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DBMS > Cachelot.io vs. CrateDB vs. HEAVY.AI vs. Ingres

System Properties Comparison Cachelot.io vs. CrateDB vs. HEAVY.AI vs. Ingres

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Editorial information provided by DB-Engines
NameCachelot.io  Xexclude from comparisonCrateDB  Xexclude from comparisonHEAVY.AI infoFormerly named 'OmniSci', rebranded to 'HEAVY.AI' in March 2022  Xexclude from comparisonIngres  Xexclude from comparison
DescriptionIn-memory caching systemDistributed Database based on LuceneA high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardwareWell established RDBMS
Primary database modelKey-value storeDocument store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS
Relational DBMSRelational DBMS
Secondary database modelsRelational DBMSSpatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.00
Rank#396  Overall
#64  Key-value stores
Score0.73
Rank#229  Overall
#37  Document stores
#5  Spatial DBMS
#16  Search engines
#19  Time Series DBMS
#8  Vector DBMS
Score2.10
Rank#126  Overall
#61  Relational DBMS
Score4.67
Rank#77  Overall
#42  Relational DBMS
Websitecachelot.iocratedb.comgithub.com/­heavyai/­heavydb
www.heavy.ai
www.actian.com/­databases/­ingres
Technical documentationcratedb.com/­docsdocs.heavy.aidocs.actian.com/­ingres
DeveloperCrateHEAVY.AI, Inc.Actian Corporation
Initial release2015201320161974 infooriginally developed at University Berkely in early 1970s
Current release5.10, January 202211.2, May 2022
License infoCommercial or Open SourceOpen Source infoSimplified BSD LicenseOpen SourceOpen Source infoApache Version 2; enterprise edition availablecommercial
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
CrateDB Cloud: a distributed SQL database that spreads data and processing across an elastic cluster of shared nothing nodes. CrateDB Cloud enables data insights at scale on Microsoft Azure, AWS and Google Cloud Platform.
Implementation languageC++JavaC++ and CUDAC
Server operating systemsFreeBSD
Linux
OS X
All Operating Systems, including Kubernetes with CrateDB Kubernetes Operator supportLinuxAIX
HP Open VMS
HP-UX
Linux
Solaris
Windows
Data schemeschema-freeFlexible Schema (defined schema, partial schema, schema free)yesyes
Typing infopredefined data types such as float or datenoyesyesyes
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 infobut tools for importing/exporting data from/to XML-files available
Secondary indexesnoyesnoyes
SQL infoSupport of SQLnoyes, but no triggers and constraints, and PostgreSQL compatibilityyesyes
APIs and other access methodsMemcached protocolADO.NET
JDBC
ODBC
PostgreSQL wire protocol
Prometheus Remote Read/Write
RESTful HTTP API
JDBC
ODBC
Thrift
Vega
.NET Client API
JDBC
ODBC
proprietary protocol (OpenAPI)
Supported programming languages.Net
C
C++
ColdFusion
Erlang
Java
Lisp
Lua
OCaml
OCaml
Perl
PHP
Python
Ruby
.NET
Erlang
Go infocommunity maintained client
Java
JavaScript (Node.js) infocommunity maintained client
Perl infocommunity maintained client
PHP
Python
R
Ruby infocommunity maintained client
Scala infocommunity maintained client
All languages supporting JDBC/ODBC/Thrift
Python
Server-side scripts infoStored proceduresnouser defined functions (Javascript)noyes
Triggersnononoyes
Partitioning methods infoMethods for storing different data on different nodesnoneShardingSharding infoRound robinhorizontal partitioning infoIngres Star to access multiple databases simultaneously
Replication methods infoMethods for redundantly storing data on multiple nodesnoneConfigurable replication on table/partition-levelMulti-source replicationIngres Replicator
MapReduce infoOffers an API for user-defined Map/Reduce methodsnononono
Consistency concepts infoMethods to ensure consistency in a distributed systemnoneEventual Consistency
Read-after-write consistency on record level
Immediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynononoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanono infounique row identifiers can be used for implementing an optimistic concurrency control strategynoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes infoMVCC
Durability infoSupport for making data persistentnoyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonoyesno
User concepts infoAccess controlnorights management via user accountsfine grained access rights according to SQL-standardfine grained access rights according to SQL-standard
More information provided by the system vendor
Cachelot.ioCrateDBHEAVY.AI infoFormerly named 'OmniSci', rebranded to 'HEAVY.AI' in March 2022Ingres
Specific characteristicsThe enterprise database for time series, documents, and vectors. Distributed - Native...
» more
Competitive advantagesResponse time in milliseconds: e ven for complex ad-hoc queries. Massive scaling...
» more
Typical application scenarios​ IoT: accelerate your IIoT projects with CrateDB, delivering real-time analytics...
» more
Key customersAcross all continents, CrateDB is used by companies of all sizes to meet the most...
» more
Market metricsThe CrateDB open source project was started in 2013 Honorable Mention in 2021 Gartner®...
» more
Licensing and pricing modelsSee CrateDB pricing >
» more

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More resources
Cachelot.ioCrateDBHEAVY.AI infoFormerly named 'OmniSci', rebranded to 'HEAVY.AI' in March 2022Ingres
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