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MySQL is a reliable and widely used DBMS that utilizes SQL and a relational model to manage data. MySQL is installed as part of LAMP in Linux, but you can install it separately.Even in Ubuntu 24.04, installing MySQL is straightforward. This guide outlines the steps to follow. Read on! Step-By-Step Guide to Install MySQL on Ubuntu 24.04 If you have a user account on your Ubuntu 24.04 and have sudo privileges, installing MySQL requires you to follow the procedure below. Step 1: Update the System’s Repository When installing packages on Ubuntu, you should update the system’s repository to refresh the sources list. Doing so ensures the MySQL package you install is the latest stable version. $ sudo apt update Step 2: Install MySQL Server Once the package index updates, the next step is to install the MySQL server package using the below command. $ sudo apt install mysql-server After the installation, start the MySQL service on your Ubuntu 24.04. $ sudo systemctl start mysql.service Step 3: Configure MySQL Before we can start working with MySQL, we need to make a couple of configurations. First, access the MySQL shell using the command below. $ sudo mysql Once the shell opens up, set a password for your ’root’ using the below syntax. ALTER USER ‘root’@’localhost’ IDENTIFIED WITH mysql_native_password BY ‘your_password’; We’ve also specified to use the mysql_native_password authentication method. Exit the MySQL shell. exit; Step 4: Run the MySQL Script One interesting feature of MySQL is that it offers a script that you should run to quickly set it up. The script prompts you to specify different settings based on your preference. For example, you will be prompted to set a password for the root user. Go through each prompt and respond accordingly. $ sudo mysql_secure_installation Step 5: Modify the Authentication Method After successfully running the MySQL installation script, you should change the authentication method and set it to use the auth_socket plugin. Start by accessing your MySQL shell using the root account. $ mysql -u root -p Once logged in, run the below command to modify the authentication plugin. ALTER USER ‘root’@’localhost’ IDENTIFIED WITH auth_socket; Step 6: Create a MySQL User So far, we have only access to MySQL using the root account. We should create a new user and specify what privileges they should have. When creating a new user, you must add their username and the login password using the syntax below. create user ‘username’@’localhost’ IDENTIFIED BY ‘password’; Now that the user is created, we need to specify what privileges the user has when using MySQL. For instance, you can give them privileges, such as CREATE, ALTER, etc., on a specific or all the databases. Here’s an example where we’ve specified a few privileges to the added user on all available databases. Feel free to specify whichever privileges are ideal for your user. GRANT CREATE, ALTER, INSERT, UPDATE, SELECT on *.* TO ‘username’@’localhost’ WITH GRANT OPTION; For the new user and the privileges to apply, flush the privileges and exit MySQL. flush privileges; Step 7: Confirm the Created User As the last step, we should verify that our user can access the database and has the specified privileges. Start by checking the MySQL service to ensure it is running. $ sudo systemctl status mysql Next, access MySQL using the credentials of the user you added in the previous step. $ mysql -u username -p A successful login confirms that you’ve successfully installed MySQL, configured it, and added a new user. Conclusion MySQL is a relational DBMS widely used for various purposes. It supports SQL in managing data, and this post discusses all the steps you should follow to install it on Ubuntu 24.04. Hopefully, you’ve installed MySQL on your Ubuntu 24.04 with the help of the covered steps. View the full article

Managing databases is an essential part of business. It helps you store and retrieve data on the go. With the latest technologies in the market, deciding which database management system is the best has become tough. One such necessary factor is the cost considerations of choosing a database. MySQL is a free database management system […]View the full article

The post How to Install Apache, MySQL/MariaDB and PHP in Linux first appeared on Tecmint: Linux Howtos, Tutorials & Guides .This how-to guide explains how to install the latest version of Apache, MySQL (or MariaDB), and PHP, along with the required PHP modules, on RHEL-based The post How to Install Apache, MySQL/MariaDB and PHP in Linux first appeared on Tecmint: Linux Howtos, Tutorials & Guides.View the full article

In today’s data-driven world, efficient workflow management and secure storage are essential for the success of any project or organization. If you have large datasets in a cloud-based project management platform like Hive, you can smoothly migrate them to a relational database management system (RDBMS), like MySQL. Now, you must be wondering why you should […]View the full article

