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DBMS > Fujitsu Enterprise Postgres vs. Google Cloud Spanner vs. Hawkular Metrics vs. Sphinx vs. Splice Machine

System Properties Comparison Fujitsu Enterprise Postgres vs. Google Cloud Spanner vs. Hawkular Metrics vs. Sphinx vs. Splice Machine

Editorial information provided by DB-Engines
NameFujitsu Enterprise Postgres  Xexclude from comparisonGoogle Cloud Spanner  Xexclude from comparisonHawkular Metrics  Xexclude from comparisonSphinx  Xexclude from comparisonSplice Machine  Xexclude from comparison
DescriptionEnterprise-grade PostgreSQL-based DBMS with security enhancements such as Transparent Data Encryption and Data Masking, plus high-availability and performance improvement features.A horizontally scalable, globally consistent, relational database service. It is the externalization of the core Google database that runs the biggest aspects of Google, like Ads and Google Play.Hawkular metrics is the metric storage of the Red Hat sponsored Hawkular monitoring system. It is based on Cassandra.Open source search engine for searching in data from different sources, e.g. relational databasesOpen-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark
Primary database modelRelational DBMSRelational DBMSTime Series DBMSSearch engineRelational DBMS
Secondary database modelsDocument store
Spatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.22
Rank#304  Overall
#137  Relational DBMS
Score2.69
Rank#101  Overall
#51  Relational DBMS
Score0.01
Rank#377  Overall
#39  Time Series DBMS
Score5.97
Rank#56  Overall
#5  Search engines
Score0.54
Rank#244  Overall
#114  Relational DBMS
Websitewww.postgresql.fastware.comcloud.google.com/­spannerwww.hawkular.orgsphinxsearch.comsplicemachine.com
Technical documentationwww.postgresql.fastware.com/­product-manualscloud.google.com/­spanner/­docswww.hawkular.org/­hawkular-metrics/­docs/­user-guidesphinxsearch.com/­docssplicemachine.com/­how-it-works
DeveloperPostgreSQL Global Development Group, Fujitsu Australia Software TechnologyGoogleCommunity supported by Red HatSphinx Technologies Inc.Splice Machine
Initial release2017201420012014
Current releaseFujitsu Enterprise Postgres 14, January 20223.5.1, February 20233.1, March 2021
License infoCommercial or Open SourcecommercialcommercialOpen Source infoApache 2.0Open Source infoGPL version 2, commercial licence availableOpen Source infoAGPL 3.0, commercial license available
Cloud-based only infoOnly available as a cloud servicenoyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageCJavaC++Java
Server operating systemsLinux
Windows
hostedLinux
OS X
Windows
FreeBSD
Linux
NetBSD
OS X
Solaris
Windows
Linux
OS X
Solaris
Windows
Data schemeyesyesschema-freeyesyes
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.nono
Secondary indexesyesyesnoyes infofull-text index on all search fieldsyes
SQL infoSupport of SQLyesyes infoQuery statements complying to ANSI 2011noSQL-like query language (SphinxQL)yes
APIs and other access methodsADO.NET
JDBC
native C library
ODBC
streaming API for large objects
gRPC (using protocol buffers) API
JDBC infoAt present, JDBC supports read-only queries. No support for DDL or DML statements.
RESTful HTTP API
HTTP RESTProprietary protocolJDBC
Native Spark Datasource
ODBC
Supported programming languages.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl
Go
Java
JavaScript (Node.js)
Python
Go
Java
Python
Ruby
C++ infounofficial client library
Java
Perl infounofficial client library
PHP
Python
Ruby infounofficial client library
C#
C++
Java
JavaScript (Node.js)
Python
R
Scala
Server-side scripts infoStored proceduresuser defined functionsnononoyes infoJava
Triggersyesnoyes infovia Hawkular Alertingnoyes
Partitioning methods infoMethods for storing different data on different nodespartitioning by range, list and by hashShardingSharding infobased on CassandraSharding infoPartitioning is done manually, search queries against distributed index is supportedShared Nothhing Auto-Sharding, Columnar Partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationMulti-source replication with 3 replicas for regional instances.selectable replication factor infobased on CassandranoneMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyes infousing Google Cloud DataflownonoYes, via Full Spark Integration
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyEventual Consistency infobased on Cassandra
Immediate Consistency infobased on Cassandra
Immediate Consistency
Foreign keys infoReferential integrityyesyes infoby using interleaved tables, this features focuses more on performance improvements than on referential integritynonoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACID infoStrict serializable isolationnonoACID
Concurrency infoSupport for concurrent manipulation of datayes, multi-version concurrency control (MVCC)yesyesyesyes, multi-version concurrency control (MVCC)
Durability infoSupport for making data persistentyesyesyesyes infoThe original contents of fields are not stored in the Sphinx index.yes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonoyes
User concepts infoAccess controlfine grained access rights according to SQL-standardAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)nonoAccess rights for users, groups and roles according to SQL-standard

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More resources
Fujitsu Enterprise PostgresGoogle Cloud SpannerHawkular MetricsSphinxSplice Machine
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