DBMS > EJDB vs. Heroic vs. Hypertable vs. Hyprcubd vs. Microsoft Azure Synapse Analytics

System Properties Comparison EJDB vs. Heroic vs. Hypertable vs. Hyprcubd vs. Microsoft Azure Synapse Analytics

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

Elastic, large scale data warehouse service leveraging the broad eco-system of SQL Server

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	19.93
Rank	#31	Overall
	#19	Relational DBMS

Website

github.com/Softmotions/ejdb

github.com/spotify/heroic

hyprcubd.com (offline)

azure.microsoft.com/services/synapse-analytics

Technical documentation

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

spotify.github.io/heroic

docs.microsoft.com/azure/synapse-analytics

Developer

Softmotions

Spotify

Hypertable Inc.

Hyprcubd, Inc.

Microsoft

Initial release

2012

2014

2009

2016

Current release

0.9.8.11, March 2016

License

Commercial or Open Source

Open Source

GPLv2

Open Source

Apache 2.0

Open Source

GNU version 3. Commercial license available

commercial

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++

Server operating systems

server-less

Linux
OS X
Windows

an inofficial Windows port is available

hosted

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)

ADO.NET
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

C#
Java
PHP

Server-side scripts

Stored procedures

Transact SQL

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Sharding, horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

selectable replication factor on file system level

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

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

docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-table-constraints

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

token access

yes

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