Amazon Aurora MySQL zero-ETL integration with Amazon Redshift is now supported in 11 additional regions, enabling near real-time analytics and machine learning (ML) using Amazon Redshift. Based on your analytics needs, you can include or exclude specific databases and tables from an existing or a new zero-ETL integration and selectively bring data into Amazon Redshift. View the full article

MySQL provides several replication configuration options. However, ensuring it is done correctly may take time and effort, with considerable choices. Replication is a crucial initial step to enhance availability in MySQL databases. A properly designed replication architecture can significantly impact the accessibility of your data and prevent potential management complications. This article will delve into […] View the full article

Amazon Relational Database Service (Amazon RDS) for MySQL zero-ETL integration with Amazon Redshift was announced in preview at AWS re:Invent 2023 for Amazon RDS for MySQL version 8.0.28 or higher. In this post, we provide step-by-step guidance on how to get started with near real-time operational analytics using this feature. This post is a continuation of the zero-ETL series that started with Getting started guide for near-real time operational analytics using Amazon Aurora zero-ETL integration with Amazon Redshift. Challenges Customers across industries today are looking to use data to their competitive advantage and increase revenue and customer engagement by implementing near real time analytics use cases like personalization strategies, fraud detection, inventory monitoring, and many more. There are two broad approaches to analyzing operational data for these use cases: Analyze the data in-place in the operational database (such as read replicas, federated query, and analytics accelerators) Move the data to a data store optimized for running use case-specific queries such as a data warehouse The zero-ETL integration is focused on simplifying the latter approach. The extract, transform, and load (ETL) process has been a common pattern for moving data from an operational database to an analytics data warehouse. ELT is where the extracted data is loaded as is into the target first and then transformed. ETL and ELT pipelines can be expensive to build and complex to manage. With multiple touchpoints, intermittent errors in ETL and ELT pipelines can lead to long delays, leaving data warehouse applications with stale or missing data, further leading to missed business opportunities. Alternatively, solutions that analyze data in-place may work great for accelerating queries on a single database, but such solutions aren’t able to aggregate data from multiple operational databases for customers that need to run unified analytics. Zero-ETL Unlike the traditional systems where data is siloed in one database and the user has to make a trade-off between unified analysis and performance, data engineers can now replicate data from multiple RDS for MySQL databases into a single Redshift data warehouse to derive holistic insights across many applications or partitions. Updates in transactional databases are automatically and continuously propagated to Amazon Redshift so data engineers have the most recent information in near real time. There is no infrastructure to manage and the integration can automatically scale up and down based on the data volume. At AWS, we have been making steady progress towards bringing our zero-ETL vision to life. The following sources are currently supported for zero-ETL integrations: Amazon Aurora MySQL-Compatible Edition (generally available) Amazon Aurora PostgreSQL-Compatible Edition (preview) Amazon RDS for MySQL (preview) Amazon DynamoDB (limited preview) When you create a zero-ETL integration for Amazon Redshift, you continue to pay for underlying source database and target Redshift database usage. Refer to Zero-ETL integration costs (Preview) for further details. With zero-ETL integration with Amazon Redshift, the integration replicates data from the source database into the target data warehouse. The data becomes available in Amazon Redshift within seconds, allowing you to use the analytics features of Amazon Redshift and capabilities like data sharing, workload optimization autonomics, concurrency scaling, machine learning, and many more. You can continue with your transaction processing on Amazon RDS or Amazon Aurora while simultaneously using Amazon Redshift for analytics workloads such as reporting and dashboards. The following diagram illustrates this architecture. Solution overview Let’s consider TICKIT, a fictional website where users buy