DBDB.io The Encyclopedia of Database Systems · Est. 2017
Database of Databases

Database Entry

Yellowbrick


Yellowbrick is a relational data warehouse that is optimized for flash-based storage.[04][05][06]

Country of Origin
US
Start Year
2014 [13]
Project Type
Commercial
Written in
C, C++, Go, Java, Python
Derived From
PostgreSQL
Compatible With
PostgreSQL
Operating Systems
Hosted, Linux
License
Proprietary

Database Entry

Yellowbrick


Yellowbrick is a relational data warehouse that is optimized for flash-based storage.[04][05][06]

Concurrency Control[06]


Yellowbrick uses append-only MVCC with vacuum garbage collection.

Data Model[06]


Yellowbrick supports the boolean, integer, decimal, floating point, string, date/time, and UUID types available in PostgreSQL, as well as new data types for IP and MAC addresses.

Foreign Keys[07]


Hardware Acceleration[06][08]


Yellowbrick’s on-premise servers utilize a dual-core FPGA to accelerate table scans by performing file parsing, decompression, predicate evaluation, and Bloom filtering. The FPGA accelerator is also used for shuffling data between nodes, which happens via RDMA.

Indexes[06]


Yellowbrick does not support indexes.

Isolation Levels[06]


Yellowbrick universally uses the READ COMMITTED isolation level.

Joins[09]


Yellowbrick supports hash, sort-merge, and nested loop joins.

Logging[06]


Yellowbrick uses PostgreSQL's transaction log.

Parallel Execution[06][09]


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.

Query Compilation[06]


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.

Query Execution[06]


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.

Query Interface[06][10]


SQL

Yellowbrick is compatible with the PostgreSQL dialect and wire protocol, and it uses the PostgreSQL JDBC, ODBC, and ADO.NET drivers.

Storage Architecture


Storage Model


Stored Procedures[11]


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.

System Architecture


Views[12]


Yellowbrick supports virtual views only.

Derivative Systems
Floe Floe
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