DBMS > Google BigQuery vs. HarperDB vs. Hypertable vs. RavenDB vs. Valentina Server

System Properties Comparison Google BigQuery vs. HarperDB vs. Hypertable vs. RavenDB vs. Valentina Server

Editorial information provided by DB-Engines

Name

Hypertable has stopped its further development with March 2016 and is removed from the DB-Engines ranking.

Description

Large scale data warehouse service with append-only tables

Ultra-low latency distributed database with an intuitive REST API supporting NoSQL and SQL (including joins). Deployment of functions and databases simultaneously with a consolidated node-level architecture.

An open source BigTable implementation based on distributed file systems such as Hadoop

Open Source Operational and Transactional Enterprise NoSQL Document Database

Object-relational database and reports server

Primary database model

Secondary database models

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	0.55
Rank	#248	Overall
	#38	Document stores

Score	2.92
Rank	#101	Overall
	#18	Document stores

Score	0.17
Rank	#327	Overall
	#145	Relational DBMS

Website

cloud.google.com/bigquery

www.harperdb.io

ravendb.net

www.valentina-db.net

Technical documentation

cloud.google.com/bigquery/docs

docs.harperdb.io/docs

ravendb.net/docs

valentina-db.com/docs/dokuwiki/v5/doku.php

Developer

Google

HarperDB

Hypertable Inc.

Hibernating Rhinos

Paradigma Software

Initial release

2010

2017

2009

2010

1999

Current release

3.1, August 2021

0.9.8.11, March 2016

5.4, July 2022

5.7.5

License

Commercial or Open Source

commercial

free community edition available

Open Source

GNU version 3. Commercial license available

Open Source

AGPL version 3, commercial license available

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Node.js

C++

Server operating systems

hosted

Linux
OS X

Linux
OS X
Windows

an inofficial Windows port is available

Linux
macOS
Raspberry Pi
Windows

Linux
OS X
Windows

Data scheme

yes

dynamic schema

schema-free

yes

Typing

predefined data types such as float or date

yes

JSON data types

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

restricted

only exact value or prefix value scans

yes

SQL

Support of SQL

yes

SQL-like data manipulation statements

SQL-like query language (RQL)

yes

APIs and other access methods

RESTful HTTP/JSON API

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

C++ API
Thrift

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

ODBC

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
C
C#
C++
ColdFusion
D
Dart
Delphi
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
MatLab
Objective C
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Rust
Scala
Swift

C++
Java
Perl
PHP
Python
Ruby

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

.Net
C
C#
C++
Objective-C
PHP
Ruby
Visual Basic
Visual Basic.NET

Server-side scripts

Stored procedures

user defined functions

in JavaScript

Custom Functions

since release 3.1

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

A table resides as a whole on one (or more) nodes in a cluster

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

the nodes on which a table resides can be defined

selectable replication factor on file system level

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

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

Transaction concepts

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

Since BigQuery is designed for querying data

Atomic execution of specific operations

ACID, Cluster-wide transaction available

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, using LMDB

yes

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)

Access rights for users and roles

Authorization levels configured per client per database

fine grained access rights according to SQL-standard

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