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DBMS > Apache Impala vs. Quasardb vs. Splice Machine

System Properties Comparison Apache Impala vs. Quasardb vs. Splice Machine

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Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisonQuasardb  Xexclude from comparisonSplice Machine  Xexclude from comparison
DescriptionAnalytic DBMS for HadoopDistributed, high-performance timeseries databaseOpen-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark
Primary database modelRelational DBMSTime Series DBMSRelational 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
Score0.14
Rank#332  Overall
#29  Time Series DBMS
Score0.54
Rank#250  Overall
#114  Relational DBMS
Websiteimpala.apache.orgquasar.aisplicemachine.com
Technical documentationimpala.apache.org/­impala-docs.htmldoc.quasar.ai/­mastersplicemachine.com/­how-it-works
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaquasardbSplice Machine
Initial release201320092014
Current release4.1.0, June 20223.14.1, January 20243.1, March 2021
License infoCommercial or Open SourceOpen Source infoApache Version 2commercial infoFree community edition, Non-profit organizations and non-commercial usage are eligible for free licensesOpen Source infoAGPL 3.0, commercial license available
Cloud-based only infoOnly available as a cloud servicenonono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++C++Java
Server operating systemsLinuxBSD
Linux
OS X
Windows
Linux
OS X
Solaris
Windows
Data schemeyesschema-freeyes
Typing infopredefined data types such as float or dateyesyes infointeger and binaryyes
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.nono
Secondary indexesyesyes infowith tagsyes
SQL infoSupport of SQLSQL-like DML and DDL statementsSQL-like query languageyes
APIs and other access methodsJDBC
ODBC
HTTP APIJDBC
Native Spark Datasource
ODBC
Supported programming languagesAll languages supporting JDBC/ODBC.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R
C#
C++
Java
JavaScript (Node.js)
Python
R
Scala
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reducenoyes infoJava
Triggersnonoyes
Partitioning methods infoMethods for storing different data on different nodesShardingSharding infoconsistent hashingShared Nothhing Auto-Sharding, Columnar Partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factorSource-replica replication with selectable replication factorMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReducewith Hadoop integrationYes, via Full Spark Integration
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynonoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDACID
Concurrency infoSupport for concurrent manipulation of datayesyesyes, multi-version concurrency control (MVCC)
Durability infoSupport for making data persistentyesyes infoby using LevelDByes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyes infoTransient modeyes
User concepts infoAccess controlAccess rights for users, groups and roles infobased on Apache Sentry and KerberosCryptographically strong user authentication and audit trailAccess rights for users, groups and roles according to SQL-standard

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More resources
Apache ImpalaQuasardbSplice Machine
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