and sell tickets online for sporting events, shows, and concerts. The transactional data from this website is loaded into an Amazon RDS for MySQL 8.0.28 (or higher version) database. The company’s business analysts want to generate metrics to identify ticket movement over time, success rates for sellers, and the best-selling events, venues, and seasons. They would like to get these metrics in near real time using a zero-ETL integration. The integration is set up between Amazon RDS for MySQL (source) and Amazon Redshift (destination). The transactional data from the source gets refreshed in near real time on the destination, which processes analytical queries. You can use either the serverless option or an encrypted RA3 cluster for Amazon Redshift. For this post, we use a provisioned RDS database and a Redshift provisioned data warehouse. The following diagram illustrates the high-level architecture. The following are the steps needed to set up zero-ETL integration. These steps can be done automatically by the zero-ETL wizard, but you will require a restart if the wizard changes the setting for Amazon RDS or Amazon Redshift. You could do these steps manually, if not already configured, and perform the restarts at your convenience. For the complete getting started guides, refer to Working with Amazon RDS zero-ETL integrations with Amazon Redshift (preview) and Working with zero-ETL integrations. Configure the RDS for MySQL source with a custom DB parameter group. Configure the Redshift cluster to enable case-sensitive identifiers. Configure the required permissions. Create the zero-ETL integration. Create a database from the integration in Amazon Redshift. Configure the RDS for MySQL source with a customized DB parameter group To create an RDS for MySQL database, complete the following steps: On the Amazon RDS console, create a DB parameter group called zero-etl-custom-pg. Zero-ETL integration works by using binary logs (binlogs) generated by MySQL database. To enable binlogs on Amazon RDS for MySQL, a specific set of parameters must be enabled. Set the following binlog cluster parameter settings: binlog_format = ROW binlog_row_image = FULL binlog_checksum = NONE In addition, make sure that the binlog_row_value_options parameter is not set to PARTIAL_JSON. By default, this parameter is not set. Choose Databases in the navigation pane, then choose Create database. For Engine Version, choose MySQL 8.0.28 (or higher). For Templates, select Production. For Availability and durability, select either Multi-AZ DB instance or Single DB instance (Multi-AZ DB clusters are not supported, as of this writing). For DB instance identifier, enter zero-etl-source-rms. Under Instance configuration, select Memory optimized classes and choose the instance db.r6g.large, which should be sufficient for TICKIT use case. Under Additional configuration, for DB cluster parameter group, choose the parameter group you created earlier (zero-etl-custom-pg). Choose Create database. In a couple of minutes, it should spin up an RDS for MySQL database as the source for zero-ETL integration. Configure the Redshift destination After you create your source DB cluster, you must create and configure a target data warehouse in Amazon Redshift. The data warehouse must meet the following requirements: Using an RA3 node type (ra3.16xlarge, ra3.4xlarge, or ra3.xlplus) or Amazon Redshift Serverless Encrypted (if using a provisioned cluster) For our use case, create a Redshift cluster by completing the following steps: On the Amazon Redshift console, choose Configurations and then choose Workload management. In the parameter group section, choose Create. Create a new parameter group named zero-etl-rms. Choose Edit parameters and change the value of enable_case_sensitive_identifier to True. Choose Save. You can also use the AWS Command Line Interface (AWS CLI) command update-workgroup for Redshift Serverless: aws redshift-serverless update-workgroup --workgroup-name <your-workgroup-name> --config-parameters parameterKey=enable_case_sensitive_identifier,parameterValue=true Choose Provisioned clusters dashboard. At the top of you console window, you will see a Try new Amazon Redshift features in preview banner. Choose Create preview cluster. For Preview track, chose preview_2023. For Node type, choose one of the supported node types (for this post, we use ra3.xlplus). Under Additional configurations, expand Database configurations. For Parameter groups, choose zero-etl-rms. For Encryption, select Use AWS Key Management Service. Choose Create cluster. The cluster should become Available in a few minutes. Navigate to the namespace zero-etl-target-rs-ns