DBMS > Apache IoTDB vs. Drizzle vs. Heroic vs. Tarantool

System Properties Comparison Apache IoTDB vs. Drizzle vs. Heroic vs. Tarantool

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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

An IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and Flink

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

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

In-memory computing platform with a flexible data schema for efficiently building high-performance applications

Primary database model

Time Series DBMS

Relational DBMS

Time Series DBMS

Document store
Key-value store
Relational DBMS

Secondary database models

Spatial DBMS

with Tarantool/GIS extension

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

Score	1.18
Rank	#173	Overall
	#15	Time Series DBMS

Score	0.51
Rank	#255	Overall
	#21	Time Series DBMS

Score	1.72
Rank	#144	Overall
	#25	Document stores
	#25	Key-value stores
	#66	Relational DBMS

Website

iotdb.apache.org

github.com/spotify/heroic

www.tarantool.io

Technical documentation

iotdb.apache.org/UserGuide/Master/QuickStart/QuickStart.html

spotify.github.io/heroic

www.tarantool.io/en/doc

Developer

Apache Software Foundation

Drizzle project, originally started by Brian Aker

Spotify

Initial release

2018

2008

2014

2008

Current release

1.1.0, April 2023

7.2.4, September 2012

2.10.0, May 2022

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

GNU GPL

Open Source

Apache 2.0

Open Source

BSD-2, source-available extensions (modules), commercial licenses for Tarantool Enterprise

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

C++

Java

C and C++

Server operating systems

All OS with a Java VM (>= 1.8)

FreeBSD
Linux
OS X

BSD
Linux
macOS

Data scheme

yes

schema-free

Flexible data schema: relational definition for tables with ability to store json-like documents in columns

Typing

predefined data types such as float or date

yes

string, double, decimal, uuid, integer, blob, boolean, datetime

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

via Elasticsearch

yes

SQL

Support of SQL

SQL-like query language

yes

with proprietary extensions

Full-featured ANSI SQL support

APIs and other access methods

JDBC
Native API

JDBC

HQL (Heroic Query Language, a JSON-based language)
HTTP API

Open binary protocol

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

C
C++
Java
PHP

C
C#
C++
Erlang
Go
Java
JavaScript
Lua
Perl
PHP
Python
Rust

Server-side scripts

Stored procedures

yes

Lua, C and SQL stored procedures

Triggers

yes

hooks for callbacks inside the server can be used.

yes, before/after data modification events, on replication events, client session events

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning (by time range) + vertical partitioning (by deviceId)

Sharding

Sharding, partitioned with virtual buckets by user defined affinity key. Live resharding for scale up and scale down without maintenance downtime.

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicas

Multi-source replication
Source-replica replication

yes

Asynchronous replication with multi-master option
Configurable replication topology (full-mesh, chain, star)
Synchronous quorum replication (with Raft)

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

Eventual Consistency
Immediate Consistency

Casual consistency across sharding partitions
Eventual consistency within replicaset partition

when using asyncronous replication
Immediate Consistency within single instance
Sequential consistency including linearizable read within replicaset partition

when using Raft

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

ACID, with serializable isolation and linearizable read (within partition); Configurable MVCC (within partition); No cross-shard distributed transactions

Concurrency

Support for concurrent manipulation of data

yes

yes, cooperative multitasking

Durability

Support for making data persistent

yes

yes, write ahead logging

In-memory capabilities

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

yes

yes, full featured in-memory storage engine with persistence

User concepts

Access control

yes

Pluggable authentication mechanisms

e.g. LDAP, HTTP

Access Control Lists
Mutual TLS authentication for Tarantol Enterprise
Password based authentication
Role-based access control (RBAC) and LDAP for Tarantol Enterprise
Users and Roles

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