DBMS > Badger vs. EsgynDB vs. Google BigQuery vs. Riak KV vs. ToroDB

System Properties Comparison Badger vs. EsgynDB vs. Google BigQuery vs. Riak KV vs. ToroDB

Editorial information provided by DB-Engines

Name

ToroDB seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

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

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

Large scale data warehouse service with append-only tables

Distributed, fault tolerant key-value store

A MongoDB-compatible JSON document store, built on top of PostgreSQL

Primary database model

Key-value store

Relational DBMS

Key-value store

with links between data sets and object tags for the creation of secondary indexes

Document store

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

Score	0.22
Rank	#320	Overall
	#47	Key-value stores

Score	0.25
Rank	#312	Overall
	#138	Relational DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	4.01
Rank	#79	Overall
	#9	Key-value stores

Website

github.com/dgraph-io/badger

www.esgyn.cn

cloud.google.com/bigquery

github.com/torodb/server

Technical documentation

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

cloud.google.com/bigquery/docs

www.tiot.jp/riak-docs/riak/kv/latest

Developer

DGraph Labs

Esgyn

Google

OpenSource, formerly Basho Technologies

8Kdata

Initial release

2017

2015

2010

2009

2016

Current release

3.2.0, December 2022

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

Apache version 2, commercial enterprise edition

Open Source

AGPL-V3

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

C++, Java

Erlang

Java

Server operating systems

BSD
Linux
OS X
Solaris
Windows

Linux

hosted

Linux
OS X

All OS with a Java 7 VM

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

string, integer, double, boolean, date, object_id

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

restricted

SQL

Support of SQL

yes

APIs and other access methods

ADO.NET
JDBC
ODBC

RESTful HTTP/JSON API

HTTP API
Native Erlang Interface

Supported programming languages

All languages supporting JDBC/ODBC/ADO.Net

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

Server-side scripts

Stored procedures

Java Stored Procedures

user defined functions

in JavaScript

Erlang

Triggers

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

Sharding

no "single point of failure"

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication between multi datacenters

selectable replication factor

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Eventual Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

links between data sets can be stored

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

In-memory capabilities

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

User concepts

Access control

fine grained access rights according to SQL-standard

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

yes, using Riak Security

Access rights for users and roles

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