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DBMS > Apache Impala vs. GridDB vs. LevelDB vs. TDengine

System Properties Comparison Apache Impala vs. GridDB vs. LevelDB vs. TDengine

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Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisonGridDB  Xexclude from comparisonLevelDB  Xexclude from comparisonTDengine  Xexclude from comparison
DescriptionAnalytic DBMS for HadoopScalable in-memory time series database optimized for IoT and Big DataEmbeddable fast key-value storage library that provides an ordered mapping from string keys to string valuesTime Series DBMS and big data platform
Primary database modelRelational DBMSTime Series DBMSKey-value storeTime Series DBMS
Secondary database modelsDocument storeKey-value store
Relational DBMS
Relational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score13.77
Rank#40  Overall
#24  Relational DBMS
Score1.95
Rank#128  Overall
#10  Time Series DBMS
Score2.35
Rank#111  Overall
#19  Key-value stores
Score2.60
Rank#107  Overall
#8  Time Series DBMS
Websiteimpala.apache.orggriddb.netgithub.com/­google/­leveldbgithub.com/­taosdata/­TDengine
tdengine.com
Technical documentationimpala.apache.org/­impala-docs.htmldocs.griddb.netgithub.com/­google/­leveldb/­blob/­main/­doc/­index.mddocs.tdengine.com
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaToshiba CorporationGoogleTDEngine, previously Taos Data
Initial release2013201320112019
Current release4.1.0, June 20225.1, August 20221.23, February 20213.0, August 2022
License infoCommercial or Open SourceOpen Source infoApache Version 2Open Source infoAGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also availableOpen Source infoBSDOpen Source infoAGPL V3, also commercial editions 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++C
Server operating systemsLinuxLinuxIllumos
Linux
OS X
Windows
Linux
Windows
Data schemeyesyesschema-freeyes
Typing infopredefined data types such as float or dateyesyes infonumerical, string, blob, geometry, boolean, timestampnoyes
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 indexesyesyesnono
SQL infoSupport of SQLSQL-like DML and DDL statementsSQL92, SQL-like TQL (Toshiba Query Language)noStandard SQL with extensions for time-series applications
APIs and other access methodsJDBC
ODBC
JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API
JDBC
RESTful HTTP API
Supported programming languagesAll languages supporting JDBC/ODBCC
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
C++
Go
Java info3rd party binding
JavaScript (Node.js) info3rd party binding
Python info3rd party binding
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Rust
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reducenonono
Triggersnoyesnoyes, via alarm monitoring
Partitioning methods infoMethods for storing different data on different nodesShardingShardingnoneSharding
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factorSource-replica replicationnoneyes
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReduceConnector for using GridDB as an input source and output destination for Hadoop MapReduce jobsno
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyImmediate consistency within container, eventual consistency across containersImmediate Consistency
Foreign keys infoReferential integritynononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACID at container levelno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyes infowith automatic compression on writesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyes
User concepts infoAccess controlAccess rights for users, groups and roles infobased on Apache Sentry and KerberosAccess rights for users can be defined per databasenoyes
More information provided by the system vendor
Apache ImpalaGridDBLevelDBTDengine
Specific characteristicsGridDB is a highly scalable, in-memory time series database optimized for IoT and...
» more
TDengine™ is a next generation data historian purpose-built for Industry 4.0 and...
» more
Competitive advantages1. Optimized for IoT Equipped with Toshiba's proprietary key-container data model...
» more
High Performance at any Scale: TDengine is purpose-built for handling massive industrial...
» more
Typical application scenariosFactory IoT, Automative Industry, Energy, BEMS, Smart Community, Monitoring system.
» more
TDengine is designed for Industrial IoT scenarios, including: Manufacturing Connected...
» more
Key customersDenso International [see use case ] An Electric Power company [see use case ] Ishinomaki...
» more
Market metricsGitHub trending repository
» more
TDengine has garnered over 22,500 stars on GitHub and is used in over 50 countries...
» more
Licensing and pricing modelsOpen Source license (AGPL v3 & Apache v2) Commercial license (subscription)
» more
TDengine OSS is an open source, cloud native time series database. It includes built-in...
» more
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Apache ImpalaGridDBLevelDBTDengine
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