For some time i am working on creating index for very large data sets (around 190 million). I have a BTree which can insert data sets (typically an object)/search for key and while i searched how to persist the data into files in disk, i came across this amazing article (http://www.javaworld.com/article/2076333/java-web-development/use-a-randomaccessfile-to-build-a-low-level-database.html#resources). This pretty much gives me the starting point.
Here they are indexing String key to binary object (blob). They have the file format where they have divided it into 3 regions, header(stores start point of indexes), index(stores index and its corresponding location) and data region (stores data). They are using RandomAccessFile to get the data.
How do i define similar file format for btree. All i know is for every read made to disk, i have to get one node(typically one block 512 bytes). There are many similar questions on how to persist but it is little difficult to understand the big picture on why we decide on something that we implemented like this question (Persisting B-Tree nodes to RandomAccessFile -[SOLVED]). Please share your thoughts.
Here is an alternative take on the question, based on problem specifics that have become known in the meantime. This post is based on the following assumptions:
Efficient representation of filenames in a database is a different topic that cannot be addressed here. Should the filenames be awkward - longish on average and/or Unicode - then the hashing solution will punish you with increased disk read counts (more overflows, more chaining) or reduced average occupancy (more wasted space). A B-tree solution reacts somewhat more benignly, though, since an optimum tree can be constructed in any case.
The most efficient solution in this situation - and the simplest to implement by a wide margin - is hashing, since your keys are perfect hashes already. Take the first 23 bits of the hash as the page number, and lay out the pages like this:
page header uint32_t next_page uint16_t key_count key/offset vector uint16_t value_offset; byte key; ... unallocated space ... last arrived filename ... 2nd arrived filename 1st arrived filename
Values (filenames) are stored from the end of the page downwards, prefixed with their 16-bit length, and the key/offset vector grows upwards. That way neither low/high key counts nor short/long values can cause unnecessary waste of space, as would be the case with fixed-size structures. Nor do you have to parse variable-length structures during key searches. Apart from that I've aimed for the greatest possible simplicity - no premature optimisation. The bottom of the heap can be stored in the page header, in
KO.[PH.key_count].value_offset (my preference), or computed as
KO.Take(PH.key_count).Select(r => r.value_offset).Min(), whatever pleases you most.
The key/offset vector needs to be kept sorted on the keys so that you can use binary search but the values can be written as they arrive, they do not need to be in any particular order. If the page overflows, allocate a new one just like it at the current end of the file (growing the file by one page) and stash its page number in the appropriate header slot. This means that you can binary search within a page but all chained pages need to be read and searched one by one. Also, you do not need any kind of file header, since the file size is otherwise available and that's the only piece of global management information that needs to be maintained.
Create the file as a sparse file with the number of pages as indicated by your chosen number of hash key bits (e.g. 8388608 pages for 23 bits). Empty pages in a sparse file don't take up any disk space and read as all 0s, which works perfectly fine with our page layout/semantics. Extend the file by one page whenever you need to allocate an overflow page. Note: the 'sparse file' thing isn't very important here since almost all pages will have been written to when you're done building the file.
For maximum efficiency you need to run some analyses on your data. In my simulation - with random numbers as stand-ins for the hashes, and on the assumption that average filename size is 62 bytes or less - the optimum turned out to be making 2^23 = 8388608 buckets/pages. This means that you take the first 23 bit of the hash as the page number to load. Here are the details:
# bucket statistics for K = 23 and N = 190000000 ... 7336,5 ms average occupancy 22,6 records 0 empty buckets (min: 3 records) 310101/8388608 buckets with 32+ records (3,7%)
That keeps the chaining to a minimum, on average you need to read just 1.04 pages per search. Increasing the hash key size by one single bit to 24 reduces the expected number of overflowing pages to 3 but doubles the file size and reduces average occupancy to 11.3 records per page/bucket. Reducing the key to 22 bits means that almost all pages (98.4%) can be expected to overflow - meaning the file is virtually the same size as that for 23 bits but you have to do twice as many disk reads per search.
Hence you see how important it is to run a detailed analysis on the data to decide on the proper number of bits to use for hash addressing. You should run an analysis that uses the actual filename sizes and tracks the per-page overhead, to see what the actual picture looks like for 22 bits to 24 bits. It'll take a while to run but that's still way faster than building a multi-gigabyte file blindly and then finding that you have wasted 70% of space or that searches take significantly more than 1.05 page reads on average.
Any B-tree based solution would be much more involved (read: complicated) but could not reduce the page read count per search below 1.000, for obvious reasons, and even that only on the assumption that a sufficient number of internal nodes can be kept cached in memory. If your system has such humongous amounts of RAM that data pages can be cached to a significant degree then the hashing solution will benefit just as much as one that is based on some kind of B-tree.
As much as I would like an excuse for building a screamingly fast hybrid radix/B+tree, the hashing solution delivers essentially the same performance for a tiny fraction of the effort. The only thing where B-treeish solutions can outdo hashing here is space efficiency, since it is trivial to construct an optimum tree for existing pre-sorted data.