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DBMS > Apache Pinot vs. Google Cloud Datastore vs. GridGain vs. Hawkular Metrics vs. STSdb

System Properties Comparison Apache Pinot vs. Google Cloud Datastore vs. GridGain vs. Hawkular Metrics vs. STSdb

Editorial information provided by DB-Engines
NameApache Pinot  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonGridGain  Xexclude from comparisonHawkular Metrics  Xexclude from comparisonSTSdb  Xexclude from comparison
DescriptionRealtime distributed OLAP datastore, designed to answer OLAP queries with low latencyAutomatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformGridGain is an in-memory computing platform, built on Apache IgniteHawkular metrics is the metric storage of the Red Hat sponsored Hawkular monitoring system. It is based on Cassandra.Key-Value Store with special method for indexing infooptimized for high performance using a special indexing method
Primary database modelRelational DBMSDocument storeKey-value store
Relational DBMS
Time Series DBMSKey-value store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.40
Rank#270  Overall
#125  Relational DBMS
Score4.47
Rank#76  Overall
#12  Document stores
Score1.47
Rank#154  Overall
#26  Key-value stores
#72  Relational DBMS
Score0.00
Rank#379  Overall
#40  Time Series DBMS
Score0.04
Rank#360  Overall
#52  Key-value stores
Websitepinot.apache.orgcloud.google.com/­datastorewww.gridgain.comwww.hawkular.orggithub.com/­STSSoft/­STSdb4
Technical documentationdocs.pinot.apache.orgcloud.google.com/­datastore/­docswww.gridgain.com/­docs/­index.htmlwww.hawkular.org/­hawkular-metrics/­docs/­user-guide
DeveloperApache Software Foundation and contributorsGoogleGridGain Systems, Inc.Community supported by Red HatSTS Soft SC
Initial release20152008200720142011
Current release1.0.0, September 2023GridGain 8.5.14.0.8, September 2015
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercialcommercialOpen Source infoApache 2.0Open Source infoGPLv2, 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 languageJavaJava, C++, .NetJavaC#
Server operating systemsAll OS with a Java JDK11 or higherhostedLinux
OS X
Solaris
Windows
Linux
OS X
Windows
Windows
Data schemeyesschema-freeyesschema-freeyes
Typing infopredefined data types such as float or dateyesyes, details hereyesyesyes infoprimitive types and user defined types (classes)
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.noyesno
Secondary indexesyesyesnono
SQL infoSupport of SQLSQL-like query languageSQL-like query language (GQL)ANSI-99 for query and DML statements, subset of DDLnono
APIs and other access methodsJDBCgRPC (using protocol buffers) API
RESTful HTTP/JSON API
HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data
HTTP REST.NET Client API
Supported programming languagesGo
Java
Python
.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
C#
C++
Java
PHP
Python
Ruby
Scala
Go
Java
Python
Ruby
C#
Java
Server-side scripts infoStored proceduresusing Google App Engineyes (compute grid and cache interceptors can be used instead)nono
TriggersCallbacks using the Google Apps Engineyes (cache interceptors and events)yes infovia Hawkular Alertingno
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioningShardingShardingSharding infobased on Cassandranone
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication using Paxosyes (replicated cache)selectable replication factor infobased on Cassandranone
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infousing Google Cloud Dataflowyes (compute grid and hadoop accelerator)nono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate 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 ConsistencyEventual Consistency infobased on Cassandra
Immediate Consistency infobased on Cassandra
Foreign keys infoReferential integrityyes infovia ReferenceProperties or Ancestor pathsnonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACID infoSerializable Isolation within Transactions, Read Committed outside of TransactionsACIDnono
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
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.noyesno
User concepts infoAccess controlAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Security Hooks for custom implementationsnono

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More resources
Apache PinotGoogle Cloud DatastoreGridGainHawkular MetricsSTSdb
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