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DBMS > Apache Impala vs. GeoSpock vs. Greenplum

System Properties Comparison Apache Impala vs. GeoSpock vs. Greenplum

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Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisonGeoSpock  Xexclude from comparisonGreenplum  Xexclude from comparison
GeoSpock seems to be discontinued. Therefore it will be excluded from the DB-Engines ranking.
DescriptionAnalytic DBMS for HadoopSpatial and temporal data processing engine for extreme data scaleAnalytic Database platform built on PostgreSQL. Full name is Pivotal Greenplum Database infoA logical database in Greenplum is an array of individual PostgreSQL databases working together to present a single database image.
Primary database modelRelational DBMSRelational DBMSRelational DBMS
Secondary database modelsDocument storeTime Series DBMSDocument store
Spatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score14.03
Rank#40  Overall
#24  Relational DBMS
Score9.23
Rank#47  Overall
#30  Relational DBMS
Websiteimpala.apache.orggeospock.comgreenplum.org
Technical documentationimpala.apache.org/­impala-docs.htmldocs.greenplum.org
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaGeoSpockPivotal Software Inc.
Initial release20132005
Current release4.1.0, June 20222.0, September 20197.0.0, September 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2commercialOpen Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++Java, Javascript
Server operating systemsLinuxhostedLinux
Data schemeyesyesyes
Typing infopredefined data types such as float or dateyesyesyes
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.nonoyes infosince Version 4.2
Secondary indexesyestemporal, categoricalyes
SQL infoSupport of SQLSQL-like DML and DDL statementsANSI SQL for query only (using Presto)yes
APIs and other access methodsJDBC
ODBC
JDBCJDBC
ODBC
Supported programming languagesAll languages supporting JDBC/ODBCC
Java
Perl
Python
R
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reducenoyes
Triggersnonoyes
Partitioning methods infoMethods for storing different data on different nodesShardingAutomatic shardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factorSource-replica replication
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 integritynonoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyes
Durability infoSupport for making data persistentyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonono
User concepts infoAccess controlAccess rights for users, groups and roles infobased on Apache Sentry and KerberosAccess rights for users can be defined per tablefine grained access rights according to SQL-standard

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More resources
Apache ImpalaGeoSpockGreenplum
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