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DBMS > Drizzle vs. Google Cloud Bigtable vs. Hazelcast vs. Hive

System Properties Comparison Drizzle vs. Google Cloud Bigtable vs. Hazelcast vs. Hive

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Editorial information provided by DB-Engines
NameDrizzle  Xexclude from comparisonGoogle Cloud Bigtable  Xexclude from comparisonHazelcast  Xexclude from comparisonHive  Xexclude from comparison
Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.
DescriptionMySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.A widely adopted in-memory data griddata warehouse software for querying and managing large distributed datasets, built on Hadoop
Primary database modelRelational DBMSKey-value store
Wide column store
Key-value storeRelational DBMS
Secondary database modelsDocument store infoJSON support with IMDG 3.12
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score3.15
Rank#95  Overall
#14  Key-value stores
#8  Wide column stores
Score5.46
Rank#61  Overall
#7  Key-value stores
Score59.76
Rank#18  Overall
#12  Relational DBMS
Websitecloud.google.com/­bigtablehazelcast.comhive.apache.org
Technical documentationcloud.google.com/­bigtable/­docshazelcast.org/­imdg/­docscwiki.apache.org/­confluence/­display/­Hive/­Home
DeveloperDrizzle project, originally started by Brian AkerGoogleHazelcastApache Software Foundation infoinitially developed by Facebook
Initial release2008201520082012
Current release7.2.4, September 20125.3.6, November 20233.1.3, April 2022
License infoCommercial or Open SourceOpen Source infoGNU GPLcommercialOpen Source infoApache Version 2; commercial licenses availableOpen Source infoApache Version 2
Cloud-based only infoOnly available as a cloud servicenoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++JavaJava
Server operating systemsFreeBSD
Linux
OS X
hostedAll OS with a Java VMAll OS with a Java VM
Data schemeyesschema-freeschema-freeyes
Typing infopredefined data types such as float or dateyesnoyesyes
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.noyes infothe object must implement a serialization strategy
Secondary indexesyesnoyesyes
SQL infoSupport of SQLyes infowith proprietary extensionsnoSQL-like query languageSQL-like DML and DDL statements
APIs and other access methodsJDBCgRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)
JCache
JPA
Memcached protocol
RESTful HTTP API
JDBC
ODBC
Thrift
Supported programming languagesC
C++
Java
PHP
C#
C++
Go
Java
JavaScript (Node.js)
Python
.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala
C++
Java
PHP
Python
Server-side scripts infoStored proceduresnonoyes infoEvent Listeners, Executor Servicesyes infouser defined functions and integration of map-reduce
Triggersno infohooks for callbacks inside the server can be used.noyes infoEventsno
Partitioning methods infoMethods for storing different data on different nodesShardingShardingShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication
Source-replica replication
Internal replication in Colossus, and regional replication between two clusters in different zonesyes infoReplicated Mapselectable replication factor
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyesyesyes infoquery execution via MapReduce
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)Immediate Consistency or Eventual Consistency selectable by user infoRaft Consensus AlgorithmEventual Consistency
Foreign keys infoReferential integrityyesnonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDAtomic single-row operationsone or two-phase-commit; repeatable reads; read commitedno
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.noyes
User concepts infoAccess controlPluggable authentication mechanisms infoe.g. LDAP, HTTPAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Role-based access controlAccess rights for users, groups and roles

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More resources
DrizzleGoogle Cloud BigtableHazelcastHive
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