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DBMS > Apache Impala vs. Drizzle vs. Fujitsu Enterprise Postgres vs. SQream DB vs. TimescaleDB

System Properties Comparison Apache Impala vs. Drizzle vs. Fujitsu Enterprise Postgres vs. SQream DB vs. TimescaleDB

Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisonDrizzle  Xexclude from comparisonFujitsu Enterprise Postgres  Xexclude from comparisonSQream DB  Xexclude from comparisonTimescaleDB  Xexclude from comparison
Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.
DescriptionAnalytic DBMS for HadoopMySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.Enterprise-grade PostgreSQL-based DBMS with security enhancements such as Transparent Data Encryption and Data Masking, plus high-availability and performance improvement features.a GPU-based, columnar RDBMS for big data analytics workloadsA time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL
Primary database modelRelational DBMSRelational DBMSRelational DBMSRelational DBMSTime Series DBMS
Secondary database modelsDocument storeDocument store
Spatial DBMS
Relational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score12.45
Rank#40  Overall
#24  Relational DBMS
Score0.37
Rank#278  Overall
#128  Relational DBMS
Score0.74
Rank#224  Overall
#103  Relational DBMS
Score4.46
Rank#71  Overall
#5  Time Series DBMS
Websiteimpala.apache.orgwww.postgresql.fastware.comsqream.comwww.timescale.com
Technical documentationimpala.apache.org/­impala-docs.htmlwww.postgresql.fastware.com/­product-manualsdocs.sqream.comdocs.timescale.com
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaDrizzle project, originally started by Brian AkerPostgreSQL Global Development Group, Fujitsu Australia Software TechnologySQream TechnologiesTimescale
Initial release2013200820172017
Current release4.1.0, June 20227.2.4, September 2012Fujitsu Enterprise Postgres 14, January 20222022.1.6, December 20222.15.0, May 2024
License infoCommercial or Open SourceOpen Source infoApache Version 2Open Source infoGNU GPLcommercialcommercialOpen Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenonononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++C++CC++, CUDA, Haskell, Java, ScalaC
Server operating systemsLinuxFreeBSD
Linux
OS X
Linux
Windows
LinuxLinux
OS X
Windows
Data schemeyesyesyesyesyes
Typing infopredefined data types such as float or dateyesyesyesyes, ANSI Standard SQL Typesnumerics, 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.noyes
Secondary indexesyesyesyesnoyes
SQL infoSupport of SQLSQL-like DML and DDL statementsyes infowith proprietary extensionsyesyesyes infofull PostgreSQL SQL syntax
APIs and other access methodsJDBC
ODBC
JDBCADO.NET
JDBC
native C library
ODBC
streaming API for large objects
.Net
JDBC
ODBC
ADO.NET
JDBC
native C library
ODBC
streaming API for large objects
Supported programming languagesAll languages supporting JDBC/ODBCC
C++
Java
PHP
.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl
C++
Java
JavaScript (Node.js)
Python
.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-reducenouser defined functionsuser defined functions in Pythonuser defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell
Triggersnono infohooks for callbacks inside the server can be used.yesnoyes
Partitioning methods infoMethods for storing different data on different nodesShardingShardingpartitioning by range, list and by hashhorizontal and vertical partitioningyes, across time and space (hash partitioning) attributes
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factorMulti-source replication
Source-replica replication
Source-replica replicationnoneSource-replica replication with hot standby and reads on replicas info
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReducenononono
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyImmediate ConsistencyImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynoyesyesnoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDACIDACIDACID
Concurrency infoSupport for concurrent manipulation of datayesyesyes, multi-version concurrency control (MVCC)yesyes
Durability infoSupport for making data persistentyesyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nono
User concepts infoAccess controlAccess rights for users, groups and roles infobased on Apache Sentry and KerberosPluggable authentication mechanisms infoe.g. LDAP, HTTPfine grained access rights according to SQL-standardfine grained access rights according to SQL-standard
More information provided by the system vendor
Apache ImpalaDrizzleFujitsu Enterprise PostgresSQream DBTimescaleDB
Specific characteristics100% compatible with community PostgreSQL
» more
Competitive advantagesBuilt-in TDE and Data Masking security. In-Memory Columnar Index, and a high speed...
» more
Typical application scenariosTransactional payments applications, reporting and mixed workloads.
» more
Market metricsOver 30 years experience in database technology. Over 20 years in Postgres development...
» more
Licensing and pricing modelsCore based licensing
» more

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More resources
Apache ImpalaDrizzleFujitsu Enterprise PostgresSQream DBTimescaleDB
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