DBMS > BoltDB vs. Couchbase vs. Heroic vs. Teradata Aster vs. Tibero

System Properties Comparison BoltDB vs. Couchbase vs. Heroic vs. Teradata Aster vs. Tibero

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

An embedded key-value store for Go.

A distributed document store with integrated cache, a powerful search engine, in-built operational and analytical capabilities, and an embedded mobile database

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

Platform for big data analytics on multistructured data sources and types

A secure RDBMS, designed for easy portability from Oracle

Primary database model

Secondary database models

Key-value store

originating from the former Membase product and supporting the Memcached protocol
Spatial DBMS

using the Geocouch extension
Search engine
Time Series DBMS
Vector DBMS

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

Score	0.74
Rank	#220	Overall
	#31	Key-value stores

Score	17.30
Rank	#36	Overall
	#5	Document stores

Score	0.51
Rank	#255	Overall
	#21	Time Series DBMS

Score	1.78
Rank	#140	Overall
	#64	Relational DBMS

Website

github.com/boltdb/bolt

www.couchbase.com

github.com/spotify/heroic

us.tmaxsoft.com/products/tibero

Technical documentation

docs.couchbase.com

spotify.github.io/heroic

technet.tmaxsoft.com/upload/download/online/tibero/pver-20150504-000002/index.html

Developer

Couchbase, Inc.

Spotify

Teradata

TmaxSoft

Initial release

2013

2011

2014

2005

2003

Current release

Server: 7.2, June 2023; Mobile: 3.1, March 2022; Couchbase Capella (DBaaS), June 2023

6, April 2015

License

Commercial or Open Source

Open Source

MIT License

Open Source

Business Source License (BSL 1.1); Commercial licenses also available

Open Source

Apache 2.0

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C, C++, Go and Erlang

Java

C and Assembler

Server operating systems

BSD
Linux
OS X
Solaris
Windows

Linux
OS X
Windows

Linux

AIX
HP-UX
Linux
Solaris
Windows

Data scheme

schema-free

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

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

yes

Typing

predefined data types such as float or date

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

in Aster File Store

yes

Secondary indexes

yes

via Elasticsearch

yes

SQL

Support of SQL

SQL++, extends ANSI SQL to JSON for operational, transactional, and analytic use cases

yes

APIs and other access methods

CLI Client
HTTP REST
Kafka Connector
Native language bindings for CRUD, Query, Search and Analytics APIs
Spark Connector
Spring Data

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

ADO.NET
JDBC
ODBC
OLE DB

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)
Tibero CLI

Supported programming languages

.Net
C
Go
Java
JavaScript

Node.js
Kotlin
PHP
Python
Ruby
Scala

C
C#
C++
Java
Python
R

C
C#
C++
Cobol
Java
Objective-C
Perl
PHP
Python
Ruby
Visual Basic

Server-side scripts

Stored procedures

Functions and timers in JavaScript and UDFs in Java, Python, SQL++

R packages

Persistent Stored Procedure (PSM)

Triggers

yes

via the TAP protocol

yes

Partitioning methods

Methods for storing different data on different nodes

none

Automatic Sharding

Sharding

horizontal partitioning

by range, hash, list or composite

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication

including cross data center replication
Source-replica replication

yes

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

Multi-source replication
Source-replica replication

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

none

Eventual Consistency
Immediate Consistency

selectable on a per-operation basis

Eventual Consistency
Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

In-memory capabilities

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

yes

Ephemeral buckets

planned for next version

User concepts

Access control

User and Administrator separation with password-based and LDAP integrated Authentication. Role-base access control.

fine grained access rights according to SQL-standard

fine grained access rights according to SQL-standard (SQL 92, SQL 99)

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