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wireproto: add streams to frame-based protocol...
wireproto: add streams to frame-based protocol Previously, the frame-based protocol was just a series of frames, with each frame associated with a request ID. In order to scale the protocol, we'll want to enable the use of compression. While it is possible to enable compression at the socket/pipe level, this has its disadvantages. The big one is it undermines the point of frames being standalone, atomic units that can be read and written: if you add compression above the framing protocol, you are back to having a stream-based protocol as opposed to something frame-based. So in order to preserve frames, compression needs to occur at the frame payload level. Compressing each frame's payload individually will limit compression ratios because the window size of the compressor will be limited by the max frame size, which is 32-64kb as currently defined. It will also add CPU overhead, as it is more efficient for compressors to operate on fewer, larger blocks of data than more, smaller blocks. So compressing each frame independently is out. This means we need to compress each frame's payload as if it is part of a larger stream. The simplest approach is to have 1 stream per connection. This could certainly work. However, it has disadvantages (documented below). We could also have 1 stream per RPC/command invocation. (This is the model HTTP/2 goes with.) This also has disadvantages. The main disadvantage to one global stream is that it has the very real potential to create CPU bottlenecks doing compression. Networks are only getting faster and the performance of single CPU cores has been relatively flat. Newer compression formats like zstandard offer better CPU cycle efficiency than predecessors like zlib. But it still all too common to saturate your CPU with compression overhead long before you saturate the network pipe. The main disadvantage with streams per request is that you can't reap the benefits of the compression context for multiple requests. For example, if you send 1000 RPC requests (or HTTP/2 requests for that matter), the response to each would have its own compression context. The overall size of the raw responses would be larger because compression contexts wouldn't be able to reference data from another request or response. The approach for streams as implemented in this commit is to support N streams per connection and for streams to potentially span requests and responses. As explained by the added internals docs, this facilitates servers and clients delegating independent streams and compression to independent threads / CPU cores. This helps alleviate the CPU bottleneck of compression. This design also allows compression contexts to be reused across requests/responses. This can result in improved compression ratios and less overhead for compressors and decompressors having to build new contexts. Another feature that was defined was the ability for individual frames within a stream to declare whether that individual frame's payload uses the content encoding (read: compression) defined by the stream. The idea here is that some servers may serve data from a combination of caches and dynamic resolution. Data coming from caches may be pre-compressed. We want to facilitate servers being able to essentially stream bytes from caches to the wire with minimal overhead. Being able to mix and match with frames are compressed within a stream enables these types of advanced server functionality. This commit defines the new streams mechanism. Basic code for supporting streams in frames has been added. But that code is seriously lacking and doesn't fully conform to the defined protocol. For example, we don't close any streams. And support for content encoding within streams is not yet implemented. The change was rather invasive and I didn't think it would be reasonable to implement the entire feature in a single commit. For the record, I would have loved to reuse an existing multiplexing protocol to build the new wire protocol on top of. However, I couldn't find a protocol that offers the performance and scaling characteristics that I desired. Namely, it should support multiple compression contexts to facilitate scaling out to multiple CPU cores and compression contexts should be able to live longer than single RPC requests. HTTP/2 *almost* fits the bill. But the semantics of HTTP message exchange state that streams can only live for a single request-response. We /could/ tunnel on top of HTTP/2 streams and frames with HEADER and DATA frames. But there's no guarantee that HTTP/2 libraries and proxies would allow us to use HTTP/2 streams and frames without the HTTP message exchange semantics defined in RFC 7540 Section 8. Other RPC protocols like gRPC tunnel are built on top of HTTP/2 and thus preserve its semantics of stream per RPC invocation. Even QUIC does this. We could attempt to invent a higher-level stream that spans HTTP/2 streams. But this would be violating HTTP/2 because there is no guarantee that HTTP/2 streams are routed to the same server. The best we can do - which is what this protocol does - is shoehorn all request and response data into a single HTTP message and create streams within. At that point, we've defined a Content-Type in HTTP parlance. It just so happens our media type can also work as a standalone, stream-based protocol, without leaning on HTTP or similar protocol. Differential Revision: https://phab.mercurial-scm.org/D2907

