DBMS > atoti vs. Google Cloud Datastore vs. LeanXcale vs. searchxml vs. TimesTen

System Properties Comparison atoti vs. Google Cloud Datastore vs. LeanXcale vs. searchxml vs. TimesTen

Editorial information provided by DB-Engines

Name

Description

An in-memory DBMS combining transactional and analytical processing to handle the aggregation of ever-changing data.

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

DBMS for structured and unstructured content wrapped with an application server

In-Memory RDBMS compatible to Oracle

Primary database model

Object oriented DBMS

Document store

Key-value store
Relational DBMS

Native XML DBMS
Search engine

Relational DBMS

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

Score	0.61
Rank	#243	Overall
	#10	Object oriented DBMS

Score	4.36
Rank	#72	Overall
	#12	Document stores

Score	0.36
Rank	#280	Overall
	#40	Key-value stores
	#129	Relational DBMS

Score	0.03
Rank	#390	Overall
	#7	Native XML DBMS
	#24	Search engines

Score	1.36
Rank	#161	Overall
	#75	Relational DBMS

Website

atoti.io

cloud.google.com/datastore

www.leanxcale.com

www.searchxml.net/category/products

www.oracle.com/database/technologies/related/timesten.html

Technical documentation

docs.atoti.io

cloud.google.com/datastore/docs

www.searchxml.net/support/handouts

docs.oracle.com/database/timesten-18.1

Developer

ActiveViam

Google

LeanXcale

informationpartners gmbh

Oracle, TimesTen Performance Software, HP

originally founded in HP Labs it was acquired by Oracle in 2005

Initial release

2008

2015

1998

Current release

1.0

11 Release 2 (11.2.2.8.0)

License

Commercial or Open Source

commercial

free versions 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

hosted

Windows

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Data scheme

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

yes, details here

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

Secondary indexes

yes

SQL

Support of SQL

Multidimensional Expressions (MDX)

SQL-like query language (GQL)

yes

through Apache Derby

yes

APIs and other access methods

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

JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector

RESTful HTTP API
WebDAV
XQuery
XSLT

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Supported programming languages

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

C
Java
Scala

C++

most other programming languages supported via APIs

C
C++
Java
PL/SQL

Server-side scripts

Stored procedures

Python

using Google App Engine

yes

on the application server

PL/SQL

Triggers

Callbacks using the Google Apps Engine

Partitioning methods

Methods for storing different data on different nodes

Sharding, horizontal partitioning

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication using Paxos

yes

sychronisation to multiple collections

Multi-source replication
Source-replica replication

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

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

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

yes

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

ACID

multiple readers, single writer

ACID

Concurrency

Support for concurrent manipulation of data

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

by means of logfiles and checkpoints

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

Domain, group and role-based access control at the document level and for application services

fine grained access rights according to SQL-standard

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