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DBMS > Apache Phoenix vs. Google Cloud Bigtable vs. HugeGraph vs. InterSystems Caché vs. IRONdb

System Properties Comparison Apache Phoenix vs. Google Cloud Bigtable vs. HugeGraph vs. InterSystems Caché vs. IRONdb

Editorial information provided by DB-Engines
NameApache Phoenix  Xexclude from comparisonGoogle Cloud Bigtable  Xexclude from comparisonHugeGraph  Xexclude from comparisonInterSystems Caché  Xexclude from comparisonIRONdb  Xexclude from comparison
Caché is a deprecated database engine which is substituted with InterSystems IRIS. It therefore is removed from the DB-Engines Ranking.IRONdb seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.
DescriptionA scale-out RDBMS with evolutionary schema built on Apache HBaseGoogle's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.A fast-speed and highly-scalable Graph DBMSA multi-model DBMS and application serverA distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicity
Primary database modelRelational DBMSKey-value store
Wide column store
Graph DBMSKey-value store
Object oriented DBMS
Relational DBMS
Time Series DBMS
Secondary database modelsDocument store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.97
Rank#126  Overall
#59  Relational DBMS
Score3.26
Rank#92  Overall
#13  Key-value stores
#8  Wide column stores
Score0.13
Rank#336  Overall
#32  Graph DBMS
Websitephoenix.apache.orgcloud.google.com/­bigtablegithub.com/­hugegraph
hugegraph.apache.org
www.intersystems.com/­products/­cachewww.circonus.com/solutions/time-series-database/
Technical documentationphoenix.apache.orgcloud.google.com/­bigtable/­docshugegraph.apache.org/­docsdocs.intersystems.comdocs.circonus.com/irondb/category/getting-started
DeveloperApache Software FoundationGoogleBaiduInterSystemsCirconus LLC.
Initial release20142015201819972017
Current release5.0-HBase2, July 2018 and 4.15-HBase1, December 20190.92018.1.4, May 2020V0.10.20, January 2018
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercialOpen Source infoApache Version 2.0commercialcommercial
Cloud-based only infoOnly available as a cloud servicenoyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaJavaC and C++
Server operating systemsLinux
Unix
Windows
hostedLinux
macOS
Unix
AIX
HP Open VMS
HP-UX
Linux
OS X
Solaris
Windows
Linux
Data schemeyes infolate-bound, schema-on-read capabilitiesschema-freeyesdepending on used data modelschema-free
Typing infopredefined data types such as float or dateyesnoyesyesyes infotext, numeric, histograms
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.nononoyesno
Secondary indexesyesnoyes infoalso supports composite index and range indexyesno
SQL infoSupport of SQLyesnonoyesSQL-like query language (Circonus Analytics Query Language: CAQL)
APIs and other access methodsJDBCgRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)
Java API
RESTful HTTP API
TinkerPop Gremlin
.NET Client API
JDBC
ODBC
RESTful HTTP API
HTTP API
Supported programming languagesC
C#
C++
Go
Groovy
Java
PHP
Python
Scala
C#
C++
Go
Java
JavaScript (Node.js)
Python
Groovy
Java
Python
C#
C++
Java
.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala
Server-side scripts infoStored proceduresuser defined functionsnoasynchronous Gremlin script jobsyesyes, in Lua
Triggersnononoyesno
Partitioning methods infoMethods for storing different data on different nodesShardingShardingyes infodepending on used storage backend, e.g. Cassandra and HBasenoneAutomatic, metric affinity per node
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 infodepending on used storage backend, e.g. Cassandra and HBaseSource-replica replicationconfigurable replication factor, datacenter aware
MapReduce infoOffers an API for user-defined Map/Reduce methodsHadoop integrationyesvia hugegraph-sparknono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual ConsistencyImmediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)Eventual ConsistencyImmediate ConsistencyImmediate consistency per node, eventual consistency across nodes
Foreign keys infoReferential integritynonoyes infoedges in graphyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDAtomic single-row operationsACIDACIDno
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.yesnoyesyesno
User concepts infoAccess controlAccess Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancyAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Users, roles and permissionsAccess rights for users, groups and rolesno

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More resources
Apache PhoenixGoogle Cloud BigtableHugeGraphInterSystems CachéIRONdb
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