DBMS > Apache Pinot vs. Blazegraph vs. EJDB vs. Google Cloud Datastore

System Properties Comparison Apache Pinot vs. Blazegraph vs. EJDB vs. Google Cloud Datastore

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Editorial information provided by DB-Engines

Name

Amazon has acquired Blazegraph's domain and (probably) product. It is said that Amazon Neptune is based on Blazegraph.

Description

Realtime distributed OLAP datastore, designed to answer OLAP queries with low latency

High-performance graph database supporting Semantic Web (RDF/SPARQL) and Graph Database (tinkerpop3, blueprints, vertex-centric) APIs with scale-out and High Availability.

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

Primary database model

Relational DBMS

Graph DBMS
RDF store

Document store

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

Score	0.38
Rank	#275	Overall
	#126	Relational DBMS

Score	0.81
Rank	#213	Overall
	#19	Graph DBMS
	#8	RDF stores

Score	0.31
Rank	#296	Overall
	#44	Document stores

Score	4.36
Rank	#72	Overall
	#12	Document stores

Website

pinot.apache.org

blazegraph.com

github.com/Softmotions/ejdb

cloud.google.com/datastore

Technical documentation

docs.pinot.apache.org

wiki.blazegraph.com

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

cloud.google.com/datastore/docs

Developer

Apache Software Foundation and contributors

Blazegraph

Softmotions

Google

Initial release

2015

2006

2012

2008

Current release

1.0.0, September 2023

2.1.5, March 2019

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

extended commercial license available

Open Source

GPLv2

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

Server operating systems

All OS with a Java JDK11 or higher

Linux
OS X
Windows

server-less

hosted

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

RDF literal types

yes

string, integer, double, bool, date, object_id

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

SQL

Support of SQL

SQL-like query language

SPARQL is used as query language

SQL-like query language (GQL)

APIs and other access methods

JDBC

Java API
RESTful HTTP API
SPARQL QUERY
SPARQL UPDATE
TinkerPop 3

in-process shared library

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

Supported programming languages

Go
Java
Python

.Net
C
C++
Java
JavaScript
PHP
Python
Ruby

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

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

Server-side scripts

Stored procedures

yes

using Google App Engine

Triggers

Callbacks using the Google Apps Engine

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

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

Immediate Consistency or Eventual Consistency depending on configuration

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

yes

Relationships in Graphs

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

Concurrency

Support for concurrent manipulation of data

yes

Read/Write Locking

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.

User concepts

Access control

Security and Authentication via Web Application Container (Tomcat, Jetty)

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

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More resources

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