Timeseries and ASOF Joins

Pairing up timeseries data when the timestamps don’t match exactly (in Pandas, Polars, DuckDB, Postgres & QuestDB)

Graph Query Interfaces: A Comparison Between SQL and Cypher

Featuring DuckDB & KuzuDB

Some Notes on Vector Indexing in DuckDB

Once you’ve indexed your vectors for similarity search, be sure to check your query plans, just in case the DB decides to opt for a sequential scan

Combining Lexical and Semantic Search with Reciprocal Rank Fusion

Best of both worlds sort of thing

Vector Indexing and Search with DuckDB & FastEmbed

Using DuckDB for vector/semantic search

DuckDB JIT Compiled UDFs with Numba

JIT compiling your vectorized UDFs with Numba. Plus pure SQL is plenty fast if you can figure out how to write it

Vectorized DuckDB UDFs with Rust and Python FFI

Implementing vectorized UDFs in Rust that you can use in DuckDB, with a little help from Arrow

Parquet + Zstd: Smaller faster data formats

Often, parquet files have to be compressed. For fast compression, use LZ4 or Snappy. For the highest data compression ratio, use brotli. For both, zstd

SQL Grouping sets, Rollups & Cube

Computing multiple Group-bys with less steps

Programmatically creating a DuckDB table from an Arrow schema

PyArrow lets you create an empty table. Use that instead of custom mappings to create a DuckDB schema.