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DBMS > Badger vs. IRONdb vs. Netezza vs. Spark SQL

System Properties Comparison Badger vs. IRONdb vs. Netezza vs. Spark SQL

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Editorial information provided by DB-Engines
NameBadger  Xexclude from comparisonIRONdb  Xexclude from comparisonNetezza infoAlso called PureData System for Analytics by IBM  Xexclude from comparisonSpark SQL  Xexclude from comparison
IRONdb seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.
DescriptionAn embeddable, persistent, simple and fast Key-Value Store, written purely in Go.A distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicityData warehouse and analytics appliance part of IBM PureSystemsSpark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelKey-value storeTime Series DBMSRelational DBMSRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.22
Rank#320  Overall
#47  Key-value stores
Score8.59
Rank#45  Overall
#29  Relational DBMS
Score18.04
Rank#33  Overall
#20  Relational DBMS
Websitegithub.com/­dgraph-io/­badgerwww.circonus.com/solutions/time-series-database/www.ibm.com/­products/­netezzaspark.apache.org/­sql
Technical documentationgodoc.org/­github.com/­dgraph-io/­badgerdocs.circonus.com/irondb/category/getting-startedspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperDGraph LabsCirconus LLC.IBMApache Software Foundation
Initial release2017201720002014
Current releaseV0.10.20, January 20183.5.0 ( 2.13), September 2023
License infoCommercial or Open SourceOpen Source infoApache 2.0commercialcommercialOpen Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageGoC and C++Scala
Server operating systemsBSD
Linux
OS X
Solaris
Windows
LinuxLinux infoincluded in applianceLinux
OS X
Windows
Data schemeschema-freeschema-freeyesyes
Typing infopredefined data types such as float or datenoyes infotext, numeric, histogramsyesyes
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 indexesnonoyesno
SQL infoSupport of SQLnoSQL-like query language (Circonus Analytics Query Language: CAQL)yesSQL-like DML and DDL statements
APIs and other access methodsHTTP APIJDBC
ODBC
OLE DB
JDBC
ODBC
Supported programming languagesGo.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala
C
C++
Fortran
Java
Lua
Perl
Python
R
Java
Python
R
Scala
Server-side scripts infoStored proceduresnoyes, in Luayesno
Triggersnononono
Partitioning methods infoMethods for storing different data on different nodesnoneAutomatic, metric affinity per nodeShardingyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesnoneconfigurable replication factor, datacenter awareSource-replica replicationnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonoyes
Consistency concepts infoMethods to ensure consistency in a distributed systemnoneImmediate consistency per node, eventual consistency across nodes
Foreign keys infoReferential integritynononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonoACIDno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonono
User concepts infoAccess controlnonoUsers with fine-grained authorization conceptno

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More resources
BadgerIRONdbNetezza infoAlso called PureData System for Analytics by IBMSpark SQL
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