DuckDB is a high-performance, embeddable, in-process analytical SQL database engine written in C++. It provides a full SQL dialect with vectorized execution, columnar storage, advanced compression (ALP, Chimp, dictionary, FSST), adaptive indexing (ART), and a rich optimizer with rule-based and cost-based transformations. The system is designed for OLAP workloads, supporting complex queries with window functions, recursive CTEs, pivot operations, and lambda expressions. It exposes multiple client APIs: a C API (v1 and v2), C++ library, Swift bindings, and a command-line shell. Extensibility is a first-class concern via a plugin system for table/scalar/aggregate functions, custom storage backends, and data format readers (Parquet, CSV, JSON, Arrow). The repository includes extensive benchmark suites (TPC-H, TPC-DS, ClickBench, LDBC, IMDB, recursive CTE) and a comprehensive test infrastructure. While primarily an application (database engine), it also contains research-grade components in query optimization, compression algorithms, and parallel execution, giving it a hybrid character.
ClickHouse is a high-performance, column-oriented OLAP database management system written primarily in C++. It is designed for real-time analytics on large datasets, supporting SQL queries, distributed execution, and various storage engines (MergeTree, ObjectStorage, etc.). The architecture is monolithic but modular, with distinct layers for parsing (Parsers), planning (Analyzer/Planner), execution (Processors), and storage (Storages). Key entry points include the server binary (`clickhouse-server`) and client tools (`clickhouse-client`). It relies heavily on C++ templates, RAII, and a plugin-like registration system for functions and storage engines. It supports distributed queries via ZooKeeper/Keeper and integrates with various cloud storage providers (S3, Azure, GCS).
ParadeDB is a PostgreSQL extension that provides full-text search, vector search, and analytics directly within the database, eliminating the need for a separate search service. The core engine is `pg_search`, which implements a custom index access method (`paradedb`/`bm25`) using Tantivy for search indexing and DataFusion for analytic aggregations. It supports BM25 scoring, phrase/term/boolean queries, fuzzy matching, proximity search, hybrid search (combining full-text and vector), and a rich set of tokenizers (including jieba, ICU, Lindera, ngram). The extension integrates deeply with PostgreSQL's planner through custom scan nodes (BaseScan, AggregateScan, JoinScan, MPP) to push down search, aggregation, and join operations into the index. It also offers columnar storage via fast fields, parallel query execution, and WAL-level integration for crash safety. Architecturally, the system is a Rust library compiled as a shared library loaded by PostgreSQL, using the `pgrx` framework for FFI. Entry points include SQL functions for search (e.g., `paradedb.query`, `pdb.score`), DDL for index creation, and custom plan/exec hooks. Data flows: user SQL queries are intercepted by the planner, which decides whether to use a custom scan path; if so, Tantivy or DataFusion executes the search/aggregation/join, producing tuples that are optionally post-processed by PostgreSQL (e.g., heap fetches, visibility checks). The extension relies on external dependencies: Tantivy (forked for block storage), DataFusion (for aggregate execution), ICU (for Unicode tokenization), Lindera (for Japanese tokenization), and Apache Arrow (for columnar data exchange).
Databend is an open-source, cloud-native distributed SQL query engine and data warehouse, built in Rust. It provides a full SQL interface for analytical workloads, supporting operations such as SELECT, INSERT, COPY INTO/OUT, MERGE INTO, and complex aggregations with window functions, subqueries, and joins. The architecture follows a storage-compute separation model: a metadata service (Meta) manages schemas, users, and catalogs via a Raft-based consensus layer, while query nodes handle SQL parsing, planning, optimization (Cascades optimizer with rule-based and cost-based passes), and distributed execution over a pipelined framework. Multiple storage engines are supported, including a native columnar Fuse engine optimized for cloud object stores (S3, MinIO, Azure Blob), as well as external formats like Iceberg, Delta Lake, Hive, Paimon, Parquet, and ORC. The system exposes access via MySQL wire protocol, HTTP API, Flight SQL, and gRPC, and includes enterprise features such as data masking, row access policies, materialized views, streams, network policies, and role-based access control.