DBMS > Apache Kylin vs. EJDB vs. Google Cloud Datastore vs. Manticore Search

System Properties Comparison Apache Kylin vs. EJDB vs. Google Cloud Datastore vs. Manticore Search

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Editorial information provided by DB-Engines

Name

Description

A distributed analytics engine for big data, providing a SQL interface and multi-dimensional analysis (OLAP) and leveraging the Hadoop stack

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

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

Multi-storage database for search, including full-text search.

Primary database model

Relational DBMS

Document store

Search engine

Secondary database models

Time Series DBMS

using the Manticore Columnar Library

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

Score	1.14
Rank	#171	Overall
	#77	Relational DBMS

Score	0.13
Rank	#331	Overall
	#46	Document stores

Score	4.13
Rank	#71	Overall
	#12	Document stores

Score	0.23
Rank	#301	Overall
	#21	Search engines

Website

kylin.apache.org

github.com/Softmotions/ejdb

cloud.google.com/datastore

manticoresearch.com

Technical documentation

kylin.apache.org/docs

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

cloud.google.com/datastore/docs

manual.manticoresearch.com

Developer

Apache Software Foundation, originally contributed from eBay Inc

Softmotions

Google

Manticore Software

Initial release

2015

2012

2008

2017

Current release

3.1.0, July 2020

6.0, February 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

GPLv2

commercial

Open Source

GPL version 2

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

Linux

server-less

hosted

FreeBSD
Linux
macOS
Windows

Data scheme

yes

schema-free

Fixed schema

Typing

predefined data types such as float or date

yes

string, integer, double, bool, date, object_id

yes, details here

Int, Bigint, Float, Timestamp, Bit, Int array, Bigint array, JSON, Boolean

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.

Can index from XML

Secondary indexes

yes

full-text index on all search fields

SQL

Support of SQL

ANSI SQL for queries (using Apache Calcite)

SQL-like query language (GQL)

SQL-like query language

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

in-process shared library

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

Binary API
RESTful HTTP/JSON API
RESTful HTTP/SQL API
SQL over MySQL

Supported programming languages

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

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

Elixir
Go
Java
JavaScript (Node.js)
Perl
PHP
Python

Server-side scripts

Stored procedures

using Google App Engine

user defined functions

Triggers

Callbacks using the Google Apps Engine

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Partitioning is done manually, search queries against distributed index is supported

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication using Paxos

Synchronous replication based on Galera library

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

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.

Foreign keys

Referential integrity

typically not needed, however similar functionality with collection joins possible

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

yes

isolated transactions for atomic changes and binary logging for safe writes

Concurrency

Support for concurrent manipulation of data

yes

Read/Write Locking

yes

Durability

Support for making data persistent

yes

The original contents of fields are not stored in the Manticore index.

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

User concepts

Access control

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

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