Yellowbrick is a relational data warehouse that is optimized for flash-based storage.
Multi-version Concurrency Control (MVCC)
Yellowbrick uses append-only MVCC with vacuum garbage collection.
Nested Loop Join Hash Join Sort-Merge Join
Yellowbrick supports hash, sort-merge, and nested loop joins.
Yellowbrick uses intra-operator parallelism, where each thread operates on a different chunk of data, and threads are synchronized to each execute the same operators simultaneously. Yellowbrick schedules execution operators that process a given packet of data to be as close to each other as possible to minimize data movement.
Yellowbrick partitions query plans into segments and converts them into C++ code. Segments are then compiled into machine code in parallel using a modified version of LLVM which is memory-resident with its ASTs pre-loaded. Compiled object files are cached and reused.
Yellowbrick also has a specialized pattern compiler for LIKE
, SIMILAR TO
, regular expressions, and date/time parsing. Yellowbrick generates finite state machines for these patterns and compiles them to machine code using LLVM.
Unlike systems which constrain their query plans to be trees, Yellowbrick uses graph query plans, which allow for execution nodes to have more than one consumer. The execution engine operates on a push-based model, passing cache-resident buffers between operators. Yellowbrick uses AVX SIMD instructions to evaluate expressions and predicate filters.
Yellowbrick supports PL/pgSQL stored procedures (CREATE PROCEDURE
) but not user-defined functions (CREATE FUNCTION
). Unlike in PostgreSQL, stored procedures in Yellowbrick can return values and be called from SELECT
statements, but only when there is no table-referencing FROM
clause.
Triggers are not supported.