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DBMS > Badger vs. Hawkular Metrics vs. Speedb vs. Vertica

System Properties Comparison Badger vs. Hawkular Metrics vs. Speedb vs. Vertica

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Editorial information provided by DB-Engines
NameBadger  Xexclude from comparisonHawkular Metrics  Xexclude from comparisonSpeedb  Xexclude from comparisonVertica infoOpenText™ Vertica™  Xexclude from comparison
DescriptionAn embeddable, persistent, simple and fast Key-Value Store, written purely in Go.Hawkular metrics is the metric storage of the Red Hat sponsored Hawkular monitoring system. It is based on Cassandra.An embeddable, high performance key-value store optimized for write-intensive workloads, which can be used as a drop-in replacement for RocksDBCloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.
Primary database modelKey-value storeTime Series DBMSKey-value storeRelational DBMS infoColumn oriented
Secondary database modelsSpatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.14
Rank#331  Overall
#49  Key-value stores
Score0.00
Rank#379  Overall
#40  Time Series DBMS
Score0.21
Rank#315  Overall
#45  Key-value stores
Score10.68
Rank#43  Overall
#27  Relational DBMS
Websitegithub.com/­dgraph-io/­badgerwww.hawkular.orgwww.speedb.iowww.vertica.com
Technical documentationgodoc.org/­github.com/­dgraph-io/­badgerwww.hawkular.org/­hawkular-metrics/­docs/­user-guidevertica.com/­documentation
DeveloperDGraph LabsCommunity supported by Red HatSpeedbOpenText infopreviously Micro Focus and Hewlett Packard
Initial release2017201420202005
Current release12.0.3, January 2023
License infoCommercial or Open SourceOpen Source infoApache 2.0Open Source infoApache 2.0Open Source infoApache Version 2.0; commercial license availablecommercial infoLimited community edition free
Cloud-based only infoOnly available as a cloud servicenononono infoon-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageGoJavaC++C++
Server operating systemsBSD
Linux
OS X
Solaris
Windows
Linux
OS X
Windows
Linux
Windows
Linux
Data schemeschema-freeschema-freeschema-freeYes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.
Typing infopredefined data types such as float or datenoyesnoyes
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.nononono
Secondary indexesnononoNo Indexes Required. Different internal optimization strategy, but same functionality included.
SQL infoSupport of SQLnononoFull 1999 standard plus machine learning, time series and geospatial. Over 650 functions.
APIs and other access methodsHTTP RESTADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL infocharacter-based, interactive, front-end utility
Supported programming languagesGoGo
Java
Python
Ruby
C
C++
Go
Java
Perl
Python
Ruby
C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Server-side scripts infoStored proceduresnononoyes, PostgreSQL PL/pgSQL, with minor differences
Triggersnoyes infovia Hawkular Alertingyes, called Custom Alerts
Partitioning methods infoMethods for storing different data on different nodesnoneSharding infobased on Cassandrahorizontal partitioninghorizontal partitioning, hierarchical partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesnoneselectable replication factor infobased on CassandrayesMulti-source replication infoOne, or more copies of data replicated across nodes, or object-store used for repository.
MapReduce infoOffers an API for user-defined Map/Reduce methodsnononono infoBi-directional Spark integration
Consistency concepts infoMethods to ensure consistency in a distributed systemnoneEventual Consistency infobased on Cassandra
Immediate Consistency infobased on Cassandra
Immediate Consistency
Foreign keys infoReferential integritynononoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonoyesACID
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.nonoyesno
User concepts infoAccess controlnononofine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash
More information provided by the system vendor
BadgerHawkular MetricsSpeedbVertica infoOpenText™ Vertica™
Specific characteristicsSpeedb is an embedded key-value storage engine for versatile use cases. It was designed...
» more
Deploy-anywhere database for large-scale analytical deployments. Deploy off-cloud,...
» more
Competitive advantagesSpeedb Open-source rebases on RocksDB's latest versions, with enhanced capabilities...
» more
Fast, scalable, and capable of high concurrency. Separation of compute/storage leverages...
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Typical application scenariosCommunication and network analytics, Embedded analytics, Fraud monitoring and Risk...
» more
Key customersAbiba Systems, Adform, adMarketplace, AmeriPride, Anritsu, AOL, Avito, Auckland Transport,...
» more
Licensing and pricing modelsOpen source - Speedb OSS is released under an Apache license and can be found on...
» more
Cost-based models and subscription-based models are both available. One license is...
» more

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BadgerHawkular MetricsSpeedbVertica infoOpenText™ Vertica™
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