DBMS > SingleStore vs. Transwarp KunDB vs. Vertica

System Properties Comparison SingleStore vs. Transwarp KunDB vs. Vertica

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Editorial information provided by DB-Engines

Name

Description

MySQL wire-compliant distributed RDBMS that combines an in-memory row-oriented and a disc-based column-oriented storage with patented universal storage to handle transactional and analytical workloads in one single table type

OLTP DBMS based on a distributed architecture and highly compatible with MySQL and Oracle

Cloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.

Primary database model

Relational DBMS

Column oriented

Secondary database models

Document store
Spatial DBMS
Time Series DBMS
Vector DBMS

Spatial DBMS
Time Series DBMS

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

Score	3.69
Rank	#70	Overall
	#37	Relational DBMS

Score	0.00
Rank	#385	Overall
	#157	Relational DBMS

Score	9.78
Rank	#42	Overall
	#26	Relational DBMS

Website

www.singlestore.com

www.transwarp.cn/en/product/kundb

www.vertica.com

Technical documentation

docs.singlestore.com

vertica.com/documentation

Developer

SingleStore Inc.

Transwarp

OpenText

previously Micro Focus and Hewlett Packard

Initial release

2013

2005

Current release

8.5, January 2024

12.0.3, January 2023

License

Commercial or Open Source

commercial

free developer edition available

commercial

Limited community edition free

Cloud-based only

Only available as a cloud service

on-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

C++, Go

C++

Server operating systems

Linux

64 bit version required

Linux

Data scheme

yes

Yes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.

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

No Indexes Required. Different internal optimization strategy, but same functionality included.

SQL

Support of SQL

yes

but no triggers and foreign keys

yes

Full 1999 standard plus machine learning, time series and geospatial. Over 650 functions.

APIs and other access methods

Cluster Management API

as HTTP Rest and CLI
HTTP API
JDBC
MongoDB API
ODBC

ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL

character-based, interactive, front-end utility

Supported programming languages

Bash
C
C#
Java
JavaScript (Node.js)
Python

C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R

Server-side scripts

Stored procedures

yes

yes, PostgreSQL PL/pgSQL, with minor differences

Triggers

yes

yes, called Custom Alerts

Partitioning methods

Methods for storing different data on different nodes

Sharding

hash partitioning

horizontal partitioning

horizontal partitioning, hierarchical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

stores two copies of each physical data partition on two separate nodes

Multi-source replication

One, or more copies of data replicated across nodes, or object-store used for repository.

MapReduce

Offers an API for user-defined Map/Reduce methods

can define user-defined aggregate functions for map-reduce-style calculations

Bi-directional Spark integration

Consistency concepts

Methods to ensure consistency in a distributed system

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, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

All updates are persistent, including those to disk-based columnstores and memory-based row stores. Transaction commits are supported via write-ahead log.

yes

In-memory capabilities

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

yes

User concepts

Access control

Fine grained access control via users, groups and roles

yes

fine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash

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More resources

DB-Engines blog posts

Turbocharge Your Application Development Using WebAssembly With SingleStoreDB
17 October 2022, Akmal Chaudhri, SingleStore (sponsor)

Cloud-Based Analytics With SingleStoreDB
9 June 2022, Akmal Chaudhri, SingleStore (sponsor)

SingleStore: The Increasing Momentum of Multi-Model Database Systems
14 February 2022, Akmal Chaudhri, SingleStore (sponsor)

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Recent citations in the news

SingleStore Appoints Nadeem Asghar as Chief Technology Officer
17 April 2025, citybiz

SingleStore Acquires BryteFlow to Accelerate Adoption of Real-Time Analytics, GenAI
3 October 2024, Business Wire

Announcing watsonx.ai and SingleStore for generative AI applications
16 October 2024, IBM

SingleStore acquires BryteFlow to boost data ingestion capabilities
3 October 2024, InfoWorld

SingleStore acquires BryteFlow to better digest ERP and CRM data
4 October 2024, Blocks and Files

provided by Google News

Speeding Up Vertica Queries
12 May 2025, Taboola.com

Introducing the Future of Data Analysis: A Revolutionary Tool for Vertica Users
25 October 2024, OpenText Blogs

New browser-based query editor for OpenText Core Analytics Database accelerates and simplifies querying your data
25 November 2024, OpenText Blogs

Leveraging Vertica Performance by Reducing CPU System Calls
23 January 2025, Taboola.com

Querying a Vertica data source in Amazon Athena using the Athena Federated Query SDK
11 February 2021, Amazon Web Services

provided by Google News

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