DBMS > Hive vs. Rockset vs. Sadas Engine vs. SingleStore

System Properties Comparison Hive vs. Rockset vs. Sadas Engine vs. SingleStore

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Editorial information provided by DB-Engines

Name

Description

data warehouse software for querying and managing large distributed datasets, built on Hadoop

A scalable, reliable search and analytics service in the cloud, built on RocksDB

SADAS Engine is a columnar DBMS specifically designed for high performance in data warehouse environments

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

Primary database model

Relational DBMS

Document store

Relational DBMS

Secondary database models

Relational DBMS
Search engine

Document store
Spatial DBMS
Time Series DBMS
Vector DBMS

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

Score	61.17
Rank	#18	Overall
	#12	Relational DBMS

Score	0.79
Rank	#211	Overall
	#35	Document stores

Score	0.00
Rank	#383	Overall
	#158	Relational DBMS

Score	5.60
Rank	#62	Overall
	#35	Relational DBMS

Website

Technical documentation

cwiki.apache.org/confluence/display/Hive/Home

docs.rockset.com

www.sadasengine.com/en/sadas-engine-download-free-trial-and-documentation/#documentation

docs.singlestore.com

Developer

Apache Software Foundation

initially developed by Facebook

Rockset

SADAS s.r.l.

SingleStore Inc.

Initial release

2012

2019

2006

2013

Current release

3.1.3, April 2022

8.0

8.5, January 2024

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

free trial version available

commercial

free developer edition 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.

SingleStoreDB Cloud: The world's fastest, modern cloud database for both operational (OLTP) and analytical (OLAP) workloads. Available instantly with multi-cloud and hybrid-cloud capabilities

Implementation language

Java

C++

C++, Go

Server operating systems

All OS with a Java VM

hosted

AIX
Linux
Windows

Linux

64 bit version required

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

dynamic typing

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.

ingestion from XML files supported

Secondary indexes

yes

all fields are automatically indexed

yes

SQL

Support of SQL

SQL-like DML and DDL statements

Read-only SQL queries, including JOINs

yes

but no triggers and foreign keys

APIs and other access methods

JDBC
ODBC
Thrift

HTTP REST

JDBC
ODBC
Proprietary protocol

Cluster Management API

as HTTP Rest and CLI
HTTP API
JDBC
MongoDB API
ODBC

Supported programming languages

C++
Java
PHP
Python

Go
Java
JavaScript (Node.js)
Python

.Net
C
C#
C++
Groovy
Java
PHP
Python

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

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

Automatic sharding

horizontal partitioning

Sharding

hash partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes

none

Source-replica replication

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

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

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

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

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

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

In-memory capabilities

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

yes

managed by 'Learn by Usage'

yes

User concepts

Access control

Access rights for users, groups and roles

Access rights for users and organizations can be defined via Rockset console

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

Fine grained access control via users, groups and roles

More information provided by the system vendor

Specific characteristics

SingleStore offers a fully-managed , distributed, highly-scalable SQL database designed...
» more

Competitive advantages

SingleStore’s competitive advantages include: Easy and Simplified Architecture with...
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Typical application scenarios

Driving Fast Analytics: SingleStore delivers the fastest and most scalable reporting...
» more

Key customers

IEX Cloud : Improves Financial Data Distribution Speed 15x with Singlestore DB Comcast,...
» more

Market metrics

Customers in various industries worldwide including US and International Industry...
» more

Licensing and pricing models

F ree Tier and Enterprise Edition
» more

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More resources

DB-Engines blog posts

Why is Hadoop not listed in the DB-Engines Ranking?
13 May 2013, Paul Andlinger

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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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