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DBMS > Apache Impala vs. Datomic vs. Hypertable

System Properties Comparison Apache Impala vs. Datomic vs. Hypertable

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Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisonDatomic  Xexclude from comparisonHypertable  Xexclude from comparison
Hypertable has stopped its further development with March 2016 and is removed from the DB-Engines ranking.
DescriptionAnalytic DBMS for HadoopDatomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durabilityAn open source BigTable implementation based on distributed file systems such as Hadoop
Primary database modelRelational DBMSRelational DBMSWide column store
Secondary database modelsDocument store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score14.03
Rank#40  Overall
#24  Relational DBMS
Score1.76
Rank#145  Overall
#66  Relational DBMS
Websiteimpala.apache.orgwww.datomic.com
Technical documentationimpala.apache.org/­impala-docs.htmldocs.datomic.com
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaCognitectHypertable Inc.
Initial release201320122009
Current release4.1.0, June 20221.0.6735, June 20230.9.8.11, March 2016
License infoCommercial or Open SourceOpen Source infoApache Version 2commercial infolimited edition freeOpen Source infoGNU version 3. 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++Java, ClojureC++
Server operating systemsLinuxAll OS with a Java VMLinux
OS X
Windows infoan inofficial Windows port is available
Data schemeyesyesschema-free
Typing infopredefined data types such as float or dateyesyesno
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 indexesyesyesrestricted infoonly exact value or prefix value scans
SQL infoSupport of SQLSQL-like DML and DDL statementsnono
APIs and other access methodsJDBC
ODBC
RESTful HTTP APIC++ API
Thrift
Supported programming languagesAll languages supporting JDBC/ODBCClojure
Java
C++
Java
Perl
PHP
Python
Ruby
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reduceyes infoTransaction Functionsno
TriggersnoBy using transaction functionsno
Partitioning methods infoMethods for storing different data on different nodesShardingnone infoBut extensive use of caching in the application peersSharding
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factornone infoBut extensive use of caching in the application peersselectable replication factor on file system level
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReducenoyes
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDno
Concurrency infoSupport for concurrent manipulation of datayesyesyes
Durability infoSupport for making data persistentyesyes infousing external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)yes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyes inforecommended only for testing and development
User concepts infoAccess controlAccess rights for users, groups and roles infobased on Apache Sentry and Kerberosnono

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More resources
Apache ImpalaDatomicHypertable
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