How does it handle compression selection?

It provides a decision matrix for codecs, recommending Gorilla for floats, DoubleDelta for timestamps, and T64 for counters to balance compression ratios with read speed.

Does it support ClickHouse Cloud?

Yes, it includes specific architectural patterns for ClickHouse Cloud, including SharedMergeTree configurations and cloud-native scaling practices.

What version of ClickHouse does this skill support?

The skill is optimized for ClickHouse v24.4 and newer, covering modern features like SharedMergeTree and improved lightweight updates.

Can it help with DBeaver or User Management?

While this skill focuses on architecture, it acts as a hub that delegates user creation to clickhouse-cloud-management and client configuration to clickhouse-pydantic-config.

ClickHouse Architect

Name: ClickHouse Architect
Author: terrylica

byterrylica

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Gestión de Bases de Datos

Designs high-performance schemas and optimizes query structures for ClickHouse v24.4+ environments.

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ClickHouse Architect is a specialized authority for data modeling and performance tuning within the ClickHouse ecosystem. It provides prescriptive guidance for both self-hosted (ReplicatedMergeTree) and Cloud (SharedMergeTree) deployments, focusing on critical optimizations like ORDER BY key selection, column-specific compression codecs, and efficient partitioning strategies. By enforcing modern best practices and avoiding common anti-patterns like part fragmentation, this skill ensures your analytical databases are built for ultra-low latency and maximum storage efficiency.

Características Principales

Prescriptive ORDER BY key selection based on column cardinality
2 GitHub stars
Automated anti-pattern detection for modern ClickHouse versions
Lifecycle-aware PARTITION BY strategy design
Performance acceleration via Projections and Materialized Views
Smart compression codec mapping (Gorilla, DoubleDelta, ZSTD, LZ4)

Casos de Uso

Designing real-time analytical schemas for high-volume telemetry data
Architecting migrations from self-hosted clusters to ClickHouse Cloud
Optimizing existing table structures to reduce memory usage and query time

Acerca de

Características Principales

Prescriptive ORDER BY key selection based on column cardinality
2 GitHub stars
Automated anti-pattern detection for modern ClickHouse versions
Lifecycle-aware PARTITION BY strategy design
Performance acceleration via Projections and Materialized Views
Smart compression codec mapping (Gorilla, DoubleDelta, ZSTD, LZ4)

Casos de Uso

Designing real-time analytical schemas for high-volume telemetry data
Architecting migrations from self-hosted clusters to ClickHouse Cloud
Optimizing existing table structures to reduce memory usage and query time