DBMS > Badger vs. Postgres-XL vs. Sphinx vs. StarRocks

System Properties Comparison Badger vs. Postgres-XL vs. Sphinx vs. StarRocks

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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.

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

Open source search engine for searching in data from different sources, e.g. relational databases

An open source, high-performance columnar analytical database that enables real-time, multi-dimensional, and highly concurrent data analytics

Forked from Apache Doris

Primary database model

Secondary database models

Document store
Spatial DBMS

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

Score	0.14
Rank	#331	Overall
	#49	Key-value stores

Score	0.49
Rank	#256	Overall
	#117	Relational DBMS

Score	5.98
Rank	#56	Overall
	#5	Search engines

Score	1.04
Rank	#189	Overall
	#88	Relational DBMS

Website

github.com/dgraph-io/badger

www.postgres-xl.org

sphinxsearch.com

www.starrocks.io

Technical documentation

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

www.postgres-xl.org/documentation

sphinxsearch.com/docs

docs.starrocks.io/en-us/latest/introduction/StarRocks_intro

Developer

DGraph Labs

Sphinx Technologies Inc.

The Linux Foundation

since Feb 2023

Initial release

2017

2014

since 2012, originally named StormDB

2001

2020

Current release

10 R1, October 2018

3.5.1, February 2023

2.5.3, March 2023

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Mozilla public license

Open Source

GPL version 2, commercial licence available

Open Source

Apache Version 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

C++

C++, Java

Server operating systems

BSD
Linux
OS X
Solaris
Windows

Linux
macOS

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Linux

Data scheme

schema-free

yes

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.

yes

XML type, but no XML query functionality

Secondary indexes

yes

full-text index on all search fields

yes

SQL

Support of SQL

yes

distributed, parallel query execution

SQL-like query language (SphinxQL)

yes

APIs and other access methods

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

Proprietary protocol

JDBC
MySQL protocol

Supported programming languages

.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Java

Server-side scripts

Stored procedures

user defined functions

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning

Sharding

Partitioning is done manually, search queries against distributed index is supported

horizontal partitioning (by range and hash)

Replication methods

Methods for redundantly storing data on multiple nodes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

MVCC

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

The original contents of fields are not stored in the Sphinx index.

yes

In-memory capabilities

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

User concepts

Access control

fine grained access rights according to SQL-standard

Role based access control and fine grained access rights

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More resources

DB-Engines blog posts

The DB-Engines ranking includes now search engines
4 February 2013, Paul Andlinger

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