DBMS > Apache Drill vs. Couchbase vs. Transwarp StellarDB

System Properties Comparison Apache Drill vs. Couchbase vs. Transwarp StellarDB

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Editorial information provided by DB-Engines

Name

Description

Schema-free SQL Query Engine for Hadoop, NoSQL and Cloud Storage

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

A distributed graph DBMS built for enterprise-level graph applications

Primary database model

Document store
Relational DBMS

Document store

Graph DBMS

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	1.95
Rank	#127	Overall
	#23	Document stores
	#60	Relational DBMS

Score	17.30
Rank	#36	Overall
	#5	Document stores

Score	0.00
Rank	#383	Overall
	#39	Graph DBMS

Website

drill.apache.org

www.couchbase.com

www.transwarp.cn/en/product/stellardb

Technical documentation

drill.apache.org/docs

docs.couchbase.com

Developer

Apache Software Foundation

Couchbase, Inc.

Transwarp

Initial release

2012

2011

Current release

1.20.3, January 2023

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

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

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

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

Server operating systems

Linux
OS X
Windows

Data scheme

schema-free

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.

Secondary indexes

yes

SQL

Support of SQL

SQL SELECT statement is SQL:2003 compliant

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

SQL-like query language

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

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

OpenCypher

Supported programming languages

C++

.Net
C
Go
Java
JavaScript

Node.js
Kotlin
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

user defined functions

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

Automatic Sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

including cross data center replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Eventual Consistency
Immediate Consistency

selectable on a per-operation basis

Foreign keys

Referential integrity

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

Depending on the underlying data source

yes

In-memory capabilities

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

Depending on the underlying data source

yes

Ephemeral buckets

User concepts

Access control

Depending on the underlying data source

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

yes

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