DBMS > Amazon SimpleDB vs. atoti vs. Blueflood vs. CrateDB vs. SQLite

System Properties Comparison Amazon SimpleDB vs. atoti vs. Blueflood vs. CrateDB vs. SQLite

Editorial information provided by DB-Engines

Name

Description

Hosted simple database service by Amazon, with the data stored in the Amazon Cloud.

There is an unrelated product called SimpleDB developed by Edward Sciore

An in-memory DBMS combining transactional and analytical processing to handle the aggregation of ever-changing data.

Scalable TimeSeries DBMS based on Cassandra

Distributed Database based on Lucene

Widely used embeddable, in-process RDBMS

Primary database model

Key-value store

Object oriented DBMS

Time Series DBMS

Document store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS

Relational DBMS

Secondary database models

Relational DBMS

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

Score	1.85
Rank	#138	Overall
	#24	Key-value stores

Score	0.56
Rank	#245	Overall
	#10	Object oriented DBMS

Score	0.06
Rank	#353	Overall
	#34	Time Series DBMS

Score	0.73
Rank	#224	Overall
	#37	Document stores
	#5	Spatial DBMS
	#16	Search engines
	#19	Time Series DBMS
	#8	Vector DBMS

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Website

aws.amazon.com/simpledb

Technical documentation

docs.aws.amazon.com/simpledb

docs.atoti.io

github.com/rax-maas/blueflood/wiki

cratedb.com/docs

www.sqlite.org/docs.html

Developer

Amazon

ActiveViam

Rackspace

Crate

Dwayne Richard Hipp

Initial release

2007

2013

2000

Current release

3.45.3 (15 April 2024), April 2024

License

Commercial or Open Source

commercial

free versions available

Open Source

Apache 2.0

Open Source

Public Domain

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.

CrateDB Cloud: a distributed SQL database that spreads data and processing across an elastic cluster of shared nothing nodes. CrateDB Cloud enables data insights at scale on Microsoft Azure, AWS and Google Cloud Platform.

Implementation language

Java

Server operating systems

hosted

Linux
OS X

All Operating Systems, including Kubernetes with CrateDB Kubernetes Operator support

server-less

Data scheme

schema-free

predefined scheme

Flexible Schema (defined schema, partial schema, schema free)

yes

dynamic column types

Typing

predefined data types such as float or date

yes

not rigid because of 'dynamic typing' concept.

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 columns are indexed automatically

yes

SQL

Support of SQL

Multidimensional Expressions (MDX)

yes, but no triggers and constraints, and PostgreSQL compatibility

yes

SQL-92 is not fully supported

APIs and other access methods

RESTful HTTP API

HTTP REST

ADO.NET
JDBC
ODBC
PostgreSQL wire protocol
Prometheus Remote Read/Write
RESTful HTTP API

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Supported programming languages

.Net
C
C++
Erlang
Java
PHP
Python
Ruby
Scala

.NET
Erlang
Go

community maintained client
Java
JavaScript (Node.js)

community maintained client
Perl

community maintained client
PHP
Python
R
Ruby

community maintained client
Scala

community maintained client

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

Server-side scripts

Stored procedures

Python

user defined functions (Javascript)

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding must be implemented in the application

Sharding, horizontal partitioning

Sharding

based on Cassandra

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

based on Cassandra

Configurable replication on table/partition-level

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Eventual Consistency
Read-after-write consistency on record level

Foreign keys

Referential integrity

yes

Transaction concepts

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

Concurrent data updates can be detected by the application

unique row identifiers can be used for implementing an optimistic concurrency control strategy

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

via file-system locks

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 and roles can be defined via the AWS Identity and Access Management (IAM)

rights management via user accounts

More information provided by the system vendor

Specific characteristics

The enterprise database for time series, documents, and vectors. Distributed - Native...
» more

Competitive advantages

Response time in milliseconds: e ven for complex ad-hoc queries. Massive scaling...
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Typical application scenarios

IoT: accelerate your IIoT projects with CrateDB, delivering real-time analytics...
» more

Key customers

Across all continents, CrateDB is used by companies of all sizes to meet the most...
» more

Market metrics

The CrateDB open source project was started in 2013 Honorable Mention in 2021 Gartner®...
» more

Licensing and pricing models

See CrateDB pricing >
» more

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