Which is the fastest web framework?

For a project I was reading some benchmarks at GitHub and google, I found an article then I was surprised, its too much interesting result for me, you can check the result below.


LanguageFrameworkAverage50th percentile90th percentileStandard deviationRequests / sThroughput
rust (1.38)nickel (0.11)0.24 ms0.20 ms0.39 ms199.3337636.674.97 Mb
ruby (2.6)syro (3.1)2.70 ms0.63 ms7.94 ms4143.6746844.331.80 Mb
ruby (2.6)roda (3.25)2.79 ms0.67 ms8.10 ms4188.6745494.672.88 Mb
rust (1.38)iron (0.6)3.04 ms2.93 ms4.49 ms1368.6721289.671.76 Mb
ruby (2.6)cuba (3.9)3.06 ms0.57 ms9.41 ms5064.3341756.673.27 Mb
ruby (2.6)rack-routing (0.0)3.84 ms0.73 ms11.38 ms5596.0033197.001.27 Mb
c (11)agoo-c (0.7)4.59 ms4.28 ms8.86 ms3377.00209196.008.02 Mb
ruby (2.6)camping (2.1)4.67 ms0.49 ms16.08 ms8578.6727517.671.74 Mb
node (12.11)sifrr (0.0)4.79 ms4.32 ms9.82 ms4054.33203003.6711.84 Mb
nim (1.0)httpbeast (0.2)5.02 ms4.54 ms9.61 ms3651.00192873.3318.20 Mb
python (3.7)japronto (0.1)5.13 ms4.63 ms9.96 ms3909.00189149.6715.00 Mb
cpp (11)drogon (1.0)5.42 ms4.90 ms9.91 ms3546.00177889.3311.44 Mb
ruby (2.6)flame (4.18)5.47 ms0.49 ms19.16 ms10784.3323469.670.90 Mb
swift (5.1)swifter (1.4)5.56 ms0.85 ms14.54 ms86445.0011871.001.01 Mb
cpp (11)evhtp (1.2)5.90 ms5.24 ms9.56 ms2929.33160195.6710.30 Mb
go (1.13)gorouter-fasthttp (4.2)6.04 ms5.38 ms9.31 ms3625.67156380.0016.64 Mb
go (1.13)fasthttprouter (0.1)6.18 ms5.11 ms9.14 ms9601.00161432.0017.23 Mb
go (1.13)atreugo (8.2)6.26 ms5.19 ms9.27 ms8758.00159818.3321.31 Mb
go (1.13)fasthttp (1.5)6.33 ms4.87 ms8.99 ms12920.33168733.3318.05 Mb
crystal (0.31)router.cr (0.2)6.46 ms5.66 ms10.55 ms3220.00149292.339.31 Mb
crystal (0.31)toro (0.4)6.47 ms5.67 ms10.55 ms3222.00148845.339.28 Mb
ruby (2.6)hanami (1.3)6.66 ms0.60 ms22.99 ms11912.3319246.009.67 Mb
crystal (0.31)raze (0.3)6.75 ms5.91 ms10.91 ms3312.00143044.338.91 Mb
java (8)rapidoid (5.5)6.80 ms5.09 ms11.01 ms13146.00163718.0019.53 Mb
crystal (0.31)kemal (0.28)7.09 ms6.44 ms10.91 ms3221.67135783.6714.71 Mb
nim (1.0)jester (0.4)7.29 ms6.65 ms11.72 ms3980.00145089.0019.33 Mb
c (11)kore (3.3)7.41 ms5.96 ms13.15 ms9455.67161905.6729.17 Mb
crystal (0.31)amber (0.3)7.67 ms7.04 ms12.12 ms3559.67126369.6715.33 Mb
ruby (2.6)sinatra (2.0)7.97 ms0.68 ms26.40 ms13397.6716038.672.76 Mb
crystal (0.31)orion (1.7)8.35 ms7.80 ms13.19 ms3888.33116803.0012.65 Mb
ruby (2.6)grape (1.2)9.51 ms0.80 ms31.25 ms15583.6713576.000.51 Mb
java (8)act (1.8)9.73 ms7.65 ms13.12 ms15954.67121682.3313.92 Mb
go (1.13)gorouter (4.2)9.88 ms8.05 ms16.18 ms9871.67105554.679.31 Mb
go (1.13)rte (0.0)9.89 ms7.84 ms15.73 ms13656.33107851.679.58 Mb
rust (1.38)actix-web (1.0)10.23 ms9.78 ms13.79 ms3258.67105115.3310.09 Mb
go (1.13)echo (4.1)10.69 ms8.56 ms19.02 ms8175.6796770.6711.26 Mb
go (1.13)violetear (7.0)10.72 ms8.90 ms16.17 ms12167.0097186.008.55 Mb
go (1.13)gin (1.4)10.99 ms8.60 ms19.20 ms10553.0096706.3311.25 Mb
