DBMS > Apache Druid vs. Firebolt vs. Hawkular Metrics vs. TerarkDB vs. Warp 10

System Properties Comparison Apache Druid vs. Firebolt vs. Hawkular Metrics vs. TerarkDB vs. Warp 10

Editorial information provided by DB-Engines

Name

Description

Open-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality data

Highly scalable cloud data warehouse and analytics product

Forked from Clickhouse

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

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

TimeSeries DBMS specialized on timestamped geo data based on LevelDB or HBase

Primary database model

Relational DBMS
Time Series DBMS

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

Score	3.25
Rank	#90	Overall
	#47	Relational DBMS
	#7	Time Series DBMS

Score	1.73
Rank	#140	Overall
	#63	Relational DBMS

Score	0.08
Rank	#366	Overall
	#39	Time Series DBMS

Score	0.08
Rank	#367	Overall
	#56	Key-value stores

Score	0.14
Rank	#344	Overall
	#32	Time Series DBMS

Website

druid.apache.org

www.firebolt.io

www.hawkular.org

github.com/bytedance/terarkdb

www.warp10.io

Technical documentation

druid.apache.org/docs/latest/design

docs.firebolt.io

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

bytedance.larkoffice.com/docs/doccnZmYFqHBm06BbvYgjsHHcKc

www.warp10.io/content/02_Getting_started

Developer

Apache Software Foundation and contributors

Firebolt Analytics Inc.

Community supported by Red Hat

ByteDance, originally Terark

SenX

Initial release

2012

2020

2014

2016

2015

Current release

29.0.1, April 2024

License

Commercial or Open Source

Open Source

Apache license v2

commercial

Open Source

Apache 2.0

commercial

restricted open source version available

Open Source

Apache License 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++

Java

Server operating systems

Linux
OS X
Unix

hosted

Linux
OS X
Windows

Data scheme

yes

schema-less columns are supported

yes

schema-free

Typing

predefined data types such as float or date

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

SQL

Support of SQL

SQL for querying

yes

APIs and other access methods

JDBC
RESTful HTTP/JSON API

.Net
ODBC
RESTful HTTP API

HTTP REST

C++ API
Java API

HTTP API
Jupyter
WebSocket

Supported programming languages

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

Go
JavaScript (Node.js)
Python

Go
Java
Python
Ruby

C++
Java

Server-side scripts

Stored procedures

yes

WarpScript

Triggers

yes

via Hawkular Alerting

Partitioning methods

Methods for storing different data on different nodes

Sharding

manual/auto, time-based

Sharding

based on Cassandra

none

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

yes, via HDFS, S3 or other storage engines

depending on storage layer

selectable replication factor

based on Cassandra

none

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

based on HBase

Foreign keys

Referential integrity

Transaction concepts

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

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

RBAC using LDAP or Druid internals for users and groups for read/write by datasource and system

Mandatory use of cryptographic tokens, containing fine-grained authorizations

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