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DBMS > Apache Druid vs. CrateDB vs. Realm vs. SpaceTime

System Properties Comparison Apache Druid vs. CrateDB vs. Realm vs. SpaceTime

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Editorial information provided by DB-Engines
NameApache Druid  Xexclude from comparisonCrateDB  Xexclude from comparisonRealm  Xexclude from comparisonSpaceTime  Xexclude from comparison
DescriptionOpen-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality dataDistributed Database based on LuceneA DBMS built for use on mobile devices that’s a fast, easy to use alternative to SQLite and Core DataSpaceTime is a spatio-temporal DBMS with a focus on performance.
Primary database modelRelational DBMS
Time Series DBMS
Document store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS
Document storeSpatial DBMS
Secondary database modelsRelational DBMSRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score3.25
Rank#90  Overall
#47  Relational DBMS
#7  Time Series DBMS
Score0.71
Rank#227  Overall
#37  Document stores
#5  Spatial DBMS
#16  Search engines
#19  Time Series DBMS
#8  Vector DBMS
Score7.41
Rank#52  Overall
#8  Document stores
Score0.03
Rank#392  Overall
#8  Spatial DBMS
Websitedruid.apache.orgcratedb.comrealm.iowww.mireo.com/­spacetime
Technical documentationdruid.apache.org/­docs/­latest/­designcratedb.com/­docsrealm.io/­docs
DeveloperApache Software Foundation and contributorsCrateRealm, acquired by MongoDB in May 2019Mireo
Initial release2012201320142020
Current release29.0.1, April 2024
License infoCommercial or Open SourceOpen Source infoApache license v2Open SourceOpen Sourcecommercial
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 languageJavaJavaC++
Server operating systemsLinux
OS X
Unix
All Operating Systems, including Kubernetes with CrateDB Kubernetes Operator supportAndroid
Backend: server-less
iOS
Windows
Linux
Data schemeyes infoschema-less columns are supportedFlexible 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.nononono
Secondary indexesyesyesyesno
SQL infoSupport of SQLSQL for queryingyes, but no triggers and constraints, and PostgreSQL compatibilitynoA subset of ANSI SQL is implemented
APIs and other access methodsJDBC
RESTful HTTP/JSON API
ADO.NET
JDBC
ODBC
PostgreSQL wire protocol
Prometheus Remote Read/Write
RESTful HTTP API
RESTful HTTP API
Supported programming languagesClojure
JavaScript
PHP
Python
R
Ruby
Scala
.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
Java infowith Android only
Objective-C
React Native
Swift
C#
C++
Python
Server-side scripts infoStored proceduresnouser defined functions (Javascript)no inforuns within the applications so server-side scripts are unnecessaryno
Triggersnonoyes infoChange Listenersno
Partitioning methods infoMethods for storing different data on different nodesSharding infomanual/auto, time-basedShardingnoneFixed-grid hypercubes
Replication methods infoMethods for redundantly storing data on multiple nodesyes, via HDFS, S3 or other storage enginesConfigurable replication on table/partition-levelnoneReal-time block device replication (DRBD)
MapReduce infoOffers an API for user-defined Map/Reduce methodsnononono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual Consistency
Read-after-write consistency on record level
Immediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynononono
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 strategyACIDno
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 infoIn-Memory realmno
User concepts infoAccess controlRBAC using LDAP or Druid internals for users and groups for read/write by datasource and systemrights management via user accountsyesyes
More information provided by the system vendor
Apache DruidCrateDBRealmSpaceTime
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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Apache DruidCrateDBRealmSpaceTime
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