DBMS > Apache Pinot vs. Bangdb vs. Google Cloud Datastore vs. Hawkular Metrics

System Properties Comparison Apache Pinot vs. Bangdb vs. Google Cloud Datastore vs. Hawkular Metrics

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Editorial information provided by DB-Engines

Name

Description

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

Converged and high performance database for device data, events, time series, document and graph

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

Hawkular metrics is the metric storage of the Red Hat sponsored Hawkular monitoring system. It is based on Cassandra.

Primary database model

Relational DBMS

Document store
Graph DBMS
Time Series DBMS

Document store

Time Series DBMS

Secondary database models

Spatial DBMS

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

Score	0.38
Rank	#275	Overall
	#126	Relational DBMS

Score	0.16
Rank	#338	Overall
	#47	Document stores
	#32	Graph DBMS
	#31	Time Series DBMS

Score	4.36
Rank	#72	Overall
	#12	Document stores

Score	0.08
Rank	#366	Overall
	#39	Time Series DBMS

Website

pinot.apache.org

bangdb.com

cloud.google.com/datastore

www.hawkular.org

Technical documentation

docs.pinot.apache.org

docs.bangdb.com

cloud.google.com/datastore/docs

www.hawkular.org/hawkular-metrics/docs/user-guide

Developer

Apache Software Foundation and contributors

Sachin Sinha, BangDB

Google

Community supported by Red Hat

Initial release

2015

2012

2008

2014

Current release

1.0.0, September 2023

BangDB 2.0, October 2021

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

BSD 3

commercial

Open Source

Apache 2.0

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, C++

Java

Server operating systems

All OS with a Java JDK11 or higher

Linux

hosted

Linux
OS X
Windows

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

yes: string, long, double, int, geospatial, stream, events

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.

Secondary indexes

yes

secondary, composite, nested, reverse, geospatial

yes

SQL

Support of SQL

SQL-like query language

SQL like support with command line tool

SQL-like query language (GQL)

APIs and other access methods

JDBC

Proprietary protocol
RESTful HTTP API

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

HTTP REST

Supported programming languages

Go
Java
Python

C
C#
C++
Java
Python

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

Go
Java
Python
Ruby

Server-side scripts

Stored procedures

using Google App Engine

Triggers

yes, Notifications (with Streaming only)

Callbacks using the Google Apps Engine

yes

via Hawkular Alerting

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding (enterprise version only). P2P based virtual network overlay with consistent hashing and chord algorithm

Sharding

based on Cassandra

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor, Knob for CAP (enterprise version only)

Multi-source replication using Paxos

selectable replication factor

based on Cassandra

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

Tunable consistency, set CAP knob accordingly

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.

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

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

Concurrency

Support for concurrent manipulation of data

yes, optimistic concurrency control

yes

Durability

Support for making data persistent

yes, implements WAL (Write ahead log) as well

yes

In-memory capabilities

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

yes, run db with in-memory only mode

User concepts

Access control

yes (enterprise version only)

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

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