[squeak-dev] [Vm-dev] [Pharo-dev] Byte & String collection hash performance; a modest proposal for change.
David T. Lewis
lewis at mail.msen.com
Mon May 1 14:48:01 UTC 2017
Does it need to be done in the VM? Why not make a class LargeString with
instance variables aString and myCalculatedHashValueForTheString. That way
you can cache the hash value and calculate it any way you want.
I know vey little about hashing, just wondering if this kind of thing can
be handled more easily in the image.
> On Mon, 1 May 2017, Levente Uzonyi wrote:
>> Well, I had started to write a reply, but I had to postpone it.
>> I mostly agree with your suggestions.
>> One thing that can be done about large strings is to cache the
>> hash value in the larger strings. Currently the object representation
>> changes when the string contains 255 or more characters. In that case an
>> additional 64 bit field is added to the object header to store its
>> If we were to use the upper 28+1 bits of that field to cache the hash,
>> there would still be 35-bits to encode length, which would be enough to
>> represent strings up to 8 GiB.
> Well, we can keep the whole range minus one bit, but then we can't store
> the hash for strings larger than 8 GiB.
>> But this would require further VM changes (e.g. at:put: would have to
>> flush the cache).
>> On Mon, 1 May 2017, Martin McClure wrote:
>>> I see no replies to this on any of the three lists it was sent to, so I
>>> guess I'll chime in.
>>> Making a primitive for #hashMultiply, probably a good idea, in some
>>> form, since doing it in Smalltalk is awkward.
>>> Only hashing every N-th character of large strings, probably a very bad
>>> idea. Performance might well get worse, the complexity does not seem
>>> justified, and it would open a sizeable security hole.
>>> More verbiage below for those interested.
>>> On 04/18/2017 07:09 PM, Eliot Miranda wrote:
>>>> Hi All,
>>>> the hash algorithm used for ByteString in Squeak and Pharo is good
>>>> for "small" strings and overkill for large strings.
>>> Why do you say it's overkill for large strings? Are there applications
>>> with large strings that are being negatively impacted by the current
>>> algorithm? Which ones, and impacted how?
>>>> It is important in many applications to get well distributed string
>>>> hashes, especially over the range of strings that constitute things
>>>> like method names, URLs, etc. Consequently, the current algorithm
>>>> includes every character in a string. This works very well for
>>>> "small" strings and results in very slow hashes (and hence long
>>>> latencies, because the hash is an uninterruptible primitive) for large
>>>> strings, where large may be several megabytes.
>>> A simple solution for the uninterruptable primitive is to not make it a
>>> primitive. Make #hashMultiply a primitive (since this particular kind
>>> numeric modulo computation is really painful in Smalltalk), and do the
>>> rest in a loop in Smalltalk. It sounds like you've done the
>>> #hashMultiply primitive already.
>>> If the overhead of calling a primitive for each character proves to be
>>> too much, even with the faster primitive calling methodologies you
>>> talked about in the "Cog Primitive Performance" thread on the Vm-dev
>>> list, a more complex primitive could take a range of bytes, so large
>>> strings would be done in batches, solving the latency problem.
>>>> Let's look at the basic hash algorithm.
>>>> In looking at this I've added a primitive for hashMultiply; primitive
>>>> #159 implements precisely self * 1664525 bitAnd: 16r0FFFFFFF for
>>>> SmallInteger and LargePositiveInteger receivers, as fast as possible
>>>> in the Cog JIT. With this machinery in place it's instructive to
>>>> compare the cost of the primitive against the non-primitive Smalltalk
>>>> First let me introduce a set of replacement hash functions, newHashN.
>>>> These hash all characters in strings up to a certain size, and then no
>>>> more than that number for larger strings. Here are newHash64 and
>>>> newHash2048, which use pure Smalltalk, including an inlined
>>>> hashMultiply written to avoid SmallInteger overflow. Also measured
>>>> are the obvious variants newHash128, newHash256, newHash512 &
>>>> So the idea here is to step through the string by 1 for strings sizes
>>>> up to N - 1, and by greater than 1 for strings of size >= N, limiting
>>>> the maximum number of characters sampled to between N // 2 and N - 1.
>>> The history of computing is littered with the bones of those who have
>>> tried this kind of thing. It doesn't end well. Yes, you get a faster
>>> hash function. And then you find, for sets of data that you or your
>>> users actually want to use, that you get collisions like crazy, and
>>> worse overall performance than you started with.
>>> Sure, it works OK for the sets of data that the designer *tested*, and
>>> probably for the sets of data that they *anticipated*. But real-world
>>> data is tricky. It includes data sets where the characters that differ
>>> are the ones that the hash thinks are unimportant, and there goes your
>>> performance, by orders of magnitude. For instance, early versions of
>>> Java used a limited number of characters to hash strings. One of the
>>> biggest compatibility-breaking changes they were forced to make in
>>> Java versions was to consider *all* characters in hashing. It turned
>>> that it was very common to hash URLs, and many distinct URLs had most
>>> their characters in common.
>>> And you don't always get to choose your data -- sometimes you have an
>>> opponent who is actively looking to create collisions as a
>>> denial-of-service attack. There was a fair-sized kerfluffle about this
>>> few years ago -- most web scripting languages made it too easy to mount
>>> this kind of attack.
>>> "...an attacker can degenerate the hash table by sending lots of
>>> colliding keys. ...making it possible to exhaust hours of CPU time
>>> a single HTTP request."
>>> To guard against this kind of attack you need a randomized element in
>>> your hash (not a bad idea for Smalltalk, actually, and pretty easy --
>>> mixing in the identity hash of the collection might be sufficient) or a
>>> cryptographic hash (not worth the computational expense for most
>>> purposes). However, even adding a randomized element would not prevent
>>> this kind of attack if you predictably completely ignore some
>>> of the input string. That just makes it *so* easy to generate data that
>>> validates, and is not equal, but causes collisions.
>>> So really, for general-purpose use (i.e. what's built into the
>>> hash *every* character of *all* strings. If someone finds that this is
>>> performance problem in a real-world situation, it can be addressed in
>>> application-specific way.
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