Dear Monks,

I am looking to reduce the startup time and also the memory impact of an application which uses a large hash of hashes to hold data.

One obvious way occurred to me and that was to tie the hash to a file / database, so that i wouldn't have to repopulate it with every run of the program and also that it wouldn't be held in memory. Unfortunately I don't have a lot of experience in these matters.

Looking around CPAN and the monastery, folks seemed very keen on three main candidates:

Tie::DBI

Tie hashes to SQL databases

DBM::Deep (Re: Can I tie a hash of hashes to a file?)

"A unique flat-file database module, written in pure perl"

BerkleyDB::Hash (Re: Managing a graph with large number of nodes)

BerkelyDB based obv.!

I wanted to write some benchmarks to test which was the fastest, most space efficient, and simplest to use, but never really got very far:

Tie::DBI

You must pre-create the database, using DBI

While you can subsequently use the tied hash like a normal hash, doing the tie is fairly complex and requires messing around with flags

BerkeleyDB::Hash

Again, you must pre-create the database and mess around with a lot of setup flags, just to get it working...

I was put off, because I just wanted a simple 'plug and play' solution... However:

DBM::Deep

Very simple interface

"True multi-level hash/array support (unlike MLDBM, which is faked), hybrid OO / tie() interface, cross-platform FTPable files, ACID transactions, and is quite fast. Can handle millions of keys and unlimited levels without significant slow-down. Written from the ground-up in pure perl -- this is NOT a wrapper around a C-based DBM. Out-of-the-box compatibility with Unix, Mac OS X and Windows."

Relatively simple flags (but more if you want to go deeper):

tie %hash, "DBM::Deep", {
      file => "foo.db",
      locking => 1,
      autoflush => 1
  };
[download]

So my questions:

am I being stupid / impatient / missing a serious performance perk / lazy / etc... by discounting everything but DBM::Deep?

Are there any other other approaches I should consider, which offer the same advantages of DBM::Deep, but do it in a sufficiently different way to warrant Benchmarking?

And for those that have experience of it - are there any pitfalls i should be aware of from DBM::Deep?

Finally - am I even on the right track to solving my original problem (see 500 lines above...)!?

Sorry for the long question and thanks in advance!

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