Question

Suppose that I have a huge SQLite file (say, 500[MB]) stored in Amazon S3.
Can a python script that is run on a small EC2 instance directly access and modify that SQLite file? or must I first copy the file to the EC2 instance, change it there and then copy over to S3?

Will the I/O be efficient?

Here’s what I am trying to do. As I wrote, I have a 500[MB] SQLite file in S3. I’d like to start say 10 different Amazon EC2 instances that will each read a subset of the file and do some processing (every instance will handle a different subset of the 500[MB] SQLite file). Then, once processing is done, every instance will update only the subset of the data it dealt with (as explained, there will be no overlap of data among processes).

For example, suppose that the SQLite file has say 1M rows:

instance 1 will deal with (and update) rows 0 - 100000

instance 2 will will deal with (and update) rows 100001 - 200000

…………………….

instance 10 will deal with (and update) rows 900001 - 1000000

---

Is it at all possible? Does it sound OK? any suggestions / ideas are welcome.

Answer 1

I’d like to start say 10 different Amazon EC2 instances that will each read a subset of the file and do some processing (every instance will handle a different subset of the 500[MB] SQLite file)

You cannot do this with SQLite; on amazon infrastructure or otherwise. sqlite performs database level write locking. unless all ten nodes are performing reads exclusively, you will not attain any kind of concurrency. Even the SQLite website says so.

Situations Where Another RDBMS May Work Better

• Client/Server Applications
• High-volume Websites
• Very large datasets
• High Concurrency

Have you considered PostgreSQL?