If you find Berkeley too slow, or too memory expensive in practice, you might reconsider the md5 hashing suggestion. I've implemented this for my own purposes now, and the highlights are:

Indexing 100_000_000, 160 byte records in a data file.

1. The index requires 2.23 GB for 100 Million records. Data record size does not affect the index size. It is a fixed 24-bytes/record.
2. Building and sorting the index: 3 hours (worst case; under 1 hour best);
3. Accessing 10_000 randomly chosen records: Under 3 minutes.

   That's locating in the index entry and reading the record combined.

   Worse timing: 1000 trials of binsearch ( 37.753s total), 37.753ms/trial

   Best timing: 10000 trials of binsearch ( 175.755s total), 17.576ms/trial

   Update: A 100,000 thrials just completed:

   100000 trials of binsearch ( 1,643s total), 16.437ms/trial

4. Insert/delete* a record: Currently 1.2 seconds.

   This can be improved, I believe substantially.

   Insertion appends the new record to the end of the data file, and inserts the appropriate index entry.

   * Deletion consists of removing the index entry and adding it to a "deleted.idx" file.

   The actual record remains in-place until a compaction process is run. The latter is not part of the timing above.

The above is hitting the disk (or system caches) for every read. I have some ideas for adding my own buffering , but they need testing.

The test system was XP/NTFS on a 2.2 Ghz processor with a WDC WD800BB-75CAA0 75GB HD.

The datafile is compressed, the index not.

For contrast, I gave up waiting for Berkeley to build a BTree index from a pre-sorted file of index entries after 18+ hours and 57% complete. Hence, I don't have figures for access/insertions or deletions.