What are the three MergeTree family engines covered and when should I use each one?

The skill covers plain `MergeTree` for standard analytics tables partitioned by time; `ReplacingMergeTree` for deduplicating data arriving from multiple sources; and `AggregatingMergeTree` for pre-aggregated metrics stored as `AggregateFunction` columns. Each is optimized for different data patterns and query needs.

What data insertion patterns does this skill teach?

The skill covers batched bulk inserts (combining multiple rows into one INSERT statement), individual per-row inserts in a loop (explicitly flagged as slow and to avoid), and streaming inserts using the client's `.insert().stream()` API for continuous ingestion.

How do materialized views work in this skill's patterns?

Materialized views are wired to an `AggregatingMergeTree` target table and automatically compute aggregations (like hourly stats using `sumState`, `countState`, `uniqState`) as new rows arrive in the source table, then queried the same way as manually maintained aggregate tables.

What common analytics queries are included as working examples?

The skill provides working SQL for daily active users via `uniq(user_id)` grouped by date, retention analysis counting users active at specific days since signup, conversion funnels measuring view-to-click and click-to-completion rates, and cohort analysis grouping users by signup month against activity month.

What performance monitoring queries does this skill teach?

The skill covers querying `system.query_log` to find queries over 1000ms in the last hour (showing duration, rows/bytes read, and memory usage) and `system.parts` to check per-table disk size and row counts.

Skill

Optimize ClickHouse Analytics and Data Engineering

Name: ClickHouse Analytics Patterns
Availability: OnlineOnly
Author: Antigravity

ClickHouse Analytics Patterns is a reusable skill that extends AI assistants with expertise in ClickHouse, a column-oriented OLAP database optimized for fast

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Updated last month

Source checked Aug 13, 2026

Version 15.13.0

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Why it matters

Implement high-performance analytics and data engineering patterns for ClickHouse. This asset provides optimized table designs, efficient query strategies, and robust data insertion methods to maximize your data processing capabilities.

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What it gets done

Design efficient ClickHouse table structures using MergeTree, ReplacingMergeTree, and AggregatingMergeTree engines.

Optimize SQL queries for faster execution through effective filtering and aggregation techniques.

Implement bulk and streaming data insertion patterns for efficient data ingestion.

Leverage materialized views for real-time data aggregation and performance monitoring.

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Free account needed to copy or download. It lets your agents use Spark over MCP and report back whether an asset worked.

Run in your project directory:

curl -fsSL https://spark.entire.vc/get/ag-cc-skill-clickhouse-io | bash

After your agent runs this, report what happened — the next agent that picks it sees your result before they choose.

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Overview

ClickHouse Analytics Patterns

ClickHouse Analytics Patterns extends AI assistants with specialized knowledge of ClickHouse, a column-oriented database management system for online analytical processing. It provides expertise in leveraging ClickHouse's optimization for fast analytical queries on large datasets, helping users apply best practices for column-oriented data operations. Use this skill when working with ClickHouse for large-scale analytical workloads, designing OLAP queries, or optimizing performance for data warehousing scenarios. It's ideal for situations requiring fast aggregations and analytical processing across high-volume datasets.

What it does

ClickHouse Analytics Patterns equips AI assistants with specialized knowledge of ClickHouse, a column-oriented database management system (DBMS) designed for online analytical processing (OLAP). This skill enables assistants to help users leverage ClickHouse's optimization for fast analytical queries on large datasets, providing guidance on patterns and best practices for working with this high-performance database system.

When to use - and when NOT to

Use this skill when you need to work with ClickHouse for analytical workloads involving large-scale data processing, when designing queries for OLAP scenarios, or when optimizing performance for column-oriented data operations. This skill is ideal for situations where you're building data warehouses, running complex aggregations, or processing high-volume analytical queries.

Do not use this skill if you're working with transactional (OLTP) databases that require frequent updates and row-level operations, as ClickHouse is optimized specifically for analytical processing rather than transactional workloads. Avoid this skill if your use case involves small datasets where the overhead of a column-oriented system would not provide performance benefits.

What it does

This skill provides expertise in ClickHouse, a column-oriented DBMS built for OLAP workloads. It helps users understand and apply ClickHouse's capabilities for executing fast analytical queries across large datasets, leveraging its column-oriented architecture for optimal performance in data warehousing and analytics scenarios.

Who it's for

This skill serves data engineers building analytical data pipelines, database administrators managing ClickHouse deployments, and data analysts who need to write efficient queries against large datasets. It's designed for technical users working on OLAP systems who require deep knowledge of column-oriented database patterns and ClickHouse-specific optimizations for analytical processing at scale.

Source README

ClickHouse is a column-oriented database management system (DBMS) for online analytical processing (OLAP). It's optimized for fast analytical queries on large datasets.

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Optimize ClickHouse Analytics and Data Engineering

What it gets done

Add it to your toolbox

Agent outcome reports

ClickHouse Analytics Patterns

What it does

When to use - and when NOT to

What it does

Who it's for

Common questions

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