DBMS > Amazon DynamoDB vs. Dolt vs. Google Cloud Datastore vs. SingleStore

System Properties Comparison Amazon DynamoDB vs. Dolt vs. Google Cloud Datastore vs. SingleStore

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Editorial information provided by DB-Engines

Name

Description

Hosted, scalable database service by Amazon with the data stored in Amazons cloud

A MySQL compatible DBMS with Git-like versioning of data and schema

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

MySQL wire-compliant distributed RDBMS that combines an in-memory row-oriented and a disc-based column-oriented storage with patented universal storage to handle transactional and analytical workloads in one single table type

Primary database model

Document store
Key-value store

Relational DBMS

Document store

Relational DBMS

Secondary database models

Document store

Document store
Spatial DBMS
Time Series DBMS
Vector DBMS

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

Score	74.45
Rank	#17	Overall
	#3	Document stores
	#2	Key-value stores

Score	1.02
Rank	#191	Overall
	#89	Relational DBMS

Score	4.36
Rank	#72	Overall
	#12	Document stores

Score	5.38
Rank	#62	Overall
	#35	Relational DBMS

Website

aws.amazon.com/dynamodb

github.com/dolthub/dolt
www.dolthub.com

cloud.google.com/datastore

www.singlestore.com

Technical documentation

docs.aws.amazon.com/dynamodb

docs.dolthub.com

cloud.google.com/datastore/docs

docs.singlestore.com

Developer

Amazon

DoltHub Inc

Google

SingleStore Inc.

Initial release

2012

2018

2008

2013

Current release

8.5, January 2024

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

Open Source

Apache Version 2.0

commercial

free developer edition 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.

SingleStoreDB Cloud: The world's fastest, modern cloud database for both operational (OLTP) and analytical (OLAP) workloads. Available instantly with multi-cloud and hybrid-cloud capabilities

Implementation language

C++, Go

Server operating systems

hosted

Linux
macOS
Windows

hosted

Linux

64 bit version required

Data scheme

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

yes, details here

yes

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

yes

SQL-like query language (GQL)

yes

but no triggers and foreign keys

APIs and other access methods

RESTful HTTP API

CLI Client
HTTP REST

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

Cluster Management API

as HTTP Rest and CLI
HTTP API
JDBC
MongoDB API
ODBC

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

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

Bash
C
C#
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

currently in alpha release

using Google App Engine

yes

Triggers

yes

by integration with AWS Lambda

yes

Callbacks using the Google Apps Engine

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

hash partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

A database can be cloned to multiple locations and be used there in isolation. Data/schema changes can be pushed/pulled explicitly between locations.

Multi-source replication using Paxos

Source-replica replication

stores two copies of each physical data partition on two separate nodes

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

yes

using Google Cloud Dataflow

can define user-defined aggregate functions for map-reduce-style calculations

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.

Immediate Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

Transaction concepts

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

ACID

ACID across one or more tables within a single AWS account and region

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

Durability

Support for making data persistent

yes

All updates are persistent, including those to disk-based columnstores and memory-based row stores. Transaction commits are supported via write-ahead log.

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)

Only one user is configurable, and must be specified in the config file at startup

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

Fine grained access control via users, groups and roles

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

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Key customers

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Market metrics

Customers in various industries worldwide including US and International Industry...
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Licensing and pricing models

F ree Tier and Enterprise Edition
» more

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