skipping indexes for analytics, these structures are your shortcut to efficiency. AI2sql empowers you to bypass manual coding and instantly generate the best index strategies—no coding required, instant results, and enterprise-ready. Trusted by 50 000+ developers at companies like Stripe & Shopify. Try AI2sql Free – Generate index in clickhouse Solutions.