DBMS > Google BigQuery vs. HEAVY.AI vs. HugeGraph vs. Kinetica vs. RavenDB

System Properties Comparison Google BigQuery vs. HEAVY.AI vs. HugeGraph vs. Kinetica vs. RavenDB

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

A fast-speed and highly-scalable Graph DBMS

Fully vectorized database across both GPUs and CPUs

Open Source Operational and Transactional Enterprise NoSQL Document Database

Primary database model

Secondary database models

Spatial DBMS

Spatial DBMS
Time Series DBMS

Graph DBMS
Spatial DBMS
Time Series DBMS

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

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	0.64
Rank	#236	Overall
	#109	Relational DBMS

Score	2.92
Rank	#101	Overall
	#18	Document stores

Website

cloud.google.com/bigquery

github.com/heavyai/heavydb
www.heavy.ai

github.com/hugegraph
hugegraph.apache.org

www.kinetica.com

ravendb.net

Technical documentation

cloud.google.com/bigquery/docs

docs.heavy.ai

hugegraph.apache.org/docs

docs.kinetica.com

ravendb.net/docs

Developer

Google

HEAVY.AI, Inc.

Baidu

Kinetica

Hibernating Rhinos

Initial release

2010

2016

2018

2012

2010

Current release

5.10, January 2022

0.9

7.1, August 2021

5.4, July 2022

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; enterprise edition available

Open Source

Apache Version 2.0

commercial

Open Source

AGPL version 3, commercial license available

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++ and CUDA

Java

C, C++

Server operating systems

hosted

Linux

Linux
macOS
Unix

Linux

Linux
macOS
Raspberry Pi
Windows

Data scheme

yes

schema-free

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

SQL-like DML and DDL statements

SQL-like query language (RQL)

APIs and other access methods

RESTful HTTP/JSON API

JDBC
ODBC
Thrift
Vega

Java API
RESTful HTTP API
TinkerPop Gremlin

JDBC
ODBC
RESTful HTTP API

.NET Client API
F# Client API
Go Client API
Java Client API
NodeJS Client API
PHP Client API
Python Client API
RESTful HTTP API

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

All languages supporting JDBC/ODBC/Thrift
Python

Groovy
Java
Python

C++
Java
JavaScript (Node.js)
Python

.Net
C#
F#
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

in JavaScript

asynchronous Gremlin script jobs

user defined functions

yes

Triggers

yes

triggers when inserted values for one or more columns fall within a specified range

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Round robin

yes

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

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

yes

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

Source-replica replication

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

via hugegraph-spark

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Default ACID transactions on the local node (eventually consistent across the cluster). Atomic operations with cluster-wide ACID transactions. Eventual consistency for indexes and full-text search indexes.

Foreign keys

Referential integrity

yes

edges in graph

yes

Transaction concepts

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

Since BigQuery is designed for querying data

ACID

ACID, Cluster-wide transaction available

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.

yes

GPU vRAM or System RAM

User concepts

Access control

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

Google Cloud Identity & Access Management (IAM)

fine grained access rights according to SQL-standard

Users, roles and permissions

Access rights for users and roles on table level

Authorization levels configured per client per database

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.