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DBMS > Apache Impala vs. Bangdb vs. Datomic vs. SingleStore

System Properties Comparison Apache Impala vs. Bangdb vs. Datomic vs. SingleStore

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Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisonBangdb  Xexclude from comparisonDatomic  Xexclude from comparisonSingleStore infoformer name was MemSQL  Xexclude from comparison
DescriptionAnalytic DBMS for HadoopConverged and high performance database for device data, events, time series, document and graphDatomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durabilityMySQL wire-compliant distributed RDBMS that combines an in-memory row-oriented and a disc-based column-oriented storage with patented universal storage to handle transactional and analytical workloads in one single table type
Primary database modelRelational DBMSDocument store
Graph DBMS
Time Series DBMS
Relational DBMSRelational DBMS
Secondary database modelsDocument storeSpatial DBMSDocument store
Spatial DBMS
Time Series DBMS
Vector DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score10.63
Rank#40  Overall
#24  Relational DBMS
Score0.07
Rank#346  Overall
#47  Document stores
#35  Graph DBMS
#32  Time Series DBMS
Score1.55
Rank#144  Overall
#67  Relational DBMS
Score4.02
Rank#74  Overall
#39  Relational DBMS
Websiteimpala.apache.orgbangdb.comwww.datomic.comwww.singlestore.com
Technical documentationimpala.apache.org/­impala-docs.htmldocs.bangdb.comdocs.datomic.comdocs.singlestore.com
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaSachin Sinha, BangDBCognitectSingleStore Inc.
Initial release2013201220122013
Current release4.1.0, June 2022BangDB 2.0, October 20211.0.7180, July 20248.5, January 2024
License infoCommercial or Open SourceOpen Source infoApache Version 2Open Source infoBSD 3commercial infolimited edition freecommercial infofree developer edition available
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++C, C++Java, ClojureC++, Go
Server operating systemsLinuxLinuxAll OS with a Java VMLinux info64 bit version required
Data schemeyesschema-freeyesyes
Typing infopredefined data types such as float or dateyesyes: string, long, double, int, geospatial, stream, eventsyesyes
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 indexesyesyes infosecondary, composite, nested, reverse, geospatialyesyes
SQL infoSupport of SQLSQL-like DML and DDL statementsSQL like support with command line toolnoyes infobut no triggers and foreign keys
APIs and other access methodsJDBC
ODBC
Proprietary protocol
RESTful HTTP API
RESTful HTTP APICluster Management API infoas HTTP Rest and CLI
HTTP API
JDBC
MongoDB API
ODBC
Supported programming languagesAll languages supporting JDBC/ODBCC
C#
C++
Java
Python
Clojure
Java
Bash
C
C#
Java
JavaScript (Node.js)
Python
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reducenoyes infoTransaction Functionsyes
Triggersnoyes, Notifications (with Streaming only)By using transaction functionsno
Partitioning methods infoMethods for storing different data on different nodesShardingSharding (enterprise version only). P2P based virtual network overlay with consistent hashing and chord algorithmnone infoBut extensive use of caching in the application peersSharding infohash partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factorselectable replication factor, Knob for CAP (enterprise version only)none infoBut extensive use of caching in the application peersSource-replica replication infostores two copies of each physical data partition on two separate nodes
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReducenonono infocan define user-defined aggregate functions for map-reduce-style calculations
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyTunable consistency, set CAP knob accordinglyImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDACIDACID
Concurrency infoSupport for concurrent manipulation of datayesyes, optimistic concurrency controlyesyes, multi-version concurrency control (MVCC)
Durability infoSupport for making data persistentyesyes, implements WAL (Write ahead log) as wellyes infousing external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)yes infoAll updates are persistent, including those to disk-based columnstores and memory-based row stores. Transaction commits are supported via write-ahead log.
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyes, run db with in-memory only modeyes inforecommended only for testing and developmentyes
User concepts infoAccess controlAccess rights for users, groups and roles infobased on Apache Sentry and Kerberosyes (enterprise version only)noFine grained access control via users, groups and roles

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More resources
Apache ImpalaBangdbDatomicSingleStore infoformer name was MemSQL
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