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DBMS > Atos Standard Common Repository vs. Google Cloud Datastore vs. TigerGraph vs. VictoriaMetrics vs. YDB

System Properties Comparison Atos Standard Common Repository vs. Google Cloud Datastore vs. TigerGraph vs. VictoriaMetrics vs. YDB

Editorial information provided by DB-Engines
NameAtos Standard Common Repository  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonTigerGraph  Xexclude from comparisonVictoriaMetrics  Xexclude from comparisonYDB  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 PlatformA complete, distributed, parallel graph computing platform supporting web-scale data analytics in real-timeA fast, cost-effective and scalable Time Series DBMS and monitoring solutionA distributed fault-tolerant database service, with high availability, scalability, immediate consistency and ACID transactions and providing an Amazon DynamoDB compatible API
Primary database modelDocument store
Key-value store
Document storeGraph DBMSTime Series DBMSDocument store
Relational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score4.47
Rank#76  Overall
#12  Document stores
Score1.83
Rank#139  Overall
#13  Graph DBMS
Score1.32
Rank#162  Overall
#14  Time Series DBMS
Score0.35
Rank#278  Overall
#43  Document stores
#127  Relational DBMS
Websiteatos.net/en/convergence-creators/portfolio/standard-common-repositorycloud.google.com/­datastorewww.tigergraph.comvictoriametrics.comgithub.com/­ydb-platform/­ydb
ydb.tech
Technical documentationcloud.google.com/­datastore/­docsdocs.tigergraph.comdocs.victoriametrics.com
github.com/­VictoriaMetrics/­VictoriaMetrics/­wiki
ydb.tech/­en/­docs
DeveloperAtos Convergence CreatorsGoogleVictoriaMetricsYandex
Initial release20162008201720182019
Current release1703v1.91, May 2023
License infoCommercial or Open SourcecommercialcommercialcommercialOpen Source infoApache Version 2.0Open Source infoApache 2.0; commercial license available
Cloud-based only infoOnly available as a cloud servicenoyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaC++Go
Server operating systemsLinuxhostedLinuxFreeBSD
Linux
macOS
OpenBSD
Linux
Data schemeSchema and schema-less with LDAP viewsschema-freeyesFlexible Schema (defined schema, partial schema, schema free)
Typing infopredefined data types such as float or dateoptionalyes, details hereyesyes
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.yesnononono
Secondary indexesyesyesyes
SQL infoSupport of SQLnoSQL-like query language (GQL)SQL-like query language (GSQL)noSQL-like query language (YQL)
APIs and other access methodsLDAPgRPC (using protocol buffers) API
RESTful HTTP/JSON API
GSQL (TigerGraph Query Language)
Kafka
RESTful HTTP/JSON API
Graphite protocol
InfluxDB Line Protocol
OpenTSDB
Prometheus Query API
Prometheus Remote Read/Write
RESTful HTTP API (DynamoDB compatible)
Supported programming languagesAll languages with LDAP bindings.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
C++
Java
Go
Java
JavaScript (Node.js)
PHP
Python
Server-side scripts infoStored proceduresnousing Google App Engineyesnono
TriggersyesCallbacks using the Google Apps Enginenono
Partitioning methods infoMethods for storing different data on different nodesSharding infocell divisionShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesyesMulti-source replication using PaxosSynchronous replicationActive-passive shard replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infousing Google Cloud Dataflowyesnono
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 ConsistencyImmediate Consistency
Foreign keys infoReferential integritynoyes infovia ReferenceProperties or Ancestor pathsyes infoRelationships in graphsnono
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 TransactionsACIDnoACID
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.yesnononono
User concepts infoAccess controlLDAP bind authenticationAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Role-based access controlAccess rights defined for Yandex Cloud users

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More resources
Atos Standard Common RepositoryGoogle Cloud DatastoreTigerGraphVictoriaMetricsYDB
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