DBMS > LevelDB vs. OrigoDB vs. Teradata Aster vs. Tkrzw vs. XTDB

System Properties Comparison LevelDB vs. OrigoDB vs. Teradata Aster vs. Tkrzw vs. XTDB

Editorial information provided by DB-Engines

Name

Teradata Aster has been integrated into other Teradata systems and therefore will be removed from the DB-Engines ranking.

Description

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

A fully ACID in-memory object graph database

Platform for big data analytics on multistructured data sources and types

A concept of libraries, allowing an application program to store and query key-value pairs in a file. Successor of Tokyo Cabinet and Kyoto Cabinet

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

Primary database model

Key-value store

Document store
Object oriented DBMS

Relational DBMS

Key-value store

Document store

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

Score	2.35
Rank	#111	Overall
	#19	Key-value stores

Score	0.00
Rank	#383	Overall
	#53	Document stores
	#20	Object oriented DBMS

Score	0.00
Rank	#383	Overall
	#60	Key-value stores

Score	0.11
Rank	#343	Overall
	#46	Document stores

Website

github.com/google/leveldb

origodb.com

dbmx.net/tkrzw

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

Technical documentation

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

origodb.com/docs

www.xtdb.com/docs

Developer

Google

Robert Friberg et al

Teradata

Mikio Hirabayashi

Juxt Ltd.

Initial release

2011

2009

under the name LiveDB

2005

2020

2019

Current release

1.23, February 2021

0.9.3, August 2020

1.19, September 2021

License

Commercial or Open Source

Open Source

BSD

Open Source

commercial

Open Source

Apache Version 2.0

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

Illumos
Linux
OS X
Windows

Linux
Windows

Linux

Linux
macOS

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

Data scheme

schema-free

yes

Flexible Schema (defined schema, partial schema, schema free)

defined schema within the relational store; partial schema or schema free in the Aster File Store

schema-free

Typing

predefined data types such as float or date

User defined using .NET types and collections

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.

can be achieved using .NET

yes

in Aster File Store

Secondary indexes

yes

SQL

Support of SQL

yes

limited SQL, making use of Apache Calcite

APIs and other access methods

.NET Client API
HTTP API
LINQ

ADO.NET
JDBC
ODBC
OLE DB

HTTP REST
JDBC

Supported programming languages

C++
Go
Java

3rd party binding
JavaScript (Node.js)

3rd party binding
Python

3rd party binding

.Net

C
C#
C++
Java
Python
R

C++
Java
Python
Ruby

Clojure
Java

Server-side scripts

Stored procedures

yes

R packages

Triggers

yes

Domain Events

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning

client side managed; servers are not synchronized

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

Source-replica replication

yes

Dimension tables are replicated across all nodes in the cluster. The number of replicas for the file store can be configured.

none

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

SQL Map-Reduce Framework

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

depending on model

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

with automatic compression on writes

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

using specific database classes

User concepts

Access control

Role based authorization

fine grained access rights according to SQL-standard

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