DBMS > Heroic vs. Tarantool vs. TempoIQ vs. Vitess

System Properties Comparison Heroic vs. Tarantool vs. TempoIQ vs. Vitess

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Editorial information provided by DB-Engines

Name

TempoIQ seems to be decommissioned. It will be removed from the DB-Engines ranking.

Description

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

Scalable analytics DBMS for sensor data, provided as a service (SaaS)

Scalable, distributed, cloud-native DBMS, extending MySQL

Primary database model

Time Series DBMS

Document store
Key-value store
Relational DBMS

Time Series DBMS

Relational DBMS

Secondary database models

Spatial DBMS

with Tarantool/GIS extension

Document store
Spatial DBMS

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

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

Score	0.82
Rank	#209	Overall
	#97	Relational DBMS

Website

github.com/spotify/heroic

www.tarantool.io

tempoiq.com (offline)

vitess.io

Technical documentation

spotify.github.io/heroic

www.tarantool.io/en/doc

vitess.io/docs

Developer

Spotify

TempoIQ

The Linux Foundation, PlanetScale

Initial release

2014

2008

2012

2013

Current release

2.10.0, May 2022

15.0.2, December 2022

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

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

commercial

Open Source

Apache Version 2.0, commercial licenses 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

Java

C and C++

Server operating systems

BSD
Linux
macOS

Docker
Linux
macOS

Data scheme

schema-free

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

schema-free

yes

Typing

predefined data types such as float or date

yes

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

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

via Elasticsearch

yes

SQL

Support of SQL

Full-featured ANSI SQL support

yes

with proprietary extensions

APIs and other access methods

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

Open binary protocol

HTTP API

ADO.NET
JDBC
MySQL protocol
ODBC

Supported programming languages

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

C#
Java
JavaScript

Node.js
Python
Ruby

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

Lua, C and SQL stored procedures

yes

proprietary syntax

Triggers

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

yes

Realtime Alerts

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

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

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

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

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

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

Eventual Consistency across shards
Immediate Consistency within a shard

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

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

ACID at shard level

Concurrency

Support for concurrent manipulation of data

yes

yes, cooperative multitasking

yes

table locks or row locks depending on storage engine

Durability

Support for making data persistent

yes

yes, write ahead logging

yes

In-memory capabilities

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

yes, full featured in-memory storage engine with persistence

yes

User concepts

Access control

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

simple authentication-based access control

Users with fine-grained authorization concept

no user groups or roles

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More resources

DB-Engines blog posts

Data processing speed and reliability: in-memory synchronous replication
9 November 2021, Vladimir Perepelytsya, Tarantool (sponsor)

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