Lindsey  Koepp

Lindsey Koepp

1602321137

Hazelcast and Spring Boot for Scalable Task Execution - A How-To Guide

Here at Peoplelogic, we aggregate and stitch together a lot of data from a lot of different sources on a regular basis – helping companies stay out of head of risks to their growth. Some of this aggregation takes a long time and we needed a way to distribute the processing of all this data across our fleet.

When we first started looking into how to solve this problem, we had a hard time finding the right combination of libraries, examples, and versions to combine Spring Boot, Quartz, and Hazelcast into a distributed task engine.  Each one works great on their own, but what if we wanted to combine them?

This article shows you exactly that (along with combining Spring Beans and Hazelcast as a bonus!) and assumes that you’re moderately familiar with the structure and setup of a Spring Boot project (or at least the Spring Framework!). We will use IntelliJ IDEA for the project, but any integrated development environment (IDE) that supports Gradle projects should work just fine.  Let’s get started!

Setup Your Project – Initializing Spring Boot and Gradle

Start with the Spring Initializr, either from inside IntelliJ IDEA or via the web, or by cloning our demo starter project at https://github.com/peoplelogic/article-starter. You’ll wind up with a project structure that looks a bit like the following:

With the project foundation in place, let’s get the project ready for Hazelcast. Open up the build.gradle file from the project you just cloned. Look for the dependencies section and add the following:

implementation 'com.hazelcast:hazelcast-all:3.12.9' 

#java #springboot #prog #good-company #scalable-task-execution #hazelcast #initialize-spring-boot #initialize-gradle

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Hazelcast and Spring Boot for Scalable Task Execution - A How-To Guide
Grace  Lesch

Grace Lesch

1639778400

PySQL Tutorial: A Database Framework for Python

PySQL 

PySQL is database framework for Python (v3.x) Language, Which is based on Python module mysql.connector, this module can help you to make your code more short and more easier. Before using this framework you must have knowledge about list, tuple, set, dictionary because all codes are designed using it. It's totally free and open source.

Tutorial Video in English (Watch Now)

IMAGE ALT TEXT HERE

Installation

Before we said that this framework is based on mysql.connector so you have to install mysql.connector first on your system. Then you can import pysql and enjoy coding!

python -m pip install mysql-connector-python

After Install mysql.connector successfully create Python file download/install pysql on the same dir where you want to create program. You can clone is using git or npm command, and you can also downlaod manually from repository site.

PyPi Command

Go to https://pypi.org/project/pysql-framework/ or use command

pip install pysql-framework

Git Command

git clone https://github.com/rohit-chouhan/pysql

Npm Command

Go to https://www.npmjs.com/package/pysql or use command

$ npm i pysql

Snippet Extention for VS Code

Install From Here https://marketplace.visualstudio.com/items?itemName=rohit-chouhan.pysql

IMAGE ALT TEXT HERE

Table of contents

Connecting a Server


To connect a database with localhost server or phpmyadmin, use connect method to establish your python with database server.

import pysql

db = pysql.connect(
    "host",
    "username",
    "password"
 )

Create a Database in Server


Creating database in server, to use this method

import pysql

db = pysql.connect(
    "host",
    "username",
    "password"
 )
 pysql.createDb(db,"demo")
 #execute: CREATE DATABASE demo

Drop Database


To drop database use this method .

Syntex Code -

pysql.dropDb([connect_obj,"table_name"])

Example Code -

pysql.dropDb([db,"demo"])
#execute:DROP DATABASE demo

Connecting a Database


To connect a database with localhost server or phpmyadmin, use connect method to establish your python with database server.

import pysql

db = pysql.connect(
    "host",
    "username",
    "password",
    "database"
 )

Creating Table in Database


To create table in database use this method to pass column name as key and data type as value.

