DBMS > Hawkular Metrics vs. HEAVY.AI vs. RocksDB vs. Rockset

System Properties Comparison Hawkular Metrics vs. HEAVY.AI vs. RocksDB vs. Rockset

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Editorial information provided by DB-Engines

Name

Description

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

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

A scalable, reliable search and analytics service in the cloud, built on RocksDB

Primary database model

Secondary database models

Spatial DBMS

Relational DBMS
Search engine

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

Score	0.00
Rank	#379	Overall
	#40	Time Series DBMS

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	3.65
Rank	#85	Overall
	#11	Key-value stores

Score	0.79
Rank	#211	Overall
	#35	Document stores

Website

www.hawkular.org

github.com/heavyai/heavydb
www.heavy.ai

rocksdb.org

rockset.com

Technical documentation

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

docs.heavy.ai

github.com/facebook/rocksdb/wiki

docs.rockset.com

Developer

Community supported by Red Hat

HEAVY.AI, Inc.

Facebook, Inc.

Rockset

Initial release

2014

2016

2013

2019

Current release

5.10, January 2022

8.11.4, April 2024

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache Version 2; enterprise edition available

Open Source

BSD

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

C++ and CUDA

C++

Server operating systems

Linux
OS X
Windows

Linux

hosted

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

dynamic typing

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.

ingestion from XML files supported

Secondary indexes

all fields are automatically indexed

SQL

Support of SQL

yes

Read-only SQL queries, including JOINs

APIs and other access methods

HTTP REST

JDBC
ODBC
Thrift
Vega

C++ API
Java API

HTTP REST

Supported programming languages

Go
Java
Python
Ruby

All languages supporting JDBC/ODBC/Thrift
Python

C
C++
Go
Java
Perl
Python
Ruby

Go
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

Triggers

yes

via Hawkular Alerting

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

Sharding

Round robin

horizontal partitioning

Automatic sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

Multi-source replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

Transaction concepts

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

yes

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 and organizations can be defined via Rockset console

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