DB-EnginesExtremeDB: mitigate connectivity issues in a DBMSEnglish
Deutsch
Knowledge Base of Relational and NoSQL Database Management Systemsprovided by solid IT

DBMS > Heroic vs. Hyprcubd vs. Splice Machine vs. Tkrzw

System Properties Comparison Heroic vs. Hyprcubd vs. Splice Machine vs. Tkrzw

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines
NameHeroic  Xexclude from comparisonHyprcubd  Xexclude from comparisonSplice Machine  Xexclude from comparisonTkrzw infoSuccessor of Tokyo Cabinet and Kyoto Cabinet  Xexclude from comparison
Hyprcubd seems to be discontinued. Therefore it is excluded from the DB-Engines ranking.
DescriptionTime Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearchServerless Time Series DBMSOpen-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 modelTime Series DBMSTime Series DBMSRelational DBMSKey-value store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.51
Rank#255  Overall
#21  Time Series DBMS
Score0.54
Rank#250  Overall
#114  Relational DBMS
Score0.00
Rank#383  Overall
#60  Key-value stores
Websitegithub.com/­spotify/­heroichyprcubd.com (offline)splicemachine.comdbmx.net/­tkrzw
Technical documentationspotify.github.io/­heroicsplicemachine.com/­how-it-works
DeveloperSpotifyHyprcubd, Inc.Splice MachineMikio Hirabayashi
Initial release201420142020
Current release3.1, March 20210.9.3, August 2020
License infoCommercial or Open SourceOpen Source infoApache 2.0commercialOpen Source infoAGPL 3.0, commercial license availableOpen Source infoApache Version 2.0
Cloud-based only infoOnly available as a cloud servicenoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaGoJavaC++
Server operating systemshostedLinux
OS X
Solaris
Windows
Linux
macOS
Data schemeschema-freeyesyesschema-free
Typing infopredefined data types such as float or dateyesyes infotime, int, uint, float, stringyesno
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.nonono
Secondary indexesyes infovia Elasticsearchnoyes
SQL infoSupport of SQLnoSQL-like query languageyesno
APIs and other access methodsHQL (Heroic Query Language, a JSON-based language)
HTTP API
gRPC (https)JDBC
Native Spark Datasource
ODBC
Supported programming languagesC#
C++
Java
JavaScript (Node.js)
Python
R
Scala
C++
Java
Python
Ruby
Server-side scripts infoStored proceduresnonoyes infoJavano
Triggersnonoyesno
Partitioning methods infoMethods for storing different data on different nodesShardingShared Nothhing Auto-Sharding, Columnar Partitioningnone
Replication methods infoMethods for redundantly storing data on multiple nodesyesMulti-source replication
Source-replica replication
none
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonoYes, via Full Spark Integrationno
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Immediate Consistency
Eventual ConsistencyImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynonoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonoACID
Concurrency infoSupport for concurrent manipulation of datayesnoyes, multi-version concurrency control (MVCC)yes
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.nonoyesyes infousing specific database classes
User concepts infoAccess controltoken accessAccess rights for users, groups and roles according to SQL-standardno

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources
HeroicHyprcubdSplice MachineTkrzw infoSuccessor of Tokyo Cabinet and Kyoto Cabinet
Recent citations in the news

Review: Google Bigtable scales with ease
7 September 2016, InfoWorld

provided by Google News

Machine learning data pipeline outfit Splice Machine files for insolvency
26 August 2021, The Register

Splice Machine Launches the Splice Machine Feature Store to Simplify Feature Engineering and Democratize Machine ...
19 January 2021, PR Newswire

Splice Machine Launches Feature Store to Simplify Feature Engineering
19 January 2021, Datanami

How Splice Machine's Data Platform for Intelligent Apps Works
29 September 2020, eWeek

Distributed SQL System Review: Snowflake vs Splice Machine
18 September 2019, Towards Data Science

provided by Google News



Share this page

Featured Products

Neo4j logo

See for yourself how a graph database can make your life easier.
Use Neo4j online for free.

Milvus logo

Vector database designed for GenAI, fully equipped for enterprise implementation.
Try Managed Milvus for Free

SingleStore logo

Database for your real-time AI and Analytics Apps.
Try it today.

RaimaDB logo

RaimaDB, embedded database for mission-critical applications. When performance, footprint and reliability matters.
Try RaimaDB for free.

Datastax Astra logo

Bring all your data to Generative AI applications with vector search enabled by the most scalable
vector database available.
Try for Free

Present your product here