Storage Architecture

Disk-oriented In-Memory Hybrid

eXtremeDB enables the developer to combine both database paradigms – in-memory and on-disk – in a single database instance. Specifying one set of data as transient (managed in memory), while choosing persistent storage for other record types, requires a simple database schema declaration.

Isolation Levels

Read Committed Serializable Repeatable Read

Query Compilation

JIT Compilation Stored Procedure Compilation

Stored Procedures

Supported

Lua, Python or C++ based stored procedures run in the context of the SQL server and therefore minimizes client-server inter-process communication and attendant network overhead, and fully utilizes the multi-core nature of modern hardware.

Storage Model

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

eXtremeDB implements row-based layout for all data types other than sequences. Row and columnar layout can be combined in hybrid data designs to optimize performance managing mixed data.

System Architecture

Shared-Nothing Shared-Memory Embedded

Concurrency Control

Multi-version Concurrency Control (MVCC) Optimistic Concurrency Control (OCC)

eXtremeDB offers a multi-version concurrency control (MVCC) optimistic transaction manager and an alternative "pessimistic" MURSIW (MUltiple Reader, Single Writer) transaction manager.

Foreign Keys

Supported

Indexes

B+Tree Hash Table R-Tree T-Tree K-D Tree Patricia/Radix Trie

eXtremeDB database offers multiple indexes, including the following:

B-Trees for common sorting and searches, insertions, and deletions R-Trees for geospatial indexing (common in GPS/navigation systems) Hash tables for quickly locating a single unique index entry Patricia trie index, which speeds searches in networking and telephony applications Trigram indexes are ideal for text searches when the exact spelling of the target object is not precisely known. It finds objects which match the maximum number of three-character strings in the entered search terms, i.e., near matches. “Custom indexes” or b-trees that allow the application to define the collating sequence of entries; this is useful in implementing soundex algorithm searches, for example KD-Trees or k-dimensional trees, for spatial and pattern-matching tasks and in applications where query predicates contain various combinations of object fields (for example, to construct Query-By-Example, or QBE features)

Query Interface

Custom API SQL Stored Procedures HTTP / REST Command-line / Shell RPC

Compression

Run-Length Encoding

Data Model

Relational