python-peps/pep-0456.txt

PEP: 456
Title: Secure and interchangeable hash algorithm
Version: $Revision$
Last-Modified: $Date$
Author: Christian Heimes <christian@python.org>
BDFL-Delegate: Nick Coghlan
Status: Accepted
Type: Standards Track
Content-Type: text/x-rst
Created: 27-Sep-2013
Python-Version: 3.4
Post-History: 06-Oct-2013, 14-Nov-2013, 20-Nov-2013
Resolution: https://mail.python.org/pipermail/python-dev/2013-November/130400.html


Abstract
========

This PEP proposes SipHash as default string and bytes hash algorithm to properly
fix hash randomization once and for all. It also proposes modifications to
Python's C code in order to unify the hash code and to make it easily
interchangeable.


Rationale
=========

Despite the last attempt [issue13703]_ CPython is still vulnerable to hash
collision DoS attacks [29c3]_ [issue14621]_. The current hash algorithm and
its randomization is not resilient against attacks. Only a proper
cryptographic hash function prevents the extraction of secret randomization
keys. Although no practical attack against a Python-based service has been
seen yet, the weakness has to be fixed. Jean-Philippe Aumasson and Daniel
J. Bernstein have already shown how the seed for the current implementation
can be recovered [poc]_.

Furthermore the current hash algorithm is hard-coded and implemented multiple
times for bytes and three different Unicode representations UCS1, UCS2 and
UCS4. This makes it impossible for embedders to replace it with a different
implementation without patching and recompiling large parts of the interpreter.
Embedders may want to choose a more suitable hash function.

Finally the current implementation code does not perform well. In the common
case it only processes one or two bytes per cycle. On a modern 64-bit processor
the code can easily be adjusted to deal with eight bytes at once.

This PEP proposes three major changes to the hash code for strings and bytes:

* SipHash [sip]_ is introduced as default hash algorithm. It is fast and small
  despite its cryptographic properties. Due to the fact that it was designed
  by well known security and crypto experts, it is safe to assume that its
  secure for the near future.

* The existing FNV code is kept for platforms without a 64-bit data type. The
  algorithm is optimized to process larger chunks per cycle.

* Calculation of the hash of strings and bytes is moved into a single API 
  function instead of multiple specialized implementations in 
  ``Objects/object.c`` and ``Objects/unicodeobject.c``. The function takes a
  void pointer plus length and returns the hash for it.

* The algorithm can be selected at compile time. FNV is guaranteed to exist
  on all platforms. SipHash is available on the majority of modern systems.


Requirements for a hash function
================================

* It MUST be able to hash arbitrarily large blocks of memory from 1 byte up
  to the maximum ``ssize_t`` value.

* It MUST produce at least 32 bits on 32-bit platforms and at least 64 bits
  on 64-bit platforms. (Note: Larger outputs can be compressed with e.g.
  ``v ^ (v >> 32)``.)

* It MUST support hashing of unaligned memory in order to support
  hash(memoryview).

* It is highly RECOMMENDED that the length of the input influences the
  outcome, so that ``hash(b'\00') != hash(b'\x00\x00')``.

The internal interface code between the hash function and the tp_hash slots
implements special cases for zero length input and a return value of ``-1``.
An input of length ``0`` is mapped to hash value ``0``. The output ``-1``
is mapped to ``-2``.


Current implementation with modified FNV
========================================

CPython currently uses uses a variant of the Fowler-Noll-Vo hash function
[fnv]_. The variant is has been modified to reduce the amount and cost of hash
collisions for common strings. The first character of the string is added
twice, the first time with a bit shift of 7. The length of the input
string is XOR-ed to the final value. Both deviations from the original FNV
algorithm reduce the amount of hash collisions for short strings.

Recently [issue13703]_ a random prefix and suffix were added as an attempt to
randomize the hash values. In order to protect the hash secret the code still
returns ``0`` for zero length input.

