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ClickHouse Optimization Tips I Learned Today

Today, I dove deep into ClickHouse — one of the fastest columnar OLAP databases out there — and picked up several performance-focused insights. Whether you're new to ClickHouse or using it in production, these optimization techniques can seriously level up your query speed and storage efficiency.

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🧱 1. Columnar Database 101

ClickHouse is a column-based database, which means:

Only the columns needed for a query are read from disk.

This leads to:

• Faster analytics queries

• Better compression

• Efficient storage

Pro tip: Don’t store JSON strings — instead, break them into real typed columns (or use Map, Array, Tuple, Nested).

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🧮 2. Aggregation vs Join — What to Use?

Joins can be slow, especially on large datasets. Whenever possible:

• Use pre-aggregated tables

• Or denormalize your schema

ClickHouse is built for speed, but joins are inherently heavier than columnar aggregations.

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⚙️ 3. Use ORDER BY Smartly

ClickHouse’s ORDER BY is not just for sorting — it defines the primary key and how data is stored on disk.

Example:

ORDER BY (event_name, toDate(event_time), tag_id, muid)

This helps:

• Speed up queries with filters or ranges on those fields

• Improve compression

❌ You can’t change ORDER BY after creating a table.
✅ To update it, you must create a new table, copy the data, and rename it.

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🛠️ 4. Adding Columns Efficiently

ClickHouse supports altering schemas dynamically:

ALTER TABLE events
ADD COLUMN device_type String DEFAULT 'unknown' AFTER event_name;

Things to know:

• Columns can be added without rewriting existing data

• Add a DEFAULT to avoid NULLs

• Use AFTER to logically order columns (not required for performance)

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📦 5. Avoid Raw JSON — Use Real Columns or Map

Parsing JSON strings on the fly slows down queries:

-- Slower
SELECT JSONExtractString(json_col, 'user.city') FROM events;

-- Better: store 'user_city' as a column directly

If you need flexible schemas, use:

Map(String, String) or Nested structures

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📉 6. Query Less, Compress More

ClickHouse compresses columnar data extremely well when:

• You use consistent types (e.g., LowCardinality(String) for enums or repeated strings)

• Your data is sorted via a good ORDER BY

The better your ORDER BY, the more compression you get.

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🧪 What I’ll Explore Next

• Materialized Views for pre-aggregation

• PARTITION BY for large time-series data

• ClickHouse performance tuning flags and system tables

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💬 Final Thoughts

ClickHouse is blazing fast — but only if you design your tables and queries thoughtfully. Columnar magic only works if you think in columns and optimize for reads.

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If you're using ClickHouse or exploring data warehousing, follow me for more deep dives, breakdowns, and real-world use cases! 💻✨

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