go (1.13)goroute (0.0)11.04 ms8.56 ms19.05 ms13101.0096836.0011.27 Mb
go (1.13)chi (4.0)11.16 ms8.27 ms18.36 ms17216.00101013.678.96 Mb
go (1.13)beego (1.12)11.22 ms8.77 ms19.63 ms10964.0095757.678.54 Mb
go (1.13)kami (2.2)11.31 ms8.63 ms17.37 ms18533.0098168.678.66 Mb
go (1.13)webgo (3.0)11.62 ms9.32 ms18.84 ms12303.3391533.008.08 Mb
python (3.7)falcon (2.0)12.45 ms10.15 ms20.53 ms7317.6780978.0012.58 Mb
go (1.13)air (0.13)12.70 ms9.58 ms23.38 ms12702.0084610.0011.70 Mb
swift (5.1)perfect (3.1)12.99 ms13.07 ms15.50 ms4827.3374517.004.64 Mb
go (1.13)gorilla-mux (1.7)13.51 ms8.56 ms19.96 ms33361.3395175.678.43 Mb
csharp (7.3)aspnetcore (2.2)13.52 ms9.69 ms16.21 ms33059.3387579.339.45 Mb
node (12.11)polkadot (1.0)13.54 ms9.24 ms17.63 ms33076.3393298.679.27 Mb
go (1.13)gf (1.9)13.58 ms10.90 ms23.38 ms11764.6777603.008.73 Mb
php (7.3)one (1.8)13.74 ms12.47 ms23.63 ms8067.6773012.0011.12 Mb
ruby (2.6)agoo (2.11)13.96 ms13.56 ms18.36 ms3473.6770218.332.69 Mb
node (12.11)0http (1.2)15.87 ms9.40 ms17.70 ms45955.3391692.009.11 Mb
php (7.3)hyperf (1.0)16.97 ms14.55 ms32.28 ms11795.3361592.338.70 Mb
rust (1.38)gotham (0.4)17.32 ms16.37 ms24.25 ms18398.0059471.008.00 Mb
node (12.11)rayo (1.3)17.50 ms10.49 ms20.18 ms48826.3380132.337.96 Mb
node (12.11)polka (0.5)17.51 ms10.14 ms19.82 ms50286.6782259.338.17 Mb
ruby (2.6)plezi (0.16)17.71 ms16.45 ms23.31 ms8987.6755487.337.84 Mb
python (3.7)bottle (0.12)18.49 ms15.86 ms30.21 ms10116.6754975.338.98 Mb
php (7.3)sw-fw-less (preview)19.53 ms17.97 ms31.21 ms9924.0050967.007.76 Mb
python (3.7)blacksheep (0.2)19.53 ms17.34 ms32.78 ms10283.3351572.676.88 Mb
kotlin (1.3)ktor (1.2)19.86 ms12.19 ms29.10 ms52312.6772213.007.46 Mb
python (3.7)asgineer (0.7)20.12 ms17.88 ms33.06 ms10311.6749954.005.91 Mb
node (12.11)restana (3.3)20.18 ms9.67 ms19.11 ms67392.0088524.008.79 Mb
node (12.11)muneem (2.4)21.71 ms11.72 ms22.32 ms65248.0072124.007.16 Mb
python (3.7)hug (2.6)21.91 ms17.88 ms35.48 ms13931.6746597.007.66 Mb
python (3.7)starlette (0.12)22.08 ms18.90 ms37.56 ms11508.3345531.336.50 Mb
node (12.11)foxify (0.1)22.35 ms12.59 ms23.78 ms62906.6768189.009.50 Mb
clojure (1.10)coast (1.0)22.53 ms19.66 ms21.77 ms36208.0048332.005.76 Mb
php (7.3)swoft (2.0)23.21 ms22.53 ms30.76 ms6650.3341957.677.31 Mb
node (12.11)iotjs-express (0.0)24.19 ms14.39 ms26.72 ms64464.6759753.6716.09 Mb
swift (5.1)kitura-nio (2.8)25.57 ms20.16 ms23.38 ms61899.0047177.005.82 Mb
php (7.3)imi (1.0)26.63 ms25.65 ms33.72 ms6709.6736652.005.58 Mb
swift (5.1)kitura (2.8)27.10 ms20.72 ms23.75 ms66177.6746502.675.73 Mb
node (12.11)restify (8.4)28.43 ms19.08 ms31.25 ms59532.0045832.335.33 Mb
node (12.11)koa (2.8)28.76 ms13.84 ms26.71 ms85051.0060916.338.55 Mb
node (12.11)express (4.17)29.69 ms15.76 ms29.73 ms82691.3353886.008.75 Mb
java (8)spring-boot (2.1)29.70 ms16.10 ms36.25 ms86239.6747422.332.52 Mb
node (12.11)fastify (2.8)32.84 ms15.00 ms27.96 ms105969.0060039.3310.56 Mb
