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DBMS > CockroachDB vs. Faircom EDGE vs. Spark SQL vs. STSdb vs. YottaDB

System Properties Comparison CockroachDB vs. Faircom EDGE vs. Spark SQL vs. STSdb vs. YottaDB

Editorial information provided by DB-Engines
NameCockroachDB  Xexclude from comparisonFaircom EDGE infoformerly c-treeEDGE  Xexclude from comparisonSpark SQL  Xexclude from comparisonSTSdb  Xexclude from comparisonYottaDB  Xexclude from comparison
DescriptionCockroachDB is a distributed database architected for modern cloud applications. It is wire compatible with PostgreSQL and backed by a Key-Value Store, which is either RocksDB or a purpose-built derivative, called Pebble.FairCom EDGE is an Industry 4.0 solution built to integrate, collect, aggregate and synchronize mission-critical data in edge computing environmentsSpark SQL is a component on top of 'Spark Core' for structured data processingKey-Value Store with special method for indexing infooptimized for high performance using a special indexing methodA fast and solid embedded Key-value store
Primary database modelRelational DBMSKey-value store
Relational DBMS
Relational DBMSKey-value storeKey-value store
Secondary database modelsRelational DBMS infousing the Octo plugin
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score5.73
Rank#58  Overall
#34  Relational DBMS
Score0.09
Rank#362  Overall
#53  Key-value stores
#155  Relational DBMS
Score18.04
Rank#33  Overall
#20  Relational DBMS
Score0.10
Rank#357  Overall
#51  Key-value stores
Score0.28
Rank#306  Overall
#44  Key-value stores
Websitewww.cockroachlabs.comwww.faircom.com/­products/­faircom-edgespark.apache.org/­sqlgithub.com/­STSSoft/­STSdb4yottadb.com
Technical documentationwww.cockroachlabs.com/­docsdocs.faircom.com/­docs/­en/­UUID-23d4f1fd-d213-f6d5-b92e-9b7475baa14e.htmlspark.apache.org/­docs/­latest/­sql-programming-guide.htmlyottadb.com/­resources/­documentation
DeveloperCockroach LabsFairCom CorporationApache Software FoundationSTS Soft SCYottaDB, LLC
Initial release20151979201420112001
Current release24.1.0, May 2024V3, October 20203.5.0 ( 2.13), September 20234.0.8, September 2015
License infoCommercial or Open SourceOpen Source infoApache 2.0, commercial license availablecommercial infoRestricted, free version availableOpen Source infoApache 2.0Open Source infoGPLv2, commercial license availableOpen Source infoAGPL 3.0
Cloud-based only infoOnly available as a cloud servicenonononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageGoANSI C, C++ScalaC#C
Server operating systemsLinux
macOS
Windows
Android
Linux infoARM, x86
Raspbian
Windows
Linux
OS X
Windows
WindowsDocker
Linux
Data schemedynamic schemaFlexible Schema (defined schema, partial schema, schema free)yesyesschema-free
Typing infopredefined data types such as float or dateyesyes, ANSI Standard SQL Typesyesyes infoprimitive types and user defined types (classes)no
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.noyesnono
Secondary indexesyesyesnonono
SQL infoSupport of SQLyes, wire compatible with PostgreSQLyes infoANSI SQL queriesSQL-like DML and DDL statementsnoby using the Octo plugin
APIs and other access methodsJDBCADO.NET
Direct SQL
IoT Microservice layer
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC
RESTful HTTP API
JDBC
ODBC
.NET Client APIPostgreSQL wire protocol infousing the Octo plugin
Proprietary protocol
Supported programming languagesC#
C++
Clojure
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Rust
C
C#
C++
Java
JavaScript
PHP
Python
VB.Net
Java
Python
R
Scala
C#
Java
C
Go
JavaScript (Node.js)
Lua
M
Perl
Python
Rust
Server-side scripts infoStored proceduresnoyes info.Net, JavaScript, C/C++nono
Triggersnoyesnono
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioning (by key range) infoall tables are translated to an ordered KV store and then broken down into 64MB ranges, which are then used as replicas in RAFTFile partitioning infoCustomizable business rules for partitioningyes, utilizing Spark Corenone
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication using RAFTyes infoSynchronous and asynchronous realtime replication based on transaction logsnonenoneyes
MapReduce infoOffers an API for user-defined Map/Reduce methodsnononono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency
Tunable Consistency
Foreign keys infoReferential integrityyesyes infowhen using SQLnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDnonooptimistic locking
Concurrency infoSupport for concurrent manipulation of datayesyes infoacross SQL and NoSQLyesyes
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.noyesnoyes
User concepts infoAccess controlRole-based access controlFine grained user, group and file access rights managed across SQL (per ANSI standard) and NoSQL.nonoUsers and groups based on OS-security mechanisms

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More resources
CockroachDBFaircom EDGE infoformerly c-treeEDGESpark SQLSTSdbYottaDB
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