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5
A fairly random collection of things to work on next...
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1) Coming up with a catchy or at least somewhat interesting name.
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I suck at names. Currently "memory_dump" is the library, pymemdump is
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the project. I don't mind a functional name, but I don't want people
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going "ugh" when they think of using the tool. :)
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When this happens, create an official project on Launchpad, and host it
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2) (DONE @ revno 58) Tracking the memory consumed by the GC overhead.
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Objects allocated in the garbage collector (just about everything,
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strings being the notable exception) actually have a PyGC_Head
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structure allocated first. So while a 1 entry tuple *looks* like it
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is only 16 bytes, it actually has another probably 16-byte PyGC_Head
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structure allocated for each one.
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I haven't quite figured out how to tell if a given object is in the
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gc. It may just be a bit-field in the type object.
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3) Generating a Calltree output.
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1) Generating a Calltree output.
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I haven't yet understood the calltree syntax, nor how I want to
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10
exactly match things. Certainly you don't have FILE/LINE to put into
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15
.. _runsnakerun: http://www.vrplumber.com/programming/runsnakerun/
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4) Other analysis tools, like walking the ref graph.
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2) Other analysis tools, like walking the ref graph.
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I'm thinking something similar to PDB, which could let you walk
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up-and-down the reference graph, to let you figure out why that one
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At the moment, you can do this using '*' in Vim, which is at least a
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start, and one reason to use a text-compatible dump format.
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5) Easier ways to hook this into existing processes...
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I'm not really sure what to do here, but adding a function to make it
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easier to write-out and load-in the memory info, when you aren't as
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The dump file current takes ~ the same amount of memory as the actual
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objects in ram, both on disk, and then when loaded back into memory.
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6) Dump differencing utilities.
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3) Dump differencing utilities.
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This probably will make it a bit easier to see where memory is
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increasing, rather than just where it is at right now.
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7) Cheaper "dict" of MemObjects.
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At the moment, loading a 2M object dump costs 50MB for just the dict
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holding them. However each entry uses a simple object address as the
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key, which it maintains on the object itself. So instead of 3-words
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per entry, you could use 1. Further, the address isn't all that great
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as a hash key. Namely 90% of your objects are aligned on a 16-byte
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boundary, another 9% or so on a 8-byte boundary, and the random
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Integer is allocated on a 4-byte boundary. Regardless, just using
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"address & 0xFF" is going to have ~16x more collisions than doing
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something a bit more sensible. (Rotate the bits a bit.)
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Also, I'm thinking to allow you to load a dump file, and strip off
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things that may not be as interesting. Like whether you want values
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or not, or if you wanted to limit the maximum reference list to 100
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or so. I figure at more that 100, you aren't all that interested in
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an individual reference. At it might be nice to be able to analyze
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big dump files without consuming all of your memory.
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8) Full cross-platform and version compatibility.
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4) Full cross-platform and version compatibility testing.
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I'd like to support python2.4+, 32/64-bit, Win/Linux/Mac. I've tested
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a couple variants, but I don't have all of them to make sure it works
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TODO.txt
