Using Spark as a Database

You must have heard that Apache Spark is a powerful distributed data processing engine. But do you know that Spark (with the help of Hive) can also act as a database? So, in this blog, we will learn how Apache Spark can be leveraged as a database by creating tables in it and querying upon them.

Introduction

Since Spark is a database in itself, we can create databases in Spark. Once we have a database we can create tables and views in that database.

The table has got two parts – Table Data and Table Metadata. The table data resides as data files in your distributed storage. The metadata is stored in a meta-store called catalog. It includes schema info, table name, database name, column names, partitions and the physical location of actual data. By default, Spark comes with an in-memory catalog which is maintained per session. To persist it, Spark uses Apache Hive meta-store.

Spark Tables

In Spark, we have 2 types of tables-

1. Managed Tables

2. Unmanaged or External Tables

For managed tables, Spark manages both the table data and the metadata. It means that it creates the metadata in the meta-store and then writes the data inside a predefined directory location. This directory is the Spark SQL warehouse directory which is the base location for all the managed tables.

If we delete a managed table, Spark deletes both the metadata and table data.

Now, let’s come to the unmanaged tables. These are same as managed tables w.r.t metadata but differ in terms of data storage location. Spark only creates the metadata for these in the meta-store. When creating unmanaged tables, we must specify the location of the data directory for our table. This gives us the flexibility to store the data at a preferred location. These are useful when we want to use Spark SQL on some pre-existing data. If we delete an unmanaged table, Spark only deletes the metadata and doesn’t touch the table data. Makes sense!

We have some added benefits with managed tables such as bucketing and sorting. Future Spark SQL enhancements will also target managed tables and not unmanaged tables. As such, the rest of our discussion will only target managed tables.

#database #sql #spark #apache-spark #developer

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Using Spark as a Database

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Codeigniter 4 Autocomplete Textbox From Database using Typeahead JS - Tuts Make

Autocomplete textbox search from database in codeigniter 4 using jQuery Typeahead js. In this tutorial, you will learn how to implement an autocomplete search or textbox search with database using jquery typehead js example.

This tutorial will show you step by step how to implement autocomplete search from database in codeigniter 4 app using typeahead js.

Autocomplete Textbox Search using jQuery typeahead Js From Database in Codeigniter

  • Download Codeigniter Latest
  • Basic Configurations
  • Create Table in Database
  • Setup Database Credentials
  • Create Controller
  • Create View
  • Create Route
  • Start Development Server

https://www.tutsmake.com/codeigniter-4-autocomplete-textbox-from-database-using-typeahead-js/

#codeigniter 4 ajax autocomplete search #codeigniter 4 ajax autocomplete search from database #autocomplete textbox in jquery example using database in codeigniter #search data from database in codeigniter 4 using ajax #how to search and display data from database in codeigniter 4 using ajax #autocomplete in codeigniter 4 using typeahead js

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System Databases in SQL Server

Introduction

In SSMS, we many of may noticed System Databases under the Database Folder. But how many of us knows its purpose?. In this article lets discuss about the System Databases in SQL Server.

System Database

Fig. 1 System Databases

There are five system databases, these databases are created while installing SQL Server.

  • Master
  • Model
  • MSDB
  • Tempdb
  • Resource
Master
  • This database contains all the System level Information in SQL Server. The Information in form of Meta data.
  • Because of this master database, we are able to access the SQL Server (On premise SQL Server)
Model
  • This database is used as a template for new databases.
  • Whenever a new database is created, initially a copy of model database is what created as new database.
MSDB
  • This database is where a service called SQL Server Agent stores its data.
  • SQL server Agent is in charge of automation, which includes entities such as jobs, schedules, and alerts.
TempDB
  • The Tempdb is where SQL Server stores temporary data such as work tables, sort space, row versioning information and etc.
  • User can create their own version of temporary tables and those are stored in Tempdb.
  • But this database is destroyed and recreated every time when we restart the instance of SQL Server.
Resource
  • The resource database is a hidden, read only database that holds the definitions of all system objects.
  • When we query system object in a database, they appear to reside in the sys schema of the local database, but in actually their definitions reside in the resource db.

#sql server #master system database #model system database #msdb system database #sql server system databases #ssms #system database #system databases in sql server #tempdb system database

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How to Store Form Data in Database using PHP -2020

In this php code to insert form data into mysql database. I will show you simple way of how to create an html form that stores data in a mysql database using php.

#How to store form data in database using php

  1. Create html form
  2. Create mysql database connection file
  3. Create php file to insert form data into mysql database

https://www.tutsmake.com/php-code-insert-data-into-mysql-database-from-form/

#php code for inserting data into database from form #how to insert data in mysql using php form #how to insert data into database in php using xampp #how to save data from html form to a database using php #how to save data in database on button click in php

Top Spark Development Companies | Best Spark Developers - TopDevelopers.co

An extensively researched list of top Apache spark developers with ratings & reviews to help find the best spark development Companies around the world.

Our thorough research on the ace qualities of the best Big Data Spark consulting and development service providers bring this list of companies. To predict and analyze businesses and in the scenarios where prompt and fast data processing is required, Spark application will greatly be effective for various industry-specific management needs. The companies listed here have been skillfully boosting businesses through effective Spark consulting and customized Big Data solutions.

Check out this list of Best Spark Development Companies with Best Spark Developers.

#spark development service providers #top spark development companies #best big data spark development #spark consulting #spark developers #spark application

Using Spark as a Database

You must have heard that Apache Spark is a powerful distributed data processing engine. But do you know that Spark (with the help of Hive) can also act as a database? So, in this blog, we will learn how Apache Spark can be leveraged as a database by creating tables in it and querying upon them.

Introduction

Since Spark is a database in itself, we can create databases in Spark. Once we have a database we can create tables and views in that database.

The table has got two parts – Table Data and Table Metadata. The table data resides as data files in your distributed storage. The metadata is stored in a meta-store called catalog. It includes schema info, table name, database name, column names, partitions and the physical location of actual data. By default, Spark comes with an in-memory catalog which is maintained per session. To persist it, Spark uses Apache Hive meta-store.

Spark Tables

In Spark, we have 2 types of tables-

1. Managed Tables

2. Unmanaged or External Tables

For managed tables, Spark manages both the table data and the metadata. It means that it creates the metadata in the meta-store and then writes the data inside a predefined directory location. This directory is the Spark SQL warehouse directory which is the base location for all the managed tables.

If we delete a managed table, Spark deletes both the metadata and table data.

Now, let’s come to the unmanaged tables. These are same as managed tables w.r.t metadata but differ in terms of data storage location. Spark only creates the metadata for these in the meta-store. When creating unmanaged tables, we must specify the location of the data directory for our table. This gives us the flexibility to store the data at a preferred location. These are useful when we want to use Spark SQL on some pre-existing data. If we delete an unmanaged table, Spark only deletes the metadata and doesn’t touch the table data. Makes sense!

We have some added benefits with managed tables such as bucketing and sorting. Future Spark SQL enhancements will also target managed tables and not unmanaged tables. As such, the rest of our discussion will only target managed tables.

#database #sql #spark #apache-spark #developer