Linux load average and other silly metrics

Databases are predominantly run on Linux operating systems in cloud and on-premises environments. Metrics such as %CPU, load average, I/O wait, and free memory are commonly used to measure performance. However, interpreting these metrics correctly can be challenging. In this interactive live demo, we will run a workload and explore the intricacies of performance monitoring using the "top" command. We will also discuss the limitations and common misinterpretations when relying solely on these metrics.

YugabyteDB, distributed PostgreSQL: why & how?

In the world of PostgreSQL-compatible databases, a newcomer is YugabyteDB. Same protocol, similar open-source license, re-using Postgres query layer but on a distributed storage plugged in place of heap tables and btree indexes. We will explain the reason for it, why it cannot be just an extension, and why it re-uses PostgreSQL. Similarities and differences, with an overview of the underlying technology: LSM Trees, distributed storage, logical clock, consensus protocols.

The goal is to answer the questions like:
- why another database instead of an extension?
- when can it can substitute to standard PostgreSQL, and why?
- when it is not a good choice and PostgreSQL is the best fit?
- what is distributed vs. sharding when it comes to scale-out?

Hibernate: Mapping Strategies and Their Database Performance Impact

Your database administrator has raised concerns about inefficient queries generated by Hibernate without providing any explanation? This can be challenging because ORMs abstract away the complexities of what is executed in the database, and every database has a unique implementation. The communication gap between developers and operations further complicates the issue.

In this session, we will explore some Hibernate mapping strategies and analyze the SQL queries generated by them. We will also discuss the impact of these strategies on database performance and provide insights for Java developers to optimize code execution or improve their indexing strategy. Developers can improve application efficiency by understanding how their queries work on the database.

YugabyteDB: Distributed PostgreSQL on Kubernetes

Unlike traditional SQL databases, with their monolithic architecture, YugabyteDB brings horizontal scalability and resilience to the table. Our demo on Amazon EKS will delve into the core motivations behind adopting this cloud-native DB: elasticity and resilience, all while remaining PostgreSQL compatible and open-source.

pg_hint_plan - hinting without surprises

I use pg_hint_plan to learn about the optimizer, to understand the query planner choices, and sometimes, workaround a problem in production. But hinting is not easy. Fixing the exact plan needs more than one hint. Here are some tips and examples to use pg_hint_plan without surprises.

Harness the Power of a Single Database for Microservices

This session will delve into the benefits of using Distributed SQL for microservices. We will discuss the challenges of managing multiple databases in a microservices environment, such as complexity, inefficiency, and cost. Our focus will be on YugabyteDB, an Open Source and PostgreSQL-compatible distributed SQL database, which offers a potential solution to these challenges. Modern SQL databases are multi-model, including relational, document, text search, timeseries and more and YugabyteDB is multi-API. Combined with horizontal scalability, Distributed SQL offers a single database solution for microservices.

Anatomy of a distributed SQL database (YugabyteDB)

Porting all the features of PostgreSQL to a distributed database that “scales” horizontally is a challenge. But also the opportunity to modernize the underlying technologies of the DB, which becomes “cloud-native”: consensus protocols, logical clocks, automatic sharding. And to replace B-Tree indexes with LSM Tree and SSTables, more suited to SSD and distributed storage.

YugabyteDB is open-source, and we will go into the details of the architecture, at the crossroads of PostgreSQL, Spanner, Cassandra, RocksDB… to better understand the reasons for a new database, and its underlying technology. We will discuss the advantages and the challenges of this unique architecture design: re-using the PostgreSQL query layer, plugged on top of a distributed storage and transaction layer

Scaling Out PostgreSQL Applications

PostgreSQL is popular for its powerful features, but scaling it can be challenging. Alternatives still open source and compatible with PostgreSQL, like sharding (Citus) or distributed SQL (YugabyteDB), are available. This presentation will show you how to quickly determine if your PostgreSQL application can be smoothly migrated to sharding over PostgreSQL or distributed YugabyteDB. This will allow you to take advantage of the resilience and flexibility of a cloud-native database.

This presentation lets developers quickly assess whether their PostgreSQL applications are ready for alternatives like Citus or YugabyteDB, saving time by avoiding unnecessary testing of non-viable solutions.

Document Data Modeling and Denormalization in SQL and NoSQL

As database technologies evolve, the distinctions between SQL and NoSQL become less clear. NoSQL databases like MongoDB may recommend some referencing and joins rather than embedding all entities into one document. At the same time, SQL databases allow denormalization and can store and index JSON documents. Some even provide MongoDB-compatible APIs for querying relational data, as seen in Oracle Database and PostgreSQL when used with FerretDB.

This session explores the core principles of data modeling. Should you embed multiple entities within a document or define references and foreign keys? What factors influence this decision: performance, flexibility, or query access patterns? We will examine the trade-offs between normalization and denormalization using examples relevant to SQL tables or NoSQL collections. Whether you are working with documents or rows, this talk will give you a deeper understanding of the appropriate modeling approach to persist your application objects.

Connection Pools: Scaling Your Database Access Efficiently

It's important to manage database connections efficiently for high-performance applications. This session will explore various approaches to handling connection pools for SQL databases. We'll learn how to configure them properly to achieve scalability without putting too much pressure on the database. We'll also compare using a connection pool in the application with in-database solutions, such as those found in Oracle or YugabyteDB, or external connection pooling solutions available for PostgreSQL databases.

Connection pools are often misconfigured, leading to database saturation rather than improved performance. This provides comprehensive insights for utilizing the optimal configuration.

Beyond PostgreSQL: Distributed SQL with YugabyteDB

Postgres is a widely used open-source database for OLTP, and its powerful SQL features are continually improving. However, it faces operational challenges such as process per connection, vacuum issues, resilience to failure, and downtime for upgrades. These issues require a different storage architecture. Distributed SQL, inspired by Spanner, addresses some of these issues but often has limited SQL support. It is also essential to remain Open Source when offering an alternative to PostgreSQL

YugabyteDB overcomes these limitations by utilizing PostgreSQL code for SQL processing on multiple active nodes and employing distributed transactional storage to scale horizontally. This marks the evolution of databases towards Distributed SQL databases. We will demonstrate YugabyteDB's resilience and scalability.

This session brings the concepts of Distributed SQL and a live demo showing PostgreSQL compatibility, elasticity, and resilience