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tagmerge.py
269 lines | 11.3 KiB | text/x-python | PythonLexer
# tagmerge.py - merge .hgtags files
#
# Copyright 2014 Angel Ezquerra <angel.ezquerra@gmail.com>
#
# This software may be used and distributed according to the terms of the
# GNU General Public License version 2 or any later version.
# This module implements an automatic merge algorithm for mercurial's tag files
#
# The tagmerge algorithm implemented in this module is able to resolve most
# merge conflicts that currently would trigger a .hgtags merge conflict. The
# only case that it does not (and cannot) handle is that in which two tags point
# to different revisions on each merge parent _and_ their corresponding tag
# histories have the same rank (i.e. the same length). In all other cases the
# merge algorithm will choose the revision belonging to the parent with the
# highest ranked tag history. The merged tag history is the combination of both
# tag histories (special care is taken to try to combine common tag histories
# where possible).
#
# In addition to actually merging the tags from two parents, taking into
# account the base, the algorithm also tries to minimize the difference
# between the merged tag file and the first parent's tag file (i.e. it tries to
# make the merged tag order as as similar as possible to the first parent's tag
# file order).
#
# The algorithm works as follows:
# 1. read the tags from p1, p2 and the base
# - when reading the p1 tags, also get the line numbers associated to each
# tag node (these will be used to sort the merged tags in a way that
# minimizes the diff to p1). Ignore the file numbers when reading p2 and
# the base
# 2. recover the "lost tags" (i.e. those that are found in the base but not on
# p1 or p2) and add them back to p1 and/or p2
# - at this point the only tags that are on p1 but not on p2 are those new
# tags that were introduced in p1. Same thing for the tags that are on p2
# but not on p2
# 3. take all tags that are only on p1 or only on p2 (but not on the base)
# - Note that these are the tags that were introduced between base and p1
# and between base and p2, possibly on separate clones
# 4. for each tag found both on p1 and p2 perform the following merge algorithm:
# - the tags conflict if their tag "histories" have the same "rank" (i.e.
# length) AND the last (current) tag is NOT the same
# - for non conflicting tags:
# - choose which are the high and the low ranking nodes
# - the high ranking list of nodes is the one that is longer.
# In case of draw favor p1
# - the merged node list is made of 3 parts:
# - first the nodes that are common to the beginning of both
# the low and the high ranking nodes
# - second the non common low ranking nodes
# - finally the non common high ranking nodes (with the last
# one being the merged tag node)
# - note that this is equivalent to putting the whole low ranking
# node list first, followed by the non common high ranking nodes
# - note that during the merge we keep the "node line numbers", which will
# be used when writing the merged tags to the tag file
# 5. write the merged tags taking into account to their positions in the first
# parent (i.e. try to keep the relative ordering of the nodes that come
# from p1). This minimizes the diff between the merged and the p1 tag files
# This is done by using the following algorithm
# - group the nodes for a given tag that must be written next to each other
# - A: nodes that come from consecutive lines on p1
# - B: nodes that come from p2 (i.e. whose associated line number is
# None) and are next to one of the a nodes in A
# - each group is associated with a line number coming from p1
# - generate a "tag block" for each of the groups
# - a tag block is a set of consecutive "node tag" lines belonging to
# the same tag and which will be written next to each other on the
# merged tags file
# - sort the "tag blocks" according to their associated number line
# - put blocks whose nodes come all from p2 first
# - write the tag blocks in the sorted order
from __future__ import absolute_import
from .i18n import _
from .node import (
hex,
nullid,
)
from .import (
tags as tagsmod,
util,
)
hexnullid = hex(nullid)
def readtagsformerge(ui, repo, lines, fn='', keeplinenums=False):
'''read the .hgtags file into a structure that is suitable for merging
Depending on the keeplinenums flag, clear the line numbers associated
with each tag. This is done because only the line numbers of the first
parent are useful for merging.
'''
filetags = tagsmod._readtaghist(ui, repo, lines, fn=fn, recode=None,
calcnodelines=True)[1]
for tagname, taginfo in filetags.items():
if not keeplinenums:
for el in taginfo:
el[1] = None
return filetags
def grouptagnodesbyline(tagnodes):
'''
Group nearby nodes (i.e. those that must be written next to each other)
The input is a list of [node, position] pairs, corresponding to a given tag
The position is the line number where the node was found on the first parent
.hgtags file, or None for those nodes that came from the base or the second
parent .hgtags files.
This function groups those [node, position] pairs, returning a list of
groups of nodes that must be written next to each other because their
positions are consecutive or have no position preference (because their
position is None).
The result is a list of [position, [consecutive node list]]
'''
firstlinenum = None
for hexnode, linenum in tagnodes:
firstlinenum = linenum
if firstlinenum is not None:
break
if firstlinenum is None:
return [[None, [el[0] for el in tagnodes]]]
tagnodes[0][1] = firstlinenum
groupednodes = [[firstlinenum, []]]
prevlinenum = firstlinenum
for hexnode, linenum in tagnodes:
if linenum is not None and linenum - prevlinenum > 1:
groupednodes.append([linenum, []])
groupednodes[-1][1].append(hexnode)
if linenum is not None:
prevlinenum = linenum
return groupednodes
def writemergedtags(fcd, mergedtags):
'''
write the merged tags while trying to minimize the diff to the first parent
This function uses the ordering info stored on the merged tags dict to
generate an .hgtags file which is correct (in the sense that its contents
correspond to the result of the tag merge) while also being as close as
possible to the first parent's .hgtags file.
