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DBMS > Brytlyt vs. Ignite vs. Spark SQL vs. Splice Machine vs. Tkrzw

System Properties Comparison Brytlyt vs. Ignite vs. Spark SQL vs. Splice Machine vs. Tkrzw

Editorial information provided by DB-Engines
NameBrytlyt  Xexclude from comparisonIgnite  Xexclude from comparisonSpark SQL  Xexclude from comparisonSplice Machine  Xexclude from comparisonTkrzw infoSuccessor of Tokyo Cabinet and Kyoto Cabinet  Xexclude from comparison
DescriptionScalable GPU-accelerated RDBMS for very fast analytic and streaming workloads, leveraging PostgreSQLApache Ignite is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads, delivering in-memory speeds at petabyte scale.Spark SQL is a component on top of 'Spark Core' for structured data processingOpen-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and SparkA concept of libraries, allowing an application program to store and query key-value pairs in a file. Successor of Tokyo Cabinet and Kyoto Cabinet
Primary database modelRelational DBMSKey-value store
Relational DBMS
Relational DBMSRelational DBMSKey-value store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.29
Rank#288  Overall
#131  Relational DBMS
Score3.16
Rank#96  Overall
#15  Key-value stores
#49  Relational DBMS
Score18.96
Rank#33  Overall
#20  Relational DBMS
Score0.54
Rank#250  Overall
#114  Relational DBMS
Score0.00
Rank#383  Overall
#60  Key-value stores
Websitebrytlyt.ioignite.apache.orgspark.apache.org/­sqlsplicemachine.comdbmx.net/­tkrzw
Technical documentationdocs.brytlyt.ioapacheignite.readme.io/­docsspark.apache.org/­docs/­latest/­sql-programming-guide.htmlsplicemachine.com/­how-it-works
DeveloperBrytlytApache Software FoundationApache Software FoundationSplice MachineMikio Hirabayashi
Initial release20162015201420142020
Current release5.0, August 2023Apache Ignite 2.63.5.0 ( 2.13), September 20233.1, March 20210.9.3, August 2020
License infoCommercial or Open SourcecommercialOpen Source infoApache 2.0Open Source infoApache 2.0Open Source infoAGPL 3.0, commercial license availableOpen Source infoApache Version 2.0
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 languageC, C++ and CUDAC++, Java, .NetScalaJavaC++
Server operating systemsLinux
OS X
Windows
Linux
OS X
Solaris
Windows
Linux
OS X
Windows
Linux
OS X
Solaris
Windows
Linux
macOS
Data schemeyesyesyesyesschema-free
Typing infopredefined data types such as float or dateyesyesyesyesno
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.yes infospecific XML-type available, but no XML query functionality.yesnono
Secondary indexesyesyesnoyes
SQL infoSupport of SQLyesANSI-99 for query and DML statements, subset of DDLSQL-like DML and DDL statementsyesno
APIs and other access methodsADO.NET
JDBC
native C library
ODBC
streaming API for large objects
HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data
JDBC
ODBC
JDBC
Native Spark Datasource
ODBC
Supported programming languages.Net
C
C++
Delphi
Java
Perl
Python
Tcl
C#
C++
Java
PHP
Python
Ruby
Scala
Java
Python
R
Scala
C#
C++
Java
JavaScript (Node.js)
Python
R
Scala
C++
Java
Python
Ruby
Server-side scripts infoStored proceduresuser defined functions infoin PL/pgSQLyes (compute grid and cache interceptors can be used instead)noyes infoJavano
Triggersyesyes (cache interceptors and events)noyesno
Partitioning methods infoMethods for storing different data on different nodesShardingyes, utilizing Spark CoreShared Nothhing Auto-Sharding, Columnar Partitioningnone
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationyes (replicated cache)noneMulti-source replication
Source-replica replication
none
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyes (compute grid and hadoop accelerator)Yes, via Full Spark Integrationno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integrityyesnonoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDnoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes, multi-version concurrency control (MVCC)yes
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.yesnoyesyes infousing specific database classes
User concepts infoAccess controlfine grained access rights according to SQL-standardSecurity Hooks for custom implementationsnoAccess rights for users, groups and roles according to SQL-standardno

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More resources
BrytlytIgniteSpark SQLSplice MachineTkrzw infoSuccessor of Tokyo Cabinet and Kyoto Cabinet
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