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DBMS > Apache Phoenix vs. Bangdb vs. Google Cloud Datastore vs. InfinityDB

System Properties Comparison Apache Phoenix vs. Bangdb vs. Google Cloud Datastore vs. InfinityDB

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Editorial information provided by DB-Engines
NameApache Phoenix  Xexclude from comparisonBangdb  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonInfinityDB  Xexclude from comparison
DescriptionA scale-out RDBMS with evolutionary schema built on Apache HBaseConverged and high performance database for device data, events, time series, document and graphAutomatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformA Java embedded Key-Value Store which extends the Java Map interface
Primary database modelRelational DBMSDocument store
Graph DBMS
Time Series DBMS
Document storeKey-value store
Secondary database modelsSpatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.97
Rank#126  Overall
#59  Relational DBMS
Score0.08
Rank#347  Overall
#47  Document stores
#34  Graph DBMS
#31  Time Series DBMS
Score4.47
Rank#76  Overall
#12  Document stores
Score0.00
Rank#378  Overall
#57  Key-value stores
Websitephoenix.apache.orgbangdb.comcloud.google.com/­datastoreboilerbay.com
Technical documentationphoenix.apache.orgdocs.bangdb.comcloud.google.com/­datastore/­docsboilerbay.com/­infinitydb/­manual
DeveloperApache Software FoundationSachin Sinha, BangDBGoogleBoiler Bay Inc.
Initial release2014201220082002
Current release5.0-HBase2, July 2018 and 4.15-HBase1, December 2019BangDB 2.0, October 20214.0
License infoCommercial or Open SourceOpen Source infoApache Version 2.0Open Source infoBSD 3commercialcommercial
Cloud-based only infoOnly available as a cloud servicenonoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaC, C++Java
Server operating systemsLinux
Unix
Windows
LinuxhostedAll OS with a Java VM
Data schemeyes infolate-bound, schema-on-read capabilitiesschema-freeschema-freeyes infonested virtual Java Maps, multi-value, logical ‘tuple space’ runtime Schema upgrade
Typing infopredefined data types such as float or dateyesyes: string, long, double, int, geospatial, stream, eventsyes, details hereyes infoall Java primitives, Date, CLOB, BLOB, huge sparse arrays
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 indexesyesyes infosecondary, composite, nested, reverse, geospatialyesno infomanual creation possible, using inversions based on multi-value capability
SQL infoSupport of SQLyesSQL like support with command line toolSQL-like query language (GQL)no
APIs and other access methodsJDBCProprietary protocol
RESTful HTTP API
gRPC (using protocol buffers) API
RESTful HTTP/JSON API
Access via java.util.concurrent.ConcurrentNavigableMap Interface
Proprietary API to InfinityDB ItemSpace (boilerbay.com/­docs/­ItemSpaceDataStructures.htm)
Supported programming languagesC
C#
C++
Go
Groovy
Java
PHP
Python
Scala
C
C#
C++
Java
Python
.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Java
Server-side scripts infoStored proceduresuser defined functionsnousing Google App Engineno
Triggersnoyes, Notifications (with Streaming only)Callbacks using the Google Apps Engineno
Partitioning methods infoMethods for storing different data on different nodesShardingSharding (enterprise version only). P2P based virtual network overlay with consistent hashing and chord algorithmShardingnone
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication
Source-replica replication
selectable replication factor, Knob for CAP (enterprise version only)Multi-source replication using Paxosnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsHadoop integrationnoyes infousing Google Cloud Dataflowno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual ConsistencyTunable consistency, set CAP knob accordinglyImmediate 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 infoREAD-COMMITTED or SERIALIZED
Foreign keys infoReferential integritynonoyes infovia ReferenceProperties or Ancestor pathsno infomanual creation possible, using inversions based on multi-value capability
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDACID infoSerializable Isolation within Transactions, Read Committed outside of TransactionsACID infoOptimistic locking for transactions; no isolation for bulk loads
Concurrency infoSupport for concurrent manipulation of datayesyes, optimistic concurrency controlyesyes
Durability infoSupport for making data persistentyesyes, implements WAL (Write ahead log) as wellyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyes, run db with in-memory only modenono
User concepts infoAccess controlAccess Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancyyes (enterprise version only)Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)no

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More resources
Apache PhoenixBangdbGoogle Cloud DatastoreInfinityDB
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