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DBMS > BoltDB vs. Citus vs. CrateDB vs. Spark SQL

System Properties Comparison BoltDB vs. Citus vs. CrateDB vs. Spark SQL

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Editorial information provided by DB-Engines
NameBoltDB  Xexclude from comparisonCitus  Xexclude from comparisonCrateDB  Xexclude from comparisonSpark SQL  Xexclude from comparison
DescriptionAn embedded key-value store for Go.Scalable hybrid operational and analytics RDBMS for big data use cases based on PostgreSQLDistributed Database based on LuceneSpark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelKey-value storeRelational DBMSDocument store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS
Relational DBMS
Secondary database modelsDocument storeRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.74
Rank#220  Overall
#31  Key-value stores
Score2.21
Rank#118  Overall
#56  Relational DBMS
Score0.73
Rank#224  Overall
#37  Document stores
#5  Spatial DBMS
#16  Search engines
#19  Time Series DBMS
#8  Vector DBMS
Score18.96
Rank#33  Overall
#20  Relational DBMS
Websitegithub.com/­boltdb/­boltwww.citusdata.comcratedb.comspark.apache.org/­sql
Technical documentationdocs.citusdata.comcratedb.com/­docsspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperCrateApache Software Foundation
Initial release2013201020132014
Current release8.1, December 20183.5.0 ( 2.13), September 2023
License infoCommercial or Open SourceOpen Source infoMIT LicenseOpen Source infoAGPL, commercial license also availableOpen SourceOpen Source infoApache 2.0
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 languageGoCJavaScala
Server operating systemsBSD
Linux
OS X
Solaris
Windows
LinuxAll Operating Systems, including Kubernetes with CrateDB Kubernetes Operator supportLinux
OS X
Windows
Data schemeschema-freeyesFlexible Schema (defined schema, partial schema, schema free)yes
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.noyes infospecific XML type available, but no XML query functionalitynono
Secondary indexesnoyesyesno
SQL infoSupport of SQLnoyes infostandard, with numerous extensionsyes, but no triggers and constraints, and PostgreSQL compatibilitySQL-like DML and DDL statements
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
JDBC
ODBC
Supported programming languagesGo.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
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
Java
Python
R
Scala
Server-side scripts infoStored proceduresnouser defined functions inforealized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.user defined functions (Javascript)no
Triggersnoyesnono
Partitioning methods infoMethods for storing different data on different nodesnoneShardingShardingyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesnoneSource-replica replication infoother methods possible by using 3rd party extensionsConfigurable replication on table/partition-levelnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonono
Consistency concepts infoMethods to ensure consistency in a distributed systemnoneImmediate ConsistencyEventual Consistency
Read-after-write consistency on record level
Foreign keys infoReferential integritynoyesnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datayesACIDno infounique row identifiers can be used for implementing an optimistic concurrency control strategyno
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.nononono
User concepts infoAccess controlnofine grained access rights according to SQL-standardrights management via user accountsno
More information provided by the system vendor
BoltDBCitusCrateDBSpark SQL
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
BoltDBCitusCrateDBSpark SQL
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