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DBMS > Apache IoTDB vs. Drizzle vs. Geode vs. Google Cloud Bigtable vs. Heroic

System Properties Comparison Apache IoTDB vs. Drizzle vs. Geode vs. Google Cloud Bigtable vs. Heroic

Editorial information provided by DB-Engines
NameApache IoTDB  Xexclude from comparisonDrizzle  Xexclude from comparisonGeode  Xexclude from comparisonGoogle Cloud Bigtable  Xexclude from comparisonHeroic  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.
DescriptionAn IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and FlinkMySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.Geode is a distributed data container, pooling memory, CPU, network resources, and optionally local disk across multiple processesGoogle's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch
Primary database modelTime Series DBMSRelational DBMSKey-value storeKey-value store
Wide column store
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.18
Rank#173  Overall
#15  Time Series DBMS
Score1.92
Rank#131  Overall
#23  Key-value stores
Score3.26
Rank#92  Overall
#13  Key-value stores
#8  Wide column stores
Score0.51
Rank#255  Overall
#21  Time Series DBMS
Websiteiotdb.apache.orggeode.apache.orgcloud.google.com/­bigtablegithub.com/­spotify/­heroic
Technical documentationiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmlgeode.apache.org/­docscloud.google.com/­bigtable/­docsspotify.github.io/­heroic
DeveloperApache Software FoundationDrizzle project, originally started by Brian AkerOriginally developed by Gemstone. They outsourced the project to Apache in 2015 but still deliver a commercial version as Gemfire.GoogleSpotify
Initial release20182008200220152014
Current release1.1.0, April 20237.2.4, September 20121.1, February 2017
License infoCommercial or Open SourceOpen Source infoApache Version 2.0Open Source infoGNU GPLOpen Source infoApache Version 2; commercial licenses available as GemfirecommercialOpen Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenononoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaC++JavaJava
Server operating systemsAll OS with a Java VM (>= 1.8)FreeBSD
Linux
OS X
All OS with a Java VM infothe JDK (8 or later) is also requiredhosted
Data schemeyesyesschema-freeschema-freeschema-free
Typing infopredefined data types such as float or dateyesyesyesnoyes
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 indexesyesyesnonoyes infovia Elasticsearch
SQL infoSupport of SQLSQL-like query languageyes infowith proprietary extensionsSQL-like query language (OQL)nono
APIs and other access methodsJDBC
Native API
JDBCJava Client API
Memcached protocol
RESTful HTTP API
gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)
HQL (Heroic Query Language, a JSON-based language)
HTTP API
Supported programming languagesC
C#
C++
Go
Java
Python
Scala
C
C++
Java
PHP
.Net
All JVM based languages
C++
Groovy
Java
Scala
C#
C++
Go
Java
JavaScript (Node.js)
Python
Server-side scripts infoStored proceduresyesnouser defined functionsnono
Triggersyesno infohooks for callbacks inside the server can be used.yes infoCache Event Listenersnono
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioning (by time range) + vertical partitioning (by deviceId)ShardingShardingShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicasMulti-source replication
Source-replica replication
Multi-source replicationInternal replication in Colossus, and regional replication between two clusters in different zonesyes
MapReduce infoOffers an API for user-defined Map/Reduce methodsIntegration with Hadoop and Sparknonoyesno
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Strong Consistency with Raft
Eventual ConsistencyImmediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)Eventual Consistency
Immediate Consistency
Foreign keys infoReferential integritynoyesnonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDyes, on a single nodeAtomic single-row operationsno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyesnono
User concepts infoAccess controlyesPluggable authentication mechanisms infoe.g. LDAP, HTTPAccess rights per client and object definableAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

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More resources
Apache IoTDBDrizzleGeodeGoogle Cloud BigtableHeroic
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