DBMS > EsgynDB vs. Google Cloud Bigtable vs. Hawkular Metrics vs. RisingWave

System Properties Comparison EsgynDB vs. Google Cloud Bigtable vs. Hawkular Metrics vs. RisingWave

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Editorial information provided by DB-Engines

Name

Description

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

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

A distributed RDBMS for stream processing, wire-compatible with PostgreSQL

Primary database model

Relational DBMS

Key-value store
Wide column store

Time Series DBMS

Relational DBMS

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

Score	0.25
Rank	#312	Overall
	#138	Relational DBMS

Score	3.15
Rank	#95	Overall
	#14	Key-value stores
	#8	Wide column stores

Score	0.08
Rank	#366	Overall
	#39	Time Series DBMS

Score	0.64
Rank	#238	Overall
	#110	Relational DBMS

Website

www.esgyn.cn

cloud.google.com/bigtable

www.hawkular.org

www.risingwave.com/database

Technical documentation

cloud.google.com/bigtable/docs

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

docs.risingwave.com/docs/current/intro

Developer

Esgyn

Google

Community supported by Red Hat

RisingWave Labs

Initial release

2015

2014

2022

Current release

1.2, September 2023

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

Apache Version 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

C++, Java

Java

Rust

Server operating systems

Linux

hosted

Linux
OS X
Windows

Docker
Linux
macOS

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

Standard SQL-types and JSON

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

yes

APIs and other access methods

ADO.NET
JDBC
ODBC

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

HTTP REST

JDBC
PostgreSQL wire protocol

Supported programming languages

All languages supporting JDBC/ODBC/ADO.Net

C#
C++
Go
Java
JavaScript (Node.js)
Python

Go
Java
Python
Ruby

Go
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

Java Stored Procedures

UDFs in Python or Java

Triggers

yes

via Hawkular Alerting

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication between multi datacenters

Internal replication in Colossus, and regional replication between two clusters in different zones

selectable replication factor

based on Cassandra

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Atomic single-row operations

Concurrency

Support for concurrent manipulation of data

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

User concepts

Access control

fine grained access rights according to SQL-standard

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

Users and Roles

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