DBMS > Heroic vs. Manticore Search vs. Teradata Aster vs. Tkrzw

System Properties Comparison Heroic vs. Manticore Search vs. Teradata Aster vs. Tkrzw

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Teradata Aster has been integrated into other Teradata systems and therefore will be removed from the DB-Engines ranking.

Description

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

Multi-storage database for search, including full-text search.

Platform for big data analytics on multistructured data sources and types

A concept of libraries, allowing an application program to store and query key-value pairs in a file. Successor of Tokyo Cabinet and Kyoto Cabinet

Primary database model

Secondary database models

Time Series DBMS

using the Manticore Columnar Library

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

Score	0.57
Rank	#250	Overall
	#21	Time Series DBMS

Score	0.23
Rank	#317	Overall
	#21	Search engines

Score	0.09
Rank	#354	Overall
	#51	Key-value stores

Website

github.com/spotify/heroic

manticoresearch.com

dbmx.net/tkrzw

Technical documentation

spotify.github.io/heroic

manual.manticoresearch.com

Developer

Spotify

Manticore Software

Teradata

Mikio Hirabayashi

Initial release

2014

2017

2005

2020

Current release

6.0, February 2023

0.9.3, August 2020

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

GPL version 2

commercial

Open Source

Apache Version 2.0

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++

Server operating systems

FreeBSD
Linux
macOS
Windows

Linux

Linux
macOS

Data scheme

schema-free

Fixed schema

Flexible Schema (defined schema, partial schema, schema free)

defined schema within the relational store; partial schema or schema free in the Aster File Store

schema-free

Typing

predefined data types such as float or date

yes

Int, Bigint, Float, Timestamp, Bit, Int array, Bigint array, JSON, Boolean

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.

Can index from XML

yes

in Aster File Store

Secondary indexes

yes

via Elasticsearch

yes

full-text index on all search fields

yes

SQL

Support of SQL

SQL-like query language

yes

APIs and other access methods

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

Binary API
RESTful HTTP/JSON API
RESTful HTTP/SQL API
SQL over MySQL

ADO.NET
JDBC
ODBC
OLE DB

Supported programming languages

Elixir
Go
Java
JavaScript (Node.js)
Perl
PHP
Python

C
C#
C++
Java
Python
R

C++
Java
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

R packages

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

Partitioning is done manually, search queries against distributed index is supported

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Synchronous replication based on Galera library

yes

Dimension tables are replicated across all nodes in the cluster. The number of replicas for the file store can be configured.

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

SQL Map-Reduce Framework

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

yes

isolated transactions for atomic changes and binary logging for safe writes

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

The original contents of fields are not stored in the Manticore index.

yes

In-memory capabilities

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

yes

using specific database classes

User concepts

Access control

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.

Related products and services

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

More resources

Recent citations in the news

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

provided by Google News

Integrating Manticore Search with Apache Superset
8 August 2023, hackernoon.com

Comparing Meilisearch and Manticore Search Using Key Benchmarks
2 May 2023, hackernoon.com

Clickhouse vs Elasticsearch vs Manticore Search Query Times With a 1.7B NYC Taxi Rides Benchmark
1 June 2022, hackernoon.com

8 Google Alternatives: How to Search Crypto, the Dark Web, More
1 February 2023, Gizmodo

TF-IDF in a nutshell. Understanding TF-IDF evolution in 5… | by Sergey Nikolaev
13 April 2020, Towards Data Science

provided by Google News

Mellanox InfiniBand Helps Accelerate Teradata Aster Big Analytics Appliance
23 April 2024, Yahoo Movies UK

Northwestern Analytics Partners with Teradata Aster to Host Hackathon
23 May 2014, Northwestern Engineering

Teradata's Aster shows how the flowers of fraud bloom
23 April 2015, The Register

Case study: Siemens reduces train failures with Teradata Aster
12 September 2016, RCR Wireless News

Discover Value in Big Data with Free Aster Express
17 October 2012, PR Newswire

provided by Google News

Share this page