DBMS > Amazon DynamoDB vs. Databricks vs. etcd vs. GigaSpaces vs. Sphinx

System Properties Comparison Amazon DynamoDB vs. Databricks vs. etcd vs. GigaSpaces vs. Sphinx

Editorial information provided by DB-Engines

Name

Description

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

The Databricks Lakehouse Platform combines elements of data lakes and data warehouses to provide a unified view onto structured and unstructured data. It is based on Apache Spark.

A distributed reliable key-value store

High performance in-memory data grid platform, powering three products: Smart Cache, Smart ODS (Operational Data Store), Smart Augmented Transactions

Open source search engine for searching in data from different sources, e.g. relational databases

Primary database model

Document store
Key-value store

Document store
Relational DBMS

Key-value store

Document store
Object oriented DBMS

Values are user defined objects

Search engine

Secondary database models

Graph DBMS
Search engine

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	81.08
Rank	#15	Overall
	#2	Document stores
	#10	Relational DBMS

Score	7.03
Rank	#54	Overall
	#5	Key-value stores

Score	1.03
Rank	#188	Overall
	#32	Document stores
	#6	Object oriented DBMS

Score	5.95
Rank	#55	Overall
	#5	Search engines

Website

aws.amazon.com/dynamodb

www.databricks.com

etcd.io
github.com/etcd-io/etcd

www.gigaspaces.com

sphinxsearch.com

Technical documentation

docs.aws.amazon.com/dynamodb

docs.databricks.com

etcd.io/docs
github.com/etcd-io/etcd/tree/master/Documentation

docs.gigaspaces.com/latest/landing.html

sphinxsearch.com/docs

Developer

Amazon

Databricks

Gigaspaces Technologies

Sphinx Technologies Inc.

Initial release

2012

2013

2000

2001

Current release

3.4, August 2019

15.5, September 2020

3.5.1, February 2023

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

commercial

Open Source

Apache Version 2.0

Open Source

Apache Version 2; Commercial licenses available

Open Source

GPL version 2, commercial licence 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

Java, C++, .Net

C++

Server operating systems

hosted

FreeBSD
Linux
Windows

experimental

Linux
macOS
Solaris
Windows

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Data scheme

schema-free

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

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.

yes

XML can be used for describing objects metadata

Secondary indexes

yes

full-text index on all search fields

SQL

Support of SQL

with Databricks SQL

SQL-99 for query and DML statements

SQL-like query language (SphinxQL)

APIs and other access methods

RESTful HTTP API

JDBC
ODBC
RESTful HTTP API

gRPC
JSON over HTTP

GigaSpaces LRMI
Hibernate
JCache
JDBC
JPA
ODBC
RESTful HTTP API
Spring Data

Proprietary protocol

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

Python
R
Scala

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

.Net
C++
Java
Python
Scala

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Server-side scripts

Stored procedures

user defined functions and aggregates

yes

Triggers

yes

by integration with AWS Lambda

yes, watching key changes

yes, event driven architecture

Partitioning methods

Methods for storing different data on different nodes

Sharding

Partitioning is done manually, search queries against distributed index is supported

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Using Raft consensus algorithm to ensure data replication with strong consistency among multiple replicas.

Multi-source replication

synchronous or asynchronous
Source-replica replication

synchronous or asynchronous

none

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

yes

Map-Reduce pattern can be built with XAP task executors

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

Immediate Consistency

Consistency level configurable: ALL, QUORUM, ANY

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

The original contents of fields are not stored in the Sphinx index.

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)

Role-based access control

More information provided by the system vendor

Specific characteristics

Supported database models : In addition to the Document store and Relational DBMS...
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