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DBMS > Apache Druid vs. CrateDB vs. GigaSpaces

System Properties Comparison Apache Druid vs. CrateDB vs. GigaSpaces

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Editorial information provided by DB-Engines
NameApache Druid  Xexclude from comparisonCrateDB  Xexclude from comparisonGigaSpaces  Xexclude from comparison
DescriptionOpen-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality dataDistributed Database based on LuceneHigh performance in-memory data grid platform, powering three products: Smart Cache, Smart ODS (Operational Data Store), Smart Augmented Transactions
Primary database modelRelational DBMS
Time Series DBMS
Document store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS
Document store
Object oriented DBMS infoValues are user defined objects
Secondary database modelsRelational DBMSGraph DBMS
Search engine
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
Score0.97
Rank#192  Overall
#32  Document stores
#6  Object oriented DBMS
Websitedruid.apache.orgcratedb.comwww.gigaspaces.com
Technical documentationdruid.apache.org/­docs/­latest/­designcratedb.com/­docsdocs.gigaspaces.com/­latest/­landing.html
DeveloperApache Software Foundation and contributorsCrateGigaspaces Technologies
Initial release201220132000
Current release29.0.1, April 202415.5, September 2020
License infoCommercial or Open SourceOpen Source infoApache license v2Open SourceOpen Source infoApache Version 2; Commercial licenses available
Cloud-based only infoOnly available as a cloud servicenonono
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 languageJavaJavaJava, C++, .Net
Server operating systemsLinux
OS X
Unix
All Operating Systems, including Kubernetes with CrateDB Kubernetes Operator supportLinux
macOS
Solaris
Windows
Data schemeyes infoschema-less columns are supportedFlexible Schema (defined schema, partial schema, schema free)schema-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 infoXML can be used for describing objects metadata
Secondary indexesyesyesyes
SQL infoSupport of SQLSQL for queryingyes, but no triggers and constraints, and PostgreSQL compatibilitySQL-99 for query and DML statements
APIs and other access methodsJDBC
RESTful HTTP/JSON API
ADO.NET
JDBC
ODBC
PostgreSQL wire protocol
Prometheus Remote Read/Write
RESTful HTTP API
GigaSpaces LRMI
Hibernate
JCache
JDBC
JPA
ODBC
RESTful HTTP API
Spring Data
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
C++
Java
Python
Scala
Server-side scripts infoStored proceduresnouser defined functions (Javascript)yes
Triggersnonoyes, event driven architecture
Partitioning methods infoMethods for storing different data on different nodesSharding infomanual/auto, time-basedShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesyes, via HDFS, S3 or other storage enginesConfigurable replication on table/partition-levelMulti-source replication infosynchronous or asynchronous
Source-replica replication infosynchronous or asynchronous
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonoyes infoMap-Reduce pattern can be built with XAP task executors
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual Consistency
Read-after-write consistency on record level
Immediate Consistency infoConsistency level configurable: ALL, QUORUM, ANY
Foreign keys infoReferential integritynonono
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
Durability infoSupport for making data persistentyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonoyes
User concepts infoAccess controlRBAC using LDAP or Druid internals for users and groups for read/write by datasource and systemrights management via user accountsRole-based access control
More information provided by the system vendor
Apache DruidCrateDBGigaSpaces
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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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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More resources
Apache DruidCrateDBGigaSpaces
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