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

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

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Editorial information provided by DB-Engines

Name

Description

Converged and high performance database for device data, events, time series, document and graph

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

Document store
Graph DBMS
Time Series DBMS

Relational DBMS

Search engine

Document store

Secondary database models

Spatial DBMS

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

Score	0.08
Rank	#347	Overall
	#47	Document stores
	#34	Graph DBMS
	#31	Time Series DBMS

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

bangdb.com

spark.apache.org/sql

sphinxsearch.com

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

Technical documentation

docs.bangdb.com

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

sphinxsearch.com/docs

www.xtdb.com/docs

Developer

Sachin Sinha, BangDB

Apache Software Foundation

Sphinx Technologies Inc.

Juxt Ltd.

Initial release

2012

2014

2001

2019

Current release

BangDB 2.0, October 2021

3.5.0 ( 2.13), September 2023

3.5.1, February 2023

1.19, September 2021

License

Commercial or Open Source

Open Source

BSD 3

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

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Implementation language

C, C++

Scala

C++

Clojure

Server operating systems

Linux

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: string, long, double, int, geospatial, stream, events

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

secondary, composite, nested, reverse, geospatial

yes

full-text index on all search fields

yes

SQL

Support of SQL

SQL like support with command line tool

SQL-like DML and DDL statements

SQL-like query language (SphinxQL)

limited SQL, making use of Apache Calcite

APIs and other access methods

Proprietary protocol
RESTful HTTP API

JDBC
ODBC

Proprietary protocol

HTTP REST
JDBC

Supported programming languages

C
C#
C++
Java
Python

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

yes, Notifications (with Streaming only)

Partitioning methods

Methods for storing different data on different nodes

Sharding (enterprise version only). P2P based virtual network overlay with consistent hashing and chord algorithm

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

selectable replication factor, Knob for CAP (enterprise version only)

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

Tunable consistency, set CAP knob accordingly

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, optimistic concurrency control

yes

Durability

Support for making data persistent

yes, implements WAL (Write ahead log) as well

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.

yes, run db with in-memory only mode

User concepts

Access control

yes (enterprise version only)

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More resources

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