DB-EnginesExtremeDB for everyone with an RTOSEnglish
Deutsch
Knowledge Base of Relational and NoSQL Database Management Systemsprovided by solid IT

DBMS > Hawkular Metrics vs. Hive vs. Spark SQL vs. Trafodion

System Properties Comparison Hawkular Metrics vs. Hive vs. Spark SQL vs. Trafodion

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines
NameHawkular Metrics  Xexclude from comparisonHive  Xexclude from comparisonSpark SQL  Xexclude from comparisonTrafodion  Xexclude from comparison
Apache Trafodion has been retired in 2021. Therefore it is excluded from the DB-Engines Ranking.
DescriptionHawkular metrics is the metric storage of the Red Hat sponsored Hawkular monitoring system. It is based on Cassandra.data warehouse software for querying and managing large distributed datasets, built on HadoopSpark SQL is a component on top of 'Spark Core' for structured data processingTransactional SQL-on-Hadoop DBMS
Primary database modelTime Series DBMSRelational DBMSRelational DBMSRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.04
Rank#374  Overall
#38  Time Series DBMS
Score62.59
Rank#18  Overall
#12  Relational DBMS
Score19.15
Rank#33  Overall
#20  Relational DBMS
Websitewww.hawkular.orghive.apache.orgspark.apache.org/­sqltrafodion.apache.org
Technical documentationwww.hawkular.org/­hawkular-metrics/­docs/­user-guidecwiki.apache.org/­confluence/­display/­Hive/­Homespark.apache.org/­docs/­latest/­sql-programming-guide.htmltrafodion.apache.org/­documentation.html
DeveloperCommunity supported by Red HatApache Software Foundation infoinitially developed by FacebookApache Software FoundationApache Software Foundation, originally developed by HP
Initial release2014201220142014
Current release3.1.3, April 20223.5.0 ( 2.13), September 20232.3.0, February 2019
License infoCommercial or Open SourceOpen Source infoApache 2.0Open Source infoApache Version 2Open Source infoApache 2.0Open Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaJavaScalaC++, Java
Server operating systemsLinux
OS X
Windows
All OS with a Java VMLinux
OS X
Windows
Linux
Data schemeschema-freeyesyesyes
Typing infopredefined data types such as float or dateyesyesyesyes
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 indexesnoyesnoyes
SQL infoSupport of SQLnoSQL-like DML and DDL statementsSQL-like DML and DDL statementsyes
APIs and other access methodsHTTP RESTJDBC
ODBC
Thrift
JDBC
ODBC
ADO.NET
JDBC
ODBC
Supported programming languagesGo
Java
Python
Ruby
C++
Java
PHP
Python
Java
Python
R
Scala
All languages supporting JDBC/ODBC/ADO.Net
Server-side scripts infoStored proceduresnoyes infouser defined functions and integration of map-reducenoJava Stored Procedures
Triggersyes infovia Hawkular Alertingnonono
Partitioning methods infoMethods for storing different data on different nodesSharding infobased on CassandraShardingyes, utilizing Spark CoreSharding
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factor infobased on Cassandraselectable replication factornoneyes, via HBase
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyes infoquery execution via MapReduceyes infovia user defined functions and HBase
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency infobased on Cassandra
Immediate Consistency infobased on Cassandra
Eventual ConsistencyImmediate Consistency
Foreign keys infoReferential integritynononoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanononoACID
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 controlnoAccess rights for users, groups and rolesnofine grained access rights according to SQL-standard

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources
Hawkular MetricsHiveSpark SQLTrafodion
DB-Engines blog posts

Why is Hadoop not listed in the DB-Engines Ranking?
13 May 2013, Paul Andlinger

show all

Recent citations in the news

Waiting for Red Hat OpenShift 4.0? Too late, 4.1 has already arrived… • DEVCLASS
5 June 2019, DevClass

provided by Google News

Run Apache Hive workloads using Spark SQL with Amazon EMR on EKS | Amazon Web Services
18 October 2023, AWS Blog

Data Engineering in 2024: Predictions For Data Lakes and The Serving Layer
23 January 2024, Datanami

18 Top Big Data Tools and Technologies to Know About in 2024
24 January 2024, TechTarget

Top 80 Hadoop Interview Questions and Answers for 2024
15 February 2024, Simplilearn

What Is Apache Iceberg?
26 February 2024, IBM

provided by Google News

Use Amazon Athena with Spark SQL for your open-source transactional table formats | Amazon Web Services
24 January 2024, AWS Blog

What is Apache Spark? The big data platform that crushed Hadoop
3 April 2024, InfoWorld

Cracking the Apache Spark Interview: 80+ Top Questions and Answers for 2024
1 April 2024, Simplilearn

1.5 Years of Spark Knowledge in 8 Tips | by Michael Berk
23 December 2023, Towards Data Science

Run Apache Hive workloads using Spark SQL with Amazon EMR on EKS | Amazon Web Services
18 October 2023, AWS Blog

provided by Google News

HP Throws Trafodion Hat into OLTP Hadoop Ring
14 July 2014, Datanami

Evaluating HTAP Databases for Machine Learning Applications
2 November 2016, KDnuggets

Low-latency, distributed database architectures are critical for emerging fog applications
7 April 2022, Embedded Computing Design

provided by Google News



Share this page

Featured Products

Milvus logo

Vector database designed for GenAI, fully equipped for enterprise implementation.
Try Managed Milvus for Free

Datastax Astra logo

Bring all your data to Generative AI applications with vector search enabled by the most scalable
vector database available.
Try for Free

Ontotext logo

GraphDB allows you to link diverse data, index it for semantic search and enrich it via text analysis to build big knowledge graphs. Get it free.

SingleStore logo

Build AI apps with Vectors on SQL and JSON with milliseconds response times.
Try it today.

Neo4j logo

See for yourself how a graph database can make your life easier.
Use Neo4j online for free.

Present your product here