DBMS > AgensGraph vs. Google BigQuery vs. HarperDB vs. Oracle Rdb vs. Solr

System Properties Comparison AgensGraph vs. Google BigQuery vs. HarperDB vs. Oracle Rdb vs. Solr

Editorial information provided by DB-Engines

Name

Description

Multi-model database supporting relational and graph data models and built upon PostgreSQL

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.

A widely used distributed, scalable search engine based on Apache Lucene

Primary database model

Graph DBMS
Relational DBMS

Secondary database models

Spatial DBMS

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

Score	0.23
Rank	#315	Overall
	#26	Graph DBMS
	#140	Relational DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	0.60
Rank	#244	Overall
	#38	Document stores

Score	1.14
Rank	#178	Overall
	#80	Relational DBMS

Score	41.02
Rank	#24	Overall
	#3	Search engines

Website

bitnine.net/agensgraph

cloud.google.com/bigquery

www.harperdb.io

www.oracle.com/database/technologies/related/rdb.html

solr.apache.org

Technical documentation

bitnine.net/documentation

cloud.google.com/bigquery/docs

docs.harperdb.io/docs

www.oracle.com/database/technologies/related/rdb-doc.html

solr.apache.org/resources.html

Developer

Bitnine Global Inc.

Google

HarperDB

Oracle, originally developed by Digital Equipment Corporation (DEC)

Apache Software Foundation

Initial release

2016

2010

2017

1984

2006

Current release

2.1, December 2018

3.1, August 2021

7.4.1.1, 2021

9.6.1, May 2024

License

Commercial or Open Source

Open Source

Apache License 2.0

commercial

free community edition available

commercial

Open Source

Apache Version 2

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

Node.js

Java

Server operating systems

Linux
OS X
Windows

hosted

Linux
OS X

HP Open VMS

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Data scheme

depending on used data model

yes

dynamic schema

Flexible Schema (defined schema, partial schema, schema free)

yes

Dynamic Fields enables on-the-fly addition of new fields

Typing

predefined data types such as float or date

yes

JSON data types

yes

supports customizable data types and automatic typing

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.

yes

Secondary indexes

yes

All search fields are automatically indexed

SQL

Support of SQL

yes

SQL-like data manipulation statements

yes

Solr Parallel SQL Interface

APIs and other access methods

Cypher Query Language
JDBC

RESTful HTTP/JSON API

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

Java API
RESTful HTTP/JSON API

Supported programming languages

C
Java
JavaScript
Python

.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

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

yes

user defined functions

in JavaScript

Custom Functions

since release 3.1

Java plugins

Triggers

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

no, but can be realized using table inheritance

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

Source-replica replication

yes

the nodes on which a table resides can be defined

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

Atomic execution of specific operations

yes, on a single node

optimistic locking

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

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)

Access rights for users and roles

yes

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