DBMS > AnzoGraph DB vs. Brytlyt vs. LMDB vs. Microsoft SQL Server vs. XTDB

System Properties Comparison AnzoGraph DB vs. Brytlyt vs. LMDB vs. Microsoft SQL Server vs. XTDB

Editorial information provided by DB-Engines

Name

Description

Scalable graph database built for online analytics and data harmonization with MPP scaling, high-performance analytical algorithms and reasoning, and virtualization

Scalable GPU-accelerated RDBMS for very fast analytic and streaming workloads, leveraging PostgreSQL

A high performant, light-weight, embedded key-value database library

Microsofts flagship relational DBMS

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Secondary database models

Document store
Graph DBMS
Spatial DBMS

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	0.23
Rank	#307	Overall
	#24	Graph DBMS
	#13	RDF stores

Score	0.29
Rank	#288	Overall
	#131	Relational DBMS

Score	1.99
Rank	#125	Overall
	#21	Key-value stores

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	0.11
Rank	#343	Overall
	#46	Document stores

Website

cambridgesemantics.com/anzograph

brytlyt.io

www.symas.com/symas-embedded-database-lmdb

www.microsoft.com/en-us/sql-server

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

docs.cambridgesemantics.com/anzograph/userdoc/home.htm

docs.brytlyt.io

www.lmdb.tech/doc

learn.microsoft.com/en-US/sql/sql-server

www.xtdb.com/docs

Developer

Cambridge Semantics

Brytlyt

Symas

Microsoft

Juxt Ltd.

Initial release

2018

2016

2011

1989

2019

Current release

2.3, January 2021

5.0, August 2023

0.9.32, January 2024

SQL Server 2022, November 2022

1.19, September 2021

License

Commercial or Open Source

commercial

free trial version available

commercial

Open Source

commercial

restricted free version is available

Open Source

MIT License

Cloud-based only

Only available as a cloud service

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Implementation language

C, C++ and CUDA

C++

Clojure

Server operating systems

Linux

Linux
OS X
Windows

Linux
Unix
Windows

Linux
Windows

All OS with a Java 8 (and higher) VM
Linux

Data scheme

Schema-free and OWL/RDFS-schema support

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

yes, extensible-data-notation format

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

yes

specific XML-type available, but no XML query functionality.

yes

Secondary indexes

yes

SQL

Support of SQL

SPARQL and SPARQL* as primary query language. Cypher preview.

yes

limited SQL, making use of Apache Calcite

APIs and other access methods

Apache Mule
gRPC
JDBC
Kafka
OData access for BI tools
OpenCypher
RESTful HTTP API
SPARQL

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

HTTP REST
JDBC

Supported programming languages

C++
Java
Python

.Net
C
C++
Delphi
Java
Perl
Python
Tcl

.Net
C
C++
Clojure
Go
Haskell
Java
JavaScript (Node.js)
Lisp
Lua
MatLab
Nim
Objective C
OCaml
Perl
PHP
Python
R
Ruby
Rust
Swift
Tcl

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

Clojure
Java

Server-side scripts

Stored procedures

user defined functions and aggregates

user defined functions

in PL/pgSQL

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

none

tables can be distributed across several files (horizontal partitioning); sharding through federation

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication in MPP-Cluster

Source-replica replication

none

yes, but depending on the SQL-Server Edition

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

Kerberos/HDFS data loading

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency in MPP-Cluster

Immediate Consistency

Foreign keys

Referential integrity

not needed in graphs

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

Access rights for users and roles

fine grained access rights according to SQL-standard

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