Provided by: libfuntools-dev_1.4.8-1.1build2_amd64 
NAME
Funidx - Using Indexes to Filter Rows in a Table
SYNOPSIS
This document contains a summary of the user interface for filtering rows in binary tables
with indexes.
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.
SEE ALSO
See funtools(7) for a list of Funtools help pages