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DBMS > Brytlyt vs. CrateDB vs. Google Cloud Spanner vs. Valentina Server

System Properties Comparison Brytlyt vs. CrateDB vs. Google Cloud Spanner vs. Valentina Server

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Editorial information provided by DB-Engines
NameBrytlyt  Xexclude from comparisonCrateDB  Xexclude from comparisonGoogle Cloud Spanner  Xexclude from comparisonValentina Server  Xexclude from comparison
DescriptionScalable GPU-accelerated RDBMS for very fast analytic and streaming workloads, leveraging PostgreSQLDistributed Database based on LuceneA horizontally scalable, globally consistent, relational database service. It is the externalization of the core Google database that runs the biggest aspects of Google, like Ads and Google Play.Object-relational database and reports server
Primary database modelRelational DBMSDocument store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS
Relational DBMSRelational DBMS
Secondary database modelsRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.38
Rank#276  Overall
#127  Relational DBMS
Score0.71
Rank#227  Overall
#37  Document stores
#5  Spatial DBMS
#16  Search engines
#19  Time Series DBMS
#9  Vector DBMS
Score2.84
Rank#100  Overall
#51  Relational DBMS
Score0.21
Rank#325  Overall
#144  Relational DBMS
Websitebrytlyt.iocratedb.comcloud.google.com/­spannerwww.valentina-db.net
Technical documentationdocs.brytlyt.iocratedb.com/­docscloud.google.com/­spanner/­docsvalentina-db.com/­docs/­dokuwiki/­v5/­doku.php
DeveloperBrytlytCrateGoogleParadigma Software
Initial release2016201320171999
Current release5.0, August 20235.7.5
License infoCommercial or Open SourcecommercialOpen Sourcecommercialcommercial
Cloud-based only infoOnly available as a cloud servicenonoyesno
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, C++ and CUDAJava
Server operating systemsLinux
OS X
Windows
All Operating Systems, including Kubernetes with CrateDB Kubernetes Operator supporthostedLinux
OS X
Windows
Data schemeyesFlexible Schema (defined schema, partial schema, schema free)yesyes
Typing infopredefined data types such as float or dateyesyesyesyes
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.nono
Secondary indexesyesyesyesyes
SQL infoSupport of SQLyesyes, but no triggers and constraints, and PostgreSQL compatibilityyes infoQuery statements complying to ANSI 2011yes
APIs and other access methodsADO.NET
JDBC
native C library
ODBC
streaming API for large objects
ADO.NET
JDBC
ODBC
PostgreSQL wire protocol
Prometheus Remote Read/Write
RESTful HTTP API
gRPC (using protocol buffers) API
JDBC infoAt present, JDBC supports read-only queries. No support for DDL or DML statements.
RESTful HTTP API
ODBC
Supported programming languages.Net
C
C++
Delphi
Java
Perl
Python
Tcl
.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
Go
Java
JavaScript (Node.js)
Python
.Net
C
C#
C++
Objective-C
PHP
Ruby
Visual Basic
Visual Basic.NET
Server-side scripts infoStored proceduresuser defined functions infoin PL/pgSQLuser defined functions (Javascript)noyes
Triggersyesnonoyes
Partitioning methods infoMethods for storing different data on different nodesShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationConfigurable replication on table/partition-levelMulti-source replication with 3 replicas for regional instances.
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonoyes infousing Google Cloud Dataflowno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual Consistency
Read-after-write consistency on record level
Immediate Consistency
Foreign keys infoReferential integrityyesnoyes infoby using interleaved tables, this features focuses more on performance improvements than on referential integrityyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDno infounique row identifiers can be used for implementing an optimistic concurrency control strategyACID infoStrict serializable isolation
Concurrency infoSupport for concurrent manipulation of datayesyesyes
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.nonoyes
User concepts infoAccess controlfine grained access rights according to SQL-standardrights 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
BrytlytCrateDBGoogle Cloud SpannerValentina Server
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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BrytlytCrateDBGoogle Cloud SpannerValentina Server
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