DBMS > Apache Phoenix vs. GeoSpock vs. Google BigQuery vs. Postgres-XL

System Properties Comparison Apache Phoenix vs. GeoSpock vs. Google BigQuery vs. Postgres-XL

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Editorial information provided by DB-Engines

Name

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

Description

A scale-out RDBMS with evolutionary schema built on Apache HBase

Spatial and temporal data processing engine for extreme data scale

Large scale data warehouse service with append-only tables

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

Primary database model

Relational DBMS

Secondary database models

Time Series DBMS

Document store
Spatial DBMS

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

Score	2.06
Rank	#123	Overall
	#58	Relational DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	0.53
Rank	#254	Overall
	#117	Relational DBMS

Website

phoenix.apache.org

geospock.com

cloud.google.com/bigquery

www.postgres-xl.org

Technical documentation

phoenix.apache.org

cloud.google.com/bigquery/docs

www.postgres-xl.org/documentation

Developer

Apache Software Foundation

GeoSpock

Google

Initial release

2014

2010

2014

since 2012, originally named StormDB

Current release

5.0-HBase2, July 2018 and 4.15-HBase1, December 2019

2.0, September 2019

10 R1, October 2018

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

Mozilla public license

Cloud-based only

Only available as a cloud service

yes

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Database as a Service

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Implementation language

Java

Java, Javascript

Server operating systems

Linux
Unix
Windows

hosted

Linux
macOS

Data scheme

yes

late-bound, schema-on-read capabilities

yes

Typing

predefined data types such as float or date

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

XML type, but no XML query functionality

Secondary indexes

yes

temporal, categorical

yes

SQL

Support of SQL

yes

ANSI SQL for query only (using Presto)

yes

distributed, parallel query execution

APIs and other access methods

JDBC

RESTful HTTP/JSON API

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

Supported programming languages

C
C#
C++
Go
Groovy
Java
PHP
Python
Scala

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

Server-side scripts

Stored procedures

user defined functions

in JavaScript

user defined functions

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Automatic sharding

none

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

ACID

MVCC

Concurrency

Support for concurrent manipulation of data

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 Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancy

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)

fine grained access rights according to SQL-standard

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