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DBMS > Apache Impala vs. Datomic vs. Transwarp StellarDB

System Properties Comparison Apache Impala vs. Datomic vs. Transwarp StellarDB

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Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisonDatomic  Xexclude from comparisonTranswarp StellarDB  Xexclude from comparison
DescriptionAnalytic DBMS for HadoopDatomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durabilityA distributed graph DBMS built for enterprise-level graph applications
Primary database modelRelational DBMSRelational DBMSGraph 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
Score1.59
Rank#150  Overall
#69  Relational DBMS
Score0.00
Rank#383  Overall
#39  Graph DBMS
Websiteimpala.apache.orgwww.datomic.comwww.transwarp.cn/­en/­product/­stellardb
Technical documentationimpala.apache.org/­impala-docs.htmldocs.datomic.com
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaCognitectTranswarp
Initial release20132012
Current release4.1.0, June 20221.0.6735, June 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2commercial infolimited edition freecommercial
Cloud-based only infoOnly available as a cloud servicenonono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++Java, Clojure
Server operating systemsLinuxAll OS with a Java VM
Data schemeyesyes
Typing infopredefined data types such as float or dateyesyes
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
SQL infoSupport of SQLSQL-like DML and DDL statementsnoSQL-like query language
APIs and other access methodsJDBC
ODBC
RESTful HTTP APIOpenCypher
Supported programming languagesAll languages supporting JDBC/ODBCClojure
Java
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reduceyes infoTransaction Functions
TriggersnoBy using transaction functions
Partitioning methods infoMethods for storing different data on different nodesShardingnone infoBut extensive use of caching in the application peershorizontal partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factornone infoBut extensive use of caching in the application peers
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReduceno
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyImmediate Consistency
Foreign keys infoReferential integritynono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACID
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 Kerberosnoyes

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More resources
Apache ImpalaDatomicTranswarp StellarDB
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