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DBMS > Apache Pinot vs. CockroachDB vs. Faircom EDGE vs. Spark SQL vs. STSdb

System Properties Comparison Apache Pinot vs. CockroachDB vs. Faircom EDGE vs. Spark SQL vs. STSdb

Editorial information provided by DB-Engines
NameApache Pinot  Xexclude from comparisonCockroachDB  Xexclude from comparisonFaircom EDGE infoformerly c-treeEDGE  Xexclude from comparisonSpark SQL  Xexclude from comparisonSTSdb  Xexclude from comparison
DescriptionRealtime distributed OLAP datastore, designed to answer OLAP queries with low latencyCockroachDB 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 method
Primary database modelRelational DBMSRelational DBMSKey-value store
Relational DBMS
Relational DBMSKey-value store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.38
Rank#275  Overall
#126  Relational DBMS
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
Websitepinot.apache.orgwww.cockroachlabs.comwww.faircom.com/­products/­faircom-edgespark.apache.org/­sqlgithub.com/­STSSoft/­STSdb4
Technical documentationdocs.pinot.apache.orgwww.cockroachlabs.com/­docsdocs.faircom.com/­docs/­en/­UUID-23d4f1fd-d213-f6d5-b92e-9b7475baa14e.htmlspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperApache Software Foundation and contributorsCockroach LabsFairCom CorporationApache Software FoundationSTS Soft SC
Initial release20152015197920142011
Current release1.0.0, September 202324.1.0, May 2024V3, October 20203.5.0 ( 2.13), September 20234.0.8, September 2015
License infoCommercial or Open SourceOpen Source infoApache Version 2.0Open Source infoApache 2.0, commercial license availablecommercial infoRestricted, free version availableOpen Source infoApache 2.0Open Source infoGPLv2, commercial license available
Cloud-based only infoOnly available as a cloud servicenonononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaGoANSI C, C++ScalaC#
Server operating systemsAll OS with a Java JDK11 or higherLinux
macOS
Windows
Android
Linux infoARM, x86
Raspbian
Windows
Linux
OS X
Windows
Windows
Data schemeyesdynamic schemaFlexible Schema (defined schema, partial schema, schema free)yesyes
Typing infopredefined data types such as float or dateyesyesyes, ANSI Standard SQL Typesyesyes infoprimitive types and user defined types (classes)
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.noyesno
Secondary indexesyesyesnono
SQL infoSupport of SQLSQL-like query languageyes, wire compatible with PostgreSQLyes infoANSI SQL queriesSQL-like DML and DDL statementsno
APIs and other access methodsJDBCJDBCADO.NET
Direct SQL
IoT Microservice layer
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC
RESTful HTTP API
JDBC
ODBC
.NET Client API
Supported programming languagesGo
Java
Python
C#
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
Server-side scripts infoStored proceduresnoyes info.Net, JavaScript, C/C++nono
Triggersnoyesnono
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioninghorizontal 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 logsnonenone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonono
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 dataACIDACIDnono
Concurrency infoSupport for concurrent manipulation of datayesyes infoacross SQL and NoSQLyesyes
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.noyesno
User concepts infoAccess controlRole-based access controlFine grained user, group and file access rights managed across SQL (per ANSI standard) and NoSQL.nono

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More resources
Apache PinotCockroachDBFaircom EDGE infoformerly c-treeEDGESpark SQLSTSdb
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