1 """High-level interface for working with LBS archives.
3 This module provides an easy interface for reading from and manipulating
4 various parts of an LBS archive:
5 - listing the snapshots and segments present
6 - reading segment contents
7 - parsing snapshot descriptors and snapshot metadata logs
8 - reading and maintaining the local object database
11 from __future__ import division
12 import os, re, sha, tarfile, tempfile, thread
13 from pysqlite2 import dbapi2 as sqlite3
15 # The largest supported snapshot format that can be understood.
16 FORMAT_VERSION = (0, 6) # LBS Snapshot v0.6
18 # Maximum number of nested indirect references allowed in a snapshot.
19 MAX_RECURSION_DEPTH = 3
22 """A class which merely acts as a data container.
24 Instances of this class (or its subclasses) are merely used to store data
25 in various attributes. No methods are provided.
29 return "<%s %s>" % (self.__class__, self.__dict__)
31 CHECKSUM_ALGORITHMS = {
35 class ChecksumCreator:
36 """Compute an LBS checksum for provided data.
38 The algorithm used is selectable, but currently defaults to sha1.
41 def __init__(self, algorithm='sha1'):
42 self.algorithm = algorithm
43 self.hash = CHECKSUM_ALGORITHMS[algorithm]()
45 def update(self, data):
46 self.hash.update(data)
50 return "%s=%s" % (self.algorithm, self.hash.hexdigest())
52 class ChecksumVerifier:
53 """Verify whether a checksum from a snapshot matches the supplied data."""
55 def __init__(self, checksumstr):
56 """Create an object to check the supplied checksum."""
58 (algo, checksum) = checksumstr.split("=", 1)
59 self.checksum = checksum
60 self.hash = CHECKSUM_ALGORITHMS[algo]()
62 def update(self, data):
63 self.hash.update(data)
66 """Return a boolean indicating whether the checksum matches."""
68 result = self.hash.hexdigest()
69 return result == self.checksum
71 class LowlevelDataStore:
72 """Access to the backup store containing segments and snapshot descriptors.
74 Instances of this class are used to get direct filesystem-level access to
75 the backup data. To read a backup, a caller will ordinarily not care about
76 direct access to backup segments, but will instead merely need to access
77 objects from those segments. The ObjectStore class provides a suitable
78 wrapper around a DataStore to give this high-level access.
81 def __init__(self, path):
84 # Low-level filesystem access. These methods could be overwritten to
85 # provide access to remote data stores.
86 def lowlevel_list(self):
87 """Get a listing of files stored."""
89 return os.listdir(self.path)
91 def lowlevel_open(self, filename):
92 """Return a file-like object for reading data from the given file."""
94 return open(os.path.join(self.path, filename), 'rb')
96 def lowlevel_stat(self, filename):
97 """Return a dictionary of information about the given file.
99 Currently, the only defined field is 'size', giving the size of the
103 stat = os.stat(os.path.join(self.path, filename))
104 return {'size': stat.st_size}
106 # Slightly higher-level list methods.
