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DBMS > Apache Impala vs. Apache IoTDB vs. HBase vs. Riak TS

System Properties Comparison Apache Impala vs. Apache IoTDB vs. HBase vs. Riak TS

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Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisonApache IoTDB  Xexclude from comparisonHBase  Xexclude from comparisonRiak TS  Xexclude from comparison
DescriptionAnalytic DBMS for HadoopAn IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and FlinkWide-column store based on Apache Hadoop and on concepts of BigTableRiak TS is a distributed NoSQL database optimized for time series data and based on Riak KV
Primary database modelRelational DBMSTime Series DBMSWide column storeTime Series DBMS
Secondary database modelsDocument store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score13.77
Rank#40  Overall
#24  Relational DBMS
Score1.18
Rank#173  Overall
#15  Time Series DBMS
Score30.50
Rank#26  Overall
#2  Wide column stores
Score0.20
Rank#319  Overall
#27  Time Series DBMS
Websiteimpala.apache.orgiotdb.apache.orghbase.apache.org
Technical documentationimpala.apache.org/­impala-docs.htmliotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmlhbase.apache.org/­book.htmlwww.tiot.jp/­riak-docs/­riak/­ts/­latest
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaApache Software FoundationApache Software Foundation infoApache top-level project, originally developed by PowersetOpen Source, formerly Basho Technologies
Initial release2013201820082015
Current release4.1.0, June 20221.1.0, April 20232.3.4, January 20213.0.0, September 2022
License infoCommercial or Open SourceOpen Source infoApache Version 2Open Source infoApache Version 2.0Open Source infoApache version 2Open Source
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++JavaJavaErlang
Server operating systemsLinuxAll OS with a Java VM (>= 1.8)Linux
Unix
Windows infousing Cygwin
Linux
OS X
Data schemeyesyesschema-free, schema definition possibleschema-free
Typing infopredefined data types such as float or dateyesyesoptions to bring your own types, AVROno
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 indexesyesyesnorestricted
SQL infoSupport of SQLSQL-like DML and DDL statementsSQL-like query languagenoyes, limited
APIs and other access methodsJDBC
ODBC
JDBC
Native API
Java API
RESTful HTTP API
Thrift
HTTP API
Native Erlang Interface
Supported programming languagesAll languages supporting JDBC/ODBCC
C#
C++
Go
Java
Python
Scala
C
C#
C++
Groovy
Java
PHP
Python
Scala
C infounofficial client library
C#
C++ infounofficial client library
Clojure infounofficial client library
Dart infounofficial client library
Erlang
Go infounofficial client library
Groovy infounofficial client library
Haskell infounofficial client library
Java
JavaScript infounofficial client library
Lisp infounofficial client library
Perl infounofficial client library
PHP
Python
Ruby
Scala infounofficial client library
Smalltalk infounofficial client library
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reduceyesyes infoCoprocessors in JavaErlang
Triggersnoyesyesyes infopre-commit hooks and post-commit hooks
Partitioning methods infoMethods for storing different data on different nodesShardinghorizontal partitioning (by time range) + vertical partitioning (by deviceId)ShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factorselectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicasMulti-source replication
Source-replica replication
selectable replication factor
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReduceIntegration with Hadoop and Sparkyesyes
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyEventual Consistency
Strong Consistency with Raft
Immediate Consistency or Eventual ConsistencyEventual Consistency
Foreign keys infoReferential integritynononono infolinks between datasets can be stored
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonoSingle row ACID (across millions of columns)no
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.noyesyes
User concepts infoAccess controlAccess rights for users, groups and roles infobased on Apache Sentry and KerberosyesAccess Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABACno

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More resources
Apache ImpalaApache IoTDBHBaseRiak TS
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