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DBMS > Apache Impala vs. Apache IoTDB vs. IBM Db2 Event Store vs. Riak TS

System Properties Comparison Apache Impala vs. Apache IoTDB vs. IBM Db2 Event Store vs. Riak TS

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Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisonApache IoTDB  Xexclude from comparisonIBM Db2 Event Store  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 FlinkDistributed Event Store optimized for Internet of Things use casesRiak TS is a distributed NoSQL database optimized for time series data and based on Riak KV
Primary database modelRelational DBMSTime Series DBMSEvent Store
Time Series DBMS
Time 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
Score0.19
Rank#323  Overall
#2  Event Stores
#28  Time Series DBMS
Score0.20
Rank#319  Overall
#27  Time Series DBMS
Websiteimpala.apache.orgiotdb.apache.orgwww.ibm.com/­products/­db2-event-store
Technical documentationimpala.apache.org/­impala-docs.htmliotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmlwww.ibm.com/­docs/­en/­db2-event-storewww.tiot.jp/­riak-docs/­riak/­ts/­latest
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaApache Software FoundationIBMOpen Source, formerly Basho Technologies
Initial release2013201820172015
Current release4.1.0, June 20221.1.0, April 20232.03.0.0, September 2022
License infoCommercial or Open SourceOpen Source infoApache Version 2Open Source infoApache Version 2.0commercial infofree developer edition availableOpen Source
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++JavaC and C++Erlang
Server operating systemsLinuxAll OS with a Java VM (>= 1.8)Linux infoLinux, macOS, Windows for the developer additionLinux
OS X
Data schemeyesyesyesschema-free
Typing infopredefined data types such as float or dateyesyesyesno
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 languageyes infothrough the embedded Spark runtimeyes, limited
APIs and other access methodsJDBC
ODBC
JDBC
Native API
ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API
HTTP API
Native Erlang Interface
Supported programming languagesAll languages supporting JDBC/ODBCC
C#
C++
Go
Java
Python
Scala
C
C#
C++
Cobol
Delphi
Fortran
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Scala
Visual Basic
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-reduceyesyesErlang
Triggersnoyesnoyes 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 replicasActive-active shard replicationselectable replication factor
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReduceIntegration with Hadoop and Sparknoyes
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyEventual Consistency
Strong Consistency with Raft
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 datanononono
Concurrency infoSupport for concurrent manipulation of datayesyesNo - written data is immutableyes
Durability infoSupport for making data persistentyesyesYes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storageyes
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 Kerberosyesfine grained access rights according to SQL-standardno

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More resources
Apache ImpalaApache IoTDBIBM Db2 Event StoreRiak TS
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