107 def list_snapshots(self):
108 for f in self.lowlevel_list():
109 m = re.match(r"^snapshot-(.*)\.lbs$", f)
113 def list_segments(self):
114 for f in self.lowlevel_list():
115 m = re.match(r"^([0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12})(\.\S+)?$", f)
120 def __init__(self, data_store):
121 self.store = data_store
126 def get_cachedir(self):
127 if self.cachedir is None:
128 self.cachedir = tempfile.mkdtemp(".lbs")
132 if self.cachedir is not None:
133 # TODO: Avoid use of system, make this safer
134 os.system("rm -rf " + self.cachedir)
138 def parse_ref(refstr):
139 m = re.match(r"^([-0-9a-f]+)\/([0-9a-f]+)(\(\S+\))?(\[(\d+)\+(\d+)\])?$", refstr)
144 checksum = m.group(3)
147 if checksum is not None:
148 checksum = checksum.lstrip("(").rstrip(")")
150 if slice is not None:
151 slice = (int(m.group(5)), int(m.group(6)))
153 return (segment, object, checksum, slice)
155 def get_segment(self, segment):
156 raw = self.store.lowlevel_open(segment + ".tar.gpg")
158 (input, output) = os.popen2("lbs-filter-gpg --decrypt")
159 def copy_thread(src, dst):
162 block = src.read(BLOCK_SIZE)
163 if len(block) == 0: break
167 thread.start_new_thread(copy_thread, (raw, input))
170 def load_segment(self, segment):
171 seg = tarfile.open(segment, 'r|', self.get_segment(segment))
173 data_obj = seg.extractfile(item)
174 path = item.name.split('/')
175 if len(path) == 2 and path[0] == segment:
176 yield (path[1], data_obj.read())
178 def load_snapshot(self, snapshot):
179 file = self.store.lowlevel_open("snapshot-" + snapshot + ".lbs")
180 return file.read().splitlines(True)
182 def extract_segment(self, segment):
183 segdir = os.path.join(self.get_cachedir(), segment)
185 for (object, data) in self.load_segment(segment):
186 f = open(os.path.join(segdir, object), 'wb')
190 def load_object(self, segment, object):
191 path = os.path.join(self.get_cachedir(), segment, object)
192 if not os.access(path, os.R_OK):
193 self.extract_segment(segment)
194 if segment in self.lru_list: self.lru_list.remove(segment)
195 self.lru_list.append(segment)
196 while len(self.lru_list) > self.CACHE_SIZE:
197 os.system("rm -rf " + os.path.join(self.cachedir, self.lru_list[0]))
198 self.lru_list = self.lru_list[1:]
199 return open(path, 'rb').read()
201 def get(self, refstr):
202 """Fetch the given object and return it.
204 The input should be an object reference, in string form.
207 (segment, object, checksum, slice) = self.parse_ref(refstr)
209 data = self.load_object(segment, object)
211 if checksum is not None:
212 verifier = ChecksumVerifier(checksum)
213 verifier.update(data)
214 if not verifier.valid():
217 if slice is not None:
218 (start, length) = slice
219 data = data[start:start+length]
220 if len(data) != length: raise IndexError
224 def parse(lines, terminate=None):
225 """Generic parser for RFC822-style "Key: Value" data streams.
227 This parser can be used to read metadata logs and snapshot root descriptor
230 lines must be an iterable object which yields a sequence of lines of input.
232 If terminate is specified, it is used as a predicate to determine when to
233 stop reading input lines.
240 # Strip off a trailing newline, if present
241 if len(l) > 0 and l[-1] == "\n":
244 if terminate is not None and terminate(l):
245 if len(dict) > 0: yield dict
250 m = re.match(r"^(\w+):\s*(.*)$", l)
252 dict[m.group(1)] = m.group(2)
253 last_key = m.group(1)
254 elif len(l) > 0 and l[0].isspace() and last_key is not None:
259 if len(dict) > 0: yield dict
261 def parse_full(lines):
263 return parse(lines).next()
264 except StopIteration:
267 def parse_metadata_version(s):
268 """Convert a string with the snapshot version format to a tuple."""
270 m = re.match(r"^LBS Snapshot v(\d+(\.\d+)*)$", s)
274 return tuple([int(d) for d in m.group(1).split(".")])
276 def read_metadata(object_store, root):
277 """Iterate through all lines in the metadata log, following references."""
279 # Stack for keeping track of recursion when following references to
280 # portions of the log. The last entry in the stack corresponds to the
281 # object currently being parsed. Each entry is a list of lines which have
282 # been reversed, so that popping successive lines from the end of each list
283 # will return lines of the metadata log in order.
286 def follow_ref(refstr):
287 if len(stack) >= MAX_RECURSION_DEPTH: raise OverflowError
288 lines = object_store.get(refstr).splitlines(True)
294 while len(stack) > 0:
301 # An indirect reference which we must follow?
