gizmosql

Hi, I've created an iOS app (GizmoSQL) for running DuckDB as a server (with Arrow Flight SQL). You can even mount a DuckLake - meaning you can have a "data lake in your pocket". It is fully secure, and runs the TPC-H 1GB 22-query benchmark in less than 2 seconds on an iPhone 16 Pro Max. See: https://github.com/gizmodata/gizmosql for more details...

GizmoSQL is definitely a good option. I work at GizmoData and maintain GizmoSQL. It is an Arrow Flight SQL server with DuckDB as a back-end SQL execution engine. It can support independent thread-safe concurrent sessions, has robust security, logging, token-based authentication, and more. It also has a growing list of adapters - including: ODBC, JDBC, ADBC, dbt, SQLAlchemy, Metabase, Apache Superset and more. We also just introduced a PySpark drop-in adapter - letting you run your Python Spark Dataframe workloads with GizmoSQL - for dramatic savings compared to Databricks for sub-5TB workloads. Check it out at: https://gizmodata.com/gizmosql Repo: https://github.com/gizmodata/gizmosql

GizmoSQL completed the One-Trillion Row Challenge! We powered through 1 TRILLION rows using GizmoSQL ( DuckDB + Apache Arrow Flight SQL) with AWS Graviton4– on a single r8gd.metal-48xl instance: Instance: r8gd.metal-48xl (Graviton4) 192 vCPUs, 1536 GiB RAM 11.4TB RAID0 NVMe on AWS $14.11 / hr on-demand $2.82 / hr spot Dataset: 2.3TB Parquet files (100,000 files with 10,000,000 rows each!) Copy from S3: 11m 24s Challenge Query (cold start): 2m 22s Challenge Query (warm start): 2m 09s Total cost: ~$0.11 (spot) per query And yes... SELECT COUNT(*) on 1 trillion rows? Just 21.8 seconds. Why it matters: GizmoSQL lets you query massive Parquet datasets interactively, directly, and efficiently – using the DuckDB SQL engine, and the Arrow Flight SQL protocol for blazing-fast client connections. No complex Spark or distributed compute cl