clickhouse-io

ClickHouse database patterns, query optimization, analytics, and data engineering best practices for high-performance analytical workloads.

Quality

48%

Does it follow best practices?

Impact

Pending

No eval scenarios have been run

Securityby

Passed

No known issues

Optimize this skill with Tessl

npx tessl skill review --optimize ./docs/zh-TW/skills/clickhouse-io/SKILL.md

Evaluation results

83%

-12%

SaaS Event-Tracking Analytics Schema

Table engine and schema design

Criteria

Without context

With context

ReplacingMergeTree engine

100%

Time-based partitioning

100%

Sort key ordering

50%

LowCardinality usage

100%

Enum type usage

100%

Appropriate integer size

100%

No SELECT *

100%

Index-first WHERE filtering

100%

83%

uniq() for distinct counts

100%

quantile() for percentiles

100%

Real-Time Sales Dashboard Aggregations

Materialized views real-time aggregation

Criteria

Without context

With context

AggregatingMergeTree target table

100%

AggregateFunction column types

100%

Materialized view with *State functions

100%

Query uses *Merge functions

100%

No FINAL keyword

100%

Time partitioning on aggregation table

100%

toStartOfHour/toStartOfDay alignment

100%

No SELECT *

100%

uniq() for distinct counts

100%

Index-first WHERE filtering

100%

39%

Trade Data Ingestion Service

Bulk insert TypeScript ETL pipeline

Criteria

Without context

With context

Correct client package

100%

Bulk insert pattern

40%

100%

No per-row individual inserts

100%

MergeTree table definition

100%

Time-based partitioning

100%

LowCardinality for repeated strings

100%

ClickHouse client configuration

37%

100%

No SELECT *

100%

uniq() in analytics queries

100%

quantile() for percentile queries

100%

Repository: haniakrim21/everything-claude-code
Commit: ae2cadd

Evaluated: 18 days ago
Agent: Claude Code
Model: Claude Sonnet 4.6

Table of Contents

SaaS Event-Tracking Analytics Schema Real-Time Sales Dashboard Aggregations Trade Data Ingestion Service

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