In PostgreSQL, table bloat can negatively impact performance by increasing storage requirements and slowing down queries. pg_squeeze is a powerful tool designed to combat this issue by automatically reorganizing tables to reclaim wasted space without requiring downtime. This talk will explore the mechanics of table bloat in PostgreSQL, introduce the capabilities of pg_squeeze, and demonstrate how it helps maintain optimal database performance by performing non-blocking vacuum operations and table maintenance. Attendees will gain insights into how to integrate and configure pg_squeeze in their environments and learn about its advantages over traditional methods like VACUUM FULL. Whether you’re managing a busy production database or looking to improve PostgreSQL performance, this session will provide practical strategies to tackle table bloat effectively.
Features of postgres 17
Our idea explores the implementation of AI-driven query optimization in PostgreSQL, addressing the limitations of traditional optimization methods in handling modern database complexities. We present an innovative approach using reinforcement learning for automated index selection and query plan optimization. Our system leverages PostgreSQL’s pg_stat_statements for collecting query metrics and employs HypoPG for index simulation, while a neural network model learns optimal indexing strategies from historical query patterns. Through comprehensive testing on various workload scenarios, we will validate the model’s ability to adapt to dynamic query patterns and complex analytical workloads. The research also examines the scalability challenges and practical considerations of implementing AI optimization in production environments.
Our findings establish a foundation for future developments in self-tuning databases while offering immediate practical benefits for PostgreSQL deployments. This work contributes to the broader evolution of database management systems, highlighting the potential of AI in creating more efficient and adaptive query optimization solutions.
| In this talk, we will explore the emerging capabilities of vector search and how PostgreSQL, with its pgvector extension, is revolutionizing data retrieval by supporting AI/ML-powered vector-based indexing and search. As machine learning models generate high-dimensional vector embeddings, the need for efficient similarity searches has become critical in applications such as recommendation systems, image recognition, and natural language processing. |
This tech talk delves into the critical world of PostgreSQL query plans, providing attendees with the knowledge and tools to understand, analyze, and optimize their database queries. We’ll begin by defining query plans and emphasizing their crucial role in database performance. We’ll explore the inner workings of the PostgreSQL planner, examining how it leverages various optimization techniques like sequential scans, index scans, joins algorithms (hash join, merge join, nested loop), and more to craft the most efficient execution strategy for a given query.
The core of the talk focuses on practical analysis. Attendees will learn how to visualize and interpret query plans using EXPLAIN and ANALYZE commands, gaining insights into execution time, data access methods, and potential bottlenecks. We’ll demonstrate how to identify common performance issues like missing indexes, inefficient joins, or suboptimal query structures by deciphering the information within a query plan.
Finally, we’ll connect the dots between PostgreSQL’s optimization techniques and the resulting query plans. By understanding how the planner weighs factors like data distribution, table statistics, and available resources, attendees will be empowered to write better queries and proactively optimize their database schema for maximum performance. This session is essential for developers and database administrators seeking to unlock the full potential of PostgreSQL and ensure their applications run smoothly and efficiently.
This talk provides an introductory overview of Artificial Intelligence (AI) and Machine Learning (ML), exploring key concepts and their application in building intelligent systems. It will highlight the essential AI/ML techniques, such as supervised and unsupervised learning, and discuss practical use cases in modern industries. The session also focuses on how PostgreSQL, with its powerful extensions like PostgresML, TimescaleDB, and PostGIS, supports the development of AI-powered applications. By leveraging PostgreSQL’s ability to handle complex datasets and integrate machine learning models, participants will learn how to build scalable, intelligent solutions directly within the database environment.
Success is a multiplier of Action, External Factors and Destiny.
Out of these three, the only controllable aspect is our action. Again, action is the result of our EQ, IQ, SQ, and WQ (Willingness Quotient) together.
We all want to be successful and keep trying to motivate ourselves with external factors. We read inspirational books, listen to great personalities, and whenever possible upgrade ourselves with more knowledge and the list goes on.
Indeed these are excellent motivators, but in this process, we forget the most important source of energy, YOU!
We read other stories to feel inspired, thinking “I am not enough!”
But, the day we start accepting ourselves, introspect, understand, and align our life purpose with our routine, we find the internal POWER. This is a continuous source of motivation and energy which we need at down moments. When we feel, lonely, stuck and seek help, our inner voice is the greatest companion.
But, how many times do we consciously think about our “Subconscious”?
“Journey to Self” is our structured coaching program where we take back focus from the outside and delve deep inside to find our inner strength. Focusing on self-acceptance and personal growth
I believe everyone has POWER within them!
Let’s be the POWERHOUSE!
Human, AI, and Personalized User Experience for DB Observability: A Composable Approach
Database users across various technical levels are frequently frustrated by the time-consuming and inefficient process of identifying the root causes of issues. This process often involves navigating multiple systems or dashboards, leading to delays in finding solutions and potential downstream impacts on operations.
The challenge is compounded by the varying levels of expertise among users. It is essential to strike the right balance between specialized and generalized experiences. Oversimplification can result in the loss of critical information, while an overwhelming amount of data can alienate certain users.
Developers and designers are constantly navigating these trade-offs to deliver optimal user experiences. The integration of AI introduces an additional layer of complexity. While AI can provide personalized experiences within databases, it is crucial to maintain user trust and transparency in the process.
The concept of personalized composable observability offers a potential solution. By combining the strengths of human expertise, information balance, and AI-driven personalization, we can create intuitive and user-friendly experiences. This approach allows users to tailor their observability tools and workflows to their specific needs and preferences.
This keynote will explore how L&D had got transferred from pre AI to post AI era and its efficiency job security?
