DBMS > Amazon DynamoDB vs. Hypertable vs. LevelDB vs. Spark SQL

System Properties Comparison Amazon DynamoDB vs. Hypertable vs. LevelDB vs. Spark SQL

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Editorial information provided by DB-Engines

Name

Hypertable has stopped its further development with March 2016 and is removed from the DB-Engines ranking.

Description

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

An open source BigTable implementation based on distributed file systems such as Hadoop

Embeddable fast key-value storage library that provides an ordered mapping from string keys to string values

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

Primary database model

Document store
Key-value store

Wide column store

Key-value store

Relational DBMS

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

Score	74.07
Rank	#17	Overall
	#3	Document stores
	#2	Key-value stores

Score	2.35
Rank	#111	Overall
	#19	Key-value stores

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

aws.amazon.com/dynamodb

github.com/google/leveldb

spark.apache.org/sql

Technical documentation

docs.aws.amazon.com/dynamodb

github.com/google/leveldb/blob/main/doc/index.md

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

Developer

Amazon

Hypertable Inc.

Google

Apache Software Foundation

Initial release

2012

2009

2011

2014

Current release

0.9.8.11, March 2016

1.23, February 2021

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

Open Source

GNU version 3. Commercial license available

Open Source

BSD

Open Source

Apache 2.0

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++

Scala

Server operating systems

hosted

Linux
OS X
Windows

an inofficial Windows port is available

Illumos
Linux
OS X
Windows

Linux
OS X
Windows

Data scheme

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

restricted

only exact value or prefix value scans

SQL

Support of SQL

SQL-like DML and DDL statements

APIs and other access methods

RESTful HTTP API

C++ API
Thrift

JDBC
ODBC

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

C++
Java
Perl
PHP
Python
Ruby

C++
Go
Java

3rd party binding
JavaScript (Node.js)

3rd party binding
Python

3rd party binding

Java
Python
R
Scala

Server-side scripts

Stored procedures

Triggers

yes

by integration with AWS Lambda

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor on file system level

none

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

yes

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

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

with automatic compression on writes

yes

In-memory capabilities

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

User concepts

Access control

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

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