Source code for mail_deduplicate.deduplicate

# Copyright Kevin Deldycke <> and contributors.
# This program is Free Software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License
# as published by the Free Software Foundation; either version 2
# of the License, or (at your option) any later version.
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# 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  02111-1307, USA.
from __future__ import annotations

import textwrap
from collections import Counter, OrderedDict
from difflib import unified_diff
from functools import cached_property
from itertools import combinations
from operator import attrgetter
from pathlib import Path
from typing import TYPE_CHECKING
import logging

from boltons.dictutils import FrozenDict
from click_extra import progressbar
from click_extra.colorize import default_theme as theme
from tabulate import tabulate

from mail_deduplicate import (
from mail_deduplicate.mail_box import open_box
from mail_deduplicate.strategy import apply_strategy

    from mailbox import Mailbox, Message

STATS_DEF = OrderedDict(
        ("mail_found", "Total number of mails encountered from all mail sources."),
            "Number of mails rejected individually because they were unparsable or "
            "did not have enough metadata to compute hashes.",
            "Number of valid mails parsed and retained for deduplication.",
        ("mail_hashes", "Number of unique hashes."),
            "Number of unique mails (which where automatically added to selection).",
            "Number of duplicate mails (sum of mails in all duplicate sets with at "
            "least 2 mails).",
            "Number of mails ignored in the selection step because the whole set "
            "they belong to was skipped.",
        ("mail_discarded", "Number of mails discarded from the final selection."),
            "Number of mails kept in the final selection on which the "
            "action will be performed.",
            "Number of mails copied from their original mailbox to another.",
        ("mail_moved", "Number of mails moved from their original mailbox to another."),
        ("mail_deleted", "Number of mails deleted from their mailbox in-place."),
        ("set_total", "Total number of duplicate sets."),
            "Total number of sets containing only a single mail with no applicable "
            "strategy. They were automatically kept in the final selection.",
            "Number of sets skipped from the selection process because they had "
            "encoding issues.",
            "Number of sets skipped from the selection process because they were "
            "too dissimilar in size.",
            "Number of sets skipped from the selection process because they were "
            "too dissimilar in content.",
            "Number of sets skipped from the selection process because the strategy "
            "could not be applied.",
            "Number of valid sets on which the selection strategy was successfully "
"""All tracked statistics and their definition."""

BODY_HASHERS = FrozenDict(
        BODY_HASHER_SKIP: lambda _: "",
        BODY_HASHER_RAW: lambda m: m.hash_raw_body,
        BODY_HASHER_NORMALIZED: lambda m: m.hash_normalized_body,
"""Method used to hash the body of mails."""

