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btrfs-progs/show-blocks
Nikolay Borisov de34a33a82 btrfs-progs: Remove btrfs-debug-tree command
There is already a replacement in the face of btrfs inspect-internal
dump-tree. And this command is just a simple wrapper around it. Just
remove it and adjust the show-blocks script to call the main btrfs
binary to achieve the same effect.

Informally deprecated since 4.4.

Issue: 
Signed-off-by: Nikolay Borisov <nborisov@suse.com>
Signed-off-by: David Sterba <dsterba@suse.com>
2018-04-24 13:00:10 +02:00

326 lines
9.0 KiB
Python
Executable File

#!/usr/bin/env python
#
# Copyright (C) 2007 Oracle. All rights reserved.
#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public
# License v2 as published by the Free Software Foundation.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
# General Public License for more details.
#
# You should have received a copy of the GNU General Public
# License along with this program; if not, write to the
# Free Software Foundation, Inc., 59 Temple Place - Suite 330,
# Boston, MA 021110-1307, USA.
#
import sys, os, signal, time, commands, tempfile, random
# numpy seems to override random() with something else. Instantiate our
# own here
randgen = random.Random()
randgen.seed(50)
from optparse import OptionParser
from matplotlib import rcParams
from matplotlib.font_manager import fontManager, FontProperties
import numpy
rcParams['numerix'] = 'numpy'
rcParams['backend'] = 'Agg'
rcParams['interactive'] = 'False'
from pylab import *
class AnnoteFinder:
"""
callback for matplotlib to display an annotation when points are clicked on. The
point which is closest to the click and within xtol and ytol is identified.
Register this function like this:
scatter(xdata, ydata)
af = AnnoteFinder(xdata, ydata, annotes)
connect('button_press_event', af)
"""
def __init__(self, axis=None):
if axis is None:
self.axis = gca()
else:
self.axis= axis
self.drawnAnnotations = {}
self.links = []
def clear(self):
for k in self.drawnAnnotations.keys():
self.drawnAnnotations[k].set_visible(False)
def __call__(self, event):
if event.inaxes:
if event.button != 1:
self.clear()
draw()
return
clickX = event.xdata
clickY = event.ydata
if (self.axis is None) or (self.axis==event.inaxes):
self.drawAnnote(event.inaxes, clickX, clickY)
def drawAnnote(self, axis, x, y):
"""
Draw the annotation on the plot
"""
if self.drawnAnnotations.has_key((x,y)):
markers = self.drawnAnnotations[(x,y)]
markers.set_visible(not markers.get_visible())
draw()
else:
t = axis.text(x,y, "(%3.2f, %3.2f)"%(x,y), bbox=dict(facecolor='red',
alpha=0.8))
self.drawnAnnotations[(x,y)] = t
draw()
def loaddata(fh,delimiter=None, converters=None):
#14413824 8192 extent back ref root 5 gen 10 owner 282 num_refs 1
def iter(fh, delimiter, converters):
global total_data
global total_metadata
for i,line in enumerate(fh):
line = line.split(' ')
start = float(line[0])
len = float(line[1])
owner = float(line[10])
root = float(line[6])
if owner <= 255:
total_metadata += int(len)
else:
total_data += int(len)
if start < zoommin or (zoommax != 0 and start > zoommax):
continue
yield start
yield len
yield owner
yield root
X = numpy.fromiter(iter(fh, delimiter, converters), dtype=float)
return X
def run_debug_tree(device):
p = os.popen('btrfs inspect-internal dump-tree -e ' + device)
data = loaddata(p)
return data
def shapeit(X):
lines = len(X) / 4
X.shape = (lines, 4)
def line_picker(line, mouseevent):
if mouseevent.xdata is None: return False, dict()
print "%d %d\n", mouseevent.xdata, mouseevent.ydata
return False, dict()
def xycalc(byte):
byte = byte / bytes_per_cell
yval = floor(byte / num_cells)
xval = byte % num_cells
return (xval, yval + 1)
# record the color used for each root the first time we find it
root_colors = {}
# there are lots of good colormaps to choose from
# http://www.scipy.org/Cookbook/Matplotlib/Show_colormaps
#
meta_cmap = get_cmap("gist_ncar")
data_done = False
def plotone(a, xvals, yvals, owner, root, lines, labels):
global data_done
add_label = False
if owner:
if options.meta_only:
return
color = "blue"
label = "Data"
