Ramkumar M’s Post

Today, I want to take you on a detailed journey of what happens when you type "SELECT * FROM" in PostgreSQL. Many of us use this command daily, but few know the complex process behind it. Let's dive in! What Happens When You Type SELECT * FROM in PostgreSQL: 1. Database Server Interaction:    - Your query first reaches the PostgreSQL database server.    - The server manages multiple databases (DB1, DB2, DB3, etc.).    - It interacts with various components: Tables, Views, Indexes, and Functions. 2. PostgreSQL Server Process:    - The main PostgreSQL server process activates.    - It creates a backend process specifically for your request.    - Each client request gets its own backend process for handling. 3. Query Processing Pipeline:    a) Parser:    - Breaks your SQL query into tokens.    - Creates a parse tree.    - Checks the syntax and semantics of your query.    b) Analyzer:       - Performs a deeper analysis of what your query means.       - Generates a query tree based on the SELECT structure.    c) Rewriter:       - Applies any predefined rules to transform your query.       - May create one or more modified query trees.    d) Planner:       - Analyzes different ways to execute your query.       - Chooses the fastest execution plan.       - Optimizes for speed considering various factors.    e) Executor:       - Follows the plan created by the Planner.       - Retrieves the data rows you requested. 4. Background Processes:    While your query is being processed, several background processes are at work:    - Archiver: Helps in backing up data.    - Background Writer: Writes changed data to disk.    - Checkpointer: Ensures data is consistently saved.    - AutoVacuum: Cleans up old or unused data.    - WAL Writer: Manages the Write-Ahead Log for data safety.    - Statistics Collector: Gathers information about database operations. 5. Memory Management:    - Uses both shared memory (for all processes) and local memory (for individual processes).    - Efficiently handles data in memory for faster processing. This entire process happens within milliseconds, showcasing the power and efficiency of PostgreSQL. Understanding this process can help in writing more efficient queries and appreciating the complexity of database operations. It's fascinating how much happens behind a simple SELECT statement! What part of this process surprised you the most? Do you think knowing this will change how you write your queries? I'd love to hear your thoughts and experiences with PostgreSQL or other database systems. This graphics is re-created from one of the masterpieces by Mahesh Mallikarjunaiah ↗️

While looking for a database management system (DBMS) for our next project, we had a chance to deep dive into each DBMS internal, especially Postgres and MySQL which are two most popular open-source DBMS. We would like to maintain a series of posts to share what we learned to the community as well as keep it as our documentation. It is very important to understand the mechanism of each candidate to go to the right decision. For relational database system, we need to understand the way it stores data, how the indexes work, the scalability, compatibility and last but not least, the license and cost. In this post, we will have a look at the data store mechanism of Postgres. It is called heap organised tables.

We have often seen many Customers asking one of the very interesting questions. 🔥 We have deleted a large amount of data from some of the tables, but we still do not see the space reclaimed to disk. Why ? 🔥 🎇 Some of the database engines may also see performance degradation (no improvement) eventually, as the live tuples/records are scattered across more number of data blocks than actually required. This may be because the data that got deleted was scattered across a lot of data blocks but not really stored sequentially within a few data blocks. Queries on such tables may involve scanning more data blocks for a very few records. We call this as Table fragmentation. 🐘 This concern may not be limited to #postgres, the same is also applicable for #oracle or #sqlserver or any other database engine. 👨💼 👩💼 Database and Application Architects generally implement techniques like Partitioning and Sharding wherever applicable so that the challenges of data deletion/archival are completely avoided with a simple partition drop instead of a time and resource intensive "delete" statement. We will extend a little more on database scalability sometime later. For now, let us see what can we do to reclaim this space, if you are using PostgreSQL. 💰 Considering the resources and expertise available for #Oracle and #SQLServer like database engines, users might have already been aware of solutions to rebuild tables online in Oracle or SQL Server. 💸 Do note that such options are available only with Enterprise versions for most of the commercial database engines. For users migrating from #Oracle to #PostgreSQL or newly deploying #postgres as the database engine, you could use one of the popular OpenSource extension called : pg_repack, that can safely rebuild tables online and reclaim the space back to disk. Manisankar K from HexaCluster has published this article on pg_repack and also compared with solutions available for the Oracle Enterprise versions. If you have a similar challenge where you could not avoid a massive delete but still want to rebuild the table online to reclaim space, read this article to safely implement pg_repack for PostgreSQL. Read the full Blog Post here: https://lnkd.in/gZtmB3-P Contact HexaCluster for expert assistance for your PostgreSQL databases and for database migrations to PostgreSQL. #postgres #postgresql #oracle #sqlserver #hexacluster #databasemigration #databasemaintenance

