DBMS > Altibase vs. Apache IoTDB vs. Couchbase vs. Graphite vs. Pinecone

System Properties Comparison Altibase vs. Apache IoTDB vs. Couchbase vs. Graphite vs. Pinecone

Editorial information provided by DB-Engines

Name

Description

An enterprise grade, high-performance RDBMS

An IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and Flink

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

Data logging and graphing tool for time series data

The storage layer (fixed size database) is called Whisper

A managed, cloud-native vector database

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	1.02
Rank	#189	Overall
	#88	Relational DBMS

Score	1.31
Rank	#164	Overall
	#14	Time Series DBMS

Score	16.59
Rank	#36	Overall
	#5	Document stores

Score	4.83
Rank	#67	Overall
	#4	Time Series DBMS

Score	3.23
Rank	#92	Overall
	#2	Vector DBMS

Website

altibase.com

iotdb.apache.org

www.couchbase.com

github.com/graphite-project/graphite-web

www.pinecone.io

Technical documentation

github.com/ALTIBASE/Documents/tree/master/Manuals

iotdb.apache.org/UserGuide/Master/QuickStart/QuickStart.html

docs.couchbase.com

graphite.readthedocs.io

docs.pinecone.io/docs/overview

Developer

Altibase

Apache Software Foundation

Couchbase, Inc.

Chris Davis

Pinecone Systems, Inc

Initial release

1999

2018

2011

2006

2019

Current release

v7.3, 2023, August 2023

1.1.0, April 2023

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

License

Commercial or Open Source

commercial

Open source edition discontinued with March 2023

Open Source

Apache Version 2.0

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

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Java

C, C++, Go and Erlang

Python

Server operating systems

AIX
HP-UX
Linux

All OS with a Java VM (>= 1.8)

Linux
OS X
Windows

Linux
Unix

hosted

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

Numeric data only

String, Number, Boolean

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

SQL

Support of SQL

ANSI SQL-92

SQL-like query language

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

APIs and other access methods

JDBC
ODBC
Proprietary protocol

JDBC
Native API

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

HTTP API
Sockets

RESTful HTTP API

Supported programming languages

C
C++
Java

C
C#
C++
Go
Java
Python
Scala

.Net
C
Go
Java
JavaScript

Node.js
Kotlin
PHP
Python
Ruby
Scala

JavaScript (Node.js)
Python

Python

Server-side scripts

Stored procedures

stored procedures and stored functions

yes

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

Triggers

yes

via the TAP protocol

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning (by time range) + vertical partitioning (by deviceId)

Automatic Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicas

Multi-source replication

including cross data center replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Strong Consistency with Raft

Eventual Consistency
Immediate Consistency

selectable on a per-operation basis

none

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

locking

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

User concepts

Access control

fine grained access rights according to SQL-standard

yes

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

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