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DBMS > Apache Druid vs. Google Cloud Datastore vs. GridDB vs. SwayDB

System Properties Comparison Apache Druid vs. Google Cloud Datastore vs. GridDB vs. SwayDB

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Editorial information provided by DB-Engines
NameApache Druid  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonGridDB  Xexclude from comparisonSwayDB  Xexclude from comparison
DescriptionOpen-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality dataAutomatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformScalable in-memory time series database optimized for IoT and Big DataAn embeddable, non-blocking, type-safe key-value store for single or multiple disks and in-memory storage
Primary database modelRelational DBMS
Time Series DBMS
Document storeTime Series DBMSKey-value store
Secondary database modelsKey-value store
Relational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score3.29
Rank#95  Overall
#49  Relational DBMS
#7  Time Series DBMS
Score4.49
Rank#79  Overall
#12  Document stores
Score2.02
Rank#132  Overall
#11  Time Series DBMS
Score0.04
Rank#373  Overall
#57  Key-value stores
Websitedruid.apache.orgcloud.google.com/­datastoregriddb.netswaydb.simer.au
Technical documentationdruid.apache.org/­docs/­latest/­designcloud.google.com/­datastore/­docsdocs.griddb.net
DeveloperApache Software Foundation and contributorsGoogleToshiba CorporationSimer Plaha
Initial release2012200820132018
Current release29.0.1, April 20245.1, August 2022
License infoCommercial or Open SourceOpen Source infoApache license v2commercialOpen Source infoAGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also availableOpen Source infoGNU Affero GPL V3.0
Cloud-based only infoOnly available as a cloud servicenoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaC++Scala
Server operating systemsLinux
OS X
Unix
hostedLinux
Data schemeyes infoschema-less columns are supportedschema-freeyesschema-free
Typing infopredefined data types such as float or dateyesyes, details hereyes infonumerical, string, blob, geometry, boolean, timestampno
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 queryingSQL-like query language (GQL)SQL92, SQL-like TQL (Toshiba Query Language)no
APIs and other access methodsJDBC
RESTful HTTP/JSON API
gRPC (using protocol buffers) API
RESTful HTTP/JSON API
JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API
Supported programming languagesClojure
JavaScript
PHP
Python
R
Ruby
Scala
.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
C
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
Java
Kotlin
Scala
Server-side scripts infoStored proceduresnousing Google App Enginenono
TriggersnoCallbacks using the Google Apps Engineyesno
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 enginesMulti-source replication using PaxosSource-replica replicationnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyes infousing Google Cloud DataflowConnector for using GridDB as an input source and output destination for Hadoop MapReduce jobsno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency or Eventual Consistency depending on type of query and configuration infoStrong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.Immediate consistency within container, eventual consistency across containersImmediate Consistency
Foreign keys infoReferential integritynoyes infovia ReferenceProperties or Ancestor pathsnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACID infoSerializable Isolation within Transactions, Read Committed outside of TransactionsACID at container levelAtomic execution of operations
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.nonoyesyes
User concepts infoAccess controlRBAC using LDAP or Druid internals for users and groups for read/write by datasource and systemAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Access rights for users can be defined per databaseno
More information provided by the system vendor
Apache DruidGoogle Cloud DatastoreGridDBSwayDB
Specific characteristicsGridDB is a highly scalable, in-memory time series database optimized for IoT and...
» more
Competitive advantages1. Optimized for IoT Equipped with Toshiba's proprietary key-container data model...
» more
Typical application scenariosFactory IoT, Automative Industry, Energy, BEMS, Smart Community, Monitoring system.
» more
Key customersDenso International [see use case ] An Electric Power company [see use case ] Ishinomaki...
» more
Market metricsGitHub trending repository
» more
Licensing and pricing modelsOpen Source license (AGPL v3 & Apache v2) Commercial license (subscription)
» 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 DruidGoogle Cloud DatastoreGridDBSwayDB
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