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DBMS > Apache Impala vs. InfluxDB vs. PouchDB vs. TimescaleDB

System Properties Comparison Apache Impala vs. InfluxDB vs. PouchDB vs. TimescaleDB

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Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisonInfluxDB  Xexclude from comparisonPouchDB  Xexclude from comparisonTimescaleDB  Xexclude from comparison
DescriptionAnalytic DBMS for HadoopDBMS for storing time series, events and metricsJavaScript DBMS with an API inspired by CouchDBA time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL
Primary database modelRelational DBMSTime Series DBMSDocument storeTime Series DBMS
Secondary database modelsDocument storeSpatial DBMS infowith GEO packageRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score13.77
Rank#40  Overall
#24  Relational DBMS
Score25.83
Rank#28  Overall
#1  Time Series DBMS
Score2.28
Rank#115  Overall
#21  Document stores
Score4.64
Rank#71  Overall
#4  Time Series DBMS
Websiteimpala.apache.orgwww.influxdata.com/­products/­influxdb-overviewpouchdb.comwww.timescale.com
Technical documentationimpala.apache.org/­impala-docs.htmldocs.influxdata.com/­influxdbpouchdb.com/­guidesdocs.timescale.com
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaApache Software FoundationTimescale
Initial release2013201320122017
Current release4.1.0, June 20222.7.6, April 20247.1.1, June 20192.13.0, November 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2Open Source infoMIT-License; commercial enterprise version availableOpen SourceOpen Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC++GoJavaScriptC
Server operating systemsLinuxLinux
OS X infothrough Homebrew
server-less, requires a JavaScript environment (browser, Node.js)Linux
OS X
Windows
Data schemeyesschema-freeschema-freeyes
Typing infopredefined data types such as float or dateyesNumeric data and Stringsnonumerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data types
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 indexesyesnoyes infovia viewsyes
SQL infoSupport of SQLSQL-like DML and DDL statementsSQL-like query languagenoyes infofull PostgreSQL SQL syntax
APIs and other access methodsJDBC
ODBC
HTTP API
JSON over UDP
HTTP REST infoonly for PouchDB Server
JavaScript API
ADO.NET
JDBC
native C library
ODBC
streaming API for large objects
Supported programming languagesAll languages supporting JDBC/ODBC.Net
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Perl
PHP
Python
R
Ruby
Rust
Scala
JavaScript.Net
C
C++
Delphi
Java infoJDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reducenoView functions in JavaScriptuser defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell
Triggersnonoyesyes
Partitioning methods infoMethods for storing different data on different nodesShardingSharding infoin enterprise version onlySharding infowith a proxy-based framework, named couchdb-loungeyes, across time and space (hash partitioning) attributes
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factorselectable replication factor infoin enterprise version onlyMulti-source replication infoalso with CouchDB databases
Source-replica replication infoalso with CouchDB databases
Source-replica replication with hot standby and reads on replicas info
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReducenoyesno
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyEventual ConsistencyImmediate Consistency
Foreign keys infoReferential integritynononoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanononoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyes
Durability infoSupport for making data persistentyesyesyes infoby using IndexedDB, WebSQL or LevelDB as backendyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyes infoDepending on used storage engineyesno
User concepts infoAccess controlAccess rights for users, groups and roles infobased on Apache Sentry and Kerberossimple rights management via user accountsnofine grained access rights according to SQL-standard
More information provided by the system vendor
Apache ImpalaInfluxDBPouchDBTimescaleDB
Specific characteristicsInfluxData is the creator of InfluxDB , the open source time series database. It...
» more
Competitive advantagesTime to Value InfluxDB is available in all the popular languages and frameworks,...
» more
Typical application scenariosIoT & Sensor Monitoring Developers are witnessing the instrumentation of every available...
» more
Key customersInfluxData has more than 1,900 paying customers, including customers include MuleSoft,...
» more
Market metricsFastest-growing database to drive 27,500 GitHub stars Over 750,000 daily active instances
» more
Licensing and pricing modelsOpen source core with closed source clustering available either on-premise or on...
» more
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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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More resources
Apache ImpalaInfluxDBPouchDBTimescaleDB
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Recent citations in the news

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6 June 2023, SiliconANGLE News

Apache Impala 4 Supports Operator Multi-Threading
29 July 2021, iProgrammer

Apache Impala becomes Top-Level Project
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4 April 2024, PR Newswire

Power IoT and time-series workloads with TimescaleDB for Azure Database for PostgreSQL
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Timescale Valuation Rockets to Over $1B with $110M Round, Marking the Explosive Rise of Time-Series Data
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