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DBMS > Atos Standard Common Repository vs. Google Cloud Datastore vs. IBM Db2 Event Store vs. Solr

System Properties Comparison Atos Standard Common Repository vs. Google Cloud Datastore vs. IBM Db2 Event Store vs. Solr

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Editorial information provided by DB-Engines
NameAtos Standard Common Repository  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonIBM Db2 Event Store  Xexclude from comparisonSolr  Xexclude from comparison
This system has been discontinued and will be removed from the DB-Engines ranking.
DescriptionHighly scalable database system, designed for managing session and subscriber data in modern mobile communication networksAutomatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformDistributed Event Store optimized for Internet of Things use casesA widely used distributed, scalable search engine based on Apache Lucene
Primary database modelDocument store
Key-value store
Document storeEvent Store
Time Series DBMS
Search engine
Secondary database modelsSpatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score4.36
Rank#72  Overall
#12  Document stores
Score0.27
Rank#309  Overall
#2  Event Stores
#28  Time Series DBMS
Score41.02
Rank#24  Overall
#3  Search engines
Websiteatos.net/en/convergence-creators/portfolio/standard-common-repositorycloud.google.com/­datastorewww.ibm.com/­products/­db2-event-storesolr.apache.org
Technical documentationcloud.google.com/­datastore/­docswww.ibm.com/­docs/­en/­db2-event-storesolr.apache.org/­resources.html
DeveloperAtos Convergence CreatorsGoogleIBMApache Software Foundation
Initial release2016200820172006
Current release17032.09.6.1, May 2024
License infoCommercial or Open Sourcecommercialcommercialcommercial infofree developer edition availableOpen Source infoApache Version 2
Cloud-based only infoOnly available as a cloud servicenoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaC and C++Java
Server operating systemsLinuxhostedLinux infoLinux, macOS, Windows for the developer additionAll OS with a Java VM inforuns as a servlet in servlet container (e.g. Tomcat, Jetty is included)
Data schemeSchema and schema-less with LDAP viewsschema-freeyesyes infoDynamic Fields enables on-the-fly addition of new fields
Typing infopredefined data types such as float or dateoptionalyes, details hereyesyes infosupports customizable data types and automatic typing
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.yesnonoyes
Secondary indexesyesyesnoyes infoAll search fields are automatically indexed
SQL infoSupport of SQLnoSQL-like query language (GQL)yes infothrough the embedded Spark runtimeSolr Parallel SQL Interface
APIs and other access methodsLDAPgRPC (using protocol buffers) API
RESTful HTTP/JSON API
ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API
Java API
RESTful HTTP/JSON API
Supported programming languagesAll languages with LDAP bindings.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
C
C#
C++
Cobol
Delphi
Fortran
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Scala
Visual Basic
.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala
Server-side scripts infoStored proceduresnousing Google App EngineyesJava plugins
TriggersyesCallbacks using the Google Apps Enginenoyes infoUser configurable commands triggered on index changes
Partitioning methods infoMethods for storing different data on different nodesSharding infocell divisionShardingShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesyesMulti-source replication using PaxosActive-active shard replicationyes
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infousing Google Cloud Dataflownospark-solr: github.com/­lucidworks/­spark-solr and streaming expressions to reduce
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual Consistency depending on configurationImmediate Consistency or Eventual Consistency depending on type of query and configuration infoStrong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.Eventual ConsistencyEventual Consistency
Foreign keys infoReferential integritynoyes infovia ReferenceProperties or Ancestor pathsnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataAtomic execution of specific operationsACID infoSerializable Isolation within Transactions, Read Committed outside of Transactionsnooptimistic locking
Concurrency infoSupport for concurrent manipulation of datayesyesNo - written data is immutableyes
Durability infoSupport for making data persistentyesyesYes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storageyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesnoyesyes
User concepts infoAccess controlLDAP bind authenticationAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)fine grained access rights according to SQL-standardyes

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More resources
Atos Standard Common RepositoryGoogle Cloud DatastoreIBM Db2 Event StoreSolr
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