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DBMS > Apache Druid vs. Apache Spark (SQL) vs. GigaSpaces

System Properties Comparison Apache Druid vs. Apache Spark (SQL) vs. GigaSpaces

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Editorial information provided by DB-Engines
NameApache Druid  Xexclude from comparisonApache Spark (SQL)  Xexclude from comparisonGigaSpaces  Xexclude from comparison
DescriptionOpen-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality dataApache Spark SQL is a component on top of 'Spark Core' for structured data processingHigh 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
Relational DBMSDocument store
Object oriented DBMS infoValues are user defined objects
Secondary database modelsGraph DBMS
Search engine
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score3.18
Rank#84  Overall
#45  Relational DBMS
#7  Time Series DBMS
Score21.62
Rank#29  Overall
#18  Relational DBMS
Score1.14
Rank#169  Overall
#27  Document stores
#6  Object oriented DBMS
Websitedruid.apache.orgspark.apache.org/­sqlwww.gigaspaces.com
Technical documentationdruid.apache.org/­docs/­latest/­designspark.apache.org/­docs/­latest/­sql-programming-guide.htmldocs.gigaspaces.com/­latest/­landing.html
DeveloperApache Software Foundation and contributorsApache Software FoundationGigaspaces Technologies
Initial release201220142000
Current release30.0.0, June 20243.5.0 ( 2.13), September 202315.5, September 2020
License infoCommercial or Open SourceOpen Source infoApache license v2Open Source infoApache 2.0Open Source infoApache Version 2; Commercial licenses available
Cloud-based only infoOnly available as a cloud servicenonono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaScalaJava, C++, .Net
Server operating systemsLinux
OS X
Unix
Linux
OS X
Windows
Linux
macOS
Solaris
Windows
Data schemeyes infoschema-less columns are supportedyesschema-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 indexesyesnoyes
SQL infoSupport of SQLSQL for queryingSQL-like DML and DDL statementsSQL-99 for query and DML statements
APIs and other access methodsJDBC
RESTful HTTP/JSON API
JDBC
ODBC
GigaSpaces LRMI
Hibernate
JCache
JDBC
JPA
ODBC
RESTful HTTP API
Spring Data
Supported programming languagesClojure
JavaScript
PHP
Python
R
Ruby
Scala
Java
Python
R
Scala
.Net
C++
Java
Python
Scala
Server-side scripts infoStored proceduresnonoyes
Triggersnonoyes, event driven architecture
Partitioning methods infoMethods for storing different data on different nodesSharding infomanual/auto, time-basedyes, utilizing Spark CoreSharding
Replication methods infoMethods for redundantly storing data on multiple nodesyes, via HDFS, S3 or other storage enginesnoneMulti-source replication infosynchronous or asynchronous
Source-replica replication infosynchronous or asynchronous
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyes infoMap-Reduce pattern can be built with XAP task executors
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency infoConsistency level configurable: ALL, QUORUM, ANY
Foreign keys infoReferential integritynonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonoACID
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 systemnoRole-based access control

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More resources
Apache DruidApache Spark (SQL)GigaSpaces
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