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

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

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NameApache Druid  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonHBase  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 PlatformWide-column store based on Apache Hadoop and on concepts of BigTableMulti-storage database for search, including full-text search.
Primary database modelRelational DBMS
Time Series DBMS
Document storeWide column storeSearch engine
Secondary database modelsTime Series DBMS infousing the Manticore Columnar Library
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
Score4.47
Rank#76  Overall
#12  Document stores
Score30.50
Rank#26  Overall
#2  Wide column stores
Score0.22
Rank#312  Overall
#21  Search engines
Websitedruid.apache.orgcloud.google.com/­datastorehbase.apache.orgmanticoresearch.com
Technical documentationdruid.apache.org/­docs/­latest/­designcloud.google.com/­datastore/­docshbase.apache.org/­book.htmlmanual.manticoresearch.com
DeveloperApache Software Foundation and contributorsGoogleApache Software Foundation infoApache top-level project, originally developed by PowersetManticore Software
Initial release2012200820082017
Current release29.0.1, April 20242.3.4, January 20216.0, February 2023
License infoCommercial or Open SourceOpen Source infoApache license v2commercialOpen Source infoApache version 2Open Source infoGPL version 2
Cloud-based only infoOnly available as a cloud servicenoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaJavaC++
Server operating systemsLinux
OS X
Unix
hostedLinux
Unix
Windows infousing Cygwin
FreeBSD
Linux
macOS
Windows
Data schemeyes infoschema-less columns are supportedschema-freeschema-free, schema definition possibleFixed schema
Typing infopredefined data types such as float or dateyesyes, details hereoptions to bring your own types, AVROInt, 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 indexesyesyesnoyes infofull-text index on all search fields
SQL infoSupport of SQLSQL for queryingSQL-like query language (GQL)noSQL-like query language
APIs and other access methodsJDBC
RESTful HTTP/JSON API
gRPC (using protocol buffers) API
RESTful HTTP/JSON API
Java API
RESTful HTTP API
Thrift
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
C#
C++
Groovy
Java
PHP
Python
Scala
Elixir
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Server-side scripts infoStored proceduresnousing Google App Engineyes infoCoprocessors in Javauser defined functions
TriggersnoCallbacks using the Google Apps Engineyesno
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 PaxosMulti-source replication
Source-replica replication
Synchronous replication based on Galera library
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyes infousing Google Cloud Dataflowyesno
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
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 TransactionsSingle row ACID (across millions of columns)yes 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
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 Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABACno

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More resources
Apache DruidGoogle Cloud DatastoreHBaseManticore Search
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