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DBMS > Apache Impala vs. Apache IoTDB vs. TDSQL for MySQL vs. TimescaleDB vs. Titan

System Properties Comparison Apache Impala vs. Apache IoTDB vs. TDSQL for MySQL vs. TimescaleDB vs. Titan

Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisonApache IoTDB  Xexclude from comparisonTDSQL for MySQL  Xexclude from comparisonTimescaleDB  Xexclude from comparisonTitan  Xexclude from comparison
Titan has been decommisioned after the takeover by Datastax. It will be removed from the DB-Engines ranking. A fork has been open-sourced as JanusGraph.
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 high-performance distributed database management system with features such as automatic sharding, intelligent operation and maintenance, elastic scalability without downtime, and enterprise-grade security. It is highly compatible with MySQL.A time series DBMS optimized for fast ingest and complex queries, based on PostgreSQLTitan is a Graph DBMS optimized for distributed clusters.
Primary database modelRelational DBMSTime Series DBMSRelational DBMSTime Series DBMSGraph DBMS
Secondary database modelsDocument storeDocument store
Spatial DBMS
Relational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score13.77
Rank#40  Overall
#24  Relational DBMS
Score1.18
Rank#173  Overall
#15  Time Series DBMS
Score0.85
Rank#205  Overall
#95  Relational DBMS
Score4.64
Rank#71  Overall
#4  Time Series DBMS
Websiteimpala.apache.orgiotdb.apache.orgwww.tencentcloud.com/­products/­dcdbwww.timescale.comgithub.com/­thinkaurelius/­titan
Technical documentationimpala.apache.org/­impala-docs.htmliotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmlwww.tencentcloud.com/­document/­product/­1042docs.timescale.comgithub.com/­thinkaurelius/­titan/­wiki
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaApache Software FoundationTencentTimescaleAurelius, owned by DataStax
Initial release20132018201320172012
Current release4.1.0, June 20221.1.0, April 20232.15.0, May 2024
License infoCommercial or Open SourceOpen Source infoApache Version 2Open Source infoApache Version 2.0commercialOpen Source infoApache 2.0Open Source infoApache license, version 2.0
Cloud-based only infoOnly available as a cloud servicenonoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++JavaCJava
Server operating systemsLinuxAll OS with a Java VM (>= 1.8)hostedLinux
OS X
Windows
Linux
OS X
Unix
Windows
Data schemeyesyesyesyesyes
Typing infopredefined data types such as float or dateyesyesyesnumerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data typesyes
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 indexesyesyesyesyesyes
SQL infoSupport of SQLSQL-like DML and DDL statementsSQL-like query languageyesyes infofull PostgreSQL SQL syntaxno
APIs and other access methodsJDBC
ODBC
JDBC
Native API
JDBC
ODBC
ADO.NET
JDBC
native C library
ODBC
streaming API for large objects
Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster
Supported programming languagesAll languages supporting JDBC/ODBCC
C#
C++
Go
Java
Python
Scala
C
C#
C++
Java
PHP
Python
Ruby
.Net
C
C++
Delphi
Java infoJDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl
Clojure
Java
Python
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reduceyesyesuser defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shellyes
Triggersnoyesyesyesyes
Partitioning methods infoMethods for storing different data on different nodesShardinghorizontal partitioning (by time range) + vertical partitioning (by deviceId)Automatic shardingyes, across time and space (hash partitioning) attributesyes infovia pluggable storage backends
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
Source-replica replication
Source-replica replication with hot standby and reads on replicas infoyes
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReduceIntegration with Hadoop and Sparknonoyes infovia Faunus, a graph analytics engine
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyEventual Consistency
Strong Consistency with Raft
Immediate ConsistencyImmediate ConsistencyEventual Consistency
Immediate Consistency
Foreign keys infoReferential integritynononoyesyes infoRelationships in graph
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonoACIDACIDACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyesyesyes infoSupports various storage backends: Cassandra, HBase, Berkeley DB, Akiban, Hazelcast
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesnono
User concepts infoAccess controlAccess rights for users, groups and roles infobased on Apache Sentry and KerberosyesUsers with fine-grained authorization conceptfine grained access rights according to SQL-standardUser authentification and security via Rexster Graph Server

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More resources
Apache ImpalaApache IoTDBTDSQL for MySQLTimescaleDBTitan
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