and choose the Resource policy tab. Choose Add authorized principals. Enter either the Amazon Resource Name (ARN) of the AWS user or role, or the AWS account ID (IAM principals) that are allowed to create integrations. An account ID is stored as an ARN with root user. In the Authorized integration sources section, choose Add authorized integration source to add the ARN of the RDS for MySQL DB instance that’s the data source for the zero-ETL integration. You can find this value by going to the Amazon RDS console and navigating to the Configuration tab of the zero-etl-source-rms DB instance. Your resource policy should resemble the following screenshot. Configure required permissions To create a zero-ETL integration, your user or role must have an attached identity-based policy with the appropriate AWS Identity and Access Management (IAM) permissions. An AWS account owner can configure required permissions for users or roles who may create zero-ETL integrations. The sample policy allows the associated principal to perform the following actions: Create zero-ETL integrations for the source RDS for MySQL DB instance. View and delete all zero-ETL integrations. Create inbound integrations into the target data warehouse. This permission is not required if the same account owns the Redshift data warehouse and this account is an authorized principal for that data warehouse. Also note that Amazon Redshift has a different ARN format for provisioned and serverless clusters: Provisioned – arn:aws:redshift:{region}:{account-id}:namespace:namespace-uuid Serverless – arn:aws:redshift-serverless:{region}:{account-id}:namespace/namespace-uuid Complete the following steps to configure the permissions: On the IAM console, choose Policies in the navigation pane. Choose Create policy. Create a new policy called rds-integrations using the following JSON (replace region and account-id with your actual values): { "Version": "2012-10-17", "Statement": [{ "Effect": "Allow", "Action": [ "rds:CreateIntegration" ], "Resource": [ "arn:aws:rds:{region}:{account-id}:db:source-instancename", "arn:aws:rds:{region}:{account-id}:integration:*" ] }, { "Effect": "Allow", "Action": [ "rds:DescribeIntegration" ], "Resource": ["*"] }, { "Effect": "Allow", "Action": [ "rds:DeleteIntegration" ], "Resource": [ "arn:aws:rds:{region}:{account-id}:integration:*" ] }, { "Effect": "Allow", "Action": [ "redshift:CreateInboundIntegration" ], "Resource": [ "arn:aws:redshift:{region}:{account-id}:cluster:namespace-uuid" ] }] } Attach the policy you created to your IAM user or role permissions. Create the zero-ETL integration To create the zero-ETL integration, complete the following steps: On the Amazon RDS console, choose Zero-ETL integrations in the navigation pane. Choose Create zero-ETL integration. For Integration identifier, enter a name, for example zero-etl-demo. For Source database, choose Browse RDS databases and choose the source cluster zero-etl-source-rms. Choose Next. Under Target, for Amazon Redshift data warehouse, choose Browse Redshift data warehouses and choose the Redshift data warehouse (zero-etl-target-rs). Choose Next. Add tags and encryption, if applicable. Choose Next. Verify the integration name, source, target, and other settings. Choose Create zero-ETL integration. You can choose the integration to view the details and monitor its progress. It took about 30 minutes for the status to change from Creating to Active. The time will vary depending on the size of your dataset in the source. Create a database from the integration in Amazon Redshift To create your database from the zero-ETL integration, complete the following steps: On the Amazon Redshift console, choose Clusters in the navigation pane. Open the zero-etl-target-rs cluster. Choose Query data to open the query editor v2. Connect to the Redshift data warehouse by choosing Save. Obtain the integration_id from the svv_integration system table: select integration_id from svv_integration; -- copy this result, use in the next sql Use the integration_id from the previous step to create a new database from the integration: CREATE DATABASE zetl_source FROM INTEGRATION '<result from above>'; The integration is now complete, and an entire snapshot of the source will reflect as is in the destination. Ongoing changes will be synced in near real time. Analyze the near real time transactional data Now we can run analytics on TICKIT’s operational data. Populate the source TICKIT data To populate the source data, complete the following steps: Copy the CSV input data files into a local directory. The following is an example command: aws s3 cp 