القائمة
DuckDB glob scan patterns, connection management, and performance for kreview
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
| name | duckdb-patterns |
| description | DuckDB glob scan patterns, connection management, and performance for kreview |
| Feature | Suffix | Rows Per Sample |
|---|---|---|
| FSC.gene | .FSC.gene.parquet | ~128 |
| FSD | .FSD.chr_arm.parquet | ~39 |
| FSR | .FSR.parquet | 1 |
| OCF.ontarget | .OCF.ontarget.parquet | ~100 |
| WPS.background | .WPS_background.parquet | ~22 |
| metadata | .metadata.parquet | 1 |
# Single feature, all samples
read_parquet('results/*/*.FSC.gene.parquet', filename=true)
# Multiple features via UNION ALL
SELECT 'FSC' AS feature, * FROM read_parquet('results/*/*.FSC.gene.parquet', filename=true)
UNION ALL
SELECT 'FSD' AS feature, * FROM read_parquet('results/*/*.FSD.chr_arm.parquet', filename=true)
# ✅ Per-function connection (recommended)
def load_data():
conn = get_duckdb_conn() # from kreview.core
return conn.sql("...").df()
# ❌ Global connection (fragile in notebooks)
CONN = duckdb.connect() # dies on kernel restart
.df()SELECT col1, col2 not SELECT *SET threads TO 4 (default is all cores)SET memory_limit = '4GB' for large cohortsWhen DuckLake reaches v1.0, the migration is:
# Before (DuckDB glob):
conn.sql("SELECT * FROM read_parquet('results/*/*.FSC.gene.parquet')")
# After (DuckLake):
conn.execute("ATTACH 'ducklake:catalog.ducklake' AS eval")
conn.sql("SELECT * FROM eval.fsc_gene")
Downstream code is identical — only the data source changes.
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