DBMS > LevelDB vs. Manticore Search vs. OrigoDB vs. SQLite vs. XTDB

System Properties Comparison LevelDB vs. Manticore Search vs. OrigoDB vs. SQLite vs. XTDB

Editorial information provided by DB-Engines

Name

Description

Embeddable fast key-value storage library that provides an ordered mapping from string keys to string values

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

A fully ACID in-memory object graph database

Widely used embeddable, in-process RDBMS

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

Primary database model

Key-value store

Search engine

Document store
Object oriented DBMS

Relational DBMS

Document store

Secondary database models

Time Series DBMS

using the Manticore Columnar Library

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

Score	2.25
Rank	#115	Overall
	#19	Key-value stores

Score	0.29
Rank	#302	Overall
	#21	Search engines

Score	0.06
Rank	#380	Overall
	#50	Document stores
	#18	Object oriented DBMS

Score	111.41
Rank	#10	Overall
	#7	Relational DBMS

Score	0.18
Rank	#332	Overall
	#46	Document stores

Website

github.com/google/leveldb

manticoresearch.com

origodb.com

www.sqlite.org

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

Technical documentation

github.com/google/leveldb/blob/main/doc/index.md

manual.manticoresearch.com

origodb.com/docs

www.sqlite.org/docs.html

www.xtdb.com/docs

Developer

Google

Manticore Software

Robert Friberg et al

Dwayne Richard Hipp

Juxt Ltd.

Initial release

2011

2017

2009

under the name LiveDB

2000

2019

Current release

1.23, February 2021

6.0, February 2023

3.46.0 (23 May 2024), May 2024

1.19, September 2021

License

Commercial or Open Source

Open Source

BSD

Open Source

GPL version 2

Open Source

Public Domain

Open Source

MIT License

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Clojure

Server operating systems

Illumos
Linux
OS X
Windows

FreeBSD
Linux
macOS
Windows

Linux
Windows

server-less

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

Data scheme

schema-free

Fixed schema

yes

dynamic column types

schema-free

Typing

predefined data types such as float or date

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

User defined using .NET types and collections

yes

not rigid because of 'dynamic typing' concept.

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.

Can index from XML

can be achieved using .NET

Secondary indexes

yes

full-text index on all search fields

yes

SQL

Support of SQL

SQL-like query language

yes

SQL-92 is not fully supported

limited SQL, making use of Apache Calcite

APIs and other access methods

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

.NET Client API
HTTP API
LINQ

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

HTTP REST
JDBC

Supported programming languages

C++
Go
Java

3rd party binding
JavaScript (Node.js)

3rd party binding
Python

3rd party binding

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

.Net

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

Clojure
Java

Server-side scripts

Stored procedures

user defined functions

yes

Triggers

yes

Domain Events

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

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

horizontal partitioning

client side managed; servers are not synchronized

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

Synchronous replication based on Galera library

Source-replica replication

none

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

depending on model

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

Concurrency

Support for concurrent manipulation of data

yes

via file-system locks

yes

Durability

Support for making data persistent

yes

with automatic compression on writes

yes

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

yes

Write ahead log

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

Role based authorization

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