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ClickHouse database patterns, query optimization, analytics, and data engineering best practices for high-performance analytical workloads.
Passed 17 of 22 test cases with the skill, and 15 of 22 without it.
and 1 more in the full report
2 fixed15 passed either way5 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 2 test cases the skill fixed. Same model, same prompt, with and without the skill.
我們的 ClickHouse 資料庫最近有查詢延遲現象。請提供一組監控查詢 SQL，幫我檢查過去一小時執行時間較長的慢查詢紀錄（包含查詢識別碼、執行毫秒數、讀取資料列數與記憶體用量），以及檢查當前各個資料表佔用磁碟容量與資料筆數的統計查詢。
| Checked | Without skill | With skill |
|---|---|---|
| The slow query monitoring SQL queries system.query_log with type = 'QueryFinish'. | Passed | Passed |
| The slow query monitoring SQL filters for query_duration_ms > 1000. | Missed | Passed |
| The table size SQL queries system.parts with a filter on active parts. | Passed | Passed |
| The table size SQL includes max(modification_time) AS latest_modification. | Missed | Passed |
The instructions your model reads when the skill loads.
用於高效能分析和資料工程的 ClickHouse 特定模式。
ClickHouse 是一個列式資料庫管理系統（DBMS），用於線上分析處理（OLAP）。它針對大型資料集的快速分析查詢進行了優化。
關鍵特性：
CREATE TABLE markets_analytics (
date Date,
market_id String,
market_name String,
volume UInt64,
trades UInt32,
unique_traders UInt32,
avg_trade_size Float64,
created_at DateTime
) ENGINE = MergeTree()
PARTITION BY toYYYYMM(date)
ORDER BY (date, market_id)
SETTINGS index_granularity = 8192;
-- 用於可能有重複的資料（例如來自多個來源）
CREATE TABLE user_events (
test cases passed, vs 15 / 22 without
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More skills from the same source and nearby in the registry, with their headline benchmark lift.