ryandotsmith

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ryandotsmith

An Event Ordering Schema

Shushu receives events in no particular order and long after the event originated in the client's system. Therefore, it is generally desired to depend on Shushu not caring about the order or time in which it receives events. The following text examines thoughts on how to address such issues.

The Problem

Shushu provides a resources-group-membership API that takes a resource

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Event Buffering

Eventually platforms outgrow the single-source-tree model and become distributed systems. A common pattern in these distributed systems is distributed composition via event buffering. Here we motivate and describe this event buffering pattern.

The Problem

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-- This will give us a table that has a row for each resource but includes all of the events related to the 
-- resource on the row. Calling array_agg will load all of the data in memory which means that I can do math 
-- on the set really quickly. Also, callers of this query will be able to get all of the data for the invoice 
-- in one shot.

create view invoice as
select
 a.resource_id,
 sum(a.qty) as qty,
 array_agg(hstore(a.*)) as events_collection

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module Instruments
  def self.set_logger(l, m)
    @logger = l
    @method = m
  end

  def self.logger
    @logger
  end

ryandotsmith

Getting a Job

May 25 2010

I recently graduated from University with a B.S. in Mathematics and a minor in Computer Science. I love to write code and so naturally I began applying for computer engineering jobs. The keyword is computer engineering, NOT tech support, web ninja or css guru. What I found in the job market was shocking!

YOUR DEGREE DOES NOT MATTER. that much

ryandotsmith

The Worker Pattern

2011-05-22

Introduction

This article is the accumulation of a tutorial that was given as a training session at Red Dirt Ruby Conf (2011) and a formal talk given at Rails Conf (2011). To understand the theory behind The Worker Pattern, read over the slides and the slide notes. From there, you can follow the instructions in the Tutorial

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Process Partitioning

The Problem

When working with large, high volume, low latency systems, it is often the case that processing data sequentially becomes detrimental to the system's health. If we only allow 1 process to work on our data we run into several challenges:

• Our process may fall behind resulting in a situation which it is impossible for our process to catch up.
• Our singleton process could crash and leave our system in a degraded state.
• The average latency of data processing could be dramatically affected by outlying cases.

ryandotsmith

Distributed Systems: Tales From the Crypt

A year ago we thought it would be a good idea to start pulling apart services from our core application. Today we have a network of services with clear boundaries, well defined semantics, and concise APIs. The last year was one of the toughest and most enlightening years of my programming life. In this talk I will share the tragedies, heartaches, and victories that we experienced in our migration towards distributed systems. We will look at some patterns and anti-patterns that were discovered along the way. Of course we will be talking about Ruby, PostgreSQL, and their roles in our distributed system.

ryandotsmith

L2met

Problems to solve

• Multi-tenant
• Leverage availability of Heroku's HTTP stack
• Easiest possible DX

Architecture