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DBMS > CrateDB vs. Google Cloud Datastore vs. Heroic vs. Linter

System Properties Comparison CrateDB vs. Google Cloud Datastore vs. Heroic vs. Linter

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Editorial information provided by DB-Engines
NameCrateDB  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonHeroic  Xexclude from comparisonLinter  Xexclude from comparison
DescriptionDistributed Database based on LuceneAutomatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformTime Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearchRDBMS for high security requirements
Primary database modelDocument store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS
Document storeTime Series DBMSRelational DBMS
Secondary database modelsRelational DBMSSpatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.71
Rank#227  Overall
#37  Document stores
#5  Spatial DBMS
#16  Search engines
#19  Time Series DBMS
#9  Vector DBMS
Score4.36
Rank#72  Overall
#12  Document stores
Score0.46
Rank#265  Overall
#22  Time Series DBMS
Score0.12
Rank#350  Overall
#152  Relational DBMS
Websitecratedb.comcloud.google.com/­datastoregithub.com/­spotify/­heroiclinter.ru
Technical documentationcratedb.com/­docscloud.google.com/­datastore/­docsspotify.github.io/­heroic
DeveloperCrateGoogleSpotifyrelex.ru
Initial release2013200820141990
License infoCommercial or Open SourceOpen SourcecommercialOpen Source infoApache 2.0commercial
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.
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 languageJavaJavaC and C++
Server operating systemsAll Operating Systems, including Kubernetes with CrateDB Kubernetes Operator supporthostedAIX
Android
BSD
HP Open VMS
iOS
Linux
OS X
VxWorks
Windows
Data schemeFlexible Schema (defined schema, partial schema, schema free)schema-freeschema-freeyes
Typing infopredefined data types such as float or dateyesyes, details hereyesyes
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 indexesyesyesyes infovia Elasticsearchyes
SQL infoSupport of SQLyes, but no triggers and constraints, and PostgreSQL compatibilitySQL-like query language (GQL)noyes
APIs and other access methodsADO.NET
JDBC
ODBC
PostgreSQL wire protocol
Prometheus Remote Read/Write
RESTful HTTP API
gRPC (using protocol buffers) API
RESTful HTTP/JSON API
HQL (Heroic Query Language, a JSON-based language)
HTTP API
ADO.NET
JDBC
LINQ
ODBC
OLE DB
Oracle Call Interface (OCI)
Supported programming languages.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
.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
C
C#
C++
Java
Perl
PHP
Python
Qt
Ruby
Tcl
Server-side scripts infoStored proceduresuser defined functions (Javascript)using Google App Enginenoyes infoproprietary syntax with the possibility to convert from PL/SQL
TriggersnoCallbacks using the Google Apps Enginenoyes
Partitioning methods infoMethods for storing different data on different nodesShardingShardingShardingnone
Replication methods infoMethods for redundantly storing data on multiple nodesConfigurable replication on table/partition-levelMulti-source replication using PaxosyesSource-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyes infousing Google Cloud Dataflownono
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Read-after-write consistency on record level
Immediate Consistency or Eventual Consistency depending on type of query and configuration infoStrong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.Eventual Consistency
Immediate Consistency
Immediate Consistency
Foreign keys infoReferential integritynoyes infovia ReferenceProperties or Ancestor pathsnoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datano infounique row identifiers can be used for implementing an optimistic concurrency control strategyACID infoSerializable Isolation within Transactions, Read Committed outside of TransactionsnoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
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.nonono
User concepts infoAccess controlrights management via user accountsAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)fine grained access rights according to SQL-standard
More information provided by the system vendor
CrateDBGoogle Cloud DatastoreHeroicLinter
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
CrateDBGoogle Cloud DatastoreHeroicLinter
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