[docs]class DuplicateSet: """A set of mails sharing the same hash. Implements all the safety checks required before we can apply any selection strategy. """ def __init__(self, hash_key: str, mail_set: set[Message], conf) -> None: """Load-up the duplicate set of mail and freeze pool. Once loaded-up, the pool of parsed mails is considered frozen for the rest of the duplicate set's life. This allows aggressive caching of lazy instance attributes depending on the pool content. """ self.hash_key: str = hash_key # Mails selected after application of selection strategy. self.selection: set[Message] = set() # Mails discarded after application of selection strategy. self.discard: set[Message] = set() # Global config. self.conf = conf # Pool referencing all duplicated mails and their attributes. self.pool = frozenset(mail_set) # Set metrics. self.stats: Counter = Counter() self.stats["mail_duplicates"] += self.size logging.debug(f"{self!r} created.") def __repr__(self) -> str: """Print internal raw states for debugging.""" return f"<{self.__class__.__name__} hash={self.hash_key} size={self.size}>" @cached_property def size(self) -> int: """Returns the number of mails in the duplicate set.""" return len(self.pool) @cached_property def newest_timestamp(self): """Returns the newest timestamp among all mails in the set.""" return max(map(attrgetter("timestamp"), self.pool)) @cached_property def oldest_timestamp(self): """Returns the oldest timestamp among all mails in the set.""" return min(map(attrgetter("timestamp"), self.pool)) @cached_property def biggest_size(self): """Returns the biggest size among all mails in the set.""" return max(map(attrgetter("size"), self.pool)) @cached_property def smallest_size(self): """Returns the smallest size among all mails in the set.""" return min(map(attrgetter("size"), self.pool))
[docs] def check_differences(self): """Ensures all mail differs in the limits imposed by size and content thresholds. Compare all mails of the duplicate set with each other, both in size and content. Raise an error if we're not within the limits imposed by the threshold settings. """"Check mail differences are below the thresholds.") if self.conf.size_threshold < 0:"Skip checking for size differences.") if self.conf.content_threshold < 0:"Skip checking for content differences.") if self.conf.size_threshold < 0 and self.conf.content_threshold < 0: return # Compute differences of mail against one another. for mail_a, mail_b in combinations(self.pool, 2): # Compare mails on size. if self.conf.size_threshold > -1: size_difference = abs(mail_a.size - mail_b.size) logging.debug( f"{mail_a!r} and {mail_b!r} differs by {size_difference} bytes " "in size.", ) if size_difference > self.conf.size_threshold: raise SizeDiffAboveThreshold # Compare mails on content. if self.conf.content_threshold > -1: content_difference = self.diff(mail_a, mail_b) logging.debug( f"{mail_a!r} and {mail_b!r} differs by {content_difference} bytes " "in content.", ) if content_difference > self.conf.content_threshold: if self.conf.show_diff:, mail_b)) raise ContentDiffAboveThreshold
[docs] def diff(self, mail_a, mail_b): """Return difference in bytes between two mails' normalized body. .. todo:: Rewrite the diff algorithm to not rely on naive unified diff result parsing. """ return len( "".join( unified_diff( mail_a.body_lines, mail_b.body_lines, # Ignore difference in filename lengths and timestamps. fromfile="a", tofile="b", fromfiledate="", tofiledate="", n=0, lineterm="\n", ), ), )
[docs] def pretty_diff(self, mail_a, mail_b): """Returns a verbose unified diff between two mails' normalized body.""" return "".join( unified_diff( mail_a.body_lines, mail_b.body_lines, fromfile=f"Normalized body of {mail_a!r}", tofile=f"Normalized body of {mail_b!r}", fromfiledate=f"{mail_a.timestamp:0.2f}", tofiledate=f"{mail_b.timestamp:0.2f}", n=0, lineterm="\n", ), )
[docs] def categorize_candidates(self): """Process the list of duplicates for action. Run preliminary checks, then apply the strategy to the pool of mails. The process results in two subsets of mails: the selected and the discarded. """ # Fine-grained checks on mail differences. try: self.check_differences() except UnicodeDecodeError as expt: logging.warning("Skip set: unparsable mails due to bad encoding.") logging.debug(f"{expt}") self.stats["mail_skipped"] += self.size self.stats["set_skipped_encoding"] += 1 return except SizeDiffAboveThreshold: logging.warning("Skip set: mails are too dissimilar in size.") self.stats["mail_skipped"] += self.size self.stats["set_skipped_size"] += 1 return except ContentDiffAboveThreshold: logging.warning("Skip set: mails are too dissimilar in content.") self.stats["mail_skipped"] += self.size self.stats["set_skipped_content"] += 1 return if not self.conf.strategy: logging.warning("Skip set: no strategy to apply.") self.stats["mail_skipped"] += self.size self.stats["set_skipped_strategy"] += 1 return # Fetch the subset of selected mails from the set by applying strategy. selected = apply_strategy(self.conf.strategy, self) candidate_count = len(selected) # Duplicate sets matching as a whole are skipped altogether. if candidate_count == self.size: logging.warning( f"Skip set: all {candidate_count} mails within were selected. " "The strategy criterion was not able to discard some.", ) self.stats["mail_skipped"] += self.size self.stats["set_skipped_strategy"] += 1 return # Duplicate sets matching none are skipped altogether. if candidate_count == 0: logging.warning( "Skip set: No mail within were selected. " "The strategy criterion was not able to select some.", ) self.stats["mail_skipped"] += self.size self.stats["set_skipped_strategy"] += 1 return"{candidate_count} mail candidates selected for action.") self.stats["mail_selected"] += candidate_count self.stats["mail_discarded"] += self.size - candidate_count self.stats["set_deduplicated"] += 1 self.selection = selected self.discard = self.pool.difference(selected)
[docs]class Deduplicate: """Load-up messages, search for duplicates, apply selection strategy and perform the action. Similar messages sharing the same hash are grouped together in a ``DuplicateSet``. """ def __init__(self, conf) -> None: # Index of mail sources by their full, normalized path. So we can refer # to them in Mail instances. Also have the nice side effect of natural # deduplication of sources themselves. self.sources: dict[str, Mailbox] = {} # All mails grouped by hashes. self.mails: dict[str, set[Message]] = {} # Mails selected after application of selection strategy. self.selection: set[Message] = set() # Mails discarded after application of selection strategy. self.discard: set[Message] = set() # Global config. self.conf = conf # Deduplication statistics. self.stats: Counter = Counter(dict.fromkeys(STATS_DEF, 0))
