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DBMS > Badger vs. Firebolt vs. QuestDB vs. Vitess

System Properties Comparison Badger vs. Firebolt vs. QuestDB vs. Vitess

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Editorial information provided by DB-Engines
NameBadger  Xexclude from comparisonFirebolt  Xexclude from comparisonQuestDB  Xexclude from comparisonVitess  Xexclude from comparison
DescriptionAn embeddable, persistent, simple and fast Key-Value Store, written purely in Go.Highly scalable cloud data warehouse and analytics product infoForked from ClickhouseA high performance open source SQL database for time series dataScalable, distributed, cloud-native DBMS, extending MySQL
Primary database modelKey-value storeRelational DBMSTime Series DBMSRelational DBMS
Secondary database modelsRelational DBMSDocument store
Spatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.14
Rank#331  Overall
#49  Key-value stores
Score1.90
Rank#134  Overall
#62  Relational DBMS
Score2.52
Rank#109  Overall
#9  Time Series DBMS
Score0.82
Rank#209  Overall
#97  Relational DBMS
Websitegithub.com/­dgraph-io/­badgerwww.firebolt.ioquestdb.iovitess.io
Technical documentationgodoc.org/­github.com/­dgraph-io/­badgerdocs.firebolt.ioquestdb.io/­docsvitess.io/­docs
DeveloperDGraph LabsFirebolt Analytics Inc.QuestDB Technology IncThe Linux Foundation, PlanetScale
Initial release2017202020142013
Current release15.0.2, December 2022
License infoCommercial or Open SourceOpen Source infoApache 2.0commercialOpen Source infoApache 2.0Open Source infoApache Version 2.0, commercial licenses available
Cloud-based only infoOnly available as a cloud servicenoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageGoJava (Zero-GC), C++, RustGo
Server operating systemsBSD
Linux
OS X
Solaris
Windows
hostedLinux
macOS
Windows
Docker
Linux
macOS
Data schemeschema-freeyesyes infoschema-free via InfluxDB Line Protocolyes
Typing infopredefined data types such as float or datenoyesyesyes
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.nono
Secondary indexesnoyesnoyes
SQL infoSupport of SQLnoyesSQL with time-series extensionsyes infowith proprietary extensions
APIs and other access methods.Net
ODBC
RESTful HTTP API
HTTP REST
InfluxDB Line Protocol (TCP/UDP)
JDBC
PostgreSQL wire protocol
ADO.NET
JDBC
MySQL protocol
ODBC
Supported programming languagesGoGo
JavaScript (Node.js)
Python
C infoPostgreSQL driver
C++
Go
Java
JavaScript (Node.js)
Python
Rust infoover HTTP
Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl
Server-side scripts infoStored proceduresnononoyes infoproprietary syntax
Triggersnononoyes
Partitioning methods infoMethods for storing different data on different nodesnonehorizontal partitioning (by timestamps)Sharding
Replication methods infoMethods for redundantly storing data on multiple nodesnonedepending on storage layerSource-replica replication with eventual consistencyMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonono
Consistency concepts infoMethods to ensure consistency in a distributed systemnoneImmediate ConsistencyEventual Consistency across shards
Immediate Consistency within a shard
Foreign keys infoReferential integritynonoyes infonot for MyISAM storage engine
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACID for single-table writesACID at shard level
Concurrency infoSupport for concurrent manipulation of datayesyesyes infotable locks or row locks depending on storage engine
Durability infoSupport for making data persistentyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyes infothrough memory mapped filesyes
User concepts infoAccess controlnoUsers with fine-grained authorization concept infono user groups or roles
More information provided by the system vendor
BadgerFireboltQuestDBVitess
Specific characteristicsRelational model with native time series support Column-based storage and time partitioned...
» more
Competitive advantagesHigh ingestion throughput: peak of 4M rows/sec (TSBS Benchmark) Code optimizations...
» more
Typical application scenariosFinancial tick data Industrial IoT Application Metrics Monitoring
» more
Key customersBanks & Hedge funds, Yahoo, OKX, Airbus, Aquis Exchange, Net App, Cloudera, Airtel,...
» more
Licensing and pricing modelsOpen source Apache 2.0 QuestDB Enterprise QuestDB Cloud
» more
News

QuestDB and Raspberry Pi 5 benchmark, a pocket-sized powerhouse
8 May 2024

Build your own resource monitor with QuestDB and Grafana
6 May 2024

Does "vpmovzxbd" Scare You? Here's Why it Doesn't Have To
12 April 2024

Create an ADS-B flight radar with QuestDB and a Raspberry Pi
8 April 2024

Build a temperature IoT sensor with Raspberry Pi Pico & QuestDB
5 April 2024

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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More resources
BadgerFireboltQuestDBVitess
Recent citations in the news

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7 September 2022, CTech

Firebolt, a data warehouse startup, raises $100M at a $1.4B valuation for faster, cheaper analytics on large data sets
26 January 2022, TechCrunch

Firebolt vs Snowflake | Data Warehousing Platform Comparison
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Firebolt, Israeli Cloud Data Warehouse Startup Forklifts Forward
5 January 2021, Forbes

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QuestDB snares $12M Series A with hosted version coming soon
3 November 2021, TechCrunch

SQL Extensions for Time-Series Data in QuestDB
11 January 2021, Towards Data Science

Q&A: Nicolas Hourcard, QuestDB: The advantages of a time-series database
3 December 2020, Developer News

Comparing Different Time-Series Databases
10 February 2022, hackernoon.com

Aquis Exchange goes live with QuestDB for real time monitoring
2 November 2022, FinanceFeeds

provided by Google News

PlanetScale Unveils Distributed MySQL Database Service Based on Vitess
18 May 2021, Datanami

PlanetScale grabs YouTube-developed open-source tech, promises Vitess DBaaS with on-the-fly schema changes
18 May 2021, The Register

With Vitess 4.0, database vendor matures cloud-native platform
13 November 2019, TechTarget

Massively Scaling MySQL Using Vitess
19 February 2019, InfoQ.com

They scaled YouTube -- now they’ll shard everyone with PlanetScale
13 December 2018, TechCrunch

provided by Google News



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