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DBMS > Apache Impala vs. Hypertable vs. Riak KV vs. Spark SQL

System Properties Comparison Apache Impala vs. Hypertable vs. Riak KV vs. Spark SQL

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Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisonHypertable  Xexclude from comparisonRiak KV  Xexclude from comparisonSpark SQL  Xexclude from comparison
Hypertable has stopped its further development with March 2016 and is removed from the DB-Engines ranking.
DescriptionAnalytic DBMS for HadoopAn open source BigTable implementation based on distributed file systems such as HadoopDistributed, fault tolerant key-value storeSpark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelRelational DBMSWide column storeKey-value store infowith links between data sets and object tags for the creation of secondary indexesRelational 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
Score4.10
Rank#82  Overall
#9  Key-value stores
Score18.96
Rank#33  Overall
#20  Relational DBMS
Websiteimpala.apache.orgspark.apache.org/­sql
Technical documentationimpala.apache.org/­impala-docs.htmlwww.tiot.jp/­riak-docs/­riak/­kv/­latestspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaHypertable Inc.OpenSource, formerly Basho TechnologiesApache Software Foundation
Initial release2013200920092014
Current release4.1.0, June 20220.9.8.11, March 20163.2.0, December 20223.5.0 ( 2.13), September 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2Open Source infoGNU version 3. Commercial license availableOpen Source infoApache version 2, commercial enterprise editionOpen Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++C++ErlangScala
Server operating systemsLinuxLinux
OS X
Windows infoan inofficial Windows port is available
Linux
OS X
Linux
OS X
Windows
Data schemeyesschema-freeschema-freeyes
Typing infopredefined data types such as float or dateyesnonoyes
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 indexesyesrestricted infoonly exact value or prefix value scansrestrictedno
SQL infoSupport of SQLSQL-like DML and DDL statementsnonoSQL-like DML and DDL statements
APIs and other access methodsJDBC
ODBC
C++ API
Thrift
HTTP API
Native Erlang Interface
JDBC
ODBC
Supported programming languagesAll languages supporting JDBC/ODBCC++
Java
Perl
PHP
Python
Ruby
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
Java
Python
R
Scala
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reducenoErlangno
Triggersnonoyes infopre-commit hooks and post-commit hooksno
Partitioning methods infoMethods for storing different data on different nodesShardingShardingSharding infono "single point of failure"yes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factorselectable replication factor on file system levelselectable replication factornone
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReduceyesyes
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyImmediate ConsistencyEventual Consistency
Foreign keys infoReferential integritynonono infolinks between data sets can be storedno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanononono
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.nono
User concepts infoAccess controlAccess rights for users, groups and roles infobased on Apache Sentry and Kerberosnoyes, using Riak Securityno

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More resources
Apache ImpalaHypertableRiak KVSpark SQL
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