C code::

    Py_uhash_t x;
    Py_ssize_t len;
    /* p is either 1, 2 or 4 byte type */
    unsigned char *p;
    Py_UCS2 *p;
    Py_UCS4 *p;

    if (len == 0)
        return 0;
    x = (Py_uhash_t) _Py_HashSecret.prefix;
    x ^= (Py_uhash_t) *p << 7;
    for (i = 0; i < len; i++)
        x = (1000003 * x) ^ (Py_uhash_t) *p++;
    x ^= (Py_uhash_t) len;
    x ^= (Py_uhash_t) _Py_HashSecret.suffix;
    return x;


Which roughly translates to Python::

    def fnv(p):
        if len(p) == 0:
            return 0

        # bit mask, 2**32-1 or 2**64-1
        mask = 2 * sys.maxsize + 1

        x = hashsecret.prefix
        x = (x ^ (ord(p[0]) << 7)) & mask
        for c in p:
            x = ((1000003 * x) ^ ord(c)) & mask
        x = (x ^ len(p)) & mask
        x = (x ^ hashsecret.suffix) & mask

        if x == -1:
            x = -2

        return x


FNV is a simple multiply and XOR algorithm with no cryptographic properties.
The randomization was not part of the initial hash code, but was added as
counter measure against hash collision attacks as explained in oCERT-2011-003
[ocert]_. Because FNV is not a cryptographic hash algorithm and the dict
implementation is not fortified against side channel analysis, the
randomization secrets can be calculated by a remote attacker. The author of
this PEP strongly believes that the nature of a non-cryptographic hash
function makes it impossible to conceal the secrets.


Examined hashing algorithms
===========================

The author of this PEP has researched several hashing algorithms that are
considered modern, fast and state-of-the-art.

SipHash
-------

SipHash [sip]_ is a cryptographic pseudo random function with a 128-bit seed
and 64-bit output. It was designed by Jean-Philippe Aumasson and Daniel J.
Bernstein as a fast and secure keyed hash algorithm. It's used by Ruby, Perl,
OpenDNS, Rust, Redis, FreeBSD and more. The C reference implementation has
been released under CC0 license (public domain).

Quote from SipHash's site:

    SipHash is a family of pseudorandom functions (a.k.a. keyed hash
    functions) optimized for speed on short messages. Target applications
    include network traffic authentication and defense against hash-flooding
    DoS attacks.

siphash24 is the recommend variant with best performance. It uses 2 rounds per
message block and 4 finalization rounds. Besides the reference implementation
several other implementations are available. Some are single-shot functions,
others use a Merkle–Damgård construction-like approach with init, update and
finalize functions. Marek Majkowski C implementation csiphash [csiphash]_
defines the prototype of the function. (Note: ``k`` is split up into two
uint64_t)::

  uint64_t siphash24(const void *src, unsigned long src_sz, const char k[16])

SipHash requires a 64-bit data type and is not compatible with pure C89
platforms.


MurmurHash
----------

MurmurHash [murmur]_ is a family of non-cryptographic keyed hash function
developed by Austin Appleby. Murmur3 is the latest and fast variant of
MurmurHash. The C++ reference implementation has been released into public
domain. It features 32- or 128-bit output with a 32-bit seed. (Note: The out
parameter is a buffer with either 1 or 4 bytes.)

Murmur3's function prototypes are::

  void MurmurHash3_x86_32(const void *key, int len, uint32_t seed, void *out)

  void MurmurHash3_x86_128(const void *key, int len, uint32_t seed, void *out)

  void MurmurHash3_x64_128(const void *key, int len, uint32_t seed, void *out)

The 128-bit variants requires a 64-bit data type and are not compatible with
pure C89 platforms. The 32-bit variant is fully C89-compatible.

