DBMS > Badger vs. EsgynDB vs. Tarantool vs. TerminusDB vs. Warp 10

System Properties Comparison Badger vs. EsgynDB vs. Tarantool vs. TerminusDB vs. Warp 10

Editorial information provided by DB-Engines

Name

Description

An embeddable, persistent, simple and fast Key-Value Store, written purely in Go.

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

In-memory computing platform with a flexible data schema for efficiently building high-performance applications

Scalable Graph Database platform making enterprise data available by exploiting inferred entities and relationships

TimeSeries DBMS specialized on timestamped geo data based on LevelDB or HBase

Primary database model

Key-value store

Relational DBMS

Document store
Key-value store
Relational DBMS

Graph DBMS

Time Series DBMS

Secondary database models

Spatial DBMS

with Tarantool/GIS extension

Document store
RDF store

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

Score	0.14
Rank	#331	Overall
	#49	Key-value stores

Score	0.16
Rank	#329	Overall
	#146	Relational DBMS

Score	1.72
Rank	#144	Overall
	#25	Document stores
	#25	Key-value stores
	#66	Relational DBMS

Score	0.17
Rank	#325	Overall
	#29	Graph DBMS

Score	0.07
Rank	#349	Overall
	#32	Time Series DBMS

Website

github.com/dgraph-io/badger

Technical documentation

godoc.org/github.com/dgraph-io/badger

www.tarantool.io/en/doc

terminusdb.github.io/terminusdb/#

www.warp10.io/content/02_Getting_started

Developer

DGraph Labs

Esgyn

DataChemist Ltd.

SenX

Initial release

2017

2015

2008

2018

2015

Current release

2.10.0, May 2022

11.0.0, January 2023

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

BSD-2, source-available extensions (modules), commercial licenses for Tarantool Enterprise

Open Source

GPL V3

Open Source

Apache License 2.0

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++, Java

C and C++

Prolog, Rust

Java

Server operating systems

BSD
Linux
OS X
Solaris
Windows

Linux

BSD
Linux
macOS

Linux

Linux
OS X
Windows

Data scheme

schema-free

yes

Flexible data schema: relational definition for tables with ability to store json-like documents in columns

yes

schema-free

Typing

predefined data types such as float or date

yes

string, double, decimal, uuid, integer, blob, boolean, datetime

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.

Secondary indexes

yes

SQL

Support of SQL

yes

Full-featured ANSI SQL support

SQL-like query language (WOQL)

APIs and other access methods

ADO.NET
JDBC
ODBC

Open binary protocol

OWL
RESTful HTTP API
WOQL (Web Object Query Language)

HTTP API
Jupyter
WebSocket

Supported programming languages

All languages supporting JDBC/ODBC/ADO.Net

C
C#
C++
Erlang
Go
Java
JavaScript
Lua
Perl
PHP
Python
Rust

JavaScript
Python

Server-side scripts

Stored procedures

Java Stored Procedures

Lua, C and SQL stored procedures

yes

WarpScript

Triggers

yes, before/after data modification events, on replication events, client session events

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Sharding, partitioned with virtual buckets by user defined affinity key. Live resharding for scale up and scale down without maintenance downtime.

Graph Partitioning

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication between multi datacenters

Asynchronous replication with multi-master option
Configurable replication topology (full-mesh, chain, star)
Synchronous quorum replication (with Raft)

Journaling Streams

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Casual consistency across sharding partitions
Eventual consistency within replicaset partition

when using asyncronous replication
Immediate Consistency within single instance
Sequential consistency including linearizable read within replicaset partition

when using Raft

Immediate Consistency

based on HBase

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

ACID, with serializable isolation and linearizable read (within partition); Configurable MVCC (within partition); No cross-shard distributed transactions

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, cooperative multitasking

yes

Durability

Support for making data persistent

yes

yes, write ahead logging

yes

in-memory journaling

yes

In-memory capabilities

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

yes, full featured in-memory storage engine with persistence

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Access Control Lists
Mutual TLS authentication for Tarantol Enterprise
Password based authentication
Role-based access control (RBAC) and LDAP for Tarantol Enterprise
Users and Roles

Role-based access control

Mandatory use of cryptographic tokens, containing fine-grained authorizations

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More resources

DB-Engines blog posts

Data processing speed and reliability: in-memory synchronous replication
9 November 2021, Vladimir Perepelytsya, Tarantool (sponsor)

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