DBMS > Badger vs. Rockset vs. searchxml vs. Splice Machine

System Properties Comparison Badger vs. Rockset vs. searchxml vs. Splice Machine

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Editorial information provided by DB-Engines

Name

Description

An embeddable, persistent, simple and fast Key-Value Store, written purely in Go.

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

DBMS for structured and unstructured content wrapped with an application server

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

Primary database model

Key-value store

Document store

Native XML DBMS
Search engine

Relational DBMS

Secondary database models

Relational DBMS
Search engine

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

Score	0.14
Rank	#328	Overall
	#48	Key-value stores

Score	1.07
Rank	#180	Overall
	#31	Document stores

Score	0.00
Rank	#385	Overall
	#7	Native XML DBMS
	#24	Search engines

Score	0.54
Rank	#244	Overall
	#114	Relational DBMS

Website

github.com/dgraph-io/badger

rockset.com

www.searchxml.net/category/products

splicemachine.com

Technical documentation

godoc.org/github.com/dgraph-io/badger

docs.rockset.com

www.searchxml.net/support/handouts

splicemachine.com/how-it-works

Developer

DGraph Labs

Rockset

informationpartners gmbh

Splice Machine

Initial release

2017

2019

2015

2014

Current release

1.0

3.1, March 2021

License

Commercial or Open Source

Open Source

Apache 2.0

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++

Java

Server operating systems

BSD
Linux
OS X
Solaris
Windows

hosted

Windows

Linux
OS X
Solaris
Windows

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

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

yes

Secondary indexes

all fields are automatically indexed

yes

SQL

Support of SQL

Read-only SQL queries, including JOINs

yes

APIs and other access methods

HTTP REST

RESTful HTTP API
WebDAV
XQuery
XSLT

JDBC
Native Spark Datasource
ODBC

Supported programming languages

Go
Java
JavaScript (Node.js)
Python

C++

most other programming languages supported via APIs

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

Server-side scripts

Stored procedures

yes

on the application server

yes

Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Automatic sharding

none

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

sychronisation to multiple collections

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

none

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

multiple readers, single writer

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

User concepts

Access control

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

Domain, group and role-based access control at the document level and for application services

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

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