Aumasson, Bernstein and Boßlet have shown [sip]_ [ocert-2012-001]_ that
Murmur3 is not resilient against hash collision attacks. Therefore Murmur3
can no longer be considered as secure algorithm. It still may be an
alternative is hash collision attacks are of no concern.


CityHash
--------

CityHash [city]_ is a family of non-cryptographic hash function developed by
Geoff Pike and Jyrki Alakuijala for Google. The C++ reference implementation
has been released under MIT license. The algorithm is partly based on
MurmurHash and claims to be faster. It supports 64- and 128-bit output with a
128-bit seed as well as 32-bit output without seed.

The relevant function prototype for 64-bit CityHash with 128-bit seed is::

  uint64 CityHash64WithSeeds(const char *buf, size_t len, uint64 seed0,
                             uint64 seed1)

CityHash also offers SSE 4.2 optimizations with CRC32 intrinsic for long
inputs. All variants except CityHash32 require 64-bit data types. CityHash32
uses only 32-bit data types but it doesn't support seeding.

Like MurmurHash Aumasson, Bernstein and Boßlet have shown [sip]_ a similar
weakness in CityHash.


DJBX33A
-------

DJBX33A is a very simple multiplication and addition algorithm by Daniel
J. Bernstein. It is fast and has low setup costs but it's not secure against
hash collision attacks. Its properties make it a viable choice for small
string hashing optimization.


Other
-----

Crypto algorithms such as HMAC, MD5, SHA-1 or SHA-2 are too slow and have
high setup and finalization costs. For these reasons they are not considered
fit for this purpose. Modern AMD and Intel CPUs have AES-NI (AES instruction
set) [aes-ni]_ to speed up AES encryption. CMAC with AES-NI might be a viable
option but it's probably too slow for daily operation. (testing required)


Conclusion
----------

SipHash provides the best combination of speed and security. Developers of
other prominent projects have came to the same conclusion.


Small string optimization
=========================

Hash functions like SipHash24 have a costly initialization and finalization
code that can dominate speed of the algorithm for very short strings. On the
other hand Python calculates the hash value of short strings quite often. A
simple and fast function for especially for hashing of small strings can make
a measurable impact on performance. For example these measurements were taken
during a run of Python's regression tests. Additional measurements of other
code have shown a similar distribution.

===== ============ =======
bytes hash() calls portion
===== ============ =======
1            18709    0.2%
2           737480    9.5%
3           636178   17.6%
4          1518313   36.7%
5           643022   44.9%
6           770478   54.6%
7           525150   61.2%
8           304873   65.1%
9           297272   68.8%
10           68191   69.7%
11         1388484   87.2%
12          480786   93.3%
13           52730   93.9%
14           65309   94.8%
15           44245   95.3%
16           85643   96.4%
Total      7921678
===== ============ =======

However a fast function like DJBX33A is not as secure as SipHash24. A cutoff
at about 5 to 7 bytes should provide a decent safety margin and speed up at
the same time. The PEP's reference implementation provides such a cutoff with
``Py_HASH_CUTOFF``. The optimization is disabled by default for several
reasons. For one the security implications are unclear yet and should be
thoroughly studied before the optimization is enabled by default. Secondly
the performance benefits vary. On 64 bit Linux system with Intel Core i7
multiple runs of Python's benchmark suite [pybench]_ show an average speedups
between 3% and 5% for benchmarks such as django_v2, mako and etree with a
cutoff of 7. Benchmarks with X86 binaries and Windows X86_64 builds on the
same machine are a bit slower with small string optimization.

The state of small string optimization will be assessed during the beta phase
of Python 3.4. The feature will either be enabled with appropriate values
or the code will be removed before beta 2 is released.


