TAG
#PostgreSQL
2026-10 2 posts
-
What Happens to HNSW Recall After Adding Vectors? (1M+1M)
A 1M+1M insertion study in pgvector and Qdrant, comparing insertion order, batch boundaries, and fresh indexes. Segment telemetry and version-pinned code help explain their different behavior.
-
What Happens to HNSW Recall After Adding Vectors? (10K+10K)
A small pgvector and Qdrant experiment compares recall before and after vector insertion, then against fresh indexes over the same final data.
2026-05 8 posts
-
PostgreSQL Lakehouse (2/2) — Distributed Processing and Citus Integration
Distributed processing (Ray/Daft/Smallpond) and Citus + pg_lake FDW integration
-
PostgreSQL Lakehouse (1/2) — When DuckLake Hit a Wall, pg_lake Was There
Eight phases building a PostgreSQL-centered Lakehouse: DuckLake's libpq collision and pg_lake
-
PG-Strom SSB Benchmark — Arrow FDW Comes Before GPU
555 test cases on three servers: on heap storage PG-Strom's GPU gave 1.6x and lost to CPU on wide tables; with Arrow FDW it hit 10x at SF=100.
-
GraphDB Benchmark (2/2) — Workload Matrix and Final Recommendations
Eight graph engines on OLTP, memory, analytics, and differentiation queries, with a decision table by workload. On OLTP, RCTE tied MemGraph at 22.5K RPS.
-
LightRAG Without Apache AGE — Graph Storage in Recursive CTE
Implementing LightRAG's BaseGraphStorage on plain PostgreSQL with RCTE — why a 1-hop-dominant retrieval pattern fits flat SQL
-
Should Databases Handle AI Too?
Oracle 23ai, EDB AIDB, PostgresML, Timescale pgai, pg_aidb — five systems and the abstraction-boundary discussion
-
Replacing Elasticsearch with PostgreSQL
Korean hybrid search on PostgreSQL alone (MeCab, BM25, pgvector, in-DB RRF) over eight phases: Elasticsearch-level quality, 2–5x faster on one node.
-
GraphDB Benchmark, Eight Engines (Part 1) — Decomposing the RCTE 290x Gap
On a 1.14M-edge knowledge graph, PostgreSQL RCTE had 290x the throughput of Apache AGE, traced to a ~13ms cypher() wrapper cost and plan generation.
No matching posts.