ruby (2.6)rails (6.0)33.29 ms2.49 ms110.27 ms63071.673850.331.61 Mb
python (3.7)fastapi (0.42)36.39 ms32.15 ms60.51 ms19152.0027792.003.98 Mb
python (3.7)responder (2.0)36.72 ms34.41 ms58.42 ms16339.3327054.333.91 Mb
crystal (0.31)spider-gazelle (1.6)37.63 ms35.73 ms45.36 ms15535.3326014.671.84 Mb
python (3.7)clastic (19.9)40.15 ms33.18 ms65.65 ms19619.0024875.674.09 Mb
python (3.7)molten (0.27)40.30 ms33.85 ms66.82 ms19592.0025365.673.13 Mb
fsharp (7.3)suave (2.5)40.58 ms24.44 ms100.56 ms50407.6724596.333.30 Mb
python (3.7)flask (1.1)41.74 ms36.29 ms63.43 ms16853.6723598.003.85 Mb
crystal (0.31)lucky (0.18)42.86 ms40.21 ms52.32 ms14370.3322844.331.87 Mb
node (12.11)turbo_polka (2.0)44.09 ms42.05 ms48.93 ms22248.3322322.001.39 Mb
python (3.7)aiohttp (3.6)44.17 ms42.06 ms69.09 ms19059.3322607.003.40 Mb
python (3.7)bocadillo (0.18)52.16 ms45.99 ms87.88 ms30216.0019473.672.50 Mb
java (8)micronaut (1.2)52.78 ms23.20 ms97.08 ms128239.6724704.673.43 Mb
swift (5.1)vapor (3.3)53.36 ms17.34 ms32.92 ms219012.3348897.335.54 Mb
php (7.3)lumen (6.2)53.83 ms18.27 ms114.41 ms112798.6743774.3314.42 Mb
php (7.3)slim (4.3)54.73 ms18.51 ms112.98 ms117455.3343835.0014.43 Mb
php (7.3)zend-expressive (3.2)55.56 ms18.58 ms123.67 ms115767.3343418.3314.30 Mb
python (3.7)sanic (19.9)57.11 ms51.21 ms96.65 ms38019.0018091.002.14 Mb
php (7.3)basicphp (0.9)58.93 ms19.67 ms119.40 ms123055.3340573.0013.40 Mb
php (7.3)spiral (2.2)59.60 ms59.64 ms66.25 ms8035.6716327.331.88 Mb
php (7.3)symfony (4.3)59.85 ms19.16 ms119.08 ms133181.3340957.3313.49 Mb
php (7.3)zend-framework (3.1)59.94 ms19.09 ms127.72 ms129446.6741951.3313.82 Mb
scala (2.12)http4s (0.18)65.22 ms19.31 ms45.01 ms257133.6745286.005.27 Mb
node (12.11)hapi (18.4)66.34 ms24.26 ms46.49 ms204446.6735301.336.07 Mb
crystal (0.31)athena (0.7)67.28 ms48.80 ms180.47 ms84270.6724144.672.01 Mb
php (7.3)laravel (6.4)81.27 ms22.88 ms171.46 ms188420.3335942.3311.89 Mb
node (12.11)moleculer (0.13)85.99 ms27.18 ms60.05 ms254224.6730295.333.46 Mb
python (3.7)quart (0.10)88.90 ms75.34 ms156.00 ms46756.3311209.001.48 Mb
python (3.7)cherrypy (18.3)89.78 ms73.66 ms79.81 ms233050.001373.670.21 Mb
go (1.13)gramework (1.6)96.01 ms97.62 ms102.01 ms18890.3310148.001.72 Mb
python (3.7)tornado (5.1)101.51 ms100.19 ms126.70 ms34740.679525.331.87 Mb
python (3.7)django (2.2)105.60 ms93.93 ms163.05 ms36832.339189.331.77 Mb
java (8)javalin (3.5)125.41 ms11.66 ms290.35 ms362928.3356370.676.67 Mb
python (3.7)masonite (2.2)138.54 ms129.50 ms179.52 ms53997.676988.671.14 Mb
perl (5.3)dancer2 (2.0)162.32 ms58.98 ms364.69 ms338036.671492.000.22 Mb
crystal (0.31)onyx (0.5)193.90 ms193.25 ms226.42 ms28283.675066.000.87 Mb
scala (2.12)akkahttp (10.1)220.51 ms7.35 ms96.23 ms873321.6765406.009.38 Mb
python (3.7)cyclone (1.3)399.23 ms351.41 ms445.11 ms460804.672202.670.37 Mb
python (3.7)nameko (2.12)655.34 ms551.15 ms613.68 ms770037.671278.000.18 Mb

