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