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DBMS > Apache Impala vs. Ignite vs. Microsoft Azure AI Search vs. Riak TS

System Properties Comparison Apache Impala vs. Ignite vs. Microsoft Azure AI Search vs. Riak TS

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Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisonIgnite  Xexclude from comparisonMicrosoft Azure AI Search  Xexclude from comparisonRiak TS  Xexclude from comparison
DescriptionAnalytic DBMS for HadoopApache Ignite is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads, delivering in-memory speeds at petabyte scale.Search-as-a-service for web and mobile app developmentRiak TS is a distributed NoSQL database optimized for time series data and based on Riak KV
Primary database modelRelational DBMSKey-value store
Relational DBMS
Search engineTime Series DBMS
Secondary database modelsDocument storeVector DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score12.45
Rank#40  Overall
#24  Relational DBMS
Score3.11
Rank#96  Overall
#15  Key-value stores
#49  Relational DBMS
Score5.52
Rank#59  Overall
#6  Search engines
Score0.28
Rank#307  Overall
#27  Time Series DBMS
Websiteimpala.apache.orgignite.apache.orgazure.microsoft.com/­en-us/­services/­search
Technical documentationimpala.apache.org/­impala-docs.htmlapacheignite.readme.io/­docslearn.microsoft.com/­en-us/­azure/­searchwww.tiot.jp/­riak-docs/­riak/­ts/­latest
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaApache Software FoundationMicrosoftOpen Source, formerly Basho Technologies
Initial release2013201520152015
Current release4.1.0, June 2022Apache Ignite 2.6V13.0.0, September 2022
License infoCommercial or Open SourceOpen Source infoApache Version 2Open Source infoApache 2.0commercialOpen Source
Cloud-based only infoOnly available as a cloud servicenonoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC++C++, Java, .NetErlang
Server operating systemsLinuxLinux
OS X
Solaris
Windows
hostedLinux
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.noyesnono
Secondary indexesyesyesyesrestricted
SQL infoSupport of SQLSQL-like DML and DDL statementsANSI-99 for query and DML statements, subset of DDLnoyes, limited
APIs and other access methodsJDBC
ODBC
HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data
RESTful HTTP APIHTTP API
Native Erlang Interface
Supported programming languagesAll languages supporting JDBC/ODBCC#
C++
Java
PHP
Python
Ruby
Scala
C#
Java
JavaScript
Python
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-reduceyes (compute grid and cache interceptors can be used instead)noErlang
Triggersnoyes (cache interceptors and events)noyes infopre-commit hooks and post-commit hooks
Partitioning methods infoMethods for storing different data on different nodesShardingShardingSharding infoImplicit feature of the cloud serviceSharding
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factoryes (replicated cache)yes infoImplicit feature of the cloud serviceselectable replication factor
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReduceyes (compute grid and hadoop accelerator)noyes
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyImmediate ConsistencyImmediate ConsistencyEventual Consistency
Foreign keys infoReferential integritynononono infolinks between datasets can be stored
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDnono
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.noyesno
User concepts infoAccess controlAccess rights for users, groups and roles infobased on Apache Sentry and KerberosSecurity Hooks for custom implementationsyes infousing Azure authenticationno

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More resources
Apache ImpalaIgniteMicrosoft Azure AI SearchRiak TS
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