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DBMS > EsgynDB vs. Geode vs. RisingWave vs. Spark SQL

System Properties Comparison EsgynDB vs. Geode vs. RisingWave vs. Spark SQL

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Editorial information provided by DB-Engines
NameEsgynDB  Xexclude from comparisonGeode  Xexclude from comparisonRisingWave  Xexclude from comparisonSpark SQL  Xexclude from comparison
DescriptionEnterprise-class SQL-on-Hadoop solution, powered by Apache TrafodionGeode is a distributed data container, pooling memory, CPU, network resources, and optionally local disk across multiple processesA distributed RDBMS for stream processing, wire-compatible with PostgreSQLSpark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelRelational DBMSKey-value storeRelational DBMSRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.25
Rank#312  Overall
#138  Relational DBMS
Score1.86
Rank#134  Overall
#24  Key-value stores
Score0.64
Rank#238  Overall
#110  Relational DBMS
Score18.04
Rank#33  Overall
#20  Relational DBMS
Websitewww.esgyn.cngeode.apache.orgwww.risingwave.com/­databasespark.apache.org/­sql
Technical documentationgeode.apache.org/­docsdocs.risingwave.com/­docs/­current/­introspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperEsgynOriginally developed by Gemstone. They outsourced the project to Apache in 2015 but still deliver a commercial version as Gemfire.RisingWave LabsApache Software Foundation
Initial release2015200220222014
Current release1.1, February 20171.2, September 20233.5.0 ( 2.13), September 2023
License infoCommercial or Open SourcecommercialOpen Source infoApache Version 2; commercial licenses available as GemfireOpen Source infoApache Version 2.0Open Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++, JavaJavaRustScala
Server operating systemsLinuxAll OS with a Java VM infothe JDK (8 or later) is also requiredDocker
Linux
macOS
Linux
OS X
Windows
Data schemeyesschema-freeyesyes
Typing infopredefined data types such as float or dateyesyesStandard SQL-types and JSONyes
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.nononono
Secondary indexesyesnoyesno
SQL infoSupport of SQLyesSQL-like query language (OQL)yesSQL-like DML and DDL statements
APIs and other access methodsADO.NET
JDBC
ODBC
Java Client API
Memcached protocol
RESTful HTTP API
JDBC
PostgreSQL wire protocol
JDBC
ODBC
Supported programming languagesAll languages supporting JDBC/ODBC/ADO.Net.Net
All JVM based languages
C++
Groovy
Java
Scala
Go
Java
JavaScript (Node.js)
Python
Java
Python
R
Scala
Server-side scripts infoStored proceduresJava Stored Proceduresuser defined functionsUDFs in Python or Javano
Triggersnoyes infoCache Event Listenersnono
Partitioning methods infoMethods for storing different data on different nodesShardingShardingyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication between multi datacentersMulti-source replicationnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesnono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual Consistency
Foreign keys infoReferential integrityyesnonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDyes, on a single nodenono
Concurrency infoSupport for concurrent manipulation of datayesyesyes
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.noyesyesno
User concepts infoAccess controlfine grained access rights according to SQL-standardAccess rights per client and object definableUsers and Rolesno

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EsgynDBGeodeRisingWaveSpark SQL
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