DBMS > Dgraph vs. HarperDB vs. Oracle vs. Solr

System Properties Comparison Dgraph vs. HarperDB vs. Oracle vs. Solr

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Editorial information provided by DB-Engines

Name

Description

Distributed and scalable native Graph DBMS

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.

Widely used RDBMS

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

Primary database model

Secondary database models

Document store
Graph DBMS

with Oracle Spatial and Graph
RDF store

with Oracle Spatial and Graph
Spatial DBMS

with Oracle Spatial and Graph
Vector DBMS

since Oracle 23

Spatial DBMS

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

Score	1.45
Rank	#156	Overall
	#15	Graph DBMS

Score	0.55
Rank	#248	Overall
	#38	Document stores

Score	1236.29
Rank	#1	Overall
	#1	Relational DBMS

Score	42.91
Rank	#24	Overall
	#3	Search engines

Website

dgraph.io

www.harperdb.io

www.oracle.com/database

solr.apache.org

Technical documentation

dgraph.io/docs

docs.harperdb.io/docs

docs.oracle.com/en/database

solr.apache.org/resources.html

Developer

Dgraph Labs, Inc.

HarperDB

Oracle

Apache Software Foundation

Initial release

2016

2017

1980

2006

Current release

3.1, August 2021

23c, September 2023

9.6.0, April 2024

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

free community edition available

commercial

restricted free version is available

Open Source

Apache Version 2

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Node.js

C and C++

Java

Server operating systems

Linux
OS X
Windows

Linux
OS X

AIX
HP-UX
Linux
OS X
Solaris
Windows
z/OS

All OS with a Java VM

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

Data scheme

schema-free

dynamic schema

yes

Schemaless in JSON and XML columns

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

SQL-like data manipulation statements

yes

with proprietary extensions

Solr Parallel SQL Interface

APIs and other access methods

GraphQL query language
gRPC (using protocol buffers) API
HTTP API

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Java API
RESTful HTTP/JSON API

Supported programming languages

C#
C++
Go
Java
JavaScript (Node.js)
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
C#
C++
Clojure
Cobol
Delphi
Eiffel
Erlang
Fortran
Groovy
Haskell
Java
JavaScript
Lisp
Objective C
OCaml
Perl
PHP
Python
R
Ruby
Scala
Tcl
Visual Basic

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

Server-side scripts

Stored procedures

Custom Functions

since release 3.1

PL/SQL

also stored procedures in Java possible

Java plugins

Triggers

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

yes

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

Sharding, horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Synchronous replication via Raft

yes

the nodes on which a table resides can be defined

Multi-source replication
Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

can be realized in PL/SQL

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

Atomic execution of specific operations

ACID

isolation level can be parameterized

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

Version 12c introduced the new option 'Oracle Database In-Memory'

yes

User concepts

Access control

Planned for future releases

Access rights for users and roles

fine grained access rights according to SQL-standard

yes

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