302 if len(line) > 0 and line[0] == '@':
310 """Metadata for a single file (or directory or...) from a snapshot."""
312 # Functions for parsing various datatypes that can appear in a metadata log
316 """Decode an integer, expressed in decimal, octal, or hexadecimal."""
317 if s.startswith("0x"):
319 elif s.startswith("0"):
326 """Decode a URI-encoded (%xx escapes) string."""
327 def hex_decode(m): return chr(int(m.group(1), 16))
328 return re.sub(r"%([0-9a-f]{2})", hex_decode, s)
332 """An unecoded string."""
337 """Decode a user/group to a tuple of uid/gid followed by name."""
339 uid = MetadataItem.decode_int(items[0])
342 if items[1].startswith("(") and items[1].endswith(")"):
343 name = MetadataItem.decode_str(items[1][1:-1])
347 def decode_device(s):
348 """Decode a device major/minor number."""
349 (major, minor) = map(MetadataItem.decode_int, s.split("/"))
350 return (major, minor)
354 def __init__(self, fields, object_store):
355 """Initialize from a dictionary of key/value pairs from metadata log."""
358 self.object_store = object_store
360 self.items = self.Items()
361 for (k, v) in fields.items():
362 if k in self.field_types:
363 decoder = self.field_types[k]
364 setattr(self.items, k, decoder(v))
368 """Return an iterator for the data blocks that make up a file."""
370 # This traverses the list of blocks that make up a file, following
371 # indirect references. It is implemented in much the same way as
372 # read_metadata, so see that function for details of the technique.
374 objects = self.fields['data'].split()
378 def follow_ref(refstr):
379 if len(stack) >= MAX_RECURSION_DEPTH: raise OverflowError
380 objects = self.object_store.get(refstr).split()
382 stack.append(objects)
384 while len(stack) > 0:
391 # An indirect reference which we must follow?
392 if len(ref) > 0 and ref[0] == '@':
397 # Description of fields that might appear, and how they should be parsed.
398 MetadataItem.field_types = {
399 'name': MetadataItem.decode_str,
400 'type': MetadataItem.raw_str,
401 'mode': MetadataItem.decode_int,
402 'device': MetadataItem.decode_device,
403 'user': MetadataItem.decode_user,
404 'group': MetadataItem.decode_user,
405 'ctime': MetadataItem.decode_int,
406 'mtime': MetadataItem.decode_int,
407 'links': MetadataItem.decode_int,
408 'inode': MetadataItem.raw_str,
409 'checksum': MetadataItem.decode_str,
410 'size': MetadataItem.decode_int,
411 'contents': MetadataItem.decode_str,
412 'target': MetadataItem.decode_str,
415 def iterate_metadata(object_store, root):
416 for d in parse(read_metadata(object_store, root), lambda l: len(l) == 0):
417 yield MetadataItem(d, object_store)
420 """Access to the local database of snapshot contents and object checksums.
422 The local database is consulted when creating a snapshot to determine what
423 data can be re-used from old snapshots. Segment cleaning is performed by
424 manipulating the data in the local database; the local database also
425 includes enough data to guide the segment cleaning process.
428 def __init__(self, path, dbname="localdb.sqlite"):
429 self.db_connection = sqlite3.connect(path + "/" + dbname)
431 # Low-level database access. Use these methods when there isn't a
432 # higher-level interface available. Exception: do, however, remember to
433 # use the commit() method after making changes to make sure they are
434 # actually saved, even when going through higher-level interfaces.
436 "Commit any pending changes to the local database."
437 self.db_connection.commit()
440 "Roll back any pending changes to the local database."
441 self.db_connection.rollback()
444 "Return a DB-API cursor for directly accessing the local database."
445 return self.db_connection.cursor()
447 def garbage_collect(self):
448 """Delete entries from old snapshots from the database."""
452 # Delete old snapshots.