's3://redshift-blogs/zero-etl-integration/data/tickit' . --recursive Connect to your RDS for MySQL cluster and create a database or schema for the TICKIT data model, verify that the tables in that schema have a primary key, and initiate the load process: mysql -h <rds_db_instance_endpoint> -u admin -p password --local-infile=1 Use the following CREATE TABLE commands. Load the data from local files using the LOAD DATA command. The following is an example. Note that the input CSV file is broken into several files. This command must be run for every file if you would like to load all data. For demo purposes, a partial data load should work as well. Analyze the source TICKIT data in the destination On the Amazon Redshift console, open the query editor v2 using the database you created as part of the integration setup. Use the following code to validate the seed or CDC activity: SELECT * FROM SYS_INTEGRATION_ACTIVITY ORDER BY last_commit_timestamp DESC; You can now apply your business logic for transformations directly on the data that has been replicated to the data warehouse. You can also use performance optimization techniques like creating a Redshift materialized view that joins the replicated tables and other local tables to improve query performance for your analytical queries. Monitoring You can query the following system views and tables in Amazon Redshift to get information about your zero-ETL integrations with Amazon Redshift: SVV_INTEGRATION – Provides configuration details for your integrations SYS_INTEGRATION_ACTIVITY– Provides information about completed integration runs SVV_INTEGRATION_TABLE_STATE – Describes the table-level integration information To view the integration-related metrics published to Amazon CloudWatch, open the Amazon Redshift console. Choose Zero-ETL integrations in the navigation pane and choose the integration to display activity metrics. Available metrics on the Amazon Redshift console are integration metrics and table statistics, with table statistics providing details of each table replicated from Amazon RDS for MySQL to Amazon Redshift. Integration metrics contain table replication success and failure counts and lag details. Manual resyncs The zero-ETL integration will automatically initiate a resync if a table sync state shows as failed or resync required. But in case the auto resync fails, you can initiate a resync at table-level granularity: ALTER DATABASE zetl_source INTEGRATION REFRESH TABLES tbl1, tbl2; A table can enter a failed state for multiple reasons: The primary key was removed from the table. In such cases, you need to re-add the primary key and perform the previously mentioned ALTER command. An invalid value is encountered during replication or a new column is added to the table with an unsupported data type. In such cases, you need to remove the column with the unsupported data type and perform the previously mentioned ALTER command. An internal error, in rare cases, can cause table failure. The ALTER command should fix it. Clean up When you delete a zero-ETL integration, your transactional data isn’t deleted from the source RDS or the target Redshift databases, but Amazon RDS doesn’t send any new changes to Amazon Redshift. To delete a zero-ETL integration, complete the following steps: On the Amazon RDS console, choose Zero-ETL integrations in the navigation pane. Select the zero-ETL integration that you want to delete and choose Delete. To confirm the deletion, choose Delete. Conclusion In this post, we showed you how to set up a zero-ETL integration from Amazon RDS for MySQL to Amazon Redshift. This minimizes the need to maintain complex data pipelines and enables near real time analytics on transactional and operational data. To learn more about Amazon RDS zero-ETL integration with Amazon Redshift, refer to Working with Amazon RDS zero-ETL integrations with Amazon Redshift (preview). About the Authors Milind Oke is a senior Redshift specialist solutions architect who has worked at Amazon Web Services for three years. He is an AWS-certified SA Associate, Security Specialty and Analytics Specialty certification holder, based out of Queens, New York. Aditya Samant is a relational database industry veteran with over 2 decades of experience working with commercial and open-source databases. He currently works at Amazon Web Services as a Principal Database Specialist Solutions Architect. In his role, he spends time working with customers designing scalable, secure and robust cloud native architectures. Aditya works closely with the service teams and collaborates on designing and delivery of the new features for Amazon’s managed databases. View the full article

