History[05][06]

Deep Information Sciences was founded in 2010 based on research conducted at the University of New Hampshire. After the company went under in 2017, the source code of the DeepDB engine was released as open-source as part of a new Deep Software Foundation holding. A large portion of the source code of the system was a custom C++ implementation of the Java Development Kit software and not related to the DBMS itself.

Compression[07]

There exists prefix compression in indexes. DeepDB keeps compressed data in cache, and decompress it during operations. The system supports high-levels of compression with compact representation of keys and delta compression.

Concurrency Control[07]

Same as MySQL, it supports Multi-version Concurrency Control.

Data Model[08]

While the DeepDB storage engine implements a Key/Value model, the data model is fully relational, as specified in MySQL.

Indexes[08][09]

DeepDB uses hyper-indexing and the new CASSI(Continuous Adaptive Sequential Summarization of Info) tree for indexing. CASSI tree is a persistent data structure that supports ACID transactions. It is improved from B+Trees in that they are able to collapse the internal structure through virtualizing and summarizing. Based on the the type of data and tasks, whether transactional, data stream capture, or analytics, the tree and indexes used in queries are dynamically adjusted to maximize hardware resources. For speed of lookup, sometimes it chooses to index every column in a database table.

Logging

Storage Architecture[10][07][11]

DeepDB turns MySQL into a cloud-ready, perpetually adapting database. All files are persistently stored on the disk and it will write data into in memory file temporarily. DeepDB stores data in 3 forms including on-disk row store tables, in-memory row store tables, and on-disk column store indexes. Instead of organizing in pages, the in-memory row store, which is append only, is designed to manage single rows as much as possible. The data and indexes on disk in memory are organized into segments, with various sizes. Segments may contain summary data or metadata, so that metadata or summary data remain in cache when the segments are evicted.

The system manages cache usage using adaptive algorithms. Variable-sized segments rather than pages are used to store data. In addition, summary indexing is used to identify relevant segments.

Storage Model

DeepDB uses N-ary storage model, as in MySQL.

Storage Organization[10]

A shadow copy is maintained for recovery on system crush. Disk snapshots are maintained to roll forward and back in data history. The append-only files provide real-time, incremental and continuous backup.

System Architecture[10][02][12]

DeepDB storage engine is designed as a easy-to-install plugin replacement for MySQL's native InnoDB storage engine. Using DeepDB does not require any application or schema change. It transforms MySQL into adaptive, self-tuning and highly performant database with full ACID compliance and brings additional machine-learning metrics to MySQL. The system is architected for complex environments and supports HTAP(Hybrid Transactional Processing).