DBMS > Heroic vs. Netezza vs. SQLite vs. Tarantool vs. Yaacomo

System Properties Comparison Heroic vs. Netezza vs. SQLite vs. Tarantool vs. Yaacomo

Editorial information provided by DB-Engines

Name

Yaacomo seems to be discontinued and is removed from the DB-Engines ranking

Description

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

Data warehouse and analytics appliance part of IBM PureSystems

Widely used embeddable, in-process RDBMS

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

OpenCL based in-memory RDBMS, designed for efficiently utilizing the hardware via parallel computing

Primary database model

Time Series DBMS

Relational DBMS

Document store
Key-value store
Relational DBMS

Relational DBMS

Secondary database models

Spatial DBMS

with Tarantool/GIS extension

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

Score	0.46
Rank	#265	Overall
	#22	Time Series DBMS

Score	8.59
Rank	#45	Overall
	#29	Relational DBMS

Score	111.41
Rank	#10	Overall
	#7	Relational DBMS

Score	1.67
Rank	#143	Overall
	#25	Document stores
	#25	Key-value stores
	#65	Relational DBMS

Website

github.com/spotify/heroic

www.ibm.com/products/netezza

www.sqlite.org

www.tarantool.io

yaacomo.com

Technical documentation

spotify.github.io/heroic

www.sqlite.org/docs.html

www.tarantool.io/en/doc

Developer

Spotify

IBM

Dwayne Richard Hipp

Q2WEB GmbH

Initial release

2014

2000

2008

2009

Current release

3.46.0 (23 May 2024), May 2024

2.10.0, May 2022

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

Public Domain

Open Source

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

commercial

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 and C++

Server operating systems

Linux

included in appliance

server-less

BSD
Linux
macOS

Android
Linux
Windows

Data scheme

schema-free

yes

dynamic column types

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

yes

Typing

predefined data types such as float or date

yes

not rigid because of 'dynamic typing' concept.

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

yes

SQL-92 is not fully supported

Full-featured ANSI SQL support

yes

APIs and other access methods

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

JDBC
ODBC
OLE DB

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Open binary protocol

JDBC
ODBC

Supported programming languages

C
C++
Fortran
Java
Lua
Perl
Python
R

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

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

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

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

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

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

none

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

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

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

Immediate Consistency

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

via file-system locks

yes, cooperative multitasking

yes

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

yes, full featured in-memory storage engine with persistence

yes

User concepts

Access control

Users with fine-grained authorization concept

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

fine grained access rights according to SQL-standard

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.