Amazon Aurora MySQL zero-ETL integration with Amazon Redshift now supports data filtering, enabling you to include or exclude specific databases and tables as part of the zero-ETL integration. Based on your analytics needs, filtering of specific databases and tables helps you selectively bring data into Amazon Redshift. In addition, you can now easily manage and automate the configuration and deployment of resources needed for an Aurora MySQL zero-ETL integration with Amazon Redshift using AWS CloudFormation. View the full article

Amazon Relational Database Service (Amazon RDS) for PostgreSQL, MySQL, and MariaDB now support AWS Graviton3-based M7g and R7g database instances in US West (N. California), Asia Pacific (Hyderabad, Seoul), Canada (Central), Europe (London, Spain), and Middle East (Bahrain). Graviton3-based instances provide up to a 30% performance improvement and up to a 27% price/performance improvement (based on on-demand pricing) over Graviton2-based instances on RDS for open-source databases depending on database engine, version, and workload View the full article

An End-To-End Tutorial for Beginners View the full article

Amazon Relational Database Service (Amazon RDS) now supports a Dedicated Log Volume for PostgreSQL, MySQL, and MariaDB databases. An Amazon RDS Dedicated Log Volume allows customers to select a configuration where the most latency sensitive components of their database, the transaction logs, are stored in a separate, dedicated volume. Dedicated Log Volumes work with Provisioned IOPS storage and are recommended for databases with 5,000 GiB or more of allocated storage. View the full article

Amazon Relational Database Service (Amazon RDS) now supports M6in, M6idn, R6in, and R6idn database (DB) instances for RDS for PostgreSQL, MySQL, and MariaDB. These network optimized DB instances deliver up to 200Gbps network bandwidth, which is 300% more than similar sized M6i and R6i database instances. Enhanced network bandwidth makes M6in and R6in DB instances ideal for write-intensive workloads. M6idn and R6idn support local block storage with up to 7.6 TB of NVMe-based solid state disk (SSD) storage. View the full article

Here is a list of checklist to improve MySQL query performance... View the full article

Relational database systems such as MySQL have long been the go-to choice for handling large and complex data handling tasks. But, with the introduction of new technologies like NoSQL, many developers started to question whether traditional relational databases still have the upper hand. However, when building high-scalability cloud applications, relational databases like MySQL still have […] View the full article

Docker is an open-source platform for building, shipping, and running applications in containers. Containers provide a lightweight and portable way to package and deploy software, making it easier to move applications between environments and platforms. By using Docker to containerize your database application, you can ensure that it runs consistently across different environments, making it easier to deploy and manage. In this tutorial, we will walk through the process of containerizing a MySQL database using Docker and connecting to it using DbVisualizer. We will start with a simple example and then move on to more complex scenarios, including using Docker Compose to orchestrate multiple containers and using environment variables to configure our container. View the full article

MySQL databases are known as system databases, which contain tables for storing the information required while running the MySQL server. MySQL categorizes tables into “Grant System Tables” and “Object Information System Tables”. Additionally, database table records can be removed or deleted wherever required. SQL queries, such as “DELETE” and “TRUNCATE” can be utilized for this corresponding purpose. The outcomes of this write-up are: What is MySQL’s “DELETE FROM” Statement? How to Run the “DELETE FROM” Statement in MySQL? What is MySQL’s “TRUNCATE” Statement? How to Run the “TRUNCATE” Statement in MySQL? What is MySQL’s “DELETE FROM” Statement? When a user wants to remove or delete particular records(row) from the MySQL database tables, the “DELETE” statement can be used. It is a Data Manipulation Language command which contains the “WHERE” clause. If the condition is specified, it will delete or remove only the particular records otherwise, the whole table records will be deleted. It can save the removed logs of the deleted record. Syntax The general syntax of the “DELETE” statement is given below: DELETE FROM <table-name> WHERE <condition>; How to Run the “DELETE FROM” Statement in MySQL? Follow the provided steps to use the DELETE FROM“ statement to delete the records in MySQL. Step 1: Launch Command Prompt Initially, search for the “Command Prompt” and launch it: Step 2: Access MySQL Server Then, connect the terminal to the MySQL server by executing the “mysql” command: mysql -u root -p As you can see, we have been successfully connected with the MySQL server: Step 3: List Databases Execute the provided command to list all databases: SHOW DATABASES; From the below given displayed list of databases, we chose the highlighted database: Step 4: Change Database Change the database through the “USE” command: USE mynewdb; Step 5: View Database Tables Now, list all the tables by utilizing the “SHOW” statement: SHOW TABLES; From the provided tables list, we want to delete the records from the “std1” table: Step 6: List Table Content To show the content of the databases tables, execute the “SELECT” command: SELECT * FROM Std1; As you can see, the specified table contains two records, and we want to remove the second row: Step 7: Delete Record Next, execute the “DELETE” statement with the “WHERE” clause to remove the particular record: DELETE FROM Std1 WHERE FirstName='Fatima'; Step 8: Verification Run the “SELECT” statement to ensure the record is deleted: SELECT * FROM Std1; It can be observed that the row has been deleted from the table: Let’s check out the next section and learn about the “TRUNCATE” statement. What is MySQL’s “TRUNCATE” Statement? To delete all the existing rows in the table, the “TRUNCATE” statement can be used. It is the “Data Definition Language” command, which removes all the table’s records. However, it cannot be used with the “WHERE” clause. Unlike the “DELETE” statement, the transaction log for every removed record is not stored. Once the delete is removed through the “TRUNCATE” command, it cannot be rolled back. Syntax The general syntax of the TRUNCATE“ command is: TRUNCATE TABLE <table-name>; How to Run the “TRUNCATE” Statement in MySQL? To remove the entire records of the MySQL databases table by utilizing “TRUNCATE“, check out the provided instructions. At first, execute the “SELECT” statement to show the table content: SELECT * FROM std1; To delete all the records, run the provided command: TRUNCATE TABLE Std1; Here, the “Std1” is the target table name: To ensure the whole table record is removed or not, use the provided statement: SELECT * FROM std1; It can be observed that the table is empty: That’s all! We have briefly explained the MySQL “DELETE” and “TRUNCATE” commands. Conclusion The “DELETE FROM <table-name> WHERE <condition>;” is the Data Manipulation Language command used for deleting specific records, which can contain the WHERE clause and save the deleted logs of removed records. On the other hand, the “TRUNCATE TABLE <table-name>;” is the Data Definition Language command which removes all the records from the table, and it does not contain the “WHERE” clause. This write-up discussed the MySQL “DELETE” and “TRUNCATE” commands. View the full article

