DBMS > Google Cloud Bigtable vs. Graphite vs. Microsoft Azure Cosmos DB vs. TerminusDB vs. TimescaleDB

System Properties Comparison Google Cloud Bigtable vs. Graphite vs. Microsoft Azure Cosmos DB vs. TerminusDB vs. TimescaleDB

Editorial information provided by DB-Engines

Name

Description

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

Data logging and graphing tool for time series data

The storage layer (fixed size database) is called Whisper

Globally distributed, horizontally scalable, multi-model database service

Scalable Graph Database platform making enterprise data available by exploiting inferred entities and relationships

A time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL

Primary database model

Key-value store
Wide column store

Time Series DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Graph DBMS

Time Series DBMS

Secondary database models

Spatial DBMS

Document store
RDF store

Relational DBMS

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

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

Score	4.83
Rank	#67	Overall
	#4	Time Series DBMS

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

Score	0.23
Rank	#316	Overall
	#27	Graph DBMS

Score	4.46
Rank	#71	Overall
	#5	Time Series DBMS

Website

cloud.google.com/bigtable

github.com/graphite-project/graphite-web

azure.microsoft.com/services/cosmos-db

terminusdb.com

www.timescale.com

Technical documentation

cloud.google.com/bigtable/docs

graphite.readthedocs.io

learn.microsoft.com/azure/cosmos-db

terminusdb.github.io/terminusdb/#

docs.timescale.com

Developer

Google

Chris Davis

Microsoft

DataChemist Ltd.

Timescale

Initial release

2015

2006

2014

2018

2017

Current release

11.0.0, January 2023

2.15.0, May 2024

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

Open Source

GPL V3

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Python

Prolog, Rust

Server operating systems

hosted

Linux
Unix

hosted

Linux

Linux
OS X
Windows

Data scheme

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

Numeric data only

yes

JSON types

yes

numerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data types

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

All properties auto-indexed by default

yes

SQL

Support of SQL

SQL-like query language

SQL-like query language (WOQL)

yes

full PostgreSQL SQL syntax

APIs and other access methods

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

HTTP API
Sockets

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

OWL
RESTful HTTP API
WOQL (Web Object Query Language)

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

Supported programming languages

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

JavaScript (Node.js)
Python

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

JavaScript
Python

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

JavaScript

yes

user defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell

Triggers

JavaScript

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Implicit feature of the cloud service

Graph Partitioning

yes, across time and space (hash partitioning) attributes

Replication methods

Methods for redundantly storing data on multiple nodes

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

none

yes

Implicit feature of the cloud service

Journaling Streams

Source-replica replication with hot standby and reads on replicas

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

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

none

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomic single-row operations

Multi-item ACID transactions with snapshot isolation within a partition

ACID

Concurrency

Support for concurrent manipulation of data

yes

locking

yes

Durability

Support for making data persistent

yes

in-memory journaling

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

User concepts

Access control

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

Access rights can be defined down to the item level

Role-based access control

fine grained access rights according to SQL-standard

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