Syntex Code -


pysql.createTable([db,"table_name_to_create"],{
    "column_name":"data_type", 
    "column_name":"data_type"
})

Example Code -


pysql.createTable([db,"details"],{
    "id":"int(11) primary", 
     "name":"text", 
    "email":"varchar(50)",
    "address":"varchar(500)"
})

2nd Example Code -

Use can use any Constraint with Data Value


pysql.createTable([db,"details"],{
    "id":"int NOT NULL PRIMARY KEY", 
     "name":"varchar(20) NOT NULL", 
    "email":"varchar(50)",
    "address":"varchar(500)"
})

Drop Table in Database


To drop table in database use this method .

Syntex Code -

pysql.dropTable([connect_obj,"table_name"])

Example Code -

pysql.dropTable([db,"users"])
#execute:DROP TABLE users

Selecting data from Table


For Select data from table, you have to mention the connector object with table name. pass column names in set.

Syntex For All Data (*)-

records = pysql.selectAll([db,"table_name"])
for x in records:
  print(x)

Example - -

records = pysql.selectAll([db,"details"])
for x in records:
  print(x)
#execute: SELECT * FROM details

Syntex For Specific Column-

records = pysql.select([db,"table_name"],{"column","column"})
for x in records:
  print(x)

Example - -

records = pysql.select([db,"details"],{"name","email"})
for x in records:
  print(x)
#execute: SELECT name, email FROM details

Syntex Where and Where Not-

#For Where Column=Data
records = pysql.selectWhere([db,"table_name"],{"column","column"},("column","data"))

#For Where Not Column=Data (use ! with column)
records = pysql.selectWhere([db,"table_name"],{"column","column"},("column!","data"))
for x in records:
  print(x)

Example - -

records = pysql.selectWhere([db,"details"],{"name","email"},("county","india"))
for x in records:
  print(x)
#execute: SELECT name, email FROM details WHERE country='india'

Add New Column to Table


To add column in table, use this method to pass column name as key and data type as value. Note: you can only add one column only one call

Syntex Code -


pysql.addColumn([db,"table_name"],{
    "column_name":"data_type"
})

Example Code -


pysql.addColumn([db,"details"],{
    "email":"varchar(50)"
})
#execute: ALTER TABLE details ADD email varchar(50);

Modify Column to Table


To modify data type of column table, use this method to pass column name as key and data type as value.

Syntex Code -

pysql.modifyColumn([db,"table_name"],{
    "column_name":"new_data_type"
})

Example Code -

pysql.modifyColumn([db,"details"],{
    "email":"text"
})
#execute: ALTER TABLE details MODIFY COLUMN email text;

Drop Column from Table


Note: you can only add one column only one call

Syntex Code -

pysql.dropColumn([db,"table_name"],"column_name")

Example Code -

pysql.dropColumn([db,"details"],"name")
#execute: ALTER TABLE details DROP COLUMN name

Manual Execute Query


To execute manual SQL Query to use this method.

Syntex Code -

pysql.query(connector_object,your_query)

Example Code -

pysql.query(db,"INSERT INTO users (name) VALUES ('Rohit')")

Inserting data


For Inserting data in database, you have to mention the connector object with table name, and data as sets.

Syntex -

data =     {
    "db_column":"Data for Insert",
    "db_column":"Data for Insert"
}
pysql.insert([db,"table_name"],data)

Example Code -

data =     {
    "name":"Komal Sharma",
    "contry":"India"
}
pysql.insert([db,"users"],data)

Updating data


For Update data in database, you have to mention the connector object with table name, and data as tuple.

Syntex For Updating All Data-

data = ("column","data to update")
pysql.updateAll([db,"users"],data)

Example - -

data = ("name","Rohit")
pysql.updateAll([db,"users"],data)
#execute: UPDATE users SET name='Rohit'

Syntex For Updating Data (Where and Where Not)-

data = ("column","data to update")
#For Where Column=Data
where = ("column","data")

#For Where Not Column=Data (use ! with column)
where = ("column!","data")
pysql.update([db,"users"],data,where)

Example -

data = ("name","Rohit")
where = ("id",1)
pysql.update([db,"users"],data,where)
#execute: UPDATE users SET name='Rohit' WHERE id=1

Deleting data


For Delete data in database, you have to mention the connector object with table name.

