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DBMS > Drizzle vs. Hawkular Metrics vs. Pinecone vs. Vertica

System Properties Comparison Drizzle vs. Hawkular Metrics vs. Pinecone vs. Vertica

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Editorial information provided by DB-Engines
NameDrizzle  Xexclude from comparisonHawkular Metrics  Xexclude from comparisonPinecone  Xexclude from comparisonVertica infoOpenText™ Vertica™  Xexclude from comparison
Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.
DescriptionMySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.Hawkular metrics is the metric storage of the Red Hat sponsored Hawkular monitoring system. It is based on Cassandra.A managed, cloud-native vector databaseCloud 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 modelRelational DBMSTime Series DBMSVector DBMSRelational DBMS infoColumn oriented
Secondary database modelsSpatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.08
Rank#366  Overall
#39  Time Series DBMS
Score3.23
Rank#92  Overall
#3  Vector DBMS
Score10.06
Rank#42  Overall
#26  Relational DBMS
Websitewww.hawkular.orgwww.pinecone.iowww.vertica.com
Technical documentationwww.hawkular.org/­hawkular-metrics/­docs/­user-guidedocs.pinecone.io/­docs/­overviewvertica.com/­documentation
DeveloperDrizzle project, originally started by Brian AkerCommunity supported by Red HatPinecone Systems, IncOpenText infopreviously Micro Focus and Hewlett Packard
Initial release2008201420192005
Current release7.2.4, September 201212.0.3, January 2023
License infoCommercial or Open SourceOpen Source infoGNU GPLOpen Source infoApache 2.0commercialcommercial infoLimited community edition free
Cloud-based only infoOnly available as a cloud servicenonoyesno 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 languageC++JavaC++
Server operating systemsFreeBSD
Linux
OS X
Linux
OS X
Windows
hostedLinux
Data schemeyesschema-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 dateyesyesString, Number, Booleanyes
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.nonono
Secondary indexesyesnoNo Indexes Required. Different internal optimization strategy, but same functionality included.
SQL infoSupport of SQLyes infowith proprietary extensionsnonoFull 1999 standard plus machine learning, time series and geospatial. Over 650 functions.
APIs and other access methodsJDBCHTTP RESTRESTful HTTP APIADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL infocharacter-based, interactive, front-end utility
Supported programming languagesC
C++
Java
PHP
Go
Java
Python
Ruby
PythonC#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Server-side scripts infoStored proceduresnonoyes, PostgreSQL PL/pgSQL, with minor differences
Triggersno infohooks for callbacks inside the server can be used.yes infovia Hawkular Alertingyes, called Custom Alerts
Partitioning methods infoMethods for storing different data on different nodesShardingSharding infobased on Cassandrahorizontal partitioning, hierarchical partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication
Source-replica replication
selectable replication factor infobased on CassandraMulti-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 systemEventual Consistency infobased on Cassandra
Immediate Consistency infobased on Cassandra
Immediate Consistency
Foreign keys infoReferential integrityyesnoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnoACID
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.nonono
User concepts infoAccess controlPluggable authentication mechanisms infoe.g. LDAP, HTTPnofine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash
More information provided by the system vendor
DrizzleHawkular MetricsPineconeVertica infoOpenText™ Vertica™
Specific characteristicsDeploy-anywhere database for large-scale analytical deployments. Deploy off-cloud,...
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Competitive advantagesFast, 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...
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Key customersAbiba Systems, Adform, adMarketplace, AmeriPride, Anritsu, AOL, Avito, Auckland Transport,...
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Licensing and pricing modelsCost-based models and subscription-based models are both available. One license is...
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DrizzleHawkular MetricsPineconeVertica infoOpenText™ Vertica™
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