ClickHouse数据库模式、查询优化、分析以及高性能分析工作负载的数据工程最佳实践。
78
63%
Does it follow best practices?
Impact
87%
1.14xAverage score across 6 eval scenarios
Passed
No findings from the security scan
Fix and improve this skill with Tessl
tessl review fix ./docs/zh-CN/skills/clickhouse-io/SKILL.mdQuery optimization and monitoring
Indexed columns first in WHERE
70%
80%
uniq() for distinct counts
100%
80%
quantile() for percentiles
100%
100%
No SELECT *
100%
100%
No FINAL keyword
100%
100%
No excessive JOINs
100%
100%
sumMerge/countMerge/uniqMerge for MV queries
100%
100%
system.query_log monitoring
100%
100%
system.parts for table size
33%
100%
ClickHouse time functions
100%
100%
countIf() for conditional aggregation
100%
100%
TypeScript data ingestion patterns
Uses 'clickhouse' package
0%
100%
Port 8123
100%
25%
basicAuth configuration
0%
100%
Environment variables for credentials
100%
100%
Batch insert for bulk loading
100%
100%
No individual inserts in loop
100%
100%
Streaming insert for continuous data
0%
100%
Batch size consideration
40%
40%
toPromise() or async/await
100%
100%
No SELECT * in any queries
100%
100%
ClickHouse table schema design
MergeTree engine used
100%
100%
index_granularity setting
100%
100%
PARTITION BY toYYYYMM
100%
100%
ORDER BY indexed columns first
100%
100%
ReplacingMergeTree for deduplication
100%
100%
PRIMARY KEY on ReplacingMergeTree
0%
0%
LowCardinality for repeated strings
100%
100%
Enum for categorical data
0%
0%
Smallest appropriate integer type
66%
55%
No SELECT * in queries
100%
100%
Time-based partitioning only
100%
100%
AggregatingMergeTree or Materialized View
42%
100%
dfbf946
Table of Contents
If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.