Syntex For Delete All Data-

pysql.deleteAll([db,"table_name"])

Example - -

pysql.deleteAll([db,"users"])
#execute: DELETE FROM users

Syntex For Deleting Data (Where and Where Not)-

where = ("column","data")

pysql.delete([db,"table_name"],where)

Example -

#For Where Column=Data
where = ("id",1)

#For Where Not Column=Data (use ! with column)
where = ("id!",1)
pysql.delete([db,"users"],where)
#execute: DELETE FROM users WHERE id=1

--- Finish ---

Change Logs

[19/06/2021]
 - ConnectSever() removed and merged to Connect()
 - deleteAll() [Fixed]
 - dropTable() [Added]
 - dropDb() [Added]
 
[20/06/2021]
 - Where Not Docs [Added]

The module is designed by Rohit Chouhan, contact us for any bug report, feature or business inquiry.

Author: rohit-chouhan
Source Code: https://github.com/rohit-chouhan/pysql
License: Apache-2.0 License

#python 

Lindsey  Koepp

Lindsey Koepp

1602321137

Hazelcast and Spring Boot for Scalable Task Execution - A How-To Guide

Here at Peoplelogic, we aggregate and stitch together a lot of data from a lot of different sources on a regular basis – helping companies stay out of head of risks to their growth. Some of this aggregation takes a long time and we needed a way to distribute the processing of all this data across our fleet.

When we first started looking into how to solve this problem, we had a hard time finding the right combination of libraries, examples, and versions to combine Spring Boot, Quartz, and Hazelcast into a distributed task engine.  Each one works great on their own, but what if we wanted to combine them?

This article shows you exactly that (along with combining Spring Beans and Hazelcast as a bonus!) and assumes that you’re moderately familiar with the structure and setup of a Spring Boot project (or at least the Spring Framework!). We will use IntelliJ IDEA for the project, but any integrated development environment (IDE) that supports Gradle projects should work just fine.  Let’s get started!

Setup Your Project – Initializing Spring Boot and Gradle

Start with the Spring Initializr, either from inside IntelliJ IDEA or via the web, or by cloning our demo starter project at https://github.com/peoplelogic/article-starter. You’ll wind up with a project structure that looks a bit like the following:

With the project foundation in place, let’s get the project ready for Hazelcast. Open up the build.gradle file from the project you just cloned. Look for the dependencies section and add the following:

implementation 'com.hazelcast:hazelcast-all:3.12.9' 

#java #springboot #prog #good-company #scalable-task-execution #hazelcast #initialize-spring-boot #initialize-gradle

Were  Joyce

Were Joyce

1620751200

How to Configure the Interceptor With Spring Boot Application

In the video in this article, we take a closer look at how to configure the interceptor with the Spring Boot application! Let’s take a look!

#spring boot #spring boot tutorial #interceptor #interceptors #spring boot interceptor #spring boot tutorial for beginners

Were  Joyce

Were Joyce

1620720872

Spring vs Spring BooDifference Between Spring and Spring Boot

As an extension of the Spring Framework, Spring Boot is widely used to make development on Spring faster, more efficient and convenient. In this article, we will look at some of the parameters were using Spring Boot can drastically reduce the time and effort required in application development.

What is Spring?

Spring Boot

Difference between Spring and Spring Boot

Advantages of Spring Boot over Spring

Conclusion

#full stack development #spring #spring and spring boot #spring boot

Were  Joyce

Were Joyce

1623559620

Spring Native turns Spring apps into native executables

Spring Native beta release leverages GraalVM to compile Spring Java and Kotlin applications to native images, reducing startup time and memory overhead compared to the JVM.

Spring Native, for compiling Spring Java applications to standalone executables called native images, is now available as a beta release. Native images promise faster startup times and lower runtime memory overhead compared to the JVM.

Launched March 11 and available on start.spring.io, the Spring Native beta compiles Spring applications to native images using the GraalVM multi-language runtime. These standalone executables offer benefits including nearly instant startup (typically fewer than 100ms), instant peak performance, and lower memory consumption, at the cost of longer build times and fewer runtime optimizations than the JVM.

#spring native turns spring apps into native executables #spring native #spring #native executables #spring apps