DBMS > CrateDB vs. HarperDB vs. Hypertable vs. Riak TS

System Properties Comparison CrateDB vs. HarperDB vs. Hypertable vs. Riak TS

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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

Distributed Database based on Lucene

HarperDB takes the "stack" out of "tech stack" by combining an ultra-fast document-style data store, in-memory cache, real-time message broker, and your application components into a single distributed technology.

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

Riak TS is a distributed NoSQL database optimized for time series data and based on Riak KV

Primary database model

Document store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS

Document store

Wide column store

Time Series DBMS

Secondary database models

Relational DBMS

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

Score	0.69
Rank	#226	Overall
	#37	Document stores
	#5	Spatial DBMS
	#16	Search engines
	#19	Time Series DBMS
	#11	Vector DBMS

Score	0.55
Rank	#243	Overall
	#38	Document stores

Score	0.17
Rank	#318	Overall
	#27	Time Series DBMS

Website

cratedb.com

www.harperdb.io

Technical documentation

cratedb.com/docs

docs.harperdb.io/docs

www.tiot.jp/riak-docs/riak/ts/latest

Developer

Crate

HarperDB

Hypertable Inc.

Open Source, formerly Basho Technologies

Initial release

2013

2017

2009

2015

Current release

5.8.1, August 2024

3.1, August 2021

0.9.8.11, March 2016

3.0.0, September 2022

License

Commercial or Open Source

Open Source

commercial

free community edition available

Open Source

GNU version 3. Commercial license available

Open Source

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Node.js

C++

Erlang

Server operating systems

All Operating Systems, including Kubernetes with CrateDB Kubernetes Operator support

Linux
OS X

Linux
OS X
Windows

an inofficial Windows port is available

Linux
OS X

Data scheme

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

dynamic schema

schema-free

Typing

predefined data types such as float or date

yes

JSON data types

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

restricted

SQL

Support of SQL

yes, but no triggers and constraints, and PostgreSQL compatibility

SQL-like data manipulation statements

yes, limited

APIs and other access methods

ADO.NET
JDBC
ODBC
PostgreSQL wire protocol
Prometheus Remote Read/Write
RESTful HTTP API

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

C++ API
Thrift

HTTP API
Native Erlang Interface

Supported programming languages

.NET
Erlang
Go

community maintained client
Java
JavaScript (Node.js)

community maintained client
Perl

community maintained client
PHP
Python
R
Ruby

community maintained client
Scala

community maintained client

.Net
C
C#
C++
ColdFusion
D
Dart
Delphi
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
MatLab
Objective C
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Rust
Scala
Swift

C++
Java
Perl
PHP
Python
Ruby

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

Server-side scripts

Stored procedures

user defined functions (Javascript)

Custom Functions

since release 3.1

Erlang

Triggers

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

A table resides as a whole on one (or more) nodes in a cluster

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Configurable replication on table/partition-level

yes

the nodes on which a table resides can be defined

selectable replication factor on file system level

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Read-after-write consistency on record level

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

links between datasets can be stored

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

unique row identifiers can be used for implementing an optimistic concurrency control strategy

Atomic execution of specific operations

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, using LMDB

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

rights management via user accounts

Access rights for users and roles

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