DBMS > Badger vs. Spark SQL vs. Sphinx vs. XTDB

System Properties Comparison Badger vs. Spark SQL vs. Sphinx vs. XTDB

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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.

Spark SQL is a component on top of 'Spark Core' for structured data processing

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

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

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

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

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	5.98
Rank	#56	Overall
	#5	Search engines

Score	0.11
Rank	#343	Overall
	#46	Document stores

Website

github.com/dgraph-io/badger

spark.apache.org/sql

sphinxsearch.com

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

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

spark.apache.org/docs/latest/sql-programming-guide.html

sphinxsearch.com/docs

www.xtdb.com/docs

Developer

DGraph Labs

Apache Software Foundation

Sphinx Technologies Inc.

Juxt Ltd.

Initial release

2017

2014

2001

2019

Current release

3.5.0 ( 2.13), September 2023

3.5.1, February 2023

1.19, September 2021

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache 2.0

Open Source

GPL version 2, commercial licence available

Open Source

MIT License

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

Scala

C++

Clojure

Server operating systems

BSD
Linux
OS X
Solaris
Windows

Linux
OS X
Windows

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

All OS with a Java 8 (and higher) VM
Linux

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

yes, extensible-data-notation format

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

full-text index on all search fields

yes

SQL

Support of SQL

SQL-like DML and DDL statements

SQL-like query language (SphinxQL)

limited SQL, making use of Apache Calcite

APIs and other access methods

JDBC
ODBC

Proprietary protocol

HTTP REST
JDBC

Supported programming languages

Java
Python
R
Scala

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Clojure
Java

Server-side scripts

Stored procedures

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

yes, utilizing Spark Core

Sharding

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

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

none

Foreign keys

Referential integrity

Transaction concepts

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

ACID

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, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

In-memory capabilities

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

User concepts

Access control

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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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