[docs] def add_source(self, source_path: Path | str) -> None: """Registers a source of mails, validates and opens it. Duplicate sources of mails are not allowed, as when we perform the action, we use the path as a unique key to tie back a mail from its source. """ # Make the path absolute and resolve any symlinks. path = Path(source_path).resolve(strict=True) if str(path) in self.sources: msg = f"{path} already added." raise ValueError(msg) # Open and register the mail source. Subfolders will be registered as their # own box. boxes = open_box(path, self.conf.input_format, self.conf.force_unlock) for box in boxes: self.sources[box._path] = box # Track global mail count. mail_found = len(box)"{mail_found} mails found.") self.stats["mail_found"] += mail_found
[docs] def hash_all(self): """Browse all mails from all registered sources, compute hashes and group mails by hash. Displays a progress bar as the operation might be slow. """ f"Use [{', '.join(map(theme.choice, self.conf.hash_headers))}] headers to " "compute hashes.", ) body_hasher = BODY_HASHERS.get(self.conf.hash_body) if not body_hasher: msg = f"{self.conf.hash_body} body hasher not implemented yet." raise NotImplementedError(msg) with progressbar( length=self.stats["mail_found"], label="Hashed mails", show_pos=True, ) as progress: for box in self.sources.values(): for mail_id, mail in box.iteritems(): mail.add_box_metadata(box, mail_id) mail.conf = self.conf try: mail_hash = mail.hash_key() + body_hasher(mail) except TooFewHeaders as expt: logging.warning(f"Rejecting {mail!r}: {expt.args[0]}") self.stats["mail_rejected"] += 1 else: # Use a set to deduplicate entries pointing to the same file. self.mails.setdefault(mail_hash, set()).add(mail) self.stats["mail_retained"] += 1 progress.update(1) self.stats["mail_hashes"] += len(self.mails)
[docs] def build_sets(self): """Build the selected and discarded sets from each duplicate set. We apply the selection strategy one duplicate set at a time to keep memory footprint low and make the log easier to read. """ if self.conf.strategy: f"{theme.choice(self.conf.strategy)} strategy will be applied on each " "duplicate set to select candidates.", ) else: logging.warning("No strategy configured, skip selection.") self.stats["set_total"] = len(self.mails) for hash_key, mail_set in self.mails.items(): # Alter log level depending on set length. mail_count = len(mail_set) log_level = logging.debug if mail_count == 1 else log_level(theme.subheading(f"◼ {mail_count} mails sharing hash {hash_key}")) # Apply the selection strategy to discriminate mails within the set. duplicates = DuplicateSet(hash_key, mail_set, self.conf) duplicates.categorize_candidates() # Merge duplicate set's stats to global stats. self.stats += duplicates.stats self.selection.update(duplicates.selection) self.discard.update(duplicates.discard) # Remove from mail objects all attributes we no longer need, now that we # have built the sets of selected and discarded mails. This will save # memory and speed-up the action. # See: delete_names = ["canonical_headers", "body_lines", "subject"] for mail in duplicates.discard: for name in delete_names: if name in mail.__dict__: del mail.__dict__[name] for mail in duplicates.selection: for name in delete_names: if name in mail.__dict__: del mail.__dict__[name] if self.conf.action == "move-discarded": # Selection mails are not moved, delete payload. del mail.__dict__["_payload"]
[docs] def close_all(self): """Close all open boxes.""" for source_path, box in self.sources.items(): logging.debug(f"Close {source_path}") box.close()
[docs] def report(self): """Returns a text report of user-friendly statistics and metrics.""" output = "" for prefix, title in (("mail_", "Mails"), ("set_", "Duplicate sets")): table = [[title, "Metric", "Description"]] for stat_id, desc in STATS_DEF.items(): if stat_id.startswith(prefix): table.append( [ stat_id[len(prefix) :].replace("_", " - ").title(), self.stats[stat_id], "\n".join(textwrap.wrap(desc, 60)), ], ) output += tabulate(table, tablefmt="fancy_grid", headers="firstrow") output += "\n" return output
[docs] def check_stats(self): """Perform some high-level consistency checks on metrics. Helps users reports tricky edge-cases. """ # Box opening stats. assert self.stats["mail_found"] >= self.stats["mail_rejected"] assert self.stats["mail_found"] >= self.stats["mail_retained"] assert self.stats["mail_found"] == ( self.stats["mail_rejected"] + self.stats["mail_retained"] ) # Mail grouping by hash. assert self.stats["mail_retained"] >= self.stats["mail_unique"] assert self.stats["mail_retained"] >= self.stats["mail_duplicates"] assert self.stats["mail_retained"] == ( self.stats["mail_unique"] + self.stats["mail_duplicates"] ) # Mail selection stats. assert self.stats["mail_retained"] >= self.stats["mail_skipped"] assert self.stats["mail_retained"] >= self.stats["mail_discarded"] assert self.stats["mail_retained"] >= self.stats["mail_selected"] assert self.stats["mail_retained"] == ( self.stats["mail_skipped"] + self.stats["mail_discarded"] + self.stats["mail_selected"] ) # Action stats. assert self.stats["mail_selected"] >= self.stats["mail_copied"] if self.conf.action != "move-discarded": # The number of moved mails may be larger than the number of selected # mails for move-discarded action, because discarded mails are moved. assert self.stats["mail_selected"] >= self.stats["mail_moved"] assert self.stats["mail_selected"] >= self.stats["mail_deleted"] assert self.stats["mail_selected"] in ( self.stats["mail_copied"], self.stats["mail_moved"], self.stats["mail_deleted"], ) # Sets accounting. assert self.stats["set_total"] == self.stats["mail_hashes"] assert self.stats["set_single"] == self.stats["mail_unique"] assert self.stats["set_total"] == ( self.stats["set_single"] + self.stats["set_skipped_encoding"] + self.stats["set_skipped_size"] + self.stats["set_skipped_content"] + self.stats["set_skipped_strategy"] + self.stats["set_deduplicated"] )