C API additions
===============

All C API extension modifications are not part of the stable API.

hash secret
-----------

The ``_Py_HashSecret_t`` type of Python 2.6 to 3.3 has two members with either
32- or 64-bit length each. SipHash requires two 64-bit unsigned integers as
keys. The typedef will be changed to an union with a guaranteed size of 24
bytes on all architectures. The union provides a 128 bit random key for
SipHash24 and FNV as well as an additional value of 64 bit for the optional
small string optimization and pyexpat seed. The additional 64 bit seed ensures
that pyexpat or small string optimization cannot reveal bits of the SipHash24
seed.

memory layout on 64 bit systems::

    cccccccc cccccccc cccccccc  uc -- unsigned char[24]
    pppppppp ssssssss ........  fnv -- two Py_hash_t
    k0k0k0k0 k1k1k1k1 ........  siphash -- two PY_UINT64_T
    ........ ........ ssssssss  djbx33a -- 16 bytes padding + one Py_hash_t
    ........ ........ eeeeeeee  pyexpat XML hash salt

memory layout on 32 bit systems::

    cccccccc cccccccc cccccccc  uc -- unsigned char[24]
    ppppssss ........ ........  fnv -- two Py_hash_t
    k0k0k0k0 k1k1k1k1 ........  siphash -- two PY_UINT64_T (if available)
    ........ ........ ssss....  djbx33a -- 16 bytes padding + one Py_hash_t
    ........ ........ eeee....  pyexpat XML hash salt

new type definition::

    typedef union {
        /* ensure 24 bytes */
        unsigned char uc[24];
        /* two Py_hash_t for FNV */
        struct {
            Py_hash_t prefix;
            Py_hash_t suffix;
        } fnv;
    #ifdef PY_UINT64_T
        /* two uint64 for SipHash24 */
        struct {
            PY_UINT64_T k0;
            PY_UINT64_T k1;
        } siphash;
    #endif
        /* a different (!) Py_hash_t for small string optimization */
        struct {
            unsigned char padding[16];
            Py_hash_t suffix;
        } djbx33a;
        struct {
            unsigned char padding[16];
            Py_hash_t hashsalt;
        } expat;
    } _Py_HashSecret_t;
    PyAPI_DATA(_Py_HashSecret_t) _Py_HashSecret;

``_Py_HashSecret_t`` is initialized in ``Python/random.c:_PyRandom_Init()``
exactly once at startup.


hash function definition
------------------------

Implementation::

    typedef struct {
        /* function pointer to hash function, e.g. fnv or siphash24 */
        Py_hash_t (*const hash)(const void *, Py_ssize_t);
        const char *name;       /* name of the hash algorithm and variant */
        const int hash_bits;    /* internal size of hash value */
        const int seed_bits;    /* size of seed input */
    } PyHash_FuncDef;

    PyAPI_FUNC(PyHash_FuncDef*) PyHash_GetFuncDef(void);


autoconf
--------

A new test is added to the configure script. The test sets
``HAVE_ALIGNED_REQUIRED``, when it detects a platform, that requires aligned
memory access for integers. Must current platforms such as X86, X86_64 and
modern ARM don't need aligned data.

A new option ``--with-hash-algorithm`` enables the user to select a hash
algorithm in the configure step.


hash function selection
-----------------------

The value of the macro ``Py_HASH_ALGORITHM`` defines which hash algorithm is
used internally. It may be set to any of the three values ``Py_HASH_SIPHASH24``,
``Py_HASH_FNV`` or ``Py_HASH_EXTERNAL``. If ``Py_HASH_ALGORITHM`` is not
defined at all, then the best available algorithm is selected. On platforms
wich don't require aligned memory access (``HAVE_ALIGNED_REQUIRED`` not
defined) and an unsigned 64 bit integer type ``PY_UINT64_T``, SipHash24 is
used. On strict C89 platforms without a 64 bit data type, or architectures such
as SPARC, FNV is selected as fallback. A hash algorithm can be selected with
an autoconf option, for example ``./configure --with-hash-algorithm=fnv``.

The value ``Py_HASH_EXTERNAL`` allows 3rd parties to provide their own
implementation at compile time.


