DBMS > EJDB vs. GeoSpock vs. Google BigQuery vs. HBase vs. STSdb

System Properties Comparison EJDB vs. GeoSpock vs. Google BigQuery vs. HBase vs. STSdb

Editorial information provided by DB-Engines

Name

GeoSpock seems to be discontinued. Therefore it will be excluded from the DB-Engines ranking.

Description

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

Spatial and temporal data processing engine for extreme data scale

Large scale data warehouse service with append-only tables

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

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Primary database model

Secondary database models

Time Series DBMS

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

Score	0.31
Rank	#296	Overall
	#44	Document stores

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	27.97
Rank	#26	Overall
	#2	Wide column stores

Score	0.10
Rank	#357	Overall
	#51	Key-value stores

Website

github.com/Softmotions/ejdb

geospock.com

cloud.google.com/bigquery

hbase.apache.org

github.com/STSSoft/STSdb4

Technical documentation

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

cloud.google.com/bigquery/docs

hbase.apache.org/book.html

Developer

Softmotions

GeoSpock

Google

Apache Software Foundation

Apache top-level project, originally developed by Powerset

STS Soft SC

Initial release

2012

2010

2008

2011

Current release

2.0, September 2019

2.3.4, January 2021

4.0.8, September 2015

License

Commercial or Open Source

Open Source

GPLv2

commercial

Open Source

Apache version 2

Open Source

GPLv2, 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, Javascript

Java

Server operating systems

server-less

hosted

Linux
Unix
Windows

using Cygwin

Windows

Data scheme

schema-free

yes

schema-free, schema definition possible

yes

Typing

predefined data types such as float or date

yes

string, integer, double, bool, date, object_id

yes

options to bring your own types, AVRO

yes

primitive types and user defined types (classes)

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

temporal, categorical

SQL

Support of SQL

ANSI SQL for query only (using Presto)

yes

APIs and other access methods

in-process shared library

JDBC

RESTful HTTP/JSON API

Java API
RESTful HTTP API
Thrift

.NET Client API

Supported programming languages

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

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C
C#
C++
Groovy
Java
PHP
Python
Scala

C#
Java

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

Coprocessors in Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Automatic sharding

none

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency

Foreign keys

Referential integrity

typically not needed, however similar functionality with collection joins possible

Transaction concepts

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

Since BigQuery is designed for querying data

Single row ACID (across millions of columns)

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.

yes

User concepts

Access control

Access rights for users can be defined per table

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

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

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.