DBMS > BigObject vs. Dgraph vs. EsgynDB vs. Hypertable vs. Spark SQL

System Properties Comparison BigObject vs. Dgraph vs. EsgynDB vs. Hypertable vs. Spark SQL

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

Analytic DBMS for real-time computations and queries

Distributed and scalable native Graph DBMS

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

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

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

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

Score	0.19
Rank	#329	Overall
	#146	Relational DBMS

Score	1.53
Rank	#152	Overall
	#15	Graph DBMS

Score	0.25
Rank	#312	Overall
	#138	Relational DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Website

Technical documentation

docs.bigobject.io

dgraph.io/docs

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

BigObject, Inc.

Dgraph Labs, Inc.

Esgyn

Hypertable Inc.

Apache Software Foundation

Initial release

2015

2016

2015

2009

2014

Current release

0.9.8.11, March 2016

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

free community edition available

Open Source

Apache 2.0

commercial

Open Source

GNU version 3. Commercial license available

Open Source

Apache 2.0

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

C++, Java

C++

Scala

Server operating systems

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

Linux
OS X
Windows

Linux

Linux
OS X
Windows

an inofficial Windows port is available

Linux
OS X
Windows

Data scheme

yes

schema-free

yes

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

restricted

only exact value or prefix value scans

SQL

Support of SQL

SQL-like DML and DDL statements

yes

SQL-like DML and DDL statements

APIs and other access methods

fluentd
ODBC
RESTful HTTP API

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

ADO.NET
JDBC
ODBC

C++ API
Thrift

JDBC
ODBC

Supported programming languages

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

All languages supporting JDBC/ODBC/ADO.Net

C++
Java
Perl
PHP
Python
Ruby

Java
Python
R
Scala

Server-side scripts

Stored procedures

Lua

Java Stored Procedures

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

yes

Sharding

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

none

Synchronous replication via Raft

Multi-source replication between multi datacenters

selectable replication factor on file system level

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

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

User concepts

Access control

Planned for future releases

fine grained access rights according to SQL-standard

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