| |
|
| |
| DETAILS |
|
Welcome to another edition of South Bay Systems! This time we bring you two wonderful talks: Sugu Sougoumarane will be speaking about deconstructing consensus & its application to Multigres, & Stu Hood will be speaking about how modern full-text search engines are evolving towards columnar databases & the unique optimization challenges present in FTS.
Agenda
6:00 PM: Doors open, food & socializing
6:30 PM - 7:00 PM: Consensus Talk
7:00 PM - 7:30 PM: Full Text Search Talk
7:30 PM onward : Community socializing!
Food & beverages will be provided, courtesy of our hosts, Snowflake.
Generalized Consensus
Existing consensus protocols have rigid limits, & a new approach to consensus will be presented that accommodates more flexible, alternative implementations. Using Multigres as a case study, it will be demonstrated how this approach enables a robust High Availability solution for Postgres while maintaining the rigorous safety features of traditional consensus systems.
Speaker Bio
Sugu is currently the Head of Multigres at Supabase. As a co-creator of Vitess & a co-inventor of FlexPaxos, he has focused on increasing the flexibility of consensus algorithms & adapting them to bespoke environments.
Native Top-K Joins in ParadeDB
Recently, some have argued that modern analytical formats have evolved "beyond indexes," relying entirely on data layout & coarse metadata. This narrative breaks down against the high-cardinality, late-materialized reality of full-text search. However, search engines have their own blind spots. While they excel at low-latency retrieval, they typically lack the sophisticated query planning required to efficiently process complex, normalized data. To achieve sub-second search across relational schemas, search architecture must learn from analytics.
This talk explores that convergence through the implementation of native Top-K joins. By fusing Tantivy's inverted indexing with DataFusion's analytical execution framework, we will examine the "selectivity sweet spot" where posting lists fundamentally outperform columnar dictionary scans. We'll explore how embedding an analytical optimizer into a search database allows ParadeDB to combine dynamic score filtering with a vectorized pipeline.
Speaker Bio
Stu Hood is a software engineer at ParadeDB, focusing on database internals in Rust. His background is rooted in distributed systems, having previously worked on Twitter's distributed databases & contributed to Apache Cassandra. He is currently working on low latency columnar execution for search.
|
|
|
|
|
|
|
|
