DBMS > BoltDB vs. HugeGraph vs. Microsoft Azure Cosmos DB vs. NSDb vs. TigerGraph

System Properties Comparison BoltDB vs. HugeGraph vs. Microsoft Azure Cosmos DB vs. NSDb vs. TigerGraph

Editorial information provided by DB-Engines

Name

Description

An embedded key-value store for Go.

A fast-speed and highly-scalable Graph DBMS

Globally distributed, horizontally scalable, multi-model database service

Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes

A complete, distributed, parallel graph computing platform supporting web-scale data analytics in real-time

Primary database model

Key-value store

Graph DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Time Series DBMS

Graph DBMS

Secondary database models

Spatial DBMS

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

Score	0.80
Rank	#215	Overall
	#31	Key-value stores

Score	0.17
Rank	#335	Overall
	#31	Graph DBMS

Score	27.71
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	0.08
Rank	#369	Overall
	#40	Time Series DBMS

Score	1.80
Rank	#138	Overall
	#13	Graph DBMS

Website

github.com/boltdb/bolt

github.com/hugegraph
hugegraph.apache.org

azure.microsoft.com/services/cosmos-db

nsdb.io

www.tigergraph.com

Technical documentation

hugegraph.apache.org/docs

learn.microsoft.com/azure/cosmos-db

nsdb.io/Architecture

docs.tigergraph.com

Developer

Baidu

Microsoft

Initial release

2013

2018

2014

2017

Current release

0.9

License

Commercial or Open Source

Open Source

MIT License

Open Source

Apache Version 2.0

commercial

Open Source

Apache Version 2.0

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

Java

Java, Scala

C++

Server operating systems

BSD
Linux
OS X
Solaris
Windows

Linux
macOS
Unix

hosted

Linux
macOS

Linux

Data scheme

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

JSON types

yes: int, bigint, decimal, string

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

also supports composite index and range index

yes

All properties auto-indexed by default

all fields are automatically indexed

SQL

Support of SQL

SQL-like query language

SQL-like query language (GSQL)

APIs and other access methods

Java API
RESTful HTTP API
TinkerPop Gremlin

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

gRPC
HTTP REST
WebSocket

GSQL (TigerGraph Query Language)
Kafka
RESTful HTTP/JSON API

Supported programming languages

Groovy
Java
Python

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

Java
Scala

C++
Java

Server-side scripts

Stored procedures

asynchronous Gremlin script jobs

JavaScript

yes

Triggers

JavaScript

Partitioning methods

Methods for storing different data on different nodes

none

yes

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

Sharding

Implicit feature of the cloud service

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

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

yes

Implicit feature of the cloud service

MapReduce

Offers an API for user-defined Map/Reduce methods

via hugegraph-spark

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Eventual Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

edges in graph

yes

Relationships in graphs

Transaction concepts

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

yes

ACID

Multi-item ACID transactions with snapshot isolation within a partition

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Using Apache Lucene

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

Users, roles and permissions

Access rights can be defined down to the item level

Role-based access control

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