DBMS > Amazon DynamoDB vs. HBase vs. Prometheus vs. Warp 10 vs. XTDB

System Properties Comparison Amazon DynamoDB vs. HBase vs. Prometheus vs. Warp 10 vs. XTDB

Editorial information provided by DB-Engines

Name

Description

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

Wide-column store based on Apache Hadoop and on concepts of BigTable

Open-source Time Series DBMS and monitoring system

TimeSeries DBMS specialized on timestamped geo data based on LevelDB or HBase

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Document store
Key-value store

Wide column store

Time Series DBMS

Document store

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	30.50
Rank	#26	Overall
	#2	Wide column stores

Score	8.42
Rank	#47	Overall
	#2	Time Series DBMS

Score	0.07
Rank	#349	Overall
	#32	Time Series DBMS

Score	0.11
Rank	#343	Overall
	#46	Document stores

Website

aws.amazon.com/dynamodb

hbase.apache.org

prometheus.io

www.warp10.io

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

docs.aws.amazon.com/dynamodb

hbase.apache.org/book.html

prometheus.io/docs

www.warp10.io/content/02_Getting_started

www.xtdb.com/docs

Developer

Amazon

Apache Software Foundation

Apache top-level project, originally developed by Powerset

SenX

Juxt Ltd.

Initial release

2012

2008

2015

2019

Current release

2.3.4, January 2021

1.19, September 2021

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

Open Source

Apache version 2

Open Source

Apache 2.0

Open Source

Apache License 2.0

Open Source

MIT License

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

Server operating systems

hosted

Linux
Unix
Windows

using Cygwin

Linux
Windows

Linux
OS X
Windows

All OS with a Java 8 (and higher) VM
Linux

Data scheme

schema-free

schema-free, schema definition possible

yes

schema-free

Typing

predefined data types such as float or date

yes

options to bring your own types, AVRO

Numeric data only

yes

yes, extensible-data-notation format

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.

Import of XML data possible

Secondary indexes

yes

SQL

Support of SQL

limited SQL, making use of Apache Calcite

APIs and other access methods

RESTful HTTP API

Java API
RESTful HTTP API
Thrift

RESTful HTTP/JSON API

HTTP API
Jupyter
WebSocket

HTTP REST
JDBC

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

C
C#
C++
Groovy
Java
PHP
Python
Scala

.Net
C++
Go
Haskell
Java
JavaScript (Node.js)
Python
Ruby

Clojure
Java

Server-side scripts

Stored procedures

yes

Coprocessors in Java

yes

WarpScript

Triggers

yes

by integration with AWS Lambda

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on HBase

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication
Source-replica replication

yes

by Federation

selectable replication factor

based on HBase

yes, each node contains all data

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 or Eventual Consistency

none

Immediate Consistency

based on HBase

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

Single row ACID (across millions of columns)

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

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)

Access Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

Mandatory use of cryptographic tokens, containing fine-grained authorizations

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