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

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

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Editorial information provided by DB-Engines
NameApache Druid  Xexclude from comparisonCrateDB  Xexclude from comparisonEsgynDB  Xexclude from comparisonRealm  Xexclude from comparison
DescriptionOpen-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality dataDistributed Database based on LuceneEnterprise-class SQL-on-Hadoop solution, powered by Apache TrafodionA DBMS built for use on mobile devices that’s a fast, easy to use alternative to SQLite and Core Data
Primary database modelRelational DBMS
Time Series DBMS
Document store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS
Relational DBMSDocument store
Secondary database modelsRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score3.34
Rank#88  Overall
#48  Relational DBMS
#7  Time Series DBMS
Score0.73
Rank#224  Overall
#37  Document stores
#5  Spatial DBMS
#16  Search engines
#19  Time Series DBMS
#8  Vector DBMS
Score0.16
Rank#329  Overall
#146  Relational DBMS
Score7.60
Rank#52  Overall
#9  Document stores
Websitedruid.apache.orgcratedb.comwww.esgyn.cnrealm.io
Technical documentationdruid.apache.org/­docs/­latest/­designcratedb.com/­docsrealm.io/­docs
DeveloperApache Software Foundation and contributorsCrateEsgynRealm, acquired by MongoDB in May 2019
Initial release2012201320152014
Current release29.0.1, April 2024
License infoCommercial or Open SourceOpen Source infoApache license v2Open SourcecommercialOpen Source
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++, Java
Server operating systemsLinux
OS X
Unix
All Operating Systems, including Kubernetes with CrateDB Kubernetes Operator supportLinuxAndroid
Backend: server-less
iOS
Windows
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 indexesyesyesyesyes
SQL infoSupport of SQLSQL for queryingyes, but no triggers and constraints, and PostgreSQL compatibilityyesno
APIs and other access methodsJDBC
RESTful HTTP/JSON API
ADO.NET
JDBC
ODBC
PostgreSQL wire protocol
Prometheus Remote Read/Write
RESTful HTTP API
ADO.NET
JDBC
ODBC
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
All languages supporting JDBC/ODBC/ADO.Net.Net
Java infowith Android only
Objective-C
React Native
Swift
Server-side scripts infoStored proceduresnouser defined functions (Javascript)Java Stored Proceduresno inforuns within the applications so server-side scripts are unnecessary
Triggersnononoyes infoChange Listeners
Partitioning methods infoMethods for storing different data on different nodesSharding infomanual/auto, time-basedShardingShardingnone
Replication methods infoMethods for redundantly storing data on multiple nodesyes, via HDFS, S3 or other storage enginesConfigurable replication on table/partition-levelMulti-source replication between multi datacentersnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonoyesno
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 integritynonoyesno
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 strategyACIDACID
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.nononoyes infoIn-Memory realm
User concepts infoAccess controlRBAC using LDAP or Druid internals for users and groups for read/write by datasource and systemrights management via user accountsfine grained access rights according to SQL-standardyes
More information provided by the system vendor
Apache DruidCrateDBEsgynDBRealm
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 DruidCrateDBEsgynDBRealm
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