DBMS > EJDB vs. Hypertable vs. Riak TS vs. SAP Adaptive Server vs. Snowflake

System Properties Comparison EJDB vs. Hypertable vs. Riak TS vs. SAP Adaptive Server vs. Snowflake

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

Embeddable document-store database library with JSON representation of queries (in MongoDB style)

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

The SAP (Sybase) Adaptive Server Enterprise (ASE) is an enterprise-class RDBMS

Cloud-based data warehousing service for structured and semi-structured data

Primary database model

Secondary database models

Spatial DBMS

with Boeing's Spatial Query Server

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

Score	0.27
Rank	#297	Overall
	#44	Document stores

Score	0.20
Rank	#319	Overall
	#27	Time Series DBMS

Score	36.31
Rank	#25	Overall
	#17	Relational DBMS

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Website

github.com/Softmotions/ejdb

www.sap.com/products/technology-platform/sybase-ase.html

www.snowflake.com

Technical documentation

github.com/Softmotions/ejdb/blob/master/README.md

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

help.sap.com/docs/SAP_ASE

docs.snowflake.net/manuals/index.html

Developer

Softmotions

Hypertable Inc.

Open Source, formerly Basho Technologies

SAP, Sybase

Snowflake Computing Inc.

Initial release

2012

2009

2015

1987

2014

Current release

0.9.8.11, March 2016

3.0.0, September 2022

16.0

License

Commercial or Open Source

Open Source

GPLv2

Open Source

GNU version 3. Commercial license available

Open Source

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Erlang

C and C++

Server operating systems

server-less

Linux
OS X
Windows

an inofficial Windows port is available

Linux
OS X

AIX
HP-UX
Linux
Solaris
Unix
Windows

hosted

Data scheme

schema-free

yes

support of semi-structured data formats (JSON, XML, Avro)

Typing

predefined data types such as float or date

yes

string, integer, double, bool, date, object_id

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

Secondary indexes

restricted

only exact value or prefix value scans

restricted

yes

SQL

Support of SQL

yes, limited

yes

APIs and other access methods

in-process shared library

C++ API
Thrift

HTTP API
Native Erlang Interface

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

CLI Client
JDBC
ODBC

Supported programming languages

Actionscript
C
C#
C++
Go
Java
JavaScript (Node.js)
Lua
Objective-C
Pike
Python
Ruby

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

C
C++
Cobol
Java
Java
Perl
PHP
Python

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

Erlang

Java and Transact-SQL

user defined functions

Triggers

yes

pre-commit hooks and post-commit hooks

yes

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

horizontal partitioning

yes

Replication methods

Methods for redundantly storing data on multiple nodes

none

selectable replication factor on file system level

selectable replication factor

Multi-source replication
Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

typically not needed, however similar functionality with collection joins possible

links between datasets can be stored

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/Write Locking

yes

Durability

Support for making data persistent

yes

In-memory capabilities

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

User concepts

Access control

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept, user roles and pluggable authentication

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