Tech Presentations

I didn’t know that it’s possible to work with MySQL using Visual Studio. Apparently MySQL AB is developing a good integration tools for it, definitely targeting corporate markets.

See it on TechPresentations.org: Developing for .NET using Visual Studio

Tons of presentations from MySQL Conference and Expo 2007 (and more coming).

• Capacity planning for LAMP
• Developing High Performance Applications Using NDB/J
• Developing High Performance Applications Using NDBAPI
• Implementing Performant Databases for Multi-Play Service Architectures
• InnoDB Performance Optimization
• InnoDB performance potential in high-end environments
• Interconnect Matters for Scalability and Performance
• Lucene with MySQL
• MySQL Performance CookBook
• MySQL Server Settings Tuning
• MySQL for Oracle DBAs and Developers
• MySQL query optimizer internals and upcoming features in v5.2
• Scale-out Options with MySQL and Java
• Scaling the World’s Largest Photo Blogging Community
• The 7 Stages of Scaling Web Apps - Strategies for Architects

Presentation about Six Apart’s object-relational driver Data::ObjectDriver by Benjamen Trott at OSCON 2006 on July 27th 2006 (slides)

Google Talk by Jay Pipes on Performance tuning in MySQL (video). Given in many other places as well. Slides from different presentation available (PDF).

Given by Jeff Dean (Google) at the given University of Washington on Oct 18, 2005 (video, slides)

BigTable is a distributed storage system for managing structured data that is designed to scale to a very large size.

Interesting quotes from presentation:

• Scale is too big for commercial databases, they can’t also run on a cheap clustered servers.
• Features:
  • Distributed multy-level map
  • Fault tolerant, persistant
  • Scalabale (thousands of servers, megabytes of in-memory data, petabyte of disk data, millions/sec of r/w, efficient scans)
  • Self-managing (servers can be added/removed dynamically, servers adjust to load imbalance)
• Largest bigtable cells (data collections) ~200TB on over thousands of servers
• Built upon:
  • GFS
  • Workqueue (scheduler)
  • Lock service
  • MapReduce
• miltidimentional - row (e.g. url), col (attribute) = cell, inside cell time-based values for the cell.
• related rows (tablets) are located on the same machines for better performance
• load balancing moves tablets around
• tablets are replicated across multiple machines
• requests like “get recent X values” are possible
• columns can be configured to retain only X most recent entries
• locality groups to partition tablets
• has huge logging problems
• a lot of opportunities for compression - time-shifted data is similar, many values are the same. Using BMDiff (dictionary-based compression) - encode ~100MB/s, decode ~1000MB/s; Zippy (LZW-like) - 179MB/s, 409MB/s
• Compression experiment results: web pages compress at 9.2%, links at 13.2%, anchors at 12.7%

Update: Luke Baker made screen shots from video with all slides (not really in the right order).