A local database contains nearly 1.3 billion unique rows. Each row is indirectly associated with a specific latitude and longitude (location). Each row has a date stamp.
The problem is as follows:
- The user sets a starting/ending date, and a range of values (e.g., 100 to 105).
- The system gathers all the rows that match the given date, grouped by location.
- The system performs determines the locations that, during those dates, have a statistical likelihood of falling into the given range of values.
- The system displays all matching locations to the user.
This is a problem of speed and scale.
What is the least expensive solution architecture you can imagine that would allow such a system to retrieve results for users in under five seconds?
The environment is currently:
- PostgreSQL 8.4 (upgrade is possible; switching databases is not an option)
- R and PL/R
- WD VelociRaptor
- 8 GB RAM (Corsair G.Skill; 1.3 GHz)
- Quad core GenuineIntel 7 (2.8 GHz)
- Ubuntu 10.10
Hardware upgrades are acceptable.
Update - Database Structure
The billions of rows are in a table resembling:
id | taken | location_id | category | value1 | value2 | value3
- id - Primary key
- taken - Date assigned to the row
- location_id - Reference to the latitude/longitude
- category - A description of the data
- value1 .. 3 - The other values the user can query
taken column is typically consecutive dates per
location_id, sometimes each location has data from 1800 to 2010 (about 77,000 dates, many of them duplicated as each location has data in the same date range).
There are seven categories and the tables are already split by category (using child tables). Each category contains ~190 million rows. In the near future, the number of rows per category will exceed a billion.
There are approximately 20,000 locations and 70,000 cities. The locations are correlated to city by latitude and longitude. Assigning each location to a particular city means finding the city's boundaries, which is not a trivial task.
Some ideas I have include:
- Find a cloud service to host the database.
- Create an SSD raid stripe (great video).
- Create a table that amalgamates all the locations by city (pre-calculation).