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DBMS > Apache Druid vs. Google Cloud Datastore vs. Kinetica vs. Manticore Search

System Properties Comparison Apache Druid vs. Google Cloud Datastore vs. Kinetica vs. Manticore Search

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Editorial information provided by DB-Engines
NameApache Druid  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonKinetica  Xexclude from comparisonManticore Search  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 PlatformFully vectorized database across both GPUs and CPUsMulti-storage database for search, including full-text search.
Primary database modelRelational DBMS
Time Series DBMS
Document storeRelational DBMSSearch engine
Secondary database modelsSpatial DBMS
Time Series DBMS
Time Series DBMS infousing the Manticore Columnar Library
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
Score4.36
Rank#72  Overall
#12  Document stores
Score0.66
Rank#234  Overall
#107  Relational DBMS
Score0.29
Rank#302  Overall
#21  Search engines
Websitedruid.apache.orgcloud.google.com/­datastorewww.kinetica.commanticoresearch.com
Technical documentationdruid.apache.org/­docs/­latest/­designcloud.google.com/­datastore/­docsdocs.kinetica.commanual.manticoresearch.com
DeveloperApache Software Foundation and contributorsGoogleKineticaManticore Software
Initial release2012200820122017
Current release29.0.1, April 20247.1, August 20216.0, February 2023
License infoCommercial or Open SourceOpen Source infoApache license v2commercialcommercialOpen Source infoGPL version 2
Cloud-based only infoOnly available as a cloud servicenoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaC, C++C++
Server operating systemsLinux
OS X
Unix
hostedLinuxFreeBSD
Linux
macOS
Windows
Data schemeyes infoschema-less columns are supportedschema-freeyesFixed schema
Typing infopredefined data types such as float or dateyesyes, details hereyesInt, Bigint, Float, Timestamp, Bit, Int array, Bigint array, JSON, Boolean
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.nononoCan index from XML
Secondary indexesyesyesyesyes infofull-text index on all search fields
SQL infoSupport of SQLSQL for queryingSQL-like query language (GQL)SQL-like DML and DDL statementsSQL-like query language
APIs and other access methodsJDBC
RESTful HTTP/JSON API
gRPC (using protocol buffers) API
RESTful HTTP/JSON API
JDBC
ODBC
RESTful HTTP API
Binary API
RESTful HTTP/JSON API
RESTful HTTP/SQL API
SQL over MySQL
Supported programming languagesClojure
JavaScript
PHP
Python
R
Ruby
Scala
.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
C++
Java
JavaScript (Node.js)
Python
Elixir
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Server-side scripts infoStored proceduresnousing Google App Engineuser defined functionsuser defined functions
TriggersnoCallbacks using the Google Apps Engineyes infotriggers when inserted values for one or more columns fall within a specified rangeno
Partitioning methods infoMethods for storing different data on different nodesSharding infomanual/auto, time-basedShardingShardingSharding infoPartitioning is done manually, search queries against distributed index is supported
Replication methods infoMethods for redundantly storing data on multiple nodesyes, via HDFS, S3 or other storage enginesMulti-source replication using PaxosSource-replica replicationSynchronous replication based on Galera library
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyes infousing Google Cloud Dataflownono
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 or Eventual Consistency depending on configuration
Foreign keys infoReferential integritynoyes infovia ReferenceProperties or Ancestor pathsyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACID infoSerializable Isolation within Transactions, Read Committed outside of Transactionsnoyes infoisolated transactions for atomic changes and binary logging for safe writes
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes infoThe original contents of fields are not stored in the Manticore index.
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonoyes infoGPU vRAM or System RAM
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 and roles on table levelno

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More resources
Apache DruidGoogle Cloud DatastoreKineticaManticore Search
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