DBMS > Axibase vs. Faircom DB vs. GeoSpock vs. Google Cloud Datastore vs. LeanXcale

System Properties Comparison Axibase vs. Faircom DB vs. GeoSpock vs. Google Cloud Datastore vs. LeanXcale

Editorial information provided by DB-Engines

Name

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

Description

Scalable TimeSeries DBMS based on HBase with integrated rule engine and visualization

Native high-speed multi-model DBMS for relational and key-value store data simultaneously accessible through SQL and NoSQL APIs.

Spatial and temporal data processing engine for extreme data scale

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

A highly scalable full ACID SQL database with fast NoSQL data ingestion and GIS capabilities

Primary database model

Time Series DBMS

Key-value store
Relational DBMS

Relational DBMS

Document store

Key-value store
Relational DBMS

Secondary database models

Time Series DBMS

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

Score	0.29
Rank	#292	Overall
	#25	Time Series DBMS

Score	0.20
Rank	#318	Overall
	#48	Key-value stores
	#141	Relational DBMS

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	0.29
Rank	#291	Overall
	#41	Key-value stores
	#132	Relational DBMS

Website

axibase.com/docs/atsd/finance

www.faircom.com/products/faircom-db

geospock.com

cloud.google.com/datastore

www.leanxcale.com

Technical documentation

docs.faircom.com/docs/en/UUID-7446ae34-a1a7-c843-c894-d5322e395184.html

cloud.google.com/datastore/docs

Developer

Axibase Corporation

FairCom Corporation

GeoSpock

Google

LeanXcale

Initial release

2013

1979

2008

2015

Current release

15585

V12, November 2020

2.0, September 2019

License

Commercial or Open Source

commercial

Community Edition (single node) is free, Enterprise Edition (distributed) is paid

commercial

Restricted, free version 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

ANSI C, C++

Java, Javascript

Server operating systems

Linux

AIX
FreeBSD
HP-UX
Linux
NetBSD
OS X
QNX
SCO
Solaris
VxWorks
Windows

easily portable to other OSs

hosted

Data scheme

yes

schema free, schema optional, schema required, partial schema,

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

short, integer, long, float, double, decimal, string

yes, ANSI SQL Types, JSON, typed binary structures

yes

yes, details here

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

temporal, categorical

yes

SQL

Support of SQL

SQL-like query language

yes, ANSI SQL with proprietary extensions

ANSI SQL for query only (using Presto)

SQL-like query language (GQL)

yes

through Apache Derby

APIs and other access methods

JDBC
Proprietary protocol (Network API)
RESTful HTTP API

ADO.NET
Direct SQL
JDBC
JPA
ODBC
RESTful HTTP/JSON API
RESTful MQTT/JSON API
RPC

JDBC

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector

Supported programming languages

Go
Java
PHP
Python
R
Ruby

.Net
C
C#
C++
Java
JavaScript (Node.js and browser)
PHP
Python
Visual Basic

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

C
Java
Scala

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

using Google App Engine

Triggers

yes

Callbacks using the Google Apps Engine

Partitioning methods

Methods for storing different data on different nodes

Sharding

File partitioning, horizontal partitioning, sharding

Customizable business rules for table partitioning

Automatic sharding

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes, configurable to be parallel or serial, synchronous or asynchronous, uni-directional or bi-directional, ACID-consistent or eventually consistent (with custom conflict resolution).

Multi-source replication using Paxos

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency
Tunable consistency per server, database, table, and transaction

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Immediate Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

yes

Transaction concepts

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

tunable from ACID to Eventually Consistent

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Yes, tunable from durable to delayed durability to in-memory

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

Fine grained access rights according to SQL-standard with additional protections for files

Access rights for users can be defined per table

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

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