DBMS > EJDB vs. Manticore Search vs. Newts vs. Postgres-XL vs. Splice Machine

System Properties Comparison EJDB vs. Manticore Search vs. Newts vs. Postgres-XL vs. Splice Machine

Editorial information provided by DB-Engines

Name

Description

Embeddable document-store database library with JSON representation of queries (in MongoDB style)

Multi-storage database for search, including full-text search.

Time Series DBMS based on Cassandra

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

Primary database model

Secondary database models

Time Series DBMS

using the Manticore Columnar Library

Document store
Spatial DBMS

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

Score	0.27
Rank	#297	Overall
	#44	Document stores

Score	0.22
Rank	#312	Overall
	#21	Search engines

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	0.49
Rank	#256	Overall
	#117	Relational DBMS

Score	0.54
Rank	#250	Overall
	#114	Relational DBMS

Website

github.com/Softmotions/ejdb

manticoresearch.com

opennms.github.io/newts

www.postgres-xl.org

splicemachine.com

Technical documentation

github.com/Softmotions/ejdb/blob/master/README.md

manual.manticoresearch.com

github.com/OpenNMS/newts/wiki

www.postgres-xl.org/documentation

splicemachine.com/how-it-works

Developer

Softmotions

Manticore Software

OpenNMS Group

Splice Machine

Initial release

2012

2017

2014

since 2012, originally named StormDB

2014

Current release

6.0, February 2023

10 R1, October 2018

3.1, March 2021

License

Commercial or Open Source

Open Source

GPLv2

Open Source

GPL version 2

Open Source

Apache 2.0

Open Source

Mozilla public license

Open Source

AGPL 3.0, commercial license available

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Java

Server operating systems

server-less

FreeBSD
Linux
macOS
Windows

Linux
OS X
Windows

Linux
macOS

Linux
OS X
Solaris
Windows

Data scheme

schema-free

Fixed schema

schema-free

yes

Typing

predefined data types such as float or date

yes

string, integer, double, bool, date, object_id

Int, Bigint, Float, Timestamp, Bit, Int array, Bigint array, JSON, Boolean

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.

Can index from XML

yes

XML type, but no XML query functionality

Secondary indexes

yes

full-text index on all search fields

yes

SQL

Support of SQL

SQL-like query language

yes

distributed, parallel query execution

yes

APIs and other access methods

in-process shared library

Binary API
RESTful HTTP/JSON API
RESTful HTTP/SQL API
SQL over MySQL

HTTP REST
Java API

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

JDBC
Native Spark Datasource
ODBC

Supported programming languages

Actionscript
C
C#
C++
Go
Java
JavaScript (Node.js)
Lua
Objective-C
Pike
Python
Ruby

Elixir
Go
Java
JavaScript (Node.js)
Perl
PHP
Python

Java

.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

Server-side scripts

Stored procedures

user defined functions

yes

Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Partitioning is done manually, search queries against distributed index is supported

Sharding

based on Cassandra

horizontal partitioning

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

Synchronous replication based on Galera library

selectable replication factor

based on Cassandra

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

Foreign keys

Referential integrity

typically not needed, however similar functionality with collection joins possible

yes

Transaction concepts

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

yes

isolated transactions for atomic changes and binary logging for safe writes

ACID

MVCC

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/Write Locking

yes

yes, multi-version concurrency control (MVCC)

Durability

Support for making data persistent

yes

The original contents of fields are not stored in the Manticore index.

yes

In-memory capabilities

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

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights for users, groups and roles according to SQL-standard

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