'''
# group the node-tag pairs that must be written next to each other
for tname, taglist in list(mergedtags.items()):
mergedtags[tname] = grouptagnodesbyline(taglist)
# convert the grouped merged tags dict into a format that resembles the
# final .hgtags file (i.e. a list of blocks of 'node tag' pairs)
def taglist2string(tlist, tname):
return '\n'.join(['%s %s' % (hexnode, tname) for hexnode in tlist])
finaltags = []
for tname, tags in mergedtags.items():
for block in tags:
block[1] = taglist2string(block[1], tname)
finaltags += tags
# the tag groups are linked to a "position" that can be used to sort them
# before writing them
# the position is calculated to ensure that the diff of the merged .hgtags
# file to the first parent's .hgtags file is as small as possible
finaltags.sort(key=lambda x: -1 if x[0] is None else x[0])
# finally we can join the sorted groups to get the final contents of the
# merged .hgtags file, and then write it to disk
mergedtagstring = '\n'.join([tags for rank, tags in finaltags if tags])
fcd.write(mergedtagstring + '\n', fcd.flags())
def singletagmerge(p1nodes, p2nodes):
'''
merge the nodes corresponding to a single tag
Note that the inputs are lists of node-linenum pairs (i.e. not just lists
of nodes)
'''
if not p2nodes:
return p1nodes
if not p1nodes:
return p2nodes
# there is no conflict unless both tags point to different revisions
# and have a non identical tag history
p1currentnode = p1nodes[-1][0]
p2currentnode = p2nodes[-1][0]
if p1currentnode != p2currentnode and len(p1nodes) == len(p2nodes):
# cannot merge two tags with same rank pointing to different nodes
return None
# which are the highest ranking (hr) / lowest ranking (lr) nodes?
if len(p1nodes) >= len(p2nodes):
hrnodes, lrnodes = p1nodes, p2nodes
else:
hrnodes, lrnodes = p2nodes, p1nodes
# the lowest ranking nodes will be written first, followed by the highest
# ranking nodes
# to avoid unwanted tag rank explosion we try to see if there are some
# common nodes that can be written only once
commonidx = len(lrnodes)
for n in range(len(lrnodes)):
if hrnodes[n][0] != lrnodes[n][0]:
commonidx = n
break
lrnodes[n][1] = p1nodes[n][1]
# the merged node list has 3 parts:
# - common nodes
# - non common lowest ranking nodes
# - non common highest ranking nodes
# note that the common nodes plus the non common lowest ranking nodes is the
# whole list of lr nodes
return lrnodes + hrnodes[commonidx:]
def merge(repo, fcd, fco, fca):
'''
Merge the tags of two revisions, taking into account the base tags
Try to minimize the diff between the merged tags and the first parent tags
'''
ui = repo.ui
# read the p1, p2 and base tags
# only keep the line numbers for the p1 tags
p1tags = readtagsformerge(
ui, repo, fcd.data().splitlines(), fn="p1 tags",
keeplinenums=True)
p2tags = readtagsformerge(
ui, repo, fco.data().splitlines(), fn="p2 tags",
keeplinenums=False)
basetags = readtagsformerge(
ui, repo, fca.data().splitlines(), fn="base tags",
keeplinenums=False)
# recover the list of "lost tags" (i.e. those that were found on the base
# revision but not on one of the revisions being merged)
basetagset = set(basetags)
for n, pntags in enumerate((p1tags, p2tags)):
pntagset = set(pntags)
pnlosttagset = basetagset - pntagset
for t in pnlosttagset:
pntags[t] = basetags[t]
if pntags[t][-1][0] != hexnullid:
pntags[t].append([hexnullid, None])
conflictedtags = [] # for reporting purposes
mergedtags = util.sortdict(p1tags)
# sortdict does not implement iteritems()
for tname, p2nodes in p2tags.items():
if tname not in mergedtags:
mergedtags[tname] = p2nodes
continue
p1nodes = mergedtags[tname]
mergednodes = singletagmerge(p1nodes, p2nodes)
if mergednodes is None:
conflictedtags.append(tname)
continue
mergedtags[tname] = mergednodes
if conflictedtags:
numconflicts = len(conflictedtags)
ui.warn(_('automatic .hgtags merge failed\n'
'the following %d tags are in conflict: %s\n')
% (numconflicts, ', '.join(sorted(conflictedtags))))
return True, 1
writemergedtags(fcd, mergedtags)
ui.note(_('.hgtags merged successfully\n'))
return False, 0