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

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

Editorial information provided by DB-Engines

Name

Description

Scalable in-memory time series database optimized for IoT and Big Data

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

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

Primary database model

Time Series DBMS

Key-value store

Search engine

Document store
Object oriented DBMS

Document store

Secondary database models

Key-value store
Relational DBMS

Time Series DBMS

using the Manticore Columnar Library

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

Score	2.09
Rank	#120	Overall
	#10	Time Series DBMS

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	0.18
Rank	#332	Overall
	#46	Document stores

Website

griddb.net

github.com/google/leveldb

manticoresearch.com

origodb.com

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

Technical documentation

docs.griddb.net

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

manual.manticoresearch.com

origodb.com/docs

www.xtdb.com/docs

Developer

Toshiba Corporation

Google

Manticore Software

Robert Friberg et al

Juxt Ltd.

Initial release

2013

2011

2017

2009

under the name LiveDB

2019

Current release

5.1, August 2022

1.23, February 2021

6.0, February 2023

1.19, September 2021

License

Commercial or Open Source

Open Source

AGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also available

Open Source

BSD

Open Source

GPL version 2

Open Source

MIT License

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++

Clojure

Server operating systems

Linux

Illumos
Linux
OS X
Windows

FreeBSD
Linux
macOS
Windows

Linux
Windows

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

Data scheme

yes

schema-free

Fixed schema

yes

schema-free

Typing

predefined data types such as float or date

yes

numerical, string, blob, geometry, boolean, timestamp

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

User defined using .NET types and collections

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

SQL92, SQL-like TQL (Toshiba Query Language)

SQL-like query language

limited SQL, making use of Apache Calcite

APIs and other access methods

JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API

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

.NET Client API
HTTP API
LINQ

HTTP REST
JDBC

Supported programming languages

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

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

Clojure
Java

Server-side scripts

Stored procedures

user defined functions

yes

Triggers

yes

Domain Events

Partitioning methods

Methods for storing different data on different nodes

Sharding

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

Source-replica replication

none

Synchronous replication based on Galera library

Source-replica replication

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

Connector for using GridDB as an input source and output destination for Hadoop MapReduce jobs

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate consistency within container, eventual consistency across containers

Immediate Consistency

Foreign keys

Referential integrity

depending on model

Transaction concepts

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

ACID at container level

yes

isolated transactions for atomic changes and binary logging for safe writes

ACID

Concurrency

Support for concurrent manipulation of data

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, 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 can be defined per database

Role based authorization

More information provided by the system vendor

Specific characteristics

GridDB is a highly scalable, in-memory time series database optimized for IoT and...
» more

Competitive advantages

1. Optimized for IoT Equipped with Toshiba's proprietary key-container data model...
» more

Typical application scenarios

Factory IoT, Automative Industry, Energy, BEMS, Smart Community, Monitoring system.
» more

Key customers

Denso International [see use case ] An Electric Power company [see use case ] Ishinomaki...
» more

Market metrics

GitHub trending repository
» more

Licensing and pricing models

Open Source license (AGPL v3 & Apache v2) Commercial license (subscription)
» more

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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