Implementation::

    #if Py_HASH_ALGORITHM == Py_HASH_EXTERNAL
    extern PyHash_FuncDef PyHash_Func;
    #elif Py_HASH_ALGORITHM == Py_HASH_SIPHASH24
    static PyHash_FuncDef PyHash_Func = {siphash24, "siphash24", 64, 128};
    #elif Py_HASH_ALGORITHM == Py_HASH_FNV
    static PyHash_FuncDef PyHash_Func = {fnv, "fnv", 8 * sizeof(Py_hash_t),
                                         16 * sizeof(Py_hash_t)};
    #endif


Python API addition
===================

sys module
----------

The sys module already has a hash_info struct sequence. More fields are added
to the object to reflect the active hash algorithm and its properties.

::

    sys.hash_info(width=64,
                  modulus=2305843009213693951,
                  inf=314159,
                  nan=0,
                  imag=1000003,
                  # new fields:
                  algorithm='siphash24',
                  hash_bits=64,
                  seed_bits=128,
                  cutoff=0)


Necessary modifications to C code
=================================

_Py_HashBytes() (Objects/object.c)
----------------------------------

``_Py_HashBytes`` is an internal helper function that provides the hashing
code for bytes, memoryview and datetime classes. It currently implements FNV
for ``unsigned char *``.

The function is moved to Python/pyhash.c and modified to use the hash function
through PyHash_Func.hash(). The function signature is altered to take
a ``const void *`` as first argument. ``_Py_HashBytes`` also takes care of
special cases: it maps zero length input to ``0`` and return value of ``-1``
to ``-2``.

bytes_hash() (Objects/bytesobject.c)
------------------------------------

``bytes_hash`` uses ``_Py_HashBytes`` to provide the tp_hash slot function
for bytes objects. The function will continue to use ``_Py_HashBytes``
but withoht a type cast.

memory_hash() (Objects/memoryobject.c)
--------------------------------------

``memory_hash`` provides the tp_hash slot function for read-only memory
views if the original object is hashable, too. It's the only function that
has to support hashing of unaligned memory segments in the future. The
function will continue to use ``_Py_HashBytes`` but withoht a type cast.


unicode_hash() (Objects/unicodeobject.c)
----------------------------------------

``unicode_hash`` provides the tp_hash slot function for unicode. Right now it
implements the FNV algorithm three times for ``unsigned char*``, ``Py_UCS2``
and ``Py_UCS4``. A reimplementation of the function must take care to use the
correct length. Since the macro ``PyUnicode_GET_LENGTH`` returns the length
of the unicode string and not its size in octets, the length must be
multiplied with the size of the internal unicode kind::

    if (PyUnicode_READY(u) == -1)
        return -1;
    x = _Py_HashBytes(PyUnicode_DATA(u),
                      PyUnicode_GET_LENGTH(u) * PyUnicode_KIND(u));


generic_hash() (Modules/_datetimemodule.c)
------------------------------------------

``generic_hash`` acts as a wrapper around ``_Py_HashBytes`` for the tp_hash
slots of date, time and datetime types. timedelta objects are hashed by their
state (days, seconds, microseconds) and tzinfo objects are not hashable. The
data members of date, time and datetime types' struct are not ``void*`` aligned.
This can easily by fixed with memcpy()ing four to ten bytes to an aligned
buffer.


Performance
===========

In general the PEP 456 code with SipHash24 is about as fast as the old code
with FNV. SipHash24 seems to make better use of modern compilers, CPUs and
large L1 cache. Several benchmarks show a small speed improvement on 64 bit
CPUs such as Intel Core i5 and Intel Core i7 processes. 32 bit builds and
benchmarks on older CPUs such as an AMD Athlon X2 are slightly slower with
SipHash24. The performance increase or decrease are so small that they should
not affect any application code.

