v0.1 Architecture (Delta Lake & USING columnstore)
pg_mooncake v0.1 Archive (Delta Lake Engine)This page documents pg_mooncake v0.1, which used direct CREATE TABLE ... USING columnstore DDL and Delta Lake storage. Starting in v0.2 and PGSTY’s v0.3.0 release, pg_mooncake uses moonlink real-time columnstore mirrors (CALL mooncake.create_table + USING mooncake) and Apache Iceberg v2. For current documentation, see Introduction and v0.1 vs v0.2 & v0.3.
In pg_mooncake v0.1, table metadata is stored inside PostgreSQL catalogs for transactional consistency, while columnstore data is written as Parquet files with Delta Lake metadata on local disk or object storage.
Columnstore tables are exposed as a PostgreSQL Table Access Method (USING columnstore). Queries involving columnstore tables are routed from PostgreSQL to DuckDB and streamed back via pg_duckdb.
Read Path
Queries involving USING columnstore tables are routed from PostgreSQL to DuckDB, with minor SQL query rewrites applied to bridge syntax differences.
pg_mooncake v0.1 caches Parquet metadata and column statistics inside PostgreSQL (mooncake.data_files) and uses them for segment elimination during table scans.
Write Path
In v0.1, transactional INSERT, UPDATE, and DELETE statements run directly on USING columnstore tables. To support DELETE and UPDATE, an implicit row_id column consisting of (file_id, row_offset) identifies rows during table scans.
Each write transaction creates a new Parquet file. Because of this, pg_mooncake v0.1 is designed for batched writes (100,000–1,000,000 rows) rather than small, frequent OLTP transactions.
Local Disk Caching
On writes, data is cached on local disk (mooncake.enable_local_cache = true) to accelerate queries on recently written data before or alongside cloud object storage uploads.