DBMS > Badger vs. Google BigQuery vs. HugeGraph vs. VoltDB

System Properties Comparison Badger vs. Google BigQuery vs. HugeGraph vs. VoltDB

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Editorial information provided by DB-Engines

Name

Description

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

Large scale data warehouse service with append-only tables

A fast-speed and highly-scalable Graph DBMS

Distributed In-Memory NewSQL RDBMS

Used for OLTP applications with a high frequency of relatively simple transactions, that can hold all their data in memory

Primary database model

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

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

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	1.44
Rank	#158	Overall
	#73	Relational DBMS

Website

github.com/dgraph-io/badger

cloud.google.com/bigquery

github.com/hugegraph
hugegraph.apache.org

www.voltdb.com

Technical documentation

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

cloud.google.com/bigquery/docs

hugegraph.apache.org/docs

docs.voltdb.com

Developer

DGraph Labs

Google

Baidu

VoltDB Inc.

Initial release

2017

2010

2018

2010

Current release

0.9

11.3, April 2022

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

Apache Version 2.0

Open Source

AGPL for Community Edition, commercial license for Enterprise, AWS, and Pro Editions

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

Java

Java, C++

Server operating systems

BSD
Linux
OS X
Solaris
Windows

hosted

Linux
macOS
Unix

Linux
OS X

for development

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

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

also supports composite index and range index

yes

SQL

Support of SQL

yes

only a subset of SQL 99

APIs and other access methods

RESTful HTTP/JSON API

Java API
RESTful HTTP API
TinkerPop Gremlin

Java API
JDBC
RESTful HTTP/JSON API

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Groovy
Java
Python

C#
C++
Erlang

not officially supported
Go
Java
JavaScript

Node.js
PHP
Python

Server-side scripts

Stored procedures

user defined functions

in JavaScript

asynchronous Gremlin script jobs

Java

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

yes

depending on used storage backend, e.g. Cassandra and HBase

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

depending on used storage backend, e.g. Cassandra and HBase

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

via hugegraph-spark

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

edges in graph

FOREIGN KEY constraints are not supported

Transaction concepts

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

Since BigQuery is designed for querying data

ACID

Transactions are executed single-threaded within stored procedures

Concurrency

Support for concurrent manipulation of data

yes

Data access is serialized by the server

Durability

Support for making data persistent

yes

Snapshots and command logging

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

Google Cloud Identity & Access Management (IAM)

Users, roles and permissions

Users and roles with access to stored procedures

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