DBMS > EJDB vs. Heroic vs. Hypertable vs. Hyprcubd vs. Splice Machine

System Properties Comparison EJDB vs. Heroic vs. Hypertable vs. Hyprcubd vs. Splice Machine

Editorial information provided by DB-Engines

Name

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

Hyprcubd seems to be discontinued. Therefore it is excluded from the DB-Engines ranking.

Description

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

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

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

Serverless Time Series DBMS

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

Primary database model

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

Score	0.31
Rank	#296	Overall
	#44	Document stores

Score	0.46
Rank	#265	Overall
	#22	Time Series DBMS

Score	0.54
Rank	#252	Overall
	#115	Relational DBMS

Website

github.com/Softmotions/ejdb

github.com/spotify/heroic

hyprcubd.com (offline)

splicemachine.com

Technical documentation

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

spotify.github.io/heroic

splicemachine.com/how-it-works

Developer

Softmotions

Spotify

Hypertable Inc.

Hyprcubd, Inc.

Splice Machine

Initial release

2012

2014

2009

2014

Current release

0.9.8.11, March 2016

3.1, March 2021

License

Commercial or Open Source

Open Source

GPLv2

Open Source

Apache 2.0

Open Source

GNU version 3. Commercial license available

commercial

Open Source

AGPL 3.0, commercial license 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++

Java

Server operating systems

server-less

Linux
OS X
Windows

an inofficial Windows port is available

hosted

Linux
OS X
Solaris
Windows

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

string, integer, double, bool, date, object_id

yes

time, int, uint, float, string

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

via Elasticsearch

restricted

only exact value or prefix value scans

yes

SQL

Support of SQL

SQL-like query language

yes

APIs and other access methods

in-process shared library

HQL (Heroic Query Language, a JSON-based language)
HTTP API

C++ API
Thrift

gRPC (https)

JDBC
Native Spark Datasource
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

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

Server-side scripts

Stored procedures

yes

Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

selectable replication factor on file system level

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

typically not needed, however similar functionality with collection joins possible

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

yes, multi-version concurrency control (MVCC)

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.

yes

User concepts

Access control

token access

Access rights for users, groups and roles according to SQL-standard

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

Recent citations in the news

Review: Google Bigtable scales with ease
7 September 2016, InfoWorld

provided by Google News

TimescaleDB goes distributed; implements ‘Chunking’ over ‘Sharding’ for scaling-out
22 August 2019, Packt Hub

SQL and TimescaleDB. This article takes a closer look into… | by Alibaba Cloud
31 July 2019, DataDrivenInvestor

Decorate your Windows XP with Hyperdesk
30 July 2008, CNET

The Collective: Customize Your Computer & Your Phone With Star Trek
18 March 2009, TrekMovie

5 Free NoSQL Database Certification Courses Online in 2024
31 January 2024, Analytics India Magazine

provided by Google News

Machine learning data pipeline outfit Splice Machine files for insolvency
26 August 2021, The Register

Splice Machine Launches the Splice Machine Feature Store to Simplify Feature Engineering and Democratize Machine ...
19 January 2021, PR Newswire

Distributed SQL System Review: Snowflake vs Splice Machine
18 September 2019, Towards Data Science

Splice Machine Launches Feature Store to Simplify Feature Engineering
19 January 2021, Datanami

Splice Machine scores $15M to make Hadoop run in real time
10 February 2014, VentureBeat

provided by Google News

Share this page