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DBMS > Apache Impala vs. Datomic vs. GridDB vs. HugeGraph

System Properties Comparison Apache Impala vs. Datomic vs. GridDB vs. HugeGraph

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Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisonDatomic  Xexclude from comparisonGridDB  Xexclude from comparisonHugeGraph  Xexclude from comparison
DescriptionAnalytic DBMS for HadoopDatomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durabilityScalable in-memory time series database optimized for IoT and Big DataA fast-speed and highly-scalable Graph DBMS
Primary database modelRelational DBMSRelational DBMSTime Series DBMSGraph DBMS
Secondary database modelsDocument storeKey-value store
Relational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score12.45
Rank#40  Overall
#24  Relational DBMS
Score1.66
Rank#144  Overall
#66  Relational DBMS
Score2.09
Rank#120  Overall
#10  Time Series DBMS
Score0.17
Rank#335  Overall
#31  Graph DBMS
Websiteimpala.apache.orgwww.datomic.comgriddb.netgithub.com/­hugegraph
hugegraph.apache.org
Technical documentationimpala.apache.org/­impala-docs.htmldocs.datomic.comdocs.griddb.nethugegraph.apache.org/­docs
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaCognitectToshiba CorporationBaidu
Initial release2013201220132018
Current release4.1.0, June 20221.0.7075, December 20235.1, August 20220.9
License infoCommercial or Open SourceOpen Source infoApache Version 2commercial infolimited edition freeOpen Source infoAGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also availableOpen Source infoApache Version 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++Java, ClojureC++Java
Server operating systemsLinuxAll OS with a Java VMLinuxLinux
macOS
Unix
Data schemeyesyesyesyes
Typing infopredefined data types such as float or dateyesyesyes infonumerical, string, blob, geometry, boolean, timestampyes
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.nononono
Secondary indexesyesyesyesyes infoalso supports composite index and range index
SQL infoSupport of SQLSQL-like DML and DDL statementsnoSQL92, SQL-like TQL (Toshiba Query Language)no
APIs and other access methodsJDBC
ODBC
RESTful HTTP APIJDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API
Java API
RESTful HTTP API
TinkerPop Gremlin
Supported programming languagesAll languages supporting JDBC/ODBCClojure
Java
C
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
Groovy
Java
Python
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reduceyes infoTransaction Functionsnoasynchronous Gremlin script jobs
TriggersnoBy using transaction functionsyesno
Partitioning methods infoMethods for storing different data on different nodesShardingnone infoBut extensive use of caching in the application peersShardingyes infodepending on used storage backend, e.g. Cassandra and HBase
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factornone infoBut extensive use of caching in the application peersSource-replica replicationyes infodepending on used storage backend, e.g. Cassandra and HBase
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReducenoConnector for using GridDB as an input source and output destination for Hadoop MapReduce jobsvia hugegraph-spark
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyImmediate ConsistencyImmediate consistency within container, eventual consistency across containersEventual Consistency
Foreign keys infoReferential integritynononoyes infoedges in graph
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDACID at container levelACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyes infousing external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)yesyes
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 developmentyesyes
User concepts infoAccess controlAccess rights for users, groups and roles infobased on Apache Sentry and KerberosnoAccess rights for users can be defined per databaseUsers, roles and permissions
More information provided by the system vendor
Apache ImpalaDatomicGridDBHugeGraph
Specific characteristicsGridDB is a highly scalable, in-memory time series database optimized for IoT and...
» more
Competitive advantages1. Optimized for IoT Equipped with Toshiba's proprietary key-container data model...
» more
Typical application scenariosFactory IoT, Automative Industry, Energy, BEMS, Smart Community, Monitoring system.
» more
Key customersDenso International [see use case ] An Electric Power company [see use case ] Ishinomaki...
» more
Market metricsGitHub trending repository
» more
Licensing and pricing modelsOpen Source license (AGPL v3 & Apache v2) Commercial license (subscription)
» more

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Apache ImpalaDatomicGridDBHugeGraph
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