gyeh / cc

To run:

./run.sh

Tested on OSX 64-bit and Debian Linux 8 64-bit

Dependencies

• Java (JRE) 7 or 8

Libraries

Libraries are pre-packaged within an uber jar in the ./target directory.

• scopt - command line arg parser
• google guava
• scalacheck - property-test library
• scalatest - unit tests

Design

Preliminary runs, on a 4-core, 8GB Linux machine, indicate that WordCounter and MedianTracker takes ~2 minutes to process 20 millions tweets, which is about the number of tweets Twitter generates per hour (from http://goo.gl/eWTrtz).

Some Scala-specific idioms were set aside (ie. being more imperative) to eek out minor performance gains on the hot-path.

Because the specs of the running machine are unknown, there were no optimizations derived from JVM-tuning (e.g. heap size, GC algorithm, etc).

WordCounter

WordCounter uses an in-memory Guava Multi-Set map to handle the primary string frequency operations. The main concern is memory usage: to store a high volume of potential unique words in memory. (The number of unique words in the english language is about 1 million (http://goo.gl/f4wGg9). This is not taking to account of other languages or the various permutations due to uncleaned puncuations and grammar.)

A casual experiment with 4 million unique words from a corpus of 20 million genererated tweets, resulted in a ~350MB map on a 64-bit JVM. This makes a bit more sense considering that a 17 character Java String can be 72 bytes in memory (not accounting the hashmap and long counter overhead).

Potential further optimizations include a more efficient string encoding, tries, probabilistic sampling or alternative off-heap solutions.

MedianTracker

To be space-optimized and not track the unique counts for all tweets, MedianTracker uses a long array ('buckets') to track the unique words counts from each tweet. The array index represents the number of unique words per tweet. The array value represents the number of tweets which falls into the bucket.

To calculate the new median, 'buckets' is traversed linearly each time to reach the median. Because tweets are limited to 140 characters and we're assuming that only whitespaces can delimit words, the 'buckets' size can be bounded to a small constant (< 140), resulting in a small traversal cost.