DBMS > Alibaba Cloud TSDB vs. Couchbase vs. Hyprcubd vs. Kinetica vs. Splice Machine

System Properties Comparison Alibaba Cloud TSDB vs. Couchbase vs. Hyprcubd vs. Kinetica vs. Splice Machine

Editorial information provided by DB-Engines

Name

Hyprcubd seems to be discontinued. Therefore it is excluded from the DB-Engines ranking.

Description

A stable, reliable, and cost-effective online high-performance time series database service

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

Serverless Time Series DBMS

Fully vectorized database across both GPUs and CPUs

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

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

Spatial DBMS
Time Series DBMS

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

Score	0.29
Rank	#301	Overall
	#26	Time Series DBMS

Score	16.59
Rank	#36	Overall
	#5	Document stores

Score	0.66
Rank	#234	Overall
	#107	Relational DBMS

Score	0.54
Rank	#252	Overall
	#115	Relational DBMS

Website

www.alibabacloud.com/product/hitsdb

www.couchbase.com

hyprcubd.com (offline)

www.kinetica.com

splicemachine.com

Technical documentation

www.alibabacloud.com/help/en/time-series-database/latest/what-is-tsdb

docs.couchbase.com

docs.kinetica.com

splicemachine.com/how-it-works

Developer

Alibaba

Couchbase, Inc.

Hyprcubd, Inc.

Kinetica

Splice Machine

Initial release

2011

2012

2014

Current release

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

7.1, August 2021

3.1, March 2021

License

Commercial or Open Source

commercial

Open Source

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

commercial

Open Source

AGPL 3.0, commercial license available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C, C++, Go and Erlang

C, C++

Java

Server operating systems

hosted

Linux
OS X
Windows

hosted

Linux

Linux
OS X
Solaris
Windows

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

Numeric data and Strings

yes

time, int, uint, float, string

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

SQL-like query language

SQL-like DML and DDL statements

yes

APIs and other access methods

HTTP REST

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

gRPC (https)

JDBC
ODBC
RESTful HTTP API

JDBC
Native Spark Datasource
ODBC

Supported programming languages

Java

.Net
C
Go
Java
JavaScript

Node.js
Kotlin
PHP
Python
Ruby
Scala

C++
Java
JavaScript (Node.js)
Python

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

Server-side scripts

Stored procedures

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

user defined functions

yes

Java

Triggers

yes

via the TAP protocol

yes

triggers when inserted values for one or more columns fall within a specified range

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic Sharding

Sharding

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

including cross data center replication
Source-replica replication

Source-replica replication

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

selectable on a per-operation basis

Eventual 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

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

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

yes

GPU vRAM or System RAM

yes

User concepts

Access control

Access managed on TSDB instance level

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

token access

Access rights for users and roles on table level

Access rights for users, groups and roles according to SQL-standard

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