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DBMS > Apache Impala vs. Apache IoTDB vs. Google Cloud Spanner vs. Kdb

System Properties Comparison Apache Impala vs. Apache IoTDB vs. Google Cloud Spanner vs. Kdb

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Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisonApache IoTDB  Xexclude from comparisonGoogle Cloud Spanner  Xexclude from comparisonKdb  Xexclude from comparison
DescriptionAnalytic DBMS for HadoopAn IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and FlinkA horizontally scalable, globally consistent, relational database service. It is the externalization of the core Google database that runs the biggest aspects of Google, like Ads and Google Play.High performance Time Series DBMS
Primary database modelRelational DBMSTime Series DBMSRelational DBMSTime Series DBMS
Vector DBMS
Secondary database modelsDocument storeRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score13.77
Rank#40  Overall
#24  Relational DBMS
Score1.18
Rank#173  Overall
#15  Time Series DBMS
Score2.89
Rank#103  Overall
#52  Relational DBMS
Score7.55
Rank#53  Overall
#3  Time Series DBMS
#1  Vector DBMS
Websiteimpala.apache.orgiotdb.apache.orgcloud.google.com/­spannerkx.com
Technical documentationimpala.apache.org/­impala-docs.htmliotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmlcloud.google.com/­spanner/­docscode.kx.com
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaApache Software FoundationGoogleKx Systems, a division of First Derivatives plc
Initial release2013201820172000 infokdb was released 2000, kdb+ in 2003
Current release4.1.0, June 20221.1.0, April 20233.6, May 2018
License infoCommercial or Open SourceOpen Source infoApache Version 2Open Source infoApache Version 2.0commercialcommercial infofree 32-bit version
Cloud-based only infoOnly available as a cloud servicenonoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++Javaq
Server operating systemsLinuxAll OS with a Java VM (>= 1.8)hostedLinux
OS X
Solaris
Windows
Data schemeyesyesyesyes
Typing infopredefined data types such as float or dateyesyesyesyes
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.nononoyes
Secondary indexesyesyesyesyes infotable attribute 'grouped'
SQL infoSupport of SQLSQL-like DML and DDL statementsSQL-like query languageyes infoQuery statements complying to ANSI 2011SQL-like query language (q)
APIs and other access methodsJDBC
ODBC
JDBC
Native API
gRPC (using protocol buffers) API
JDBC infoAt present, JDBC supports read-only queries. No support for DDL or DML statements.
RESTful HTTP API
HTTP API
JDBC
Jupyter
Kafka
ODBC
WebSocket
Supported programming languagesAll languages supporting JDBC/ODBCC
C#
C++
Go
Java
Python
Scala
Go
Java
JavaScript (Node.js)
Python
C
C#
C++
Go
J
Java
JavaScript
Lua
MatLab
Perl
PHP
Python
R
Scala
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reduceyesnouser defined functions
Triggersnoyesnoyes infowith views
Partitioning methods infoMethods for storing different data on different nodesShardinghorizontal partitioning (by time range) + vertical partitioning (by deviceId)Shardinghorizontal partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factorselectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicasMulti-source replication with 3 replicas for regional instances.Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReduceIntegration with Hadoop and Sparkyes infousing Google Cloud Dataflowno infosimilar paradigm used for internal processing
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyEventual Consistency
Strong Consistency with Raft
Immediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynonoyes infoby using interleaved tables, this features focuses more on performance improvements than on referential integrityyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonoACID infoStrict serializable isolationno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesnoyes
User concepts infoAccess controlAccess rights for users, groups and roles infobased on Apache Sentry and KerberosyesAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)rights management via user accounts
More information provided by the system vendor
Apache ImpalaApache IoTDBGoogle Cloud SpannerKdb
Specific characteristicsIntegrated columnar database & programming system for streaming, real time and historical...
» more
Competitive advantagesprovides seamless scalability; runs on industry standard server platforms; is top-ranked...
» more
Typical application scenariostick database streaming sensor data massive intelligence applications oil and gas...
» more
Key customersGoldman Sachs Morgan Stanley Merrill Lynch J.P. Morgan Deutsche Bank IEX Securities...
» more
Market metricskdb+ performance and reliability proven by our customers in critical infrastructure...
» more
Licensing and pricing modelsupon request
» more

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More resources
Apache ImpalaApache IoTDBGoogle Cloud SpannerKdb
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