You can find original page https://github.com/the-benchmarker/web-frameworks#results

How to install scala and create a class on Win & Linux

1 – Verify the JDK installation on your machine. Open the shell/terminal and type java -version and javac -version.

2 – Download Scala Binaries from http://www.scala-lang.org/download/. As of writing this post Scala version is 2.11.6, so you should be getting downloaded file as scala-2.11.6.tgz. Unzip the scala-2.11.6.tgz file using the following command as shown below.

3 – tar -xvzf scala-2.11.6.tgz

4 – After unzipping, change the path to point to the directory using cd command as shown below.

5 – For instance my directory is Downloads in which Scala binaries are unzipped.

6 – Now we are in the downloads directory where Scala binaries are present. Just go to the bin directory.

7 – cd scala-2.11.6 / cd bin

8 – This is the Scala REPL shell in which we can type programs and see the outcome right in the shell.

Scala Hello World Example

class Student() {
var id:Int = 0
var age:Int = 0
def studentDetails(i:Int,a:Int) {
id = i
age = a
println(“Student Id is :”+id);
println(“Student Age is :”+age);

Output: defined class Student

Here we create a Student class and print the student details in the studentDetails method by passing student id and age as parameter. If there are no errors in the code then a message “defined class Student” is displayed.

Create the student object and invoke the studdetails method by passing the student id and age.

object Stud {
def main(args:Array[String]) {
val stu = new Student();

Returns: defined object Stud

How to get data from Twitch-API with python

Hello Everyone,

This is a small quick py script for how to call API and how to parse json to csv with pandas for the beginners.

1 – Install below libraries,

import pandas as pd
import requests
import json

2 – Set your static values, btw you can use those values with .yml file

url = “https://wind-bow.glitch.me/twitch-api/channels/”
# List of channels we want to access
channels = [“ESL_SC2”, “OgamingSC2”, “cretetion”, “freecodecamp”, “storbeck”, “habathcx”, “RobotCaleb”, “noobs2ninjas”,
“ninja”, “shroud”, “Dakotaz”, “esltv_cs”, “pokimane”, “tsm_bjergsen”, “boxbox”, “a_seagull”,
“kinggothalion”, “jahrein”, “thenadeshot”, “sivhd”, “kingrichard”]

file_name = “talih.csv”
location =”C:\\Users\\talih\\Desktop\\TwitchPy\\TwitchAPI\\”
“”” Those values can be used with .yml file “””

3 – Set your class,functions:
class apicrawler:

def __init__(self,url,channels,file_name,location):
self.url = url
self.channels = channels
self.file_name = file_name
self.location = location

def selectedchannelcrawler(url,channels,location,file_name):
channels_list = []
for channel in channels:
JSONContent = requests.get(url + channel).json()
channels_list.append([JSONContent[‘_id’], JSONContent[‘display_name’], JSONContent[‘status’],
JSONContent[‘followers’], JSONContent[‘views’]])

dataset = pd.DataFrame(channels_list)
dataset.columns = [‘Id’, ‘Name’, ‘Status’, ‘Followers’, ‘Views’]
dataset.dropna(axis = 0, how = ‘any’, inplace = True)
dataset.index = pd.RangeIndex(len(dataset.index))
dataset.to_csv(location + file_name, sep=’,’, encoding=’utf-8′)

4 – Call that class for your own values:


How to use dbt in python environment

Dbt is usefull library for dwh to create a datamart or datamarts. You can find all details in dbt official pages.

I used a few times, so i can clarify for you how you can create a dbt models and dbt configs in your own project, you can do that like below step by steps;

1 – Create a profiles.yml file for DBT Profile. Specify your db connection information etc.
2 – Create a data_model folder like project_dir
3 – Create a .yml file for main project .yml file and you will call it like project_file
4 – Create your own dbt_runner file like dbt_runner.py and set it your execution configs
5 – Create a model folder, you will put your models in that folder
6 – Create a schema or model for yourself and put into that folder a xxxx.schema.yml file
6.1 – Put some table value like below;
– somthng_id
– somthng_id
– xxx_id

In the end, you will have like below folder and schema;
# DBT Profile. Specify your DB connection information etc.
profiles.yml on the root directory
bietl_data_model folder
bietl_datamarts.yml file
dbt_runner.py python file

> models
> specification of your models bietl
> bietl_datamarts.schema.yml
> sql files for using.sql

I’m executing that dbt in airflow das but I didn’t mention it, maybe in next post.