The benchmarks were conducted on CPython default branch revision b08868fd5994
and the PEP repository [pep-456-repos]_. All upstream changes were merged
into the pep-456 branch. The "performance" CPU governor was configured and
almost all programs were stopped so the benchmarks were able to utilize
TurboBoost and the CPU caches as much as possible. The raw benchmark results
of multiple machines and platforms are made available at [benchmarks]_.


Hash value distribution
-----------------------

A good distribution of hash values is important for dict and set performance.
Both SipHash24 and FNV take the length of the input into account, so that
strings made up entirely of NULL bytes don't have the same hash value. The
last bytes of the input tend to affect the least significant bits of the hash
value, too. That attribute reduces the amount of hash collisions for strings
with a common prefix.


Typical length
--------------

Serhiy Storchaka has shown in [issue16427]_ that a modified FNV
implementation with 64 bits per cycle is able to process long strings several
times faster than the current FNV implementation.

However according to statistics [issue19183]_ a typical Python program as
well as the Python test suite have a hash ratio of about 50% small strings
between 1 and 6 bytes. Only 5% of the strings are larger than 16 bytes.


Grand Unified Python Benchmark Suite
------------------------------------

Initial tests with an experimental implementation and the Grand Unified Python
Benchmark Suite have shown minimal deviations. The summarized total runtime
of the benchmark is within 1% of the runtime of an unmodified Python 3.4
binary. The tests were run on an Intel i7-2860QM machine with a 64-bit Linux
installation. The interpreter was compiled with GCC 4.7 for 64- and 32-bit.

More benchmarks will be conducted.


Backwards Compatibility
=======================

The modifications don't alter any existing API.

The output of ``hash()`` for strings and bytes are going to be different. The
hash values for ASCII Unicode and ASCII bytes will stay equal.


Alternative counter measures against hash collision DoS
=======================================================

Three alternative countermeasures against hash collisions were discussed in
the past, but are not subject of this PEP.

1. Marc-Andre Lemburg has suggested that dicts shall count hash collisions. In
   case an insert operation causes too many collisions an exception shall be
   raised.

2. Some applications (e.g. PHP) limit the amount of keys for GET and POST
   HTTP requests. The approach effectively leverages the impact of a hash
   collision attack. (XXX citation needed)

3. Hash maps have a worst case of O(n) for insertion and lookup of keys. This
   results in a quadratic runtime during a hash collision attack. The
   introduction of a new and additional data structure with with O(log n)
   worst case behavior would eliminate the root cause. A data structures like
   red-black-tree or prefix trees (trie [trie]_) would have other benefits,
   too. Prefix trees with stringed keyed can reduce memory usage as common
   prefixes are stored within the tree structure.


Discussion
==========

Pluggable
---------

The first draft of this PEP made the hash algorithm pluggable at runtime. It
supported multiple hash algorithms in one binary to give the user the
possibility to select a hash algorithm at startup. The approach was considered
an unnecessary complication by several core committers [pluggable]_. Subsequent
versions of the PEP aim for compile time configuration.

Non-aligned memory access
-------------------------

The implementation of SipHash24 were critized because it ignores the issue
of non-aligned memory and therefore doesn't work on architectures that
requires alignment of integer types. The PEP deliberately neglects this
special case and doesn't support SipHash24 on such platforms. It's simply
not considered worth the trouble until proven otherwise. All major platforms
like X86, X86_64 and ARMv6+ can handle unaligned memory with minimal or even
no speed impact. [alignmentmyth]_

Almost every block is properly aligned anyway. At present bytes' and str's
data are always aligned. Only memoryviews can point to unaligned blocks
under rare circumstances. The PEP implementation is optimized and simplified
for the common case.

ASCII str / bytes hash collision
--------------------------------

Since the implementation of [pep-0393]_ bytes and ASCII text have the same
memory layout. Because of this the new hashing API will keep the invariant::

    hash("ascii string") == hash(b"ascii string")

for ASCII string and ASCII bytes. Equal