Checkpoints

Fuzzy

DeepDB supports last transaction checkpoints, and it takes checkpoints asynchronously.

Compression

Delta Encoding Prefix Compression

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

Concurrency Control

Multi-version Concurrency Control (MVCC)

DeepDB utilizes Multi-version Concurrency Control such that the database can be rolled back to any transactional state.

Data Model

Relational

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

Joins

Nested Loop Join

DeepDB is a storage engine full compatible with other components in MySQL. Thus, it supports the same join algorithms as MySQL, including the Nested-Loop Join Algorithm and the Block Nested-Loop Join Algorithm.

Logging

Logical Logging

DeepDB maintains state logs so that the replicas can generate the same indexes and perform the same operations from the streamed state log.

Query Interface

SQL

DeepDB is designed as a storage engine that is compatible with MySQL. Since it uses the same MySQL API, the query interface is also the same, which supports the standard SQL query interface with several additional extensions.

Storage Architecture

Disk-oriented

DeepDB runs on POSIX-based disk-oriented file-systems (e.g. SSD/HDD). All files are persistently stored on the disk and it will write data into in-memory files 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 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 the 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

Decomposition Storage Model (Columnar) N-ary Storage Model (Row/Record)

DeepDB supports transactional row storage for OLTP workloads. It also implements segmented column stores to deliver capabilities similar to OLAP databases (column store). Different from traditional column stores that maintain large and monolithic column store files, variable-sized segments are maintained in DeepDB to improve space utilization.

Storage Organization

Copy-on-Write / Shadow Paging

A shadow copy is maintained for recovery on the system crash. Disk snapshots are maintained to roll forward and back in data history. The database files including indexes, transactional state, and metadata are streamed append-only files.

System Architecture

Shared-Everything

DeepDB storage engine is designed as an easy-to-install plugin replacement for MySQL's native InnoDB storage engine. Using DeepDB does not require any application or schema change. It augments MySQL with full ACID compliance and additional machine-learning metrics. The system is architected for complex environments and supports HTAP(Hybrid Transactional Processing).