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DBMS > Faircom EDGE vs. Google Cloud Datastore vs. Splice Machine vs. VictoriaMetrics

System Properties Comparison Faircom EDGE vs. Google Cloud Datastore vs. Splice Machine vs. VictoriaMetrics

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Editorial information provided by DB-Engines
NameFaircom EDGE infoformerly c-treeEDGE  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonSplice Machine  Xexclude from comparisonVictoriaMetrics  Xexclude from comparison
DescriptionFairCom EDGE is an Industry 4.0 solution built to integrate, collect, aggregate and synchronize mission-critical data in edge computing environmentsAutomatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformOpen-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and SparkA fast, cost-effective and scalable Time Series DBMS and monitoring solution
Primary database modelKey-value store
Relational DBMS
Document storeRelational DBMSTime Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.02
Rank#368  Overall
#54  Key-value stores
#156  Relational DBMS
Score4.47
Rank#76  Overall
#12  Document stores
Score0.54
Rank#250  Overall
#114  Relational DBMS
Score1.32
Rank#162  Overall
#14  Time Series DBMS
Websitewww.faircom.com/­products/­faircom-edgecloud.google.com/­datastoresplicemachine.comvictoriametrics.com
Technical documentationdocs.faircom.com/­docs/­en/­UUID-23d4f1fd-d213-f6d5-b92e-9b7475baa14e.htmlcloud.google.com/­datastore/­docssplicemachine.com/­how-it-worksdocs.victoriametrics.com
github.com/­VictoriaMetrics/­VictoriaMetrics/­wiki
DeveloperFairCom CorporationGoogleSplice MachineVictoriaMetrics
Initial release1979200820142018
Current releaseV3, October 20203.1, March 2021v1.91, May 2023
License infoCommercial or Open Sourcecommercial infoRestricted, free version availablecommercialOpen Source infoAGPL 3.0, commercial license availableOpen Source infoApache Version 2.0
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 languageANSI C, C++JavaGo
Server operating systemsAndroid
Linux infoARM, x86
Raspbian
Windows
hostedLinux
OS X
Solaris
Windows
FreeBSD
Linux
macOS
OpenBSD
Data schemeFlexible Schema (defined schema, partial schema, schema free)schema-freeyes
Typing infopredefined data types such as float or dateyes, ANSI Standard SQL Typesyes, details hereyes
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.yesnono
Secondary indexesyesyesyes
SQL infoSupport of SQLyes infoANSI SQL queriesSQL-like query language (GQL)yesno
APIs and other access methodsADO.NET
Direct SQL
IoT Microservice layer
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC
RESTful HTTP API
gRPC (using protocol buffers) API
RESTful HTTP/JSON API
JDBC
Native Spark Datasource
ODBC
Graphite protocol
InfluxDB Line Protocol
OpenTSDB
Prometheus Query API
Prometheus Remote Read/Write
Supported programming languagesC
C#
C++
Java
JavaScript
PHP
Python
VB.Net
.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
C#
C++
Java
JavaScript (Node.js)
Python
R
Scala
Server-side scripts infoStored proceduresyes info.Net, JavaScript, C/C++using Google App Engineyes infoJavano
TriggersyesCallbacks using the Google Apps Engineyesno
Partitioning methods infoMethods for storing different data on different nodesFile partitioning infoCustomizable business rules for partitioningShardingShared Nothhing Auto-Sharding, Columnar Partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesyes infoSynchronous and asynchronous realtime replication based on transaction logsMulti-source replication using PaxosMulti-source replication
Source-replica replication
Synchronous replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyes infousing Google Cloud DataflowYes, via Full Spark Integrationno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency
Tunable Consistency
Immediate 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.Immediate ConsistencyEventual Consistency
Foreign keys infoReferential integrityyes infowhen using SQLyes infovia ReferenceProperties or Ancestor pathsyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACID infoSerializable Isolation within Transactions, Read Committed outside of TransactionsACIDno
Concurrency infoSupport for concurrent manipulation of datayes infoacross SQL and NoSQLyesyes, multi-version concurrency control (MVCC)yes
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.yesnoyesno
User concepts infoAccess controlFine grained user, group and file access rights managed across SQL (per ANSI standard) and NoSQL.Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Access rights for users, groups and roles according to SQL-standard

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More resources
Faircom EDGE infoformerly c-treeEDGEGoogle Cloud DatastoreSplice MachineVictoriaMetrics
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