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DBMS > GridGain vs. InfinityDB vs. IRONdb vs. Spark SQL vs. Transbase

System Properties Comparison GridGain vs. InfinityDB vs. IRONdb vs. Spark SQL vs. Transbase

Editorial information provided by DB-Engines
NameGridGain  Xexclude from comparisonInfinityDB  Xexclude from comparisonIRONdb  Xexclude from comparisonSpark SQL  Xexclude from comparisonTransbase  Xexclude from comparison
IRONdb seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.
DescriptionGridGain is an in-memory computing platform, built on Apache IgniteA Java embedded Key-Value Store which extends the Java Map interfaceA distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicitySpark SQL is a component on top of 'Spark Core' for structured data processingA resource-optimized, high-performance, universally applicable RDBMS
Primary database modelKey-value store
Relational DBMS
Key-value storeTime Series DBMSRelational DBMSRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.47
Rank#154  Overall
#26  Key-value stores
#72  Relational DBMS
Score0.00
Rank#378  Overall
#57  Key-value stores
Score18.96
Rank#33  Overall
#20  Relational DBMS
Score0.11
Rank#341  Overall
#150  Relational DBMS
Websitewww.gridgain.comboilerbay.comwww.circonus.com/solutions/time-series-database/spark.apache.org/­sqlwww.transaction.de/­en/­products/­transbase.html
Technical documentationwww.gridgain.com/­docs/­index.htmlboilerbay.com/­infinitydb/­manualdocs.circonus.com/irondb/category/getting-startedspark.apache.org/­docs/­latest/­sql-programming-guide.htmlwww.transaction.de/­en/­products/­transbase/­features.html
DeveloperGridGain Systems, Inc.Boiler Bay Inc.Circonus LLC.Apache Software FoundationTransaction Software GmbH
Initial release20072002201720141987
Current releaseGridGain 8.5.14.0V0.10.20, January 20183.5.0 ( 2.13), September 2023Transbase 8.3, 2022
License infoCommercial or Open SourcecommercialcommercialcommercialOpen Source infoApache 2.0commercial infofree development license
Cloud-based only infoOnly available as a cloud servicenonononono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJava, C++, .NetJavaC and C++ScalaC and C++
Server operating systemsLinux
OS X
Solaris
Windows
All OS with a Java VMLinuxLinux
OS X
Windows
FreeBSD
Linux
macOS
Solaris
Windows
Data schemeyesyes infonested virtual Java Maps, multi-value, logical ‘tuple space’ runtime Schema upgradeschema-freeyesyes
Typing infopredefined data types such as float or dateyesyes infoall Java primitives, Date, CLOB, BLOB, huge sparse arraysyes infotext, numeric, histogramsyesyes
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 indexesyesno infomanual creation possible, using inversions based on multi-value capabilitynonoyes
SQL infoSupport of SQLANSI-99 for query and DML statements, subset of DDLnoSQL-like query language (Circonus Analytics Query Language: CAQL)SQL-like DML and DDL statementsyes
APIs and other access methodsHDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data
Access via java.util.concurrent.ConcurrentNavigableMap Interface
Proprietary API to InfinityDB ItemSpace (boilerbay.com/­docs/­ItemSpaceDataStructures.htm)
HTTP APIJDBC
ODBC
ADO.NET
JDBC
ODBC
Proprietary native API
Supported programming languagesC#
C++
Java
PHP
Python
Ruby
Scala
Java.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala
Java
Python
R
Scala
C
C#
C++
Java
JavaScript
Kotlin
Objective-C
PHP
Python
Server-side scripts infoStored proceduresyes (compute grid and cache interceptors can be used instead)noyes, in Luanoyes
Triggersyes (cache interceptors and events)nononoyes
Partitioning methods infoMethods for storing different data on different nodesShardingnoneAutomatic, metric affinity per nodeyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesyes (replicated cache)noneconfigurable replication factor, datacenter awarenoneSource-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes (compute grid and hadoop accelerator)nonono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency infoREAD-COMMITTED or SERIALIZEDImmediate consistency per node, eventual consistency across nodesImmediate Consistency
Foreign keys infoReferential integritynono infomanual creation possible, using inversions based on multi-value capabilitynonoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACID infoOptimistic locking for transactions; no isolation for bulk loadsnonoyes
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 controlSecurity Hooks for custom implementationsnononofine grained access rights according to SQL-standard

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More resources
GridGainInfinityDBIRONdbSpark SQLTransbase
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