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DBMS > Apache Impala vs. QuestDB

System Properties Comparison Apache Impala vs. QuestDB

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Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisonQuestDB  Xexclude from comparison
DescriptionAnalytic DBMS for HadoopA high performance open source SQL database for time series data
Primary database modelRelational DBMSTime Series DBMS
Secondary database modelsDocument storeRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score12.45
Rank#40  Overall
#24  Relational DBMS
Score2.70
Rank#105  Overall
#8  Time Series DBMS
Websiteimpala.apache.orgquestdb.io
Technical documentationimpala.apache.org/­impala-docs.htmlquestdb.io/­docs
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaQuestDB Technology Inc
Initial release20132014
Current release4.1.0, June 2022
License infoCommercial or Open SourceOpen Source infoApache Version 2Open Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++Java (Zero-GC), C++, Rust
Server operating systemsLinuxLinux
macOS
Windows
Data schemeyesyes infoschema-free via InfluxDB Line Protocol
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 indexesyesno
SQL infoSupport of SQLSQL-like DML and DDL statementsSQL with time-series extensions
APIs and other access methodsJDBC
ODBC
HTTP REST
InfluxDB Line Protocol (TCP/UDP)
JDBC
PostgreSQL wire protocol
Supported programming languagesAll languages supporting JDBC/ODBCC infoPostgreSQL driver
C++
Go
Java
JavaScript (Node.js)
Python
Rust infoover HTTP
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reduceno
Triggersnono
Partitioning methods infoMethods for storing different data on different nodesShardinghorizontal partitioning (by timestamps)
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factorSource-replica replication with eventual consistency
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 for single-table writes
Concurrency infoSupport for concurrent manipulation of datayesyes
Durability infoSupport for making data persistentyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyes infothrough memory mapped files
User concepts infoAccess controlAccess rights for users, groups and roles infobased on Apache Sentry and Kerberos
More information provided by the system vendor
Apache ImpalaQuestDB
Specific characteristicsRelational model with native time series support Column-based storage and time partitioned...
» more
Competitive advantagesHigh ingestion throughput: peak of 4M rows/sec (TSBS Benchmark) Code optimizations...
» more
Typical application scenariosFinancial tick data Industrial IoT Application Metrics Monitoring
» more
Key customersBanks & Hedge funds, Yahoo, OKX, Airbus, Aquis Exchange, Net App, Cloudera, Airtel,...
» more
Licensing and pricing modelsOpen source Apache 2.0 QuestDB Enterprise QuestDB Cloud
» more
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More resources
Apache ImpalaQuestDB
Recent citations in the news

Apache Impala becomes Top-Level Project
28 November 2017, SDTimes.com

Cloudera Bringing Impala to AWS Cloud
28 November 2017, Datanami

Apache Doris just 'graduated': Why care about this SQL data warehouse
24 June 2022, InfoWorld

Hudi: Uber Engineering’s Incremental Processing Framework on Apache Hadoop
12 March 2017, Uber

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27 October 2017, KDnuggets

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3 November 2021, TechCrunch

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11 January 2021, Towards Data Science

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