DBMS > Amazon SimpleDB vs. Google Cloud Datastore vs. InfluxDB vs. RocksDB vs. TerarkDB

System Properties Comparison Amazon SimpleDB vs. Google Cloud Datastore vs. InfluxDB vs. RocksDB vs. TerarkDB

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

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

DBMS for storing time series, events and metrics

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

A key-value store forked from RocksDB with advanced compression algorithms. It can be used standalone or as a storage engine for MySQL and MongoDB

Primary database model

Secondary database models

Spatial DBMS

with GEO package

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

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

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	25.83
Rank	#28	Overall
	#1	Time Series DBMS

Score	3.65
Rank	#85	Overall
	#11	Key-value stores

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

Website

aws.amazon.com/simpledb

cloud.google.com/datastore

www.influxdata.com/products/influxdb-overview

rocksdb.org

github.com/bytedance/terarkdb

Technical documentation

docs.aws.amazon.com/simpledb

cloud.google.com/datastore/docs

docs.influxdata.com/influxdb

github.com/facebook/rocksdb/wiki

bytedance.larkoffice.com/docs/doccnZmYFqHBm06BbvYgjsHHcKc

Developer

Amazon

Google

Facebook, Inc.

ByteDance, originally Terark

Initial release

2007

2008

2013

2016

Current release

2.7.6, April 2024

8.11.4, April 2024

License

Commercial or Open Source

commercial

Open Source

MIT-License; commercial enterprise version available

Open Source

BSD

commercial

restricted open source version available

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++

Server operating systems

hosted

Linux
OS X

through Homebrew

Linux

Data scheme

schema-free

Typing

predefined data types such as float or date

yes, details here

Numeric data and Strings

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

SQL-like query language (GQL)

SQL-like query language

APIs and other access methods

RESTful HTTP API

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

HTTP API
JSON over UDP

C++ API
Java API

Supported programming languages

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

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

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

C
C++
Go
Java
Perl
Python
Ruby

C++
Java

Server-side scripts

Stored procedures

using Google App Engine

Triggers

Callbacks using the Google Apps Engine

Partitioning methods

Methods for storing different data on different nodes

none

Sharding must be implemented in the application

Sharding

in enterprise version only

horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication using Paxos

selectable replication factor

in enterprise version only

yes

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

Eventual Consistency
Immediate Consistency

can be specified for read operations

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.

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

Transaction concepts

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

Concurrent data updates can be detected by the application

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

yes

Concurrency

Support for concurrent manipulation of data

yes

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

Depending on used storage engine

yes

User concepts

Access control

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

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

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