DBMS > Amazon DynamoDB vs. Dolt vs. Firebolt vs. RocksDB vs. Splice Machine

System Properties Comparison Amazon DynamoDB vs. Dolt vs. Firebolt vs. RocksDB vs. Splice Machine

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

Highly scalable cloud data warehouse and analytics product

Forked from Clickhouse

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

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

Primary database model

Document store
Key-value store

Relational DBMS

Key-value store

Relational DBMS

Secondary database models

Document store

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	1.73
Rank	#140	Overall
	#63	Relational DBMS

Score	3.41
Rank	#86	Overall
	#11	Key-value stores

Score	0.54
Rank	#252	Overall
	#115	Relational DBMS

Website

aws.amazon.com/dynamodb

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

www.firebolt.io

rocksdb.org

splicemachine.com

Technical documentation

docs.aws.amazon.com/dynamodb

docs.dolthub.com

docs.firebolt.io

github.com/facebook/rocksdb/wiki

splicemachine.com/how-it-works

Developer

Amazon

DoltHub Inc

Firebolt Analytics Inc.

Facebook, Inc.

Splice Machine

Initial release

2012

2018

2020

2013

2014

Current release

9.2.1, May 2024

3.1, March 2021

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

Open Source

Apache Version 2.0

commercial

Open Source

BSD

Open Source

AGPL 3.0, commercial license 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++

Java

Server operating systems

hosted

Linux
macOS
Windows

hosted

Linux

Linux
OS X
Solaris
Windows

Data scheme

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

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

APIs and other access methods

RESTful HTTP API

CLI Client
HTTP REST

.Net
ODBC
RESTful HTTP API

C++ API
Java API

JDBC
Native Spark Datasource
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

Go
JavaScript (Node.js)
Python

C
C++
Go
Java
Perl
Python
Ruby

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

Server-side scripts

Stored procedures

yes

currently in alpha release

yes

Java

Triggers

yes

by integration with AWS Lambda

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

horizontal partitioning

Shared Nothhing Auto-Sharding, Columnar 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.

depending on storage layer

yes

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

Immediate Consistency

Foreign keys

Referential integrity

yes

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

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

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)

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

Access rights for users, groups and roles according to SQL-standard

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