MySQL is one of the oldest and most reliable open-source relational database management systems which is trusted and used by millions of users on daily basis... View the full article

Amazon Aurora Serverless v1 now supports in-place upgrade from MySQL 5.6 to 5.7. Instead of backing up and restoring the database to the new version, you can upgrade with just a few clicks using the Amazon RDS Management Console or using the latest AWS SDK or CLI. No new cluster is created in the process which means you keep the same endpoints and other characteristics of the cluster. The upgrade completes in minutes as no data needs to be copied to a new cluster volume. The upgrade can be applied immediately or during the maintenance window. Your database cluster will be unavailable during the upgrade. Review the Aurora documentation to learn more. View the full article

Following the announcement of updates in MySQL database versions 5.7 and 8.0, we have updated Amazon Relational Database Service (Amazon RDS) for MySQL to support MySQL minor versions 5.7.38, and 8.0.29. View the full article

Amazon Relational Database Service (Amazon RDS) for MySQL version 8.0 now supports M6i and R6i instances. M6i instances are the 6th generation of Amazon EC2 x86-based General Purpose compute instances, designed to provide a balance of compute, memory, storage, and network resources. R6i instances are the 6th generation of Amazon EC2 memory optimized instances, designed for memory-intensive workloads. Both M6i and R6i instances are built on the AWS Nitro System, a combination of dedicated hardware and lightweight hypervisor, which delivers practically all of the compute and memory resources of the host hardware to your instances. View the full article

Amazon Relational Database Service (Amazon RDS) now supports AWS Graviton2-based database (DB) instances in the regions of AWS GovCloud (US), Asia Pacific (Seoul), and Europe (Stockholm). Depending on DB engine, version, and workload, Graviton2 instances provide up to 35% performance improvement and up to 52% price/performance improvement over comparable current generation x86-based instances for Amazon RDS for MySQL, MariaDB, and PostgreSQL. View the full article

Starting today, you can use easily restore a new Amazon RDS for MySQL database instance from a backup of your existing MySQL 8.0 database, whether it’s running on Amazon EC2 or outside of AWS. This is done by using Percona XtraBackup to create a backup of your existing MySQL database, uploading the resulting files to an Amazon S3 bucket, and then creating a new Amazon RDS DB instance through the RDS Console or AWS Command Line Interface (CLI). View the full article

Amazon RDS Performance Insights supports an additional dimension to identify the source of high-frequency, long-running, and stuck SQL queries faster. The new Performance Insights dimension is available on Amazon RDS for MySQL, Amazon Aurora with MySQL compatibility, and Amazon RDS for MariaDB. View the full article

Amazon RDS for MySQL has been updated to support release 8.0.21 of the MySQL database. This release includes a number of bug fixes as well as functionality improvements. View the full article