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DBMS > Badger vs. Bangdb vs. QuestDB vs. Sphinx vs. Splice Machine

System Properties Comparison Badger vs. Bangdb vs. QuestDB vs. Sphinx vs. Splice Machine

Editorial information provided by DB-Engines
NameBadger  Xexclude from comparisonBangdb  Xexclude from comparisonQuestDB  Xexclude from comparisonSphinx  Xexclude from comparisonSplice Machine  Xexclude from comparison
DescriptionAn embeddable, persistent, simple and fast Key-Value Store, written purely in Go.Converged and high performance database for device data, events, time series, document and graphA high performance open source SQL database for time series dataOpen source search engine for searching in data from different sources, e.g. relational databasesOpen-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark
Primary database modelKey-value storeDocument store
Graph DBMS
Time Series DBMS
Time Series DBMSSearch engineRelational DBMS
Secondary database modelsSpatial DBMSRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.22
Rank#320  Overall
#47  Key-value stores
Score0.16
Rank#338  Overall
#47  Document stores
#32  Graph DBMS
#31  Time Series DBMS
Score2.70
Rank#105  Overall
#8  Time Series DBMS
Score5.95
Rank#55  Overall
#5  Search engines
Score0.54
Rank#252  Overall
#115  Relational DBMS
Websitegithub.com/­dgraph-io/­badgerbangdb.comquestdb.iosphinxsearch.comsplicemachine.com
Technical documentationgodoc.org/­github.com/­dgraph-io/­badgerdocs.bangdb.comquestdb.io/­docssphinxsearch.com/­docssplicemachine.com/­how-it-works
DeveloperDGraph LabsSachin Sinha, BangDBQuestDB Technology IncSphinx Technologies Inc.Splice Machine
Initial release20172012201420012014
Current releaseBangDB 2.0, October 20213.5.1, February 20233.1, March 2021
License infoCommercial or Open SourceOpen Source infoApache 2.0Open Source infoBSD 3Open Source infoApache 2.0Open Source infoGPL version 2, commercial licence availableOpen Source infoAGPL 3.0, commercial license available
Cloud-based only infoOnly available as a cloud servicenonononono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageGoC, C++Java (Zero-GC), C++, RustC++Java
Server operating systemsBSD
Linux
OS X
Solaris
Windows
LinuxLinux
macOS
Windows
FreeBSD
Linux
NetBSD
OS X
Solaris
Windows
Linux
OS X
Solaris
Windows
Data schemeschema-freeschema-freeyes infoschema-free via InfluxDB Line Protocolyesyes
Typing infopredefined data types such as float or datenoyes: string, long, double, int, geospatial, stream, eventsyesnoyes
XML support infoSome form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.nonono
Secondary indexesnoyes infosecondary, composite, nested, reverse, geospatialnoyes infofull-text index on all search fieldsyes
SQL infoSupport of SQLnoSQL like support with command line toolSQL with time-series extensionsSQL-like query language (SphinxQL)yes
APIs and other access methodsProprietary protocol
RESTful HTTP API
HTTP REST
InfluxDB Line Protocol (TCP/UDP)
JDBC
PostgreSQL wire protocol
Proprietary protocolJDBC
Native Spark Datasource
ODBC
Supported programming languagesGoC
C#
C++
Java
Python
C infoPostgreSQL driver
C++
Go
Java
JavaScript (Node.js)
Python
Rust infoover HTTP
C++ infounofficial client library
Java
Perl infounofficial client library
PHP
Python
Ruby infounofficial client library
C#
C++
Java
JavaScript (Node.js)
Python
R
Scala
Server-side scripts infoStored proceduresnonononoyes infoJava
Triggersnoyes, Notifications (with Streaming only)nonoyes
Partitioning methods infoMethods for storing different data on different nodesnoneSharding (enterprise version only). P2P based virtual network overlay with consistent hashing and chord algorithmhorizontal partitioning (by timestamps)Sharding infoPartitioning is done manually, search queries against distributed index is supportedShared Nothhing Auto-Sharding, Columnar Partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesnoneselectable replication factor, Knob for CAP (enterprise version only)Source-replica replication with eventual consistencynoneMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonononoYes, via Full Spark Integration
Consistency concepts infoMethods to ensure consistency in a distributed systemnoneTunable consistency, set CAP knob accordinglyImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynonononoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDACID for single-table writesnoACID
Concurrency infoSupport for concurrent manipulation of datayesyes, optimistic concurrency controlyesyesyes, multi-version concurrency control (MVCC)
Durability infoSupport for making data persistentyesyes, implements WAL (Write ahead log) as wellyesyes infoThe original contents of fields are not stored in the Sphinx index.yes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyes, run db with in-memory only modeyes infothrough memory mapped filesyes
User concepts infoAccess controlnoyes (enterprise version only)noAccess rights for users, groups and roles according to SQL-standard
More information provided by the system vendor
BadgerBangdbQuestDBSphinxSplice Machine
Specific characteristicsRelational model with native time series support Column-based storage and time partitioned...
» more
Competitive advantagesHigh ingestion throughput: peak of 4M rows/sec (TSBS Benchmark) Code optimizations...
» more
Typical application scenariosFinancial tick data Industrial IoT Application Metrics Monitoring
» more
Key customersBanks & Hedge funds, Yahoo, OKX, Airbus, Aquis Exchange, Net App, Cloudera, Airtel,...
» more
Licensing and pricing modelsOpen source Apache 2.0 QuestDB Enterprise QuestDB Cloud
» more
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