DBMS > IRONdb vs. Solr vs. Tarantool vs. Yaacomo

System Properties Comparison IRONdb vs. Solr vs. Tarantool vs. Yaacomo

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Editorial information provided by DB-Engines

Name

IRONdb seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

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

Description

A distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicity

A widely used distributed, scalable search engine based on Apache Lucene

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

Search engine

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	42.91
Rank	#24	Overall
	#3	Search engines

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

Website

www.circonus.com/solutions/time-series-database/

solr.apache.org

www.tarantool.io

yaacomo.com

Technical documentation

docs.circonus.com/irondb/category/getting-started

solr.apache.org/resources.html

www.tarantool.io/en/doc

Developer

Circonus LLC.

Apache Software Foundation

Q2WEB GmbH

Initial release

2017

2006

2008

2009

Current release

V0.10.20, January 2018

9.6.0, April 2024

2.10.0, May 2022

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

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

C and C++

Java

C and C++

Server operating systems

Linux

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

BSD
Linux
macOS

Android
Linux
Windows

Data scheme

schema-free

yes

Dynamic Fields enables on-the-fly addition of new fields

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

text, numeric, histograms

yes

supports customizable data types and automatic typing

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.

yes

Secondary indexes

yes

All search fields are automatically indexed

yes

SQL

Support of SQL

SQL-like query language (Circonus Analytics Query Language: CAQL)

Solr Parallel SQL Interface

Full-featured ANSI SQL support

yes

APIs and other access methods

HTTP API

Java API
RESTful HTTP/JSON API

Open binary protocol

JDBC
ODBC

Supported programming languages

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

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

Server-side scripts

Stored procedures

yes, in Lua

Java plugins

Lua, C and SQL stored procedures

Triggers

yes

User configurable commands triggered on index changes

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

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic, metric affinity per node

Sharding

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

configurable replication factor, datacenter aware

yes

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

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate consistency per node, eventual consistency across nodes

Eventual 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

optimistic locking

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

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

yes

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

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