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DBMS > Blueflood vs. Drizzle vs. Fujitsu Enterprise Postgres vs. Google Cloud Datastore

System Properties Comparison Blueflood vs. Drizzle vs. Fujitsu Enterprise Postgres vs. Google Cloud Datastore

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Editorial information provided by DB-Engines
NameBlueflood  Xexclude from comparisonDrizzle  Xexclude from comparisonFujitsu Enterprise Postgres  Xexclude from comparisonGoogle Cloud Datastore  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.
DescriptionScalable TimeSeries DBMS based on CassandraMySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.Enterprise-grade PostgreSQL-based DBMS with security enhancements such as Transparent Data Encryption and Data Masking, plus high-availability and performance improvement features.Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform
Primary database modelTime Series DBMSRelational DBMSRelational DBMSDocument store
Secondary database modelsDocument store
Spatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.13
Rank#346  Overall
#33  Time Series DBMS
Score0.37
Rank#278  Overall
#128  Relational DBMS
Score4.36
Rank#72  Overall
#12  Document stores
Websiteblueflood.iowww.postgresql.fastware.comcloud.google.com/­datastore
Technical documentationgithub.com/­rax-maas/­blueflood/­wikiwww.postgresql.fastware.com/­product-manualscloud.google.com/­datastore/­docs
DeveloperRackspaceDrizzle project, originally started by Brian AkerPostgreSQL Global Development Group, Fujitsu Australia Software TechnologyGoogle
Initial release201320082008
Current release7.2.4, September 2012Fujitsu Enterprise Postgres 14, January 2022
License infoCommercial or Open SourceOpen Source infoApache 2.0Open Source infoGNU GPLcommercialcommercial
Cloud-based only infoOnly available as a cloud servicenononoyes
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaC++C
Server operating systemsLinux
OS X
FreeBSD
Linux
OS X
Linux
Windows
hosted
Data schemepredefined schemeyesyesschema-free
Typing infopredefined data types such as float or dateyesyesyesyes, details here
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.nono
Secondary indexesnoyesyesyes
SQL infoSupport of SQLnoyes infowith proprietary extensionsyesSQL-like query language (GQL)
APIs and other access methodsHTTP RESTJDBCADO.NET
JDBC
native C library
ODBC
streaming API for large objects
gRPC (using protocol buffers) API
RESTful HTTP/JSON API
Supported programming languagesC
C++
Java
PHP
.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl
.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Server-side scripts infoStored proceduresnonouser defined functionsusing Google App Engine
Triggersnono infohooks for callbacks inside the server can be used.yesCallbacks using the Google Apps Engine
Partitioning methods infoMethods for storing different data on different nodesSharding infobased on CassandraShardingpartitioning by range, list and by hashSharding
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factor infobased on CassandraMulti-source replication
Source-replica replication
Source-replica replicationMulti-source replication using Paxos
MapReduce infoOffers an API for user-defined Map/Reduce methodsnononoyes infousing Google Cloud Dataflow
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency infobased on Cassandra
Immediate Consistency infobased on Cassandra
Immediate 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.
Foreign keys infoReferential integritynoyesyesyes infovia ReferenceProperties or Ancestor paths
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDACIDACID infoSerializable Isolation within Transactions, Read Committed outside of Transactions
Concurrency infoSupport for concurrent manipulation of datayesyesyes, multi-version concurrency control (MVCC)yes
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.nono
User concepts infoAccess controlnoPluggable authentication mechanisms infoe.g. LDAP, HTTPfine grained access rights according to SQL-standardAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

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More resources
BluefloodDrizzleFujitsu Enterprise PostgresGoogle Cloud Datastore
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