DBMS > AgensGraph vs. Google Cloud Bigtable vs. InfluxDB vs. NSDb vs. RocksDB

System Properties Comparison AgensGraph vs. Google Cloud Bigtable vs. InfluxDB vs. NSDb vs. RocksDB

Editorial information provided by DB-Engines

Name

Description

Multi-model database supporting relational and graph data models and built upon PostgreSQL

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

DBMS for storing time series, events and metrics

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

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

Primary database model

Graph DBMS
Relational DBMS

Key-value store
Wide column store

Time Series DBMS

Key-value store

Secondary database models

Spatial DBMS

with GEO package

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

Score	0.23
Rank	#315	Overall
	#26	Graph DBMS
	#140	Relational DBMS

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

Score	24.39
Rank	#28	Overall
	#1	Time Series DBMS

Score	0.08
Rank	#369	Overall
	#40	Time Series DBMS

Score	3.41
Rank	#86	Overall
	#11	Key-value stores

Website

bitnine.net/agensgraph

cloud.google.com/bigtable

www.influxdata.com/products/influxdb-overview

nsdb.io

rocksdb.org

Technical documentation

bitnine.net/documentation

cloud.google.com/bigtable/docs

docs.influxdata.com/influxdb

nsdb.io/Architecture

github.com/facebook/rocksdb/wiki

Developer

Bitnine Global Inc.

Google

Facebook, Inc.

Initial release

2016

2015

2013

2017

2013

Current release

2.1, December 2018

2.7.6, April 2024

9.2.1, May 2024

License

Commercial or Open Source

Open Source

Apache License 2.0

commercial

Open Source

MIT-License; commercial enterprise version available

Open Source

Apache Version 2.0

Open Source

BSD

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

Linux
OS X
Windows

hosted

Linux
OS X

through Homebrew

Linux
macOS

Linux

Data scheme

depending on used data model

schema-free

Typing

predefined data types such as float or date

yes

Numeric data and Strings

yes: int, bigint, decimal, string

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 fields are automatically indexed

SQL

Support of SQL

yes

SQL-like query language

APIs and other access methods

Cypher Query Language
JDBC

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

HTTP API
JSON over UDP

gRPC
HTTP REST
WebSocket

C++ API
Java API

Supported programming languages

C
Java
JavaScript
Python

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

.Net
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Perl
PHP
Python
R
Ruby
Rust
Scala

Java
Scala

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

yes

Triggers

Partitioning methods

Methods for storing different data on different nodes

no, but can be realized using table inheritance

Sharding

in enterprise version only

Sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

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

selectable replication factor

in enterprise version only

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

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

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Atomic single-row operations

yes

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

Depending on used storage engine

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)

simple rights management via user accounts

More information provided by the system vendor

Specific characteristics

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Market metrics

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Licensing and pricing models

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