DBMS > Heroic vs. OrigoDB vs. Solr vs. XTDB

System Properties Comparison Heroic vs. OrigoDB vs. Solr vs. XTDB

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

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

A fully ACID in-memory object graph database

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

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Time Series DBMS

Document store
Object oriented DBMS

Search engine

Document store

Secondary database models

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	0.00
Rank	#383	Overall
	#53	Document stores
	#20	Object oriented DBMS

Score	42.91
Rank	#24	Overall
	#3	Search engines

Score	0.11
Rank	#343	Overall
	#46	Document stores

Website

github.com/spotify/heroic

origodb.com

solr.apache.org

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

spotify.github.io/heroic

origodb.com/docs

solr.apache.org/resources.html

www.xtdb.com/docs

Developer

Spotify

Robert Friberg et al

Apache Software Foundation

Juxt Ltd.

Initial release

2014

2009

under the name LiveDB

2006

2019

Current release

9.6.0, April 2024

1.19, September 2021

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache Version 2

Open Source

MIT License

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

Clojure

Server operating systems

Linux
Windows

All OS with a Java VM

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

All OS with a Java 8 (and higher) VM
Linux

Data scheme

schema-free

yes

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

schema-free

Typing

predefined data types such as float or date

yes

User defined using .NET types and collections

yes

supports customizable data types and automatic typing

yes, extensible-data-notation format

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.

can be achieved using .NET

yes

Secondary indexes

yes

via Elasticsearch

yes

All search fields are automatically indexed

yes

SQL

Support of SQL

Solr Parallel SQL Interface

limited SQL, making use of Apache Calcite

APIs and other access methods

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

.NET Client API
HTTP API
LINQ

Java API
RESTful HTTP/JSON API

HTTP REST
JDBC

Supported programming languages

.Net

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

Clojure
Java

Server-side scripts

Stored procedures

yes

Java plugins

Triggers

yes

Domain Events

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

client side managed; servers are not synchronized

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

yes

yes, each node contains all data

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

Eventual Consistency
Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

depending on model

Transaction concepts

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

ACID

optimistic locking

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Write ahead log

yes

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

In-memory capabilities

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

yes

User concepts

Access control

Role based authorization

yes

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.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

DB-Engines blog posts

Elasticsearch replaced Solr as the most popular search engine
12 January 2016, Paul Andlinger

Enterprise Search Engines almost double their popularity in the last 12 months
2 July 2014, Paul Andlinger

The DB-Engines ranking includes now search engines
4 February 2013, Paul Andlinger

show all

Recent citations in the news

Review: Google Bigtable scales with ease
7 September 2016, InfoWorld

provided by Google News

SOLR-led walkout demands better conditions for Compass workers
27 February 2024, Daily Northwestern

Closing Bell: Solar Alliance Energy Inc flat on Tuesday (SOLR)
24 May 2024, The Globe and Mail

(SOLR) Technical Data
17 May 2024, news.stocktradersdaily.com

SOLR hosts teach-in of labor movements at Northwestern
28 January 2024, Daily Northwestern

SOLR hosts May Day amid ongoing contract negotiations
12 May 2024, Daily Northwestern

provided by Google News

Share this page