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DBMS > Ehcache vs. GeoSpock vs. Google Cloud Datastore vs. Lovefield vs. Teradata Aster

System Properties Comparison Ehcache vs. GeoSpock vs. Google Cloud Datastore vs. Lovefield vs. Teradata Aster

Editorial information provided by DB-Engines
NameEhcache  Xexclude from comparisonGeoSpock  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonLovefield  Xexclude from comparisonTeradata Aster  Xexclude from comparison
GeoSpock seems to be discontinued. Therefore it will be excluded from the DB-Engines ranking.Teradata Aster has been integrated into other Teradata systems and therefore will be removed from the DB-Engines ranking.
DescriptionA widely adopted Java cache with tiered storage optionsSpatial and temporal data processing engine for extreme data scaleAutomatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformEmbeddable relational database for web apps written in pure JavaScriptPlatform for big data analytics on multistructured data sources and types
Primary database modelKey-value storeRelational DBMSDocument storeRelational DBMSRelational DBMS
Secondary database modelsTime Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score4.64
Rank#68  Overall
#8  Key-value stores
Score4.36
Rank#72  Overall
#12  Document stores
Score0.33
Rank#286  Overall
#131  Relational DBMS
Websitewww.ehcache.orggeospock.comcloud.google.com/­datastoregoogle.github.io/­lovefield
Technical documentationwww.ehcache.org/­documentationcloud.google.com/­datastore/­docsgithub.com/­google/­lovefield/­blob/­master/­docs/­spec_index.md
DeveloperTerracotta Inc, owned by Software AGGeoSpockGoogleGoogleTeradata
Initial release2009200820142005
Current release3.10.0, March 20222.0, September 20192.1.12, February 2017
License infoCommercial or Open SourceOpen Source infoApache Version 2; commercial licenses availablecommercialcommercialOpen Source infoApache 2.0commercial
Cloud-based only infoOnly available as a cloud servicenoyesyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaJava, JavascriptJavaScript
Server operating systemsAll OS with a Java VMhostedhostedserver-less, requires a JavaScript environment (browser, Node.js) infotested with Chrome, Firefox, IE, SafariLinux
Data schemeschema-freeyesschema-freeyesFlexible Schema (defined schema, partial schema, schema free) infodefined schema within the relational store; partial schema or schema free in the Aster File Store
Typing infopredefined data types such as float or dateyesyesyes, details hereyesyes
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.nonononoyes infoin Aster File Store
Secondary indexesnotemporal, categoricalyesyesyes
SQL infoSupport of SQLnoANSI SQL for query only (using Presto)SQL-like query language (GQL)SQL-like query language infovia JavaScript builder patternyes
APIs and other access methodsJCacheJDBCgRPC (using protocol buffers) API
RESTful HTTP/JSON API
ADO.NET
JDBC
ODBC
OLE DB
Supported programming languagesJava.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
JavaScriptC
C#
C++
Java
Python
R
Server-side scripts infoStored proceduresnonousing Google App EnginenoR packages
Triggersyes infoCache Event ListenersnoCallbacks using the Google Apps EngineUsing read-only observersno
Partitioning methods infoMethods for storing different data on different nodesSharding infoby using Terracotta ServerAutomatic shardingShardingnoneSharding
Replication methods infoMethods for redundantly storing data on multiple nodesyes infoby using Terracotta ServerMulti-source replication using Paxosnoneyes infoDimension tables are replicated across all nodes in the cluster. The number of replicas for the file store can be configured.
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonoyes infousing Google Cloud Dataflownoyes infoSQL Map-Reduce Framework
Consistency concepts infoMethods to ensure consistency in a distributed systemTunable Consistency (Strong, Eventual, Weak)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.Immediate Consistency or Eventual Consistency depending on configuration
Foreign keys infoReferential integritynonoyes infovia ReferenceProperties or Ancestor pathsyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datayes infosupports JTA and can work as an XA resourcenoACID infoSerializable Isolation within Transactions, Read Committed outside of TransactionsACIDACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyes infousing a tiered cache-storage approachyesyesyes, by using IndexedDB or the cloud service Firebase Realtime Databaseyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesnonoyes infousing MemoryDBno
User concepts infoAccess controlnoAccess rights for users can be defined per tableAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)nofine grained access rights according to SQL-standard

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More resources
EhcacheGeoSpockGoogle Cloud DatastoreLovefieldTeradata Aster
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