Handle 1 Billion Events Per Day Using a Memory Gri

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Moshe Kaplan of RockeTier shows the life cycle of an affiliate marketing system that starts off as a cub handling one million events per day and ends up a lion handling 200 million to even one billion events per day. The resulting system uses ten commodity servers at a cost of $35,000.

Mr. Kaplan's paper is especially interesting because it documents a system architecture evolution we may see a lot more of in the future: database centric --> cache centric --> memory grid .

As scaling and performance requirements for complicated operations increase, leaving the entire system in memory starts to make a great deal of sense. Why use cache at all? Why shouldn't your system be all in memory from the start?

 

General Approach to Evolving the System to Scale

 

Analyze the system architecture and the main business processes. Detect the main hardware bottlenecks and the related business process causing them. Focus efforts on points of greatest return.

Rate the bottlenecks by importance and provide immediate and practical recommendation to improve performance.

Implement the recommendations to provide immediate relief to problems. Risk is reduced by avoiding a full rewrite and spending a fortune on more resources.

Plan a road map for meeting next generation solutions.

Scale up and scale out when redesign is necessary.

 

One Million Event Per Day System

The events are common advertising system operations like: ad impressions, clicks, and sales.

Typical two tier system. Impressions and banner sales are written directly to the database.

The immediate goal was to process 2.5 million events per day so something needed to be done.

 

2.5 Million Event Per Day System

PerfMon was used to check web server and DB performance counters. CPU usage was at 100% at peak usage.

Immediate fixes included: tuning SQL queries, implementing stored procedures, using a PHP compiler, removing include files and fixing other programming errors.

The changes successfully double the performance of the system within 3 months. The next goal was to handle 20 million events per day.

 

20 Million Event Per Day System

To make this scaling leap a rethinking of how the system worked was in order.

The main load of the system was validating inputs in order to prevent forgery.

A cache was maintained in the application servers to cut unnecessary database access. The result was 50% reduction in CPU utilization.

An in-memory database was used to accumulate transactions over time (impression counting, clicks, sales recording).

A periodic process was used to write transactions from the in-memory database to the database server.

This architecture could handle 20 million events using existing hardware.

Business projections required a system that could handle 200 million events.

 

200 Million Event Per Day System

The next architectural evolution was to a scale out grid product. It's not mentioned in the paper but I think GigaSpaces was used.

A Layer 7 load balancer is used to route requests to sharded application servers. Each app server supports a different set of banners.

Data is still stored in the database as the data is used for statistics, reports, billing, fraud detection and so on.

Latency was slashed because logic was separated out of the HTTP request/response loop into a separate process and database persistence is done offline.

At this point architecture supports near-linear scaling and it's projected that it can easily scale to a billion events per day.