if not data_done:
add_label = True
data_done = True
else:
if options.data_only:
return
if root not in root_colors:
color = meta_cmap(randgen.random())
label = "Meta %d" % int(root)
root_colors[root] = (color, label)
add_label = True
else:
color, label = root_colors[root]
plotlines = a.plot(xvals, yvals, 's', color=color, mfc=color, mec=color,
markersize=.23, label=label)
if add_label:
lines += plotlines
labels.append(label)
print "add label %s" % label
def parse_zoom():
def parse_num(s):
mult = 1
c = s.lower()[-1]
if c == 't':
mult = 1024 * 1024 * 1024 * 1024
elif c == 'g':
mult = 1024 * 1024 * 1024
elif c == 'm':
mult = 1024 * 1024
elif c == 'k':
mult = 1024
else:
c = None
if c:
num = int(s[:-1]) * mult
else:
num = int(s)
return num
if not options.zoom:
return (0, 0)
vals = options.zoom.split(':')
if len(vals) != 2:
sys.stderr.write("warning: unable to parse zoom %s\n" % options.zoom)
return (0, 0)
zoommin = parse_num(vals[0])
zoommax = parse_num(vals[1])
return (zoommin, zoommax)
usage = "usage: %prog [options]"
parser = OptionParser(usage=usage)
parser.add_option("-d", "--device", help="Btrfs device", default="")
parser.add_option("-i", "--input-file", help="debug-tree data", default="")
parser.add_option("-o", "--output", help="Output file", default="blocks.png")
parser.add_option("-z", "--zoom", help="Zoom", default=None)
parser.add_option("", "--data-only", help="Only print data blocks",
default=False, action="store_true")
parser.add_option("", "--meta-only", help="Only print metadata blocks",
default=False, action="store_true")
(options,args) = parser.parse_args()
if not options.device and not options.input_file:
parser.print_help()
sys.exit(1)
zoommin, zoommax = parse_zoom()
total_data = 0
total_metadata = 0
if options.device:
data = run_debug_tree(options.device)
elif options.input_file:
data = loaddata(file(options.input_file))
shapeit(data)
# try to drop out the least common data points by creating
# a histogram of the sectors seen.
sectors = data[:,0]
sizes = data[:,1]
datalen = len(data)
sectormax = numpy.max(sectors)
sectormin = 0
num_cells = 800
total_cells = num_cells * num_cells
byte_range = sectormax - sectormin
bytes_per_cell = byte_range / total_cells
f = figure(figsize=(8,6))
# Throughput goes at the bottom
a = subplot(1, 1, 1)
subplots_adjust(right=0.7)
datai = 0
xvals = []
yvals = []
last_owner = 0
last_root = 0
lines = []
labels = []
while datai < datalen:
row = data[datai]
datai += 1
byte = row[0]
size = row[1]
owner = row[2]
root = row[3]
if owner <= 255:
owner = 0
else:
owner = 1
if len(xvals) and (owner != last_owner or last_root != root):
plotone(a, xvals, yvals, last_owner, last_root, lines, labels)
xvals = []
yvals = []
cell = 0
while cell < size:
xy = xycalc(byte)
byte += bytes_per_cell
cell += bytes_per_cell
if xy:
xvals.append(xy[0])
yvals.append(xy[1])
last_owner = owner
last_root = root
if xvals:
plotone(a, xvals, yvals, last_owner, last_root, lines, labels)
# make sure the final second goes on the x axes
ticks = []
a.set_xticks(ticks)
ticks = a.get_yticks()
first_tick = ticks[1] * bytes_per_cell * num_cells
if first_tick > 1024 * 1024 * 1024 * 1024:
scale = 1024 * 1024 * 1024 * 1024;
scalestr = "TB"
elif first_tick > 1024 * 1024 * 1024:
scale = 1024 * 1024 * 1024;
scalestr = "GB"
elif first_tick > 1024 * 1024:
scale = 1024 * 1024;
scalestr = "MB"
elif first_tick > 1024:
scale = 1024;
scalestr = "KB"
else:
scalestr = "Bytes"
scale = 1
ylabels = [ str(int((x * bytes_per_cell * num_cells) / scale)) for x in ticks ]
a.set_yticklabels(ylabels)
a.set_ylabel('Disk offset (%s)' % scalestr)
a.set_xlim(0, num_cells)
a.set_title('Blocks')
a.legend(lines, labels, loc=(1.05, 0.8), shadow=True, pad=0.1, numpoints=1,
handletextsep = 0.005,
labelsep = 0.01,
markerscale=10,
prop=FontProperties(size='x-small') )
if total_data == 0:
percent_meta = 100
else:
percent_meta = (float(total_metadata) / float(total_data)) * 100
print "Total metadata bytes %d data %d ratio %.3f" % (total_metadata,
total_data, percent_meta)
print "saving graph to %s" % options.output
savefig(options.output, orientation='landscape')
show()