In a previous post I described how I could now string search WorldCat using the Research compute cluster and Hadoop. This means I can find any string of characters anywhere in a MARC record and output the OCLC numbers of matches and/or the entire record, all within a few minutes. Keep in mind we are now talking about nearly 300 million records.
String searching is not, by any means, the only way we use Hadoop. It is actually more common for us to use code (typically written in Java, Python, or Perl) to parse the records and output portions for further processing. But since I had been using such code for simply identifying records of interest, I began to wonder which method of processing was faster.
In a short experiment I quickly proved that string searching was about three times faster for simple identification and output of records than was the code I had previously been using. This is because, I believe, the code I had been using would parse the record before determining if it met my criteria. This one extra step added so much overhead to the process that it would take 15 minutes (in one test) rather than 5.
This likely means that in some cases where relatively few records would match your criteria, you would still be better off extracting the records by string searching and then running your extraction code against them off-cluster. For example, if I wanted to pull out the 245 fields of, say, about 1,000 records, I’d likely be better off extracting the records I wanted by string searching and then process that file directly without using Hadoop.
One last permutation, however. If your process is one that identifies 1,000 records in some situations and several million in another, having one process through which all operations flow is more efficient than two or more separate processes.
And so it goes. Another day and another adventure in Hadoop.