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DBMS > Apache Impala vs. Hawkular Metrics vs. Spark SQL vs. TimescaleDB vs. Tkrzw

System Properties Comparison Apache Impala vs. Hawkular Metrics vs. Spark SQL vs. TimescaleDB vs. Tkrzw

Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisonHawkular Metrics  Xexclude from comparisonSpark SQL  Xexclude from comparisonTimescaleDB  Xexclude from comparisonTkrzw infoSuccessor of Tokyo Cabinet and Kyoto Cabinet  Xexclude from comparison
DescriptionAnalytic DBMS for HadoopHawkular metrics is the metric storage of the Red Hat sponsored Hawkular monitoring system. It is based on Cassandra.Spark SQL is a component on top of 'Spark Core' for structured data processingA time series DBMS optimized for fast ingest and complex queries, based on PostgreSQLA concept of libraries, allowing an application program to store and query key-value pairs in a file. Successor of Tokyo Cabinet and Kyoto Cabinet
Primary database modelRelational DBMSTime Series DBMSRelational DBMSTime Series DBMSKey-value store
Secondary database modelsDocument storeRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score13.77
Rank#40  Overall
#24  Relational DBMS
Score0.00
Rank#379  Overall
#40  Time Series DBMS
Score18.96
Rank#33  Overall
#20  Relational DBMS
Score4.64
Rank#71  Overall
#4  Time Series DBMS
Score0.00
Rank#383  Overall
#60  Key-value stores
Websiteimpala.apache.orgwww.hawkular.orgspark.apache.org/­sqlwww.timescale.comdbmx.net/­tkrzw
Technical documentationimpala.apache.org/­impala-docs.htmlwww.hawkular.org/­hawkular-metrics/­docs/­user-guidespark.apache.org/­docs/­latest/­sql-programming-guide.htmldocs.timescale.com
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaCommunity supported by Red HatApache Software FoundationTimescaleMikio Hirabayashi
Initial release20132014201420172020
Current release4.1.0, June 20223.5.0 ( 2.13), September 20232.15.0, May 20240.9.3, August 2020
License infoCommercial or Open SourceOpen Source infoApache Version 2Open Source infoApache 2.0Open Source infoApache 2.0Open Source infoApache 2.0Open Source infoApache Version 2.0
Cloud-based only infoOnly available as a cloud servicenonononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++JavaScalaCC++
Server operating systemsLinuxLinux
OS X
Windows
Linux
OS X
Windows
Linux
OS X
Windows
Linux
macOS
Data schemeyesschema-freeyesyesschema-free
Typing infopredefined data types such as float or dateyesyesyesnumerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data typesno
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.nononoyesno
Secondary indexesyesnonoyes
SQL infoSupport of SQLSQL-like DML and DDL statementsnoSQL-like DML and DDL statementsyes infofull PostgreSQL SQL syntaxno
APIs and other access methodsJDBC
ODBC
HTTP RESTJDBC
ODBC
ADO.NET
JDBC
native C library
ODBC
streaming API for large objects
Supported programming languagesAll languages supporting JDBC/ODBCGo
Java
Python
Ruby
Java
Python
R
Scala
.Net
C
C++
Delphi
Java infoJDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl
C++
Java
Python
Ruby
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reducenonouser defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shellno
Triggersnoyes infovia Hawkular Alertingnoyesno
Partitioning methods infoMethods for storing different data on different nodesShardingSharding infobased on Cassandrayes, utilizing Spark Coreyes, across time and space (hash partitioning) attributesnone
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factorselectable replication factor infobased on CassandranoneSource-replica replication with hot standby and reads on replicas infonone
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReducenonono
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyEventual Consistency infobased on Cassandra
Immediate Consistency infobased on Cassandra
Immediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynononoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanononoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonononoyes infousing specific database classes
User concepts infoAccess controlAccess rights for users, groups and roles infobased on Apache Sentry and Kerberosnonofine grained access rights according to SQL-standardno

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More resources
Apache ImpalaHawkular MetricsSpark SQLTimescaleDBTkrzw infoSuccessor of Tokyo Cabinet and Kyoto Cabinet
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