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DBMS > Badger vs. Hazelcast vs. Hypertable vs. Spark SQL

System Properties Comparison Badger vs. Hazelcast vs. Hypertable vs. Spark SQL

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Editorial information provided by DB-Engines
NameBadger  Xexclude from comparisonHazelcast  Xexclude from comparisonHypertable  Xexclude from comparisonSpark SQL  Xexclude from comparison
Hypertable has stopped its further development with March 2016 and is removed from the DB-Engines ranking.
DescriptionAn embeddable, persistent, simple and fast Key-Value Store, written purely in Go.A widely adopted in-memory data gridAn open source BigTable implementation based on distributed file systems such as HadoopSpark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelKey-value storeKey-value storeWide column storeRelational DBMS
Secondary database modelsDocument store infoJSON support with IMDG 3.12
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.22
Rank#320  Overall
#47  Key-value stores
Score5.46
Rank#61  Overall
#7  Key-value stores
Score18.04
Rank#33  Overall
#20  Relational DBMS
Websitegithub.com/­dgraph-io/­badgerhazelcast.comspark.apache.org/­sql
Technical documentationgodoc.org/­github.com/­dgraph-io/­badgerhazelcast.org/­imdg/­docsspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperDGraph LabsHazelcastHypertable Inc.Apache Software Foundation
Initial release2017200820092014
Current release5.3.6, November 20230.9.8.11, March 20163.5.0 ( 2.13), September 2023
License infoCommercial or Open SourceOpen Source infoApache 2.0Open Source infoApache Version 2; commercial licenses availableOpen Source infoGNU version 3. Commercial license availableOpen 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 languageGoJavaC++Scala
Server operating systemsBSD
Linux
OS X
Solaris
Windows
All OS with a Java VMLinux
OS X
Windows infoan inofficial Windows port is available
Linux
OS X
Windows
Data schemeschema-freeschema-freeschema-freeyes
Typing infopredefined data types such as float or datenoyesnoyes
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.noyes infothe object must implement a serialization strategyno
Secondary indexesnoyesrestricted infoonly exact value or prefix value scansno
SQL infoSupport of SQLnoSQL-like query languagenoSQL-like DML and DDL statements
APIs and other access methodsJCache
JPA
Memcached protocol
RESTful HTTP API
C++ API
Thrift
JDBC
ODBC
Supported programming languagesGo.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala
C++
Java
Perl
PHP
Python
Ruby
Java
Python
R
Scala
Server-side scripts infoStored proceduresnoyes infoEvent Listeners, Executor Servicesnono
Triggersnoyes infoEventsnono
Partitioning methods infoMethods for storing different data on different nodesnoneShardingShardingyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesnoneyes infoReplicated Mapselectable replication factor on file system levelnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyesyes
Consistency concepts infoMethods to ensure consistency in a distributed systemnoneImmediate Consistency or Eventual Consistency selectable by user infoRaft Consensus AlgorithmImmediate Consistency
Foreign keys infoReferential integritynononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoone or two-phase-commit; repeatable reads; read commitednono
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.noyesno
User concepts infoAccess controlnoRole-based access controlnono

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BadgerHazelcastHypertableSpark SQL
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