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DBMS > Blueflood vs. CrateDB vs. Splice Machine vs. SWC-DB

System Properties Comparison Blueflood vs. CrateDB vs. Splice Machine vs. SWC-DB

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Editorial information provided by DB-Engines
NameBlueflood  Xexclude from comparisonCrateDB  Xexclude from comparisonSplice Machine  Xexclude from comparisonSWC-DB infoSuper Wide Column Database  Xexclude from comparison
DescriptionScalable TimeSeries DBMS based on CassandraDistributed Database based on LuceneOpen-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and SparkA high performance, scalable Wide Column DBMS
Primary database modelTime Series DBMSDocument store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS
Relational DBMSWide column store
Secondary database modelsRelational DBMSTime Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.13
Rank#346  Overall
#33  Time Series DBMS
Score0.71
Rank#227  Overall
#37  Document stores
#5  Spatial DBMS
#16  Search engines
#19  Time Series DBMS
#9  Vector DBMS
Score0.54
Rank#252  Overall
#115  Relational DBMS
Score0.08
Rank#364  Overall
#13  Wide column stores
Websiteblueflood.iocratedb.comsplicemachine.comgithub.com/­kashirin-alex/­swc-db
www.swcdb.org
Technical documentationgithub.com/­rax-maas/­blueflood/­wikicratedb.com/­docssplicemachine.com/­how-it-works
DeveloperRackspaceCrateSplice MachineAlex Kashirin
Initial release2013201320142020
Current release3.1, March 20210.5, April 2021
License infoCommercial or Open SourceOpen Source infoApache 2.0Open SourceOpen Source infoAGPL 3.0, commercial license availableOpen Source infoGPL V3
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 languageJavaJavaJavaC++
Server operating systemsLinux
OS X
All Operating Systems, including Kubernetes with CrateDB Kubernetes Operator supportLinux
OS X
Solaris
Windows
Linux
Data schemepredefined schemeFlexible Schema (defined schema, partial schema, schema free)yesschema-free
Typing infopredefined data types such as float or dateyesyesyes
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 indexesnoyesyes
SQL infoSupport of SQLnoyes, but no triggers and constraints, and PostgreSQL compatibilityyesSQL-like query language
APIs and other access methodsHTTP RESTADO.NET
JDBC
ODBC
PostgreSQL wire protocol
Prometheus Remote Read/Write
RESTful HTTP API
JDBC
Native Spark Datasource
ODBC
Proprietary protocol
Thrift
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
C#
C++
Java
JavaScript (Node.js)
Python
R
Scala
C++
Server-side scripts infoStored proceduresnouser defined functions (Javascript)yes infoJavano
Triggersnonoyesno
Partitioning methods infoMethods for storing different data on different nodesSharding infobased on CassandraShardingShared Nothhing Auto-Sharding, Columnar PartitioningSharding
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factor infobased on CassandraConfigurable replication on table/partition-levelMulti-source replication
Source-replica replication
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 infobased on Cassandra
Immediate Consistency infobased on Cassandra
Eventual Consistency
Read-after-write consistency on record level
Immediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynonoyes
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 strategyACID
Concurrency infoSupport for concurrent manipulation of datayesyesyes, 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.nonoyesno
User concepts infoAccess controlnorights management via user accountsAccess rights for users, groups and roles according to SQL-standard
More information provided by the system vendor
BluefloodCrateDBSplice MachineSWC-DB infoSuper Wide Column Database
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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BluefloodCrateDBSplice MachineSWC-DB infoSuper Wide Column Database
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