We have often seen many Customers asking one of the very interesting question. 🔥 We have deleted a large amount of data from some of the tables, but we still do not see the space reclaimed to disk. Why ? 🔥 🎇 Some of the database engines may also see performance degradation (no improvement) eventually, as the live tuples/records are scattered across more number of data blocks than actually required. This may be because the data that got deleted was scattered across a lot of data blocks but not really stored sequentially within a few data blocks. Queries on such tables may involve scanning more data blocks for a very few records. We call this as Table fragmentation. 🐘 This concern may not be limited to #postgres, the same is also applicable for #oracle or #sqlserver or any other database engine. 👨💼 👩💼 Database and Application Architects generally implement techniques like Partitioning and Sharding wherever applicable so that the challenges of data deletion/archival are completely avoided with a simple partition drop instead of a time and resource intensive "delete" statement. We will extend a little more on database scalability sometime later. For now, let us see what can we do to reclaim this space, if you are using PostgreSQL. 💰 Considering the resources and expertise available for #Oracle and #SQLServer like database engines, users might have already been aware of solutions to rebuild tables online in Oracle or SQL Server. 💸 Do note that such options are available only with Enterprise versions for most of the commercial database engines. For users migrating from #oracle to #PostgreSQL or newly deploying #postgres as the database engine, you could use one of the popular OpenSource extension called : pg_repack, that can safely rebuild tables online and reclaim the space back to disk. Manisankar K from HexaCluster has published this article on pg_repack and also compared with solutions available for the Oracle Enterprise versions. If you have a similar challenge where you could not avoid a massive delete but still want to rebuild the table online to reclaim space, read this article to safely implement pg_repack for PostgreSQL. Contact HexaCluster for expert assistance for your PostgreSQL databases and for database migrations to PostgreSQL. #postgres #postgresql #oracle #sqlserver #hexacluster #databasemigration #databasemaintenance

Check out this post by Brij kishore Pandey Today, I want to take you on a detailed journey of what happens when you type "SELECT * FROM" in PostgreSQL. Many of us use this command daily, but few know the complex process behind it. Let's dive in! What Happens When You Type SELECT * FROM in PostgreSQL: 1. Database Server Interaction: - Your query first reaches the PostgreSQL database server. - The server manages multiple databases (DB1, DB2, DB3, etc.). - It interacts with various components: Tables, Views, Indexes, and Functions. 2. PostgreSQL Server Process: - The main PostgreSQL server process activates. - It creates a backend process specifically for your request. - Each client request gets its own backend process for handling. 3. Query Processing Pipeline: a) Parser: - Breaks your SQL query into tokens. - Creates a parse tree. - Checks the syntax and semantics of your query. b) Analyzer: - Performs a deeper analysis of what your query means. - Generates a query tree based on the SELECT structure. c) Rewriter: - Applies any predefined rules to transform your query. - May create one or more modified query trees. d) Planner: - Analyzes different ways to execute your query. - Chooses the fastest execution plan. - Optimizes for speed considering various factors. e) Executor: - Follows the plan created by the Planner. - Retrieves the data rows you requested. 4. Background Processes: While your query is being processed, several background processes are at work: - Archiver: Helps in backing up data. - Background Writer: Writes changed data to disk. - Checkpointer: Ensures data is consistently saved. - AutoVacuum: Cleans up old or unused data. - WAL Writer: Manages the Write-Ahead Log for data safety. - Statistics Collector: Gathers information about database operations. 5. Memory Management: - Uses both shared memory (for all processes) and local memory (for individual processes). - Efficiently handles data in memory for faster processing. This entire process happens within milliseconds, showcasing the power and efficiency of PostgreSQL. Understanding this process can help in writing more efficient queries and appreciating the complexity of database operations. It's fascinating how much happens behind a simple SELECT statement! What part of this process surprised you the most? Do you think knowing this will change how you write your queries? I'd love to hear your thoughts and experiences with PostgreSQL or other database systems. This graphics is re-created from one of the masterpieces by Mahesh Mallikarjunaiah ↗️️

