DBMS > Drizzle vs. Hawkular Metrics vs. HEAVY.AI vs. Microsoft Azure Cosmos DB vs. TerarkDB

System Properties Comparison Drizzle vs. Hawkular Metrics vs. HEAVY.AI vs. Microsoft Azure Cosmos DB vs. TerarkDB

Editorial information provided by DB-Engines

Name

Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.

Description

MySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.

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

Globally distributed, horizontally scalable, multi-model database service

A key-value store forked from RocksDB with advanced compression algorithms. It can be used standalone or as a storage engine for MySQL and MongoDB

Primary database model

Relational DBMS

Time Series DBMS

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Key-value store

Secondary database models

Spatial DBMS

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	29.04
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	0.00
Rank	#383	Overall
	#60	Key-value stores

Website

www.hawkular.org

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

azure.microsoft.com/services/cosmos-db

github.com/bytedance/terarkdb

Technical documentation

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

docs.heavy.ai

learn.microsoft.com/azure/cosmos-db

bytedance.larkoffice.com/docs/doccnZmYFqHBm06BbvYgjsHHcKc

Developer

Drizzle project, originally started by Brian Aker

Community supported by Red Hat

HEAVY.AI, Inc.

Microsoft

ByteDance, originally Terark

Initial release

2008

2014

2016

2014

2016

Current release

7.2.4, September 2012

5.10, January 2022

License

Commercial or Open Source

Open Source

GNU GPL

Open Source

Apache 2.0

Open Source

Apache Version 2; enterprise edition available

commercial

restricted open source version available

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

C++ and CUDA

C++

Server operating systems

FreeBSD
Linux
OS X

Linux
OS X
Windows

Linux

hosted

Data scheme

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

JSON types

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

All properties auto-indexed by default

SQL

Support of SQL

yes

with proprietary extensions

yes

SQL-like query language

APIs and other access methods

JDBC

HTTP REST

JDBC
ODBC
Thrift
Vega

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

C++ API
Java API

Supported programming languages

C
C++
Java
PHP

Go
Java
Python
Ruby

All languages supporting JDBC/ODBC/Thrift
Python

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

C++
Java

Server-side scripts

Stored procedures

JavaScript

Triggers

hooks for callbacks inside the server can be used.

yes

via Hawkular Alerting

JavaScript

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

Sharding

Round robin

Sharding

Implicit feature of the cloud service

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

selectable replication factor

based on Cassandra

Multi-source replication

yes

Implicit feature of the cloud service

none

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Multi-item ACID transactions with snapshot isolation within a partition

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

Pluggable authentication mechanisms

e.g. LDAP, HTTP

fine grained access rights according to SQL-standard

Access rights can be defined down to the item level

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