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DBMS > Atos Standard Common Repository vs. etcd vs. Hazelcast vs. Hive vs. IRONdb

System Properties Comparison Atos Standard Common Repository vs. etcd vs. Hazelcast vs. Hive vs. IRONdb

Editorial information provided by DB-Engines
NameAtos Standard Common Repository  Xexclude from comparisonetcd  Xexclude from comparisonHazelcast  Xexclude from comparisonHive  Xexclude from comparisonIRONdb  Xexclude from comparison
This system has been discontinued and will be removed from the DB-Engines ranking.IRONdb seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.
DescriptionHighly scalable database system, designed for managing session and subscriber data in modern mobile communication networksA distributed reliable key-value storeA widely adopted in-memory data griddata warehouse software for querying and managing large distributed datasets, built on HadoopA distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicity
Primary database modelDocument store
Key-value store
Key-value storeKey-value storeRelational DBMSTime Series DBMS
Secondary database modelsDocument store infoJSON support with IMDG 3.12
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score7.25
Rank#54  Overall
#5  Key-value stores
Score5.97
Rank#57  Overall
#6  Key-value stores
Score61.17
Rank#18  Overall
#12  Relational DBMS
Websiteatos.net/en/convergence-creators/portfolio/standard-common-repositoryetcd.io
github.com/­etcd-io/­etcd
hazelcast.comhive.apache.orgwww.circonus.com/solutions/time-series-database/
Technical documentationetcd.io/­docs
github.com/­etcd-io/­etcd/­tree/­master/­Documentation
hazelcast.org/­imdg/­docscwiki.apache.org/­confluence/­display/­Hive/­Homedocs.circonus.com/irondb/category/getting-started
DeveloperAtos Convergence CreatorsHazelcastApache Software Foundation infoinitially developed by FacebookCirconus LLC.
Initial release2016200820122017
Current release17033.4, August 20195.3.6, November 20233.1.3, April 2022V0.10.20, January 2018
License infoCommercial or Open SourcecommercialOpen Source infoApache Version 2.0Open Source infoApache Version 2; commercial licenses availableOpen Source infoApache Version 2commercial
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 languageJavaGoJavaJavaC and C++
Server operating systemsLinuxFreeBSD
Linux
Windows infoexperimental
All OS with a Java VMAll OS with a Java VMLinux
Data schemeSchema and schema-less with LDAP viewsschema-freeschema-freeyesschema-free
Typing infopredefined data types such as float or dateoptionalnoyesyesyes infotext, numeric, histograms
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.yesnoyes infothe object must implement a serialization strategyno
Secondary indexesyesnoyesyesno
SQL infoSupport of SQLnonoSQL-like query languageSQL-like DML and DDL statementsSQL-like query language (Circonus Analytics Query Language: CAQL)
APIs and other access methodsLDAPgRPC
JSON over HTTP
JCache
JPA
Memcached protocol
RESTful HTTP API
JDBC
ODBC
Thrift
HTTP API
Supported programming languagesAll languages with LDAP bindings.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Rust
Scala
Tcl
.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala
C++
Java
PHP
Python
.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala
Server-side scripts infoStored proceduresnonoyes infoEvent Listeners, Executor Servicesyes infouser defined functions and integration of map-reduceyes, in Lua
Triggersyesyes, watching key changesyes infoEventsnono
Partitioning methods infoMethods for storing different data on different nodesSharding infocell divisionShardingShardingAutomatic, metric affinity per node
Replication methods infoMethods for redundantly storing data on multiple nodesyesUsing Raft consensus algorithm to ensure data replication with strong consistency among multiple replicas.yes infoReplicated Mapselectable replication factorconfigurable replication factor, datacenter aware
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyesyes infoquery execution via MapReduceno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual Consistency depending on configurationImmediate ConsistencyImmediate Consistency or Eventual Consistency selectable by user infoRaft Consensus AlgorithmEventual ConsistencyImmediate consistency per node, eventual consistency across nodes
Foreign keys infoReferential integritynonononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataAtomic execution of specific operationsnoone or two-phase-commit; repeatable reads; read commitednono
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesnoyesno
User concepts infoAccess controlLDAP bind authenticationnoRole-based access controlAccess rights for users, groups and rolesno

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More resources
Atos Standard Common RepositoryetcdHazelcastHiveIRONdb
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