pg_stat_statement – your gateway to PostgreSQL performance optimization 🐘 🚀 As a PostgreSQL DBA, it is essential to have visibility into database performance metrics to optimize database performance. pg_stat_statement is an extension that provides valuable insights into the query performance of a PostgreSQL database. ⭐ What is pg_stat_statement? pg_stat_statement records statistics about the SQL statements executed in a database. It collects query performance data such as the total execution time, the number of times the query was executed, and the total time spent executing the query. pg_stat_statement also records information about the query plan, which can help identify slow queries that require optimization. The statistics collected by pg_stat_statement can be used to monitor database performance, optimize queries, and diagnose performance problems. ⭐ How does it collect data? pg_stat_statement collects data by intercepting SQL statements sent to the database server. Whenever a query is executed, pg_stat_statement records various statistics about the query, such as its execution time, the number of rows returned, and the number of times the query has been executed. This information is stored in a system table called pg_stat_statements. ⭐ Benefits pg_stat_statement is an essential tool for PostgreSQL DBAs. It provides a wealth of information about the performance of your database queries. Some of its benefits include: ➡ Identifying slow queries: pg_stat_statement provides detailed performance metrics for each SQL statement executed in a PostgreSQL database. This information can help identify slow queries that require optimization. ➡ Optimizing database performance: By identifying slow queries, PostgreSQL DBAs can optimize database performance and improve query response times. ➡ Improving application performance: Optimizing database performance can improve application performance by reducing the time it takes for queries to execute. ➡ Track change in performance: pg_stat_statement provides a way to track changes in query performance over time, allowing you to monitor the impact of changes to your database schema or query execution plan. 👉 Overhead pg_stat_statement can cause overhead because it collects detailed performance data about every SQL statement executed in a PostgreSQL database. However, this overhead is minimal and typically has a negligible impact on database performance. The performance overhead of pg_stat_statement can be further reduced by configuring it to only record statistics for specific SQL statements or users. 👉 Limitations pg_stat_statement has a few limitations that PostgreSQL DBAs should be aware of. For example, pg_stat_statement does not record performance data for SQL statements executed within a transaction that was rolled back. Additionally, the information recorded by pg_stat_statement is not persisted across server restarts unless the server is configured to do so explicitly.

🚀 Streamline Your Data Migration with PostgreSQL's pgloader! 🚀 Hello PostgreSQL community! 🙌 Are you looking to migrate data seamlessly from various sources into PostgreSQL? Say hello to pgloader, a powerful and flexible tool designed to simplify the data loading process. Whether you're moving data from MySQL, SQLite, or CSV files, pgloader offers a robust solution to get your data where it needs to be efficiently. What is pgloader? pgloader is an open-source data loading tool that allows you to migrate data to PostgreSQL from different databases and file formats. It supports a wide range of input formats, including MySQL, SQLite, CSV, and more. pgloader automates the data conversion and loading process, ensuring a smooth and reliable migration. Key Features of pgloader 1. Broad Source Support pgloader supports various source databases and file formats, making it a versatile tool for different migration scenarios. You can migrate data from: MySQL SQLite MS SQL Server CSV files And more! 2. Automated Data Transformation pgloader handles data type conversions automatically, ensuring that your data is correctly transformed and loaded into PostgreSQL. 3. Incremental and Continuous Migration pgloader supports incremental and continuous migration, allowing you to keep your source and target databases in sync. 4. High Performance pgloader is designed for speed and efficiency. It uses parallel processing and bulk loading techniques to maximize throughput and minimize migration time, even for large datasets. 5. Easy Configuration pgloader offers a simple configuration syntax, making it easy to define your migration tasks. Advanced Features Data Transformation pgloader allows you to define complex data transformations during the migration process. You can map source columns to target columns, apply functions to transform data, and handle data type conversions seamlessly. Error Handling and Logging pgloader provides detailed logging and error handling capabilities. You can configure it to log errors, warnings, and progress, helping you troubleshoot any issues that arise during the migration. Continuous Integration Integrate pgloader into your continuous integration (CI) pipelines to automate data migrations and keep your databases in sync during development and testing. Best Practices for Using pgloader Plan Your Migration Monitor Performance Keep Data Secure Conclusion pgloader is a powerful and versatile tool that simplifies the data migration process to PostgreSQL. With its broad source support, automated data transformation, and high-performance capabilities, pgloader is an essential tool for any PostgreSQL user looking to streamline data migrations. 🔗 Learn More About pgloader at pgloader Official Documentation 🌟 https://pgloader.io/ #PostgreSQL #DataMigration #DatabaseManagement #DataLoading #TechTools #ITProfessional #Bitnine Let's streamline our data migrations with pgloader for efficient and reliable PostgreSQL integrations! 🚀

What Happens When You Type SELECT * FROM in #PostgreSQL: #shared Brij kishore Pandey Thanks for the detailed insights

Today, I want to take you on a detailed journey of what happens when you type "SELECT * FROM" in PostgreSQL

Are you looking to streamline your data insertion process in PostgreSQL? 🌟 Check out my new blog post where I break down two effective methods for inserting multiple rows. From using a single INSERT statement to leveraging the COPY command, these techniques will help you work smarter, not harder! Let's optimize your database operations! #PostgreSQL #CodingTips #DatabaseOptimization