DBMS > EsgynDB vs. Google Cloud Bigtable vs. HugeGraph vs. Microsoft SQL Server vs. Splunk

System Properties Comparison EsgynDB vs. Google Cloud Bigtable vs. HugeGraph vs. Microsoft SQL Server vs. Splunk

Editorial information provided by DB-Engines

Name

Description

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

Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

A fast-speed and highly-scalable Graph DBMS

Microsofts flagship relational DBMS

Analytics Platform for Big Data

Primary database model

Relational DBMS

Key-value store
Wide column store

Graph DBMS

Relational DBMS

Search engine

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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

Score	0.25
Rank	#312	Overall
	#138	Relational DBMS

Score	3.15
Rank	#95	Overall
	#14	Key-value stores
	#8	Wide column stores

Score	0.17
Rank	#335	Overall
	#31	Graph DBMS

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Score	89.10
Rank	#14	Overall
	#2	Search engines

Website

www.esgyn.cn

cloud.google.com/bigtable

github.com/hugegraph
hugegraph.apache.org

www.microsoft.com/en-us/sql-server

www.splunk.com

Technical documentation

cloud.google.com/bigtable/docs

hugegraph.apache.org/docs

learn.microsoft.com/en-US/sql/sql-server

docs.splunk.com/Documentation/Splunk

Developer

Esgyn

Google

Baidu

Microsoft

Splunk Inc.

Initial release

2015

2018

1989

2003

Current release

0.9

SQL Server 2022, November 2022

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

commercial

restricted free version is available

commercial

Limited free edition and free developer edition available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++, Java

Java

C++

Server operating systems

Linux

hosted

Linux
macOS
Unix

Linux
Windows

Linux
OS X
Solaris
Windows

Data scheme

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.

yes

Secondary indexes

yes

also supports composite index and range index

yes

SQL

Support of SQL

yes

Splunk Search Processing Language for search commands

APIs and other access methods

ADO.NET
JDBC
ODBC

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

Java API
RESTful HTTP API
TinkerPop Gremlin

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

HTTP REST

Supported programming languages

All languages supporting JDBC/ODBC/ADO.Net

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

Groovy
Java
Python

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

C#
Java
JavaScript
PHP
Python
Ruby

Server-side scripts

Stored procedures

Java Stored Procedures

asynchronous Gremlin script jobs

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

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

tables can be distributed across several files (horizontal partitioning); sharding through federation

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication between multi datacenters

Internal replication in Colossus, and regional replication between two clusters in different zones

yes

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

yes, but depending on the SQL-Server Edition

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via hugegraph-spark

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)

Eventual Consistency

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

edges in graph

yes

Transaction concepts

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

ACID

Atomic single-row operations

ACID

A 'Transaction' in Splunk has a different meaning: grouping related events into a single one for later searching

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

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

Users, roles and permissions

fine grained access rights according to SQL-standard

Access rights for users and roles

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