Raouf Chebri
Help developers be more productive
Genève, Switzerland
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Raouf Chebri is a Developer Advocate at Neon and loves to talk about AI, and modern web development and help developers be more productive. He worked as a Software Engineer and also an Evangelist at Microsoft and ScyllaDB before joining Neon. Raouf enjoys Mexican food, skiing, and riding his bike for hours in the Alps.
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Time Travel PostgreSQL queries with ephemeral database branches
Time Travel Queries in PostgreSQL refer to a method of querying data that allows users to see the state of the database at a specific point in the past. This feature is not inherently built into PostgreSQL but can be implemented through various techniques.
One example where Time Travel Queries are useful debugging tools is Point-in-Time Recovery (PITR).
The issue for developers is that PITR in PostgreSQL involves multiple components such as base backups, write-ahead logging (WAL), WAL archiving, and recovery procedures. Setting up and configuring these components correctly requires a good understanding of PostgreSQL's internal workings and backup strategies. Developers might find it challenging to configure these components optimally, especially in more complex environments.
In this talk, we will cover how cloud-native PostgreSQL architectures, a custom storage system and ephemeral database branches streamline the implementation of Time Travel Queries, saving developers countless hours recovering data from backups. We will cover techniques that will help application developers focus on their product rather than managing their database infrastructure.
In addition to learning how to use Time Travel Queries in PostgreSQL, developers will learn about:
- PITR, problems it solves, and its challenges
- Write-Ahead Log in PostgreSQL
- Cloud-native PostgreSQL architectures
- Ephemeral database branches
I can't wait to see you all at the talk
This talk is for any developer with basic knowledge of SQL. However, it will go over complex architectural decisions that make Time Travel Queries native in PostgreSQL.
Efficient AI apps with pgvector 0.6.0 and Neon autoscaling
pgvector is a PostgreSQL extension designed for storing vectors, and facilitating vector similarity searches. This process, crucial in AI, semantic search, and Retrieval Augmented Generation (RAG) applications, involves finding the K nearest neighbors of a given vector, typically representing a text. However, vector search can become inefficient at scale, often resulting in a sequential scan of the database, which can slow down processes and create bottlenecks. To address this, pgvector utilizes Approximate Nearest Neighbor (ANN) algorithms, such as Hierarchical Navigable Small World (HNSW), enhancing search efficiency.
A challenge for developers is the time-consuming nature of building indexes for millions of rows. Since version 0.6.0, pgvector addresses this with a parallel index build for HNSW, significantly speeding up the process but also intensifying CPU and memory usage. To optimize efficiency, we have integrated pgvector with Neon's autoscaling feature, which dynamically allocates more CPU and memory based on pgvector's requirements.
In this talk, I will demonstrate how to use the parallel index build feature in pgvector and enhance its performance with Neon's autoscaling capabilities. The session will cover:
- The fundamentals of vector search.
- An overview of ANN algorithms.
- Implementing parallel index build in HNSW.
- Utilizing Neon's autoscaling feature for improved efficiency.
This talk is for beginner developers as well as experienced ones interested in optimizing their AI applications. It will cover the fundamentals of vector search, pgvector and autoscaling and as well as deep dives into ANNs and cloud-native PostgreSQL
DeveloperWeek Europe 2023 Sessionize Event
Raouf Chebri
Help developers be more productive
Genève, Switzerland
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