This is example to build a sorted, bit-transposed dictionary vector of names of 
celestial objects from NED objects directory. 
Example shows how to do sorted search, runs benchmarks, checks self correctness. 





1. Download the fragment of catalog from 

http://bitmagic.io/data/ned.txt.gz

or use NED ADQL interface:

curl -o ned.txt "https://ned.ipac.caltech.edu/tap/sync?query=SELECT+*+FROM+ned_objdir&LANG=ADQL&REQUEST=doQuery&FORMAT=text"

It will produce a columnar report similar to this:

      bhextin      |         dec          |       gallat       |      gallon       |            prefname            |pretype |   
-------------------|----------------------|--------------------|-------------------|--------------------------------|--------|
0.0                |61.090161208000005    |55.6336642907128    |129.592988077481   |"2MASX J12201807+6105248       "|"G     "|
0.0                |56.1873074943         |60.7497277891121    |128.223612727992   |"2MASX J12325147+5611139       "|"G     "|
0.0                |58.25105334925        |58.794082336604     |126.228935296354   |"2MASS J12382714+5815046       "|"G     "|             
0.0                |57.223830104799994    |59.8791937528706    |124.806822462432   |"2MASS J12442918+5713259       "|"*     "|


OVERFLOW (more rows were available but have been truncated by the TAP service)


NOTE: Out of this columnar report we need only "prefname" - names of objects in the NED catalog.
Example will parse out the column and create a sorted STL vector, STL map (R-B tree) and
bit transposed sparse vector (BitMagic) to compare memory footprint and search performance.



2. Run xsampl05 to load the report and build index

./xsample05 -idict ned.txt -svout astra.bm -t

Expected output:

Reading input file: 1000000
Loaded 380132 dictionary names.

Performance:
1. parse input data ; 53.74 sec
2. build sparse vector; 1.276 sec
7. Save sparse vector; 4.726 ms



3. Run search benchmarks using the compressed sparse vector of "prefname"

./xsample05  -svin astra.bm -t -b


Expected output:


Picked 15000 samples. Running benchmarks.

Performance:
3. std::lower_bound() search; 427000 ops/sec; 35.08 ms
4. std::map<> search; 285000 ops/sec; 52.47 ms
5. bm::sparse_vector_scanner<> search; 21000 ops/sec; 685.2 ms
6. bm::sparse_vector_scanner<> binary search; 42000 ops/sec; 351 ms
8. Load sparse vector; 3.951 ms

