=pod

=head1 NAME



B<Funidx: Using Indexes to Filter Rows in a Table>



=head1 SYNOPSIS





This document contains a summary of the user interface for 
filtering rows in binary tables with indexes.



=head1 DESCRIPTION





Funtools Table Filtering allows rows in a
table to be selected based on the values of one or more columns in the
row. Because the actual filter code is compiled on the fly, it is very
efficient. However, for very large files (hundreds of Mb or larger),
evaluating the filter expression on each row can take a long time. Therefore,
funtools supports index files for columns, which are used automatically during
filtering to reduce dramatically the number of row evaluations performed.
The speed increase for indexed filtering can be an order of magnitude or
more, depending on the size of the file.


The funindex program creates an
index on one or more columns in a binary table. For example, to create an index
for the column pi in the file huge.fits, use:

  funindex huge.fits pi

This will create an index named huge_pi.idx.


When a filter expression is initialized for row evaluation, funtools
looks for an index file for each column in the filter expression. If
found, and if the file modification date of the index file is later
than that of the data file, then the index will be used to reduce the
number of rows that are evaluated in the filter. When 
Spatial Region Filtering is part of the
expression, the columns associated with the region are checked for index
files.


If an index file is not available for a given column, then in general,
all rows must be checked when that column is part of a filter
expression.  This is not true, however, when a non-indexed column is
part of an AND expression. In this case, only the rows that pass the
other part of the AND expression need to be checked. Thus, in some cases,
filtering speed can increase significantly even if all columns are not
indexed.


Also note that certain types of filter expression syntax cannot make
use of indices. For example, calling functions with column names as
arguments implies that all rows must be checked against the function
value. Once again, however, if this function is part of an AND
expression, then a significant improvement in speed still is possible
if the other part of the AND expression is indexed.


For example, note below the dramatic speedup in searching a 1 Gb
file using an AND filter, even when one of the columns (pha) has no
index:


  time fundisp \
  huge.fits'[idx_activate=0,idx_debug=1,pha=2348&&cir 4000 4000 1]' \
  "x y pha"
          x           y        pha                                   
 ---------- ----------- ----------                                    
    3999.48     4000.47       2348
    3999.48     4000.47       2348
    3999.48     4000.47       2348
    3999.48     4000.47       2348
    3999.48     4000.47       2348
    3999.48     4000.47       2348
    3999.48     4000.47       2348
    3999.48     4000.47       2348
    3999.48     4000.47       2348
    3999.48     4000.47       2348
    3999.48     4000.47       2348
    3999.48     4000.47       2348
    3999.48     4000.47       2348
    3999.48     4000.47       2348
    3999.48     4000.47       2348
    3999.48     4000.47       2348
    42.36u 13.07s 6:42.89 13.7%

  time fundisp \
  huge.fits'[idx_activate=1,idx_debug=1,pha=2348&&cir 4000 4000 1]' \
  "x y pha"
          x           y        pha                                    
 ---------- ----------- ----------                                    
 idxeq: [INDEF]                                   
 idxand sort: x[ROW 8037025:8070128] y[ROW 5757665:5792352]             
 idxand(1): INDEF [IDX_OR_SORT]                                   
 idxall(1): [IDX_OR_SORT]                                   
    3999.48     4000.47       2348
    3999.48     4000.47       2348
    3999.48     4000.47       2348
    3999.48     4000.47       2348
    3999.48     4000.47       2348
    3999.48     4000.47       2348
    3999.48     4000.47       2348
    3999.48     4000.47       2348
    3999.48     4000.47       2348
    3999.48     4000.47       2348
    3999.48     4000.47       2348
    3999.48     4000.47       2348
    3999.48     4000.47       2348
    3999.48     4000.47       2348
    3999.48     4000.47       2348
    3999.48     4000.47       2348
    1.55u 0.37s 1:19.80 2.4%


When all columns are indexed, the increase in speed can be even more dramatic:

  time fundisp \
  huge.fits'[idx_activate=0,idx_debug=1,pi=770&&cir 4000 4000 1]' \
  "x y pi"
          x           y         pi                                    
 ---------- ----------- ----------                                    
    3999.48     4000.47        770
    3999.48     4000.47        770
    3999.48     4000.47        770
    3999.48     4000.47        770
    3999.48     4000.47        770
    3999.48     4000.47        770
    3999.48     4000.47        770
    3999.48     4000.47        770
    3999.48     4000.47        770
    3999.48     4000.47        770
    3999.48     4000.47        770
    3999.48     4000.47        770
    3999.48     4000.47        770
    3999.48     4000.47        770
    3999.48     4000.47        770
    3999.48     4000.47        770
    42.60u 12.63s 7:28.63 12.3%

  time fundisp \
  huge.fits'[idx_activate=1,idx_debug=1,pi=770&&cir 4000 4000 1]' \
  "x y pi"
          x           y         pi                                    
 ---------- ----------- ----------                                    
 idxeq: pi start=9473025,stop=9492240 => pi[ROW 9473025:9492240]          
 idxand sort: x[ROW 8037025:8070128] y[ROW 5757665:5792352]               
 idxor sort/merge: pi[ROW 9473025:9492240] [IDX_OR_SORT]                   
 idxmerge(5): [IDX_OR_SORT] pi[ROW]                                   
 idxall(1): [IDX_OR_SORT]                                   
    3999.48     4000.47        770
    3999.48     4000.47        770
    3999.48     4000.47        770
    3999.48     4000.47        770
    3999.48     4000.47        770
    3999.48     4000.47        770
    3999.48     4000.47        770
    3999.48     4000.47        770
    3999.48     4000.47        770
    3999.48     4000.47        770
    3999.48     4000.47        770
    3999.48     4000.47        770
    3999.48     4000.47        770
    3999.48     4000.47        770
    3999.48     4000.47        770
    3999.48     4000.47        770
    1.67u 0.30s 0:24.76 7.9%



The miracle of indexed filtering (and indeed, of any indexing) is the
speed of the binary search on the index, which is of order log2(n)
instead of n. (The funtools binary search method is taken from
http://www.tbray.org/ongoing/When/200x/2003/03/22/Binary, to whom
grateful acknowledgement is made.)  This means that the larger the
file, the better the performance. Conversely, it also means that for
small files, using an index (and the overhead involved) can slow
filtering down somewhat. Our tests indicate that on a file containing
a few tens of thousands of rows, indexed filtering can be 10 to 20
percent slower than non-indexed filtering. Of course, your mileage
will vary with conditions (disk access speed, amount of available
memory, process load, etc.)


Any problem encountered during index processing will result in
indexing being turned off, and replaced by filtering all rows. You can turn
filtering off manually by setting the idx_activate variable to 0 (in a filter
expression) or the FILTER_IDX_ACTIVATE environment variable to 0 (in the global
environment). Debugging output showing how the indexes are being processed can
be displayed to stderr by setting the idx_debug variable to 1 (in a filter
expression) or the FILTER_IDX_DEBUG environment variable to 1 (in the global
environment).


Currently, indexed filtering only works with FITS binary tables and raw
event files. It does not work with text files. This restriction might be
removed in a future release.



=head1 SEE ALSO



See funtools(n) for a list of Funtools help pages



=cut
