DBMS > BigObject vs. Cachelot.io vs. Google Cloud Datastore vs. Graphite

System Properties Comparison BigObject vs. Cachelot.io vs. Google Cloud Datastore vs. Graphite

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for real-time computations and queries

In-memory caching system

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

Data logging and graphing tool for time series data

The storage layer (fixed size database) is called Whisper

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Key-value store

Document store

Time Series DBMS

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

Score	0.13
Rank	#333	Overall
	#147	Relational DBMS

Score	0.00
Rank	#383	Overall
	#60	Key-value stores

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	4.57
Rank	#73	Overall
	#5	Time Series DBMS

Website

bigobject.io

cachelot.io

cloud.google.com/datastore

github.com/graphite-project/graphite-web

Technical documentation

docs.bigobject.io

cloud.google.com/datastore/docs

graphite.readthedocs.io

Developer

BigObject, Inc.

Google

Chris Davis

Initial release

2015

2008

2006

License

Commercial or Open Source

commercial

free community edition available

Open Source

Simplified BSD License

commercial

Open Source

Apache 2.0

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

C++

Python

Server operating systems

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

FreeBSD
Linux
OS X

hosted

Linux
Unix

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

yes, details here

Numeric data only

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

SQL

Support of SQL

SQL-like DML and DDL statements

SQL-like query language (GQL)

APIs and other access methods

fluentd
ODBC
RESTful HTTP API

Memcached protocol

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

HTTP API
Sockets

Supported programming languages

.Net
C
C++
ColdFusion
Erlang
Java
Lisp
Lua
OCaml
OCaml
Perl
PHP
Python
Ruby

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

Lua

using Google App Engine

Triggers

Callbacks using the Google Apps Engine

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication using Paxos

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

none

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

yes

via ReferenceProperties or Ancestor paths

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

yes

locking

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

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

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More resources

DB-Engines blog posts

Time Series DBMS are the database category with the fastest increase in popularity
4 July 2016, Matthias Gelbmann

Time Series DBMS as a new trend?
1 June 2015, Paul Andlinger

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