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DBMS > Badger vs. Google Cloud Datastore vs. Hawkular Metrics vs. Microsoft Access vs. TimescaleDB

System Properties Comparison Badger vs. Google Cloud Datastore vs. Hawkular Metrics vs. Microsoft Access vs. TimescaleDB

Editorial information provided by DB-Engines
NameBadger  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonHawkular Metrics  Xexclude from comparisonMicrosoft Access  Xexclude from comparisonTimescaleDB  Xexclude from comparison
DescriptionAn embeddable, persistent, simple and fast Key-Value Store, written purely in Go.Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformHawkular metrics is the metric storage of the Red Hat sponsored Hawkular monitoring system. It is based on Cassandra.Microsoft Access combines a backend RDBMS (JET / ACE Engine) with a GUI frontend for data manipulation and queries. infoThe Access frontend is often used for accessing other datasources (DBMS, Excel, etc.)A time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL
Primary database modelKey-value storeDocument storeTime Series DBMSRelational DBMSTime Series DBMS
Secondary database modelsRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.14
Rank#331  Overall
#49  Key-value stores
Score4.47
Rank#76  Overall
#12  Document stores
Score0.00
Rank#379  Overall
#40  Time Series DBMS
Score104.92
Rank#11  Overall
#8  Relational DBMS
Score4.64
Rank#71  Overall
#4  Time Series DBMS
Websitegithub.com/­dgraph-io/­badgercloud.google.com/­datastorewww.hawkular.orgwww.microsoft.com/­en-us/­microsoft-365/­accesswww.timescale.com
Technical documentationgodoc.org/­github.com/­dgraph-io/­badgercloud.google.com/­datastore/­docswww.hawkular.org/­hawkular-metrics/­docs/­user-guidedeveloper.microsoft.com/­en-us/­accessdocs.timescale.com
DeveloperDGraph LabsGoogleCommunity supported by Red HatMicrosoftTimescale
Initial release20172008201419922017
Current release1902 (16.0.11328.20222), March 20192.15.0, May 2024
License infoCommercial or Open SourceOpen Source infoApache 2.0commercialOpen Source infoApache 2.0commercial infoBundled with Microsoft OfficeOpen Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenoyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageGoJavaC++C
Server operating systemsBSD
Linux
OS X
Solaris
Windows
hostedLinux
OS X
Windows
Windows infoNot a real database server, but making use of DLLsLinux
OS X
Windows
Data schemeschema-freeschema-freeschema-freeyesyes
Typing infopredefined data types such as float or datenoyes, details hereyesyesnumerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data types
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.nononoyes
Secondary indexesnoyesnoyesyes
SQL infoSupport of SQLnoSQL-like query language (GQL)noyes infobut not compliant to any SQL standardyes infofull PostgreSQL SQL syntax
APIs and other access methodsgRPC (using protocol buffers) API
RESTful HTTP/JSON API
HTTP RESTADO.NET
DAO
ODBC
OLE DB
ADO.NET
JDBC
native C library
ODBC
streaming API for large objects
Supported programming languagesGo.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Go
Java
Python
Ruby
C
C#
C++
Delphi
Java (JDBC-ODBC)
VBA
Visual Basic.NET
.Net
C
C++
Delphi
Java infoJDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl
Server-side scripts infoStored proceduresnousing Google App Enginenoyes infosince Access 2010 using the ACE-engineuser defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell
TriggersnoCallbacks using the Google Apps Engineyes infovia Hawkular Alertingyes infosince Access 2010 using the ACE-engineyes
Partitioning methods infoMethods for storing different data on different nodesnoneShardingSharding infobased on Cassandranoneyes, across time and space (hash partitioning) attributes
Replication methods infoMethods for redundantly storing data on multiple nodesnoneMulti-source replication using Paxosselectable replication factor infobased on CassandranoneSource-replica replication with hot standby and reads on replicas info
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyes infousing Google Cloud Dataflownonono
Consistency concepts infoMethods to ensure consistency in a distributed systemnoneImmediate 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.Eventual Consistency infobased on Cassandra
Immediate Consistency infobased on Cassandra
Immediate Consistency
Foreign keys infoReferential integritynoyes infovia ReferenceProperties or Ancestor pathsnoyesyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACID infoSerializable Isolation within Transactions, Read Committed outside of TransactionsnoACID infobut no files for transaction loggingACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes infobut no files for transaction loggingyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nononono
User concepts infoAccess controlnoAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)nono infoa simple user-level security was built in till version Access 2003fine grained access rights according to SQL-standard

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More resources
BadgerGoogle Cloud DatastoreHawkular MetricsMicrosoft AccessTimescaleDB
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