DBMS > Brytlyt vs. HBase vs. LokiJS vs. MarkLogic vs. RocksDB

System Properties Comparison Brytlyt vs. HBase vs. LokiJS vs. MarkLogic vs. RocksDB

Editorial information provided by DB-Engines

Name

Description

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

Wide-column store based on Apache Hadoop and on concepts of BigTable

In-memory JavaScript DBMS

Operational and transactional Enterprise NoSQL database

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

Primary database model

Relational DBMS

Wide column store

Document store

Document store
Native XML DBMS
RDF store

as of version 7
Search engine

Key-value store

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

Score	0.38
Rank	#276	Overall
	#127	Relational DBMS

Score	27.97
Rank	#26	Overall
	#2	Wide column stores

Score	0.49
Rank	#260	Overall
	#42	Document stores

Score	5.18
Rank	#63	Overall
	#11	Document stores
	#1	Native XML DBMS
	#1	RDF stores
	#7	Search engines

Score	3.41
Rank	#86	Overall
	#11	Key-value stores

Website

brytlyt.io

hbase.apache.org

github.com/techfort/LokiJS

www.marklogic.com

rocksdb.org

Technical documentation

docs.brytlyt.io

hbase.apache.org/book.html

techfort.github.io/LokiJS

docs.marklogic.com

github.com/facebook/rocksdb/wiki

Developer

Brytlyt

Apache Software Foundation

Apache top-level project, originally developed by Powerset

MarkLogic Corp.

Facebook, Inc.

Initial release

2016

2008

2014

2001

2013

Current release

5.0, August 2023

2.3.4, January 2021

11.0, December 2022

9.2.1, May 2024

License

Commercial or Open Source

commercial

Open Source

Apache version 2

Open Source

commercial

restricted free version is available

Open Source

BSD

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C, C++ and CUDA

Java

JavaScript

C++

Server operating systems

Linux
OS X
Windows

Linux
Unix
Windows

using Cygwin

server-less, requires a JavaScript environment (browser, Node.js)

Linux
OS X
Windows

Linux

Data scheme

yes

schema-free, schema definition possible

schema-free

Schema can be enforced

schema-free

Typing

predefined data types such as float or date

yes

options to bring your own types, AVRO

yes

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

via views

yes

SQL

Support of SQL

yes

SQL92

APIs and other access methods

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

Java API
RESTful HTTP API
Thrift

JavaScript API

Java API
Node.js Client API
ODBC
proprietary Optic API

Proprietary Query API, introduced with version 9
RESTful HTTP API
SPARQL
WebDAV
XDBC
XQuery
XSLT

C++ API
Java API

Supported programming languages

.Net
C
C++
Delphi
Java
Perl
Python
Tcl

C
C#
C++
Groovy
Java
PHP
Python
Scala

JavaScript

C
C#
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

in PL/pgSQL

yes

Coprocessors in Java

View functions in JavaScript

yes

via XQuery or JavaScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Multi-source replication
Source-replica replication

none

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via Hadoop Connector, HDFS Direct Access and in-database MapReduce jobs

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency

none

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Single row ACID (across millions of columns)

atomic operations within a single collection possible

ACID

can act as a resource manager in an XA/JTA transaction

yes

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Serialization of a DB in a Node.JS/Cordova/PhoneGap environment. Usage of the IndexedDB-API in a browser.

yes

In-memory capabilities

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

yes

yes, with Range Indexes

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Access Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

Role-based access control at the document and subdocument levels

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