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

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

Editorial information provided by DB-Engines

Name

Description

CockroachDB is a distributed database architected for modern cloud applications. It is wire compatible with PostgreSQL and backed by a Key-Value Store, which is either RocksDB or a purpose-built derivative, called Pebble.

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

Relational 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	5.73
Rank	#58	Overall
	#34	Relational 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

www.cockroachlabs.com

azure.microsoft.com/services/cosmos-db

nsdb.io

www.tigergraph.com

Technical documentation

www.cockroachlabs.com/docs

learn.microsoft.com/azure/cosmos-db

nsdb.io/Architecture

docs.tigergraph.com

Developer

Cockroach Labs

Microsoft

Initial release

2013

2015

2014

2017

Current release

24.1.0, May 2024

License

Commercial or Open Source

Open Source

MIT License

Open Source

Apache 2.0, commercial license available

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

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

Implementation language

Java, Scala

C++

Server operating systems

BSD
Linux
OS X
Solaris
Windows

Linux
macOS
Windows

hosted

Linux
macOS

Linux

Data scheme

schema-free

dynamic schema

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

All properties auto-indexed by default

all fields are automatically indexed

SQL

Support of SQL

yes, wire compatible with PostgreSQL

SQL-like query language

SQL-like query language (GSQL)

APIs and other access methods

JDBC

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

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

.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

JavaScript

yes

Triggers

JavaScript

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning (by key range)

all tables are translated to an ordered KV store and then broken down into 64MB ranges, which are then used as replicas in RAFT

Sharding

Implicit feature of the cloud service

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication using RAFT

yes

Implicit feature of the cloud service

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Eventual Consistency

Foreign keys

Referential integrity

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.

User concepts

Access control

Role-based access control

Access rights can be defined down to the item level

Role-based access control

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