# -*- Mode: python; coding: utf-8; indent-tabs-mode: nil -*- */
# SPDX-License-Identifier: LGPL-2.1+
+from __future__ import print_function
+
import gdb
class sd_dump_hashmaps(gdb.Command):
ulong_t = gdb.lookup_type("unsigned long")
debug_offset = gdb.parse_and_eval("(unsigned long)&((HashmapBase*)0)->debug")
- print "type, hash, indirect, entries, max_entries, buckets, creator"
+ print("type, hash, indirect, entries, max_entries, buckets, creator")
while d:
h = gdb.parse_and_eval("(HashmapBase*)((char*)%d - %d)" % (int(d.cast(ulong_t)), debug_offset))
t = ["plain", "ordered", "set"][int(h["type"])]
- print "{}, {}, {}, {}, {}, {}, {} ({}:{})".format(t, h["hash_ops"], bool(h["has_indirect"]), n_entries, d["max_entries"], n_buckets, d["func"], d["file"], d["line"])
+ print("{}, {}, {}, {}, {}, {}, {} ({}:{})".format(t, h["hash_ops"], bool(h["has_indirect"]), n_entries, d["max_entries"], n_buckets, d["func"], d["file"], d["line"]))
if arg != "" and n_entries > 0:
dib_raw_addr = storage_ptr + (all_entry_sizes[h["type"]] * n_buckets)
for dib in sorted(iter(histogram)):
if dib != 255:
- print "{:>3} {:>8} {} of entries".format(dib, histogram[dib], 100.0*histogram[dib]/n_entries)
+ print("{:>3} {:>8} {} of entries".format(dib, histogram[dib], 100.0*histogram[dib]/n_entries))
else:
- print "{:>3} {:>8} {} of slots".format(dib, histogram[dib], 100.0*histogram[dib]/n_buckets)
- print "mean DIB of entries: {}".format(sum([dib*histogram[dib] for dib in iter(histogram) if dib != 255])*1.0/n_entries)
+ print("{:>3} {:>8} {} of slots".format(dib, histogram[dib], 100.0*histogram[dib]/n_buckets))
+ print("mean DIB of entries: {}".format(sum([dib*histogram[dib] for dib in iter(histogram) if dib != 255])*1.0/n_entries))
blocks = []
current_len = 1
if len(blocks) > 1 and blocks[0][0] == blocks[0][1] and blocks[-1][0] == n_buckets - 1:
blocks[0][1] += blocks[-1][1]
blocks = blocks[0:-1]
- print "max block: {}".format(max(blocks, key=lambda a: a[1]))
- print "sum block lens: {}".format(sum(b[1] for b in blocks))
- print "mean block len: {}".format((1.0 * sum(b[1] for b in blocks) / len(blocks)))
+ print("max block: {}".format(max(blocks, key=lambda a: a[1])))
+ print("sum block lens: {}".format(sum(b[1] for b in blocks)))
+ print("mean block len: {}".format((1.0 * sum(b[1] for b in blocks) / len(blocks))))
d = d["debug_list_next"]