453 cur.execute("""delete from snapshots
454 where snapshotid < (select max(snapshotid)
457 # Delete entries in the segments_used table which are for non-existent
459 cur.execute("""delete from segments_used
460 where snapshotid not in
461 (select snapshotid from snapshots)""")
463 # Find segments which contain no objects used by any current snapshots,
464 # and delete them from the segment table.
465 cur.execute("""delete from segments where segmentid not in
466 (select segmentid from segments_used)""")
468 # Finally, delete objects contained in non-existent segments. We can't
469 # simply delete unused objects, since we use the set of unused objects
470 # to determine the used/free ratio of segments.
471 cur.execute("""delete from block_index
472 where segmentid not in
473 (select segmentid from segments)""")
476 class SegmentInfo(Struct): pass
478 def get_segment_cleaning_list(self, age_boost=0.0):
479 """Return a list of all current segments with information for cleaning.
481 Return all segments which are currently known in the local database
482 (there might be other, older segments in the archive itself), and
483 return usage statistics for each to help decide which segments to
486 The returned list will be sorted by estimated cleaning benefit, with
487 segments that are best to clean at the start of the list.
489 If specified, the age_boost parameter (measured in days) will added to
490 the age of each segment, as a way of adjusting the benefit computation
491 before a long-lived snapshot is taken (for example, age_boost might be
492 set to 7 when cleaning prior to taking a weekly snapshot).
497 cur.execute("""select segmentid, used, size, mtime,
498 julianday('now') - mtime as age from segment_info""")
500 info = self.SegmentInfo()
502 info.used_bytes = row[1]
503 info.size_bytes = row[2]
505 info.age_days = row[4]
507 # Benefit calculation: u is the estimated fraction of each segment
508 # which is utilized (bytes belonging to objects still in use
509 # divided by total size; this doesn't take compression or storage
510 # overhead into account, but should give a reasonable estimate).
512 # The total benefit is a heuristic that combines several factors:
513 # the amount of space that can be reclaimed (1 - u), an ageing
514 # factor (info.age_days) that favors cleaning old segments to young
515 # ones and also is more likely to clean segments that will be
516 # rewritten for long-lived snapshots (age_boost), and finally a
517 # penalty factor for the cost of re-uploading data (u + 0.1).
518 u = info.used_bytes / info.size_bytes
519 info.cleaning_benefit \
520 = (1 - u) * (info.age_days + age_boost) / (u + 0.1)
522 segments.append(info)
524 segments.sort(cmp, key=lambda s: s.cleaning_benefit, reverse=True)
527 def mark_segment_expired(self, segment):
528 """Mark a segment for cleaning in the local database.
530 The segment parameter should be either a SegmentInfo object or an
531 integer segment id. Objects in the given segment will be marked as
532 expired, which means that any future snapshots that would re-use those
533 objects will instead write out a new copy of the object, and thus no
534 future snapshots will depend upon the given segment.
537 if isinstance(segment, int):
539 elif isinstance(segment, self.SegmentInfo):
542 raise TypeError("Invalid segment: %s, must be of type int or SegmentInfo, not %s" % (segment, type(segment)))
545 cur.execute("update block_index set expired = 1 where segmentid = ?",
548 def balance_expired_objects(self):
549 """Analyze expired objects in segments to be cleaned and group by age.
551 Update the block_index table of the local database to group expired
552 objects by age. The exact number of buckets and the cutoffs for each
553 are dynamically determined. Calling this function after marking
554 segments expired will help in the segment cleaning process, by ensuring
555 that when active objects from clean segments are rewritten, they will
556 be placed into new segments roughly grouped by age.
559 # The expired column of the block_index table is used when generating a
560 # new LBS snapshot. A null value indicates that an object may be
561 # re-used. Otherwise, an object must be written into a new segment if
562 # needed. Objects with distinct expired values will be written into
563 # distinct segments, to allow for some grouping by age. The value 0 is
564 # somewhat special in that it indicates any rewritten objects can be
565 # placed in the same segment as completely new objects; this can be
566 # used for very young objects which have been expired, or objects not
567 # expected to be encountered.
569 # In the balancing process, all objects which are not used in any
570 # current snapshots will have expired set to 0. Objects which have
571 # been seen will be sorted by age and will have expired values set to
572 # 0, 1, 2, and so on based on age (with younger objects being assigned
573 # lower values). The number of buckets and the age cutoffs is
574 # determined by looking at the distribution of block ages.
578 # Mark all expired objects with expired = 0; these objects will later
579 # have values set to indicate groupings of objects when repacking.
580 cur.execute("""update block_index set expired = 0
581 where expired is not null""")
583 # We will want to aim for at least one full segment for each bucket
584 # that we eventually create, but don't know how many bytes that should
585 # be due to compression. So compute the average number of bytes in
586 # each expired segment as a rough estimate for the minimum size of each
587 # bucket. (This estimate could be thrown off by many not-fully-packed
588 # segments, but for now don't worry too much about that.) If we can't
589 # compute an average, it's probably because there are no expired
590 # segments, so we have no more work to do.
591 cur.execute("""select avg(size) from segments
593 (select distinct segmentid from block_index
594 where expired is not null)""")
595 segment_size_estimate = cur.fetchone()[0]
596 if not segment_size_estimate:
599 # Next, extract distribution of expired objects (number and size) by
600 # age. Save the timestamp for "now" so that the classification of
601 # blocks into age buckets will not change later in the function, after
602 # time has passed. Set any timestamps in the future to now, so we are
603 # guaranteed that for the rest of this function, age is always
605 cur.execute("select julianday('now')")
606 now = cur.fetchone()[0]
608 cur.execute("""update block_index set timestamp = ?
609 where timestamp > ? and expired is not null""",
612 cur.execute("""select round(? - timestamp) as age, count(*), sum(size)
613 from block_index where expired = 0
614 group by age order by age""", (now,))
615 distribution = cur.fetchall()
617 # Start to determine the buckets for expired objects. Heuristics used:
618 # - An upper bound on the number of buckets is given by the number of
619 # segments we estimate it will take to store all data. In fact,
620 # aim for a couple of segments per bucket.
621 # - Place very young objects in bucket 0 (place with new objects)
622 # unless there are enough of them to warrant a separate bucket.
623 # - Try not to create unnecessarily many buckets, since fewer buckets
624 # will allow repacked data to be grouped based on spatial locality
625 # (while more buckets will group by temporal locality). We want a
628 total_bytes = sum([i[2] for i in distribution])
629 target_buckets = 2 * (total_bytes / segment_size_estimate) ** 0.4
630 min_size = 1.5 * segment_size_estimate
631 target_size = max(2 * segment_size_estimate,
632 total_bytes / target_buckets)
634 print "segment_size:", segment_size_estimate
635 print "distribution:", distribution
636 print "total_bytes:", total_bytes
637 print "target_buckets:", target_buckets
638 print "min, target size:", min_size, target_size
640 # Chosen cutoffs. Each bucket consists of objects with age greater
641 # than one cutoff value, but not greater than the next largest cutoff.
644 # Starting with the oldest objects, begin grouping together into
645 # buckets of size at least target_size bytes.
646 distribution.reverse()
648 min_age_bucket = False
649 for (age, items, size) in distribution:
650 if bucket_size >= target_size \
651 or (age < MIN_AGE and not min_age_bucket):
652 if bucket_size < target_size and len(cutoffs) > 0:
659 min_age_bucket = True
661 # The last (youngest) bucket will be group 0, unless it has enough data
662 # to be of size min_size by itself, or there happen to be no objects
663 # less than MIN_AGE at all.
664 if bucket_size >= min_size or not min_age_bucket:
668 print "cutoffs:", cutoffs
670 # Update the database to assign each object to the appropriate bucket.
672 for i in range(len(cutoffs)):
673 cur.execute("""update block_index set expired = ?
674 where round(? - timestamp) > ?""",
675 (i, now, cutoffs[i]))