DBMS > atoti vs. Google Cloud Datastore vs. Machbase Neo vs. searchxml vs. TerarkDB

System Properties Comparison atoti vs. Google Cloud Datastore vs. Machbase Neo vs. searchxml vs. TerarkDB

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

TimeSeries DBMS for AIoT and BigData

DBMS for structured and unstructured content wrapped with an application server

A key-value store forked from RocksDB with advanced compression algorithms. It can be used standalone or as a storage engine for MySQL and MongoDB

Primary database model

Object oriented DBMS

Document store

Time Series DBMS

Native XML DBMS
Search engine

Key-value store

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	0.17
Rank	#337	Overall
	#30	Time Series DBMS

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

Score	0.08
Rank	#367	Overall
	#56	Key-value stores

Website

atoti.io

cloud.google.com/datastore

machbase.com

www.searchxml.net/category/products

github.com/bytedance/terarkdb

Technical documentation

docs.atoti.io

cloud.google.com/datastore/docs

machbase.com/dbms

www.searchxml.net/support/handouts

bytedance.larkoffice.com/docs/doccnZmYFqHBm06BbvYgjsHHcKc

Developer

ActiveViam

Google

Machbase

informationpartners gmbh

ByteDance, originally Terark

Initial release

2008

2013

2015

2016

Current release

V8.0, August 2023

1.0

License

Commercial or Open Source

commercial

free versions available

commercial

free test version available

commercial

restricted open source version 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

C++

Server operating systems

hosted

Linux
macOS
Windows

Windows

Data scheme

schema-free

yes

schema-free

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)

SQL-like query language

APIs and other access methods

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

gRPC
HTTP REST
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC

RESTful HTTP API
WebDAV
XQuery
XSLT

C++ API
Java API

Supported programming languages

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

C
C#
C++
Go
Java
JavaScript
PHP

via ODBC
Python
R

via ODBC
Scala

C++

most other programming languages supported via APIs

C++
Java

Server-side scripts

Stored procedures

Python

using Google App Engine

yes

on the application server

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

selectable replication factor

yes

sychronisation to multiple collections

none

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

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

multiple readers, single writer

Concurrency

Support for concurrent manipulation of data

yes, multi-version concurrency control (MVCC)

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

volatile and lookup table

yes

User concepts

Access control

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

simple password-based access control

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

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