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DBMS > Apache Impala vs. QuestDB vs. Sphinx vs. TDengine

System Properties Comparison Apache Impala vs. QuestDB vs. Sphinx vs. TDengine

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Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisonQuestDB  Xexclude from comparisonSphinx  Xexclude from comparisonTDengine  Xexclude from comparison
DescriptionAnalytic DBMS for HadoopA high performance open source SQL database for time series dataOpen source search engine for searching in data from different sources, e.g. relational databasesTime Series DBMS and big data platform
Primary database modelRelational DBMSTime Series DBMSSearch engineTime Series DBMS
Secondary database modelsDocument storeRelational DBMSRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score13.77
Rank#40  Overall
#24  Relational DBMS
Score2.52
Rank#109  Overall
#9  Time Series DBMS
Score5.98
Rank#56  Overall
#5  Search engines
Score2.60
Rank#107  Overall
#8  Time Series DBMS
Websiteimpala.apache.orgquestdb.iosphinxsearch.comgithub.com/­taosdata/­TDengine
tdengine.com
Technical documentationimpala.apache.org/­impala-docs.htmlquestdb.io/­docssphinxsearch.com/­docsdocs.tdengine.com
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaQuestDB Technology IncSphinx Technologies Inc.TDEngine, previously Taos Data
Initial release2013201420012019
Current release4.1.0, June 20223.5.1, February 20233.0, August 2022
License infoCommercial or Open SourceOpen Source infoApache Version 2Open Source infoApache 2.0Open Source infoGPL version 2, commercial licence availableOpen Source infoAGPL V3, also commercial editions available
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++Java (Zero-GC), C++, RustC++C
Server operating systemsLinuxLinux
macOS
Windows
FreeBSD
Linux
NetBSD
OS X
Solaris
Windows
Linux
Windows
Data schemeyesyes infoschema-free via InfluxDB Line Protocolyesyes
Typing infopredefined data types such as float or dateyesyesnoyes
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.nonono
Secondary indexesyesnoyes infofull-text index on all search fieldsno
SQL infoSupport of SQLSQL-like DML and DDL statementsSQL with time-series extensionsSQL-like query language (SphinxQL)Standard SQL with extensions for time-series applications
APIs and other access methodsJDBC
ODBC
HTTP REST
InfluxDB Line Protocol (TCP/UDP)
JDBC
PostgreSQL wire protocol
Proprietary protocolJDBC
RESTful HTTP API
Supported programming languagesAll languages supporting JDBC/ODBCC infoPostgreSQL driver
C++
Go
Java
JavaScript (Node.js)
Python
Rust infoover HTTP
C++ infounofficial client library
Java
Perl infounofficial client library
PHP
Python
Ruby infounofficial client library
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Rust
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reducenonono
Triggersnononoyes, via alarm monitoring
Partitioning methods infoMethods for storing different data on different nodesShardinghorizontal partitioning (by timestamps)Sharding infoPartitioning is done manually, search queries against distributed index is supportedSharding
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factorSource-replica replication with eventual consistencynoneyes
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReducenono
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyImmediate Consistency
Foreign keys infoReferential integritynononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACID for single-table writesno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyes infoThe original contents of fields are not stored in the Sphinx index.yes
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 Kerberosnoyes
More information provided by the system vendor
Apache ImpalaQuestDBSphinxTDengine
Specific characteristicsRelational model with native time series support Column-based storage and time partitioned...
» more
TDengine™ is a next generation data historian purpose-built for Industry 4.0 and...
» more
Competitive advantagesHigh ingestion throughput: peak of 4M rows/sec (TSBS Benchmark) Code optimizations...
» more
High Performance at any Scale: TDengine is purpose-built for handling massive industrial...
» more
Typical application scenariosFinancial tick data Industrial IoT Application Metrics Monitoring
» more
TDengine is designed for Industrial IoT scenarios, including: Manufacturing Connected...
» more
Key customersBanks & Hedge funds, Yahoo, OKX, Airbus, Aquis Exchange, Net App, Cloudera, Airtel,...
» more
Market metricsTDengine has garnered over 22,500 stars on GitHub and is used in over 50 countries...
» more
Licensing and pricing modelsOpen source Apache 2.0 QuestDB Enterprise QuestDB Cloud
» more
TDengine OSS is an open source, cloud native time series database. It includes built-in...
» more
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