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DBMS > Apache Impala vs. Badger vs. Dragonfly vs. Microsoft Azure Data Explorer vs. TimescaleDB

System Properties Comparison Apache Impala vs. Badger vs. Dragonfly vs. Microsoft Azure Data Explorer vs. TimescaleDB

Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisonBadger  Xexclude from comparisonDragonfly  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonTimescaleDB  Xexclude from comparison
DescriptionAnalytic DBMS for HadoopAn embeddable, persistent, simple and fast Key-Value Store, written purely in Go.A drop-in Redis replacement that scales vertically to support millions of operations per second and terabyte sized workloads, all on a single instanceFully managed big data interactive analytics platformA time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL
Primary database modelRelational DBMSKey-value storeKey-value storeRelational DBMS infocolumn orientedTime Series DBMS
Secondary database modelsDocument storeDocument store infoIf a column is of type dynamic docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-types/­dynamic then it's possible to add arbitrary JSON documents in this cell
Event Store infothis is the general usage pattern at Microsoft. Billing, Logs, Telemetry events are stored in ADX and the state of an individual entity is defined by the arg_max(timestamps)
Spatial DBMS
Search engine infosupport for complex search expressions docs.microsoft.com/­en-us/­azure/­kusto/­query/­parseoperator FTS, Geospatial docs.microsoft.com/­en-us/­azure/­kusto/­query/­geo-point-to-geohash-function distributed search -> ADX acts as a distributed search engine
Time Series DBMS infosee docs.microsoft.com/­en-us/­azure/­data-explorer/­time-series-analysis
Relational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score12.45
Rank#40  Overall
#24  Relational DBMS
Score0.22
Rank#320  Overall
#47  Key-value stores
Score0.49
Rank#261  Overall
#38  Key-value stores
Score3.80
Rank#81  Overall
#43  Relational DBMS
Score4.46
Rank#71  Overall
#5  Time Series DBMS
Websiteimpala.apache.orggithub.com/­dgraph-io/­badgergithub.com/­dragonflydb/­dragonfly
www.dragonflydb.io
azure.microsoft.com/­services/­data-explorerwww.timescale.com
Technical documentationimpala.apache.org/­impala-docs.htmlgodoc.org/­github.com/­dgraph-io/­badgerwww.dragonflydb.io/­docsdocs.microsoft.com/­en-us/­azure/­data-explorerdocs.timescale.com
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaDGraph LabsDragonflyDB team and community contributorsMicrosoftTimescale
Initial release20132017202320192017
Current release4.1.0, June 20221.0, March 2023cloud service with continuous releases2.15.0, May 2024
License infoCommercial or Open SourceOpen Source infoApache Version 2Open Source infoApache 2.0Open Source infoBSL 1.1commercialOpen Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenononoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC++GoC++C
Server operating systemsLinuxBSD
Linux
OS X
Solaris
Windows
LinuxhostedLinux
OS X
Windows
Data schemeyesschema-freescheme-freeFixed schema with schema-less datatypes (dynamic)yes
Typing infopredefined data types such as float or dateyesnostrings, hashes, lists, sets, sorted sets, bit arraysyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-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.nononoyesyes
Secondary indexesyesnonoall fields are automatically indexedyes
SQL infoSupport of SQLSQL-like DML and DDL statementsnonoKusto Query Language (KQL), SQL subsetyes infofull PostgreSQL SQL syntax
APIs and other access methodsJDBC
ODBC
Proprietary protocol infoRESP - REdis Serialization ProtocolMicrosoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
ADO.NET
JDBC
native C library
ODBC
streaming API for large objects
Supported programming languagesAll languages supporting JDBC/ODBCGoC
C#
C++
Clojure
D
Dart
Elixir
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
Lua
Objective-C
Perl
PHP
Python
R
Ruby
Rust
Scala
Swift
Tcl
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
.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-reducenoLuaYes, possible languages: KQL, Python, Ruser defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell
Triggersnonopublish/subscribe channels provide some trigger functionalityyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicyyes
Partitioning methods infoMethods for storing different data on different nodesShardingnoneSharding infoImplicit feature of the cloud serviceyes, across time and space (hash partitioning) attributes
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factornoneSource-replica replicationyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.Source-replica replication with hot standby and reads on replicas info
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReducenonoSpark connector (open source): github.com/­Azure/­azure-kusto-sparkno
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencynoneEventual ConsistencyEventual Consistency
Immediate Consistency
Immediate Consistency
Foreign keys infoReferential integritynonononoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonoAtomic execution of command blocks and scriptsnoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyes, strict serializability by the serveryesyes
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.nonoyesnono
User concepts infoAccess controlAccess rights for users, groups and roles infobased on Apache Sentry and KerberosnoPassword-based authenticationAzure Active Directory Authenticationfine grained access rights according to SQL-standard

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More resources
Apache ImpalaBadgerDragonflyMicrosoft Azure Data ExplorerTimescaleDB
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