DBMS > Amazon DynamoDB vs. Datomic vs. Spark SQL vs. SurrealDB

System Properties Comparison Amazon DynamoDB vs. Datomic vs. Spark SQL vs. SurrealDB

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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

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

Spark SQL is a component on top of 'Spark Core' for structured data processing

A fully ACID transactional, developer-friendly, multi-model DBMS

Primary database model

Document store
Key-value store

Relational DBMS

Document store
Graph 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	1.66
Rank	#144	Overall
	#66	Relational DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Score	1.02
Rank	#190	Overall
	#33	Document stores
	#18	Graph DBMS

Website

aws.amazon.com/dynamodb

www.datomic.com

spark.apache.org/sql

surrealdb.com

Technical documentation

docs.aws.amazon.com/dynamodb

docs.datomic.com

spark.apache.org/docs/latest/sql-programming-guide.html

surrealdb.com/docs

Developer

Amazon

Cognitect

Apache Software Foundation

SurrealDB Ltd

Initial release

2012

2014

2022

Current release

1.0.6735, June 2023

3.5.0 ( 2.13), September 2023

v1.5.0, May 2024

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

commercial

limited edition free

Open Source

Apache 2.0

Open Source

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

Java, Clojure

Scala

Rust

Server operating systems

hosted

All OS with a Java VM

Linux
OS X
Windows

Linux
macOS
Windows

Data scheme

schema-free

yes

schema-free

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

SQL-like DML and DDL statements

SQL-like query language

APIs and other access methods

RESTful HTTP API

JDBC
ODBC

GraphQL
RESTful HTTP API
WebSocket

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

Clojure
Java

Java
Python
R
Scala

Deno
Go
JavaScript (Node.js)
Rust

Server-side scripts

Stored procedures

yes

Transaction Functions

Triggers

yes

by integration with AWS Lambda

By using transaction functions

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

But extensive use of caching in the application peers

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

But extensive use of caching in the application peers

none

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

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

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

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

recommended only for testing and development

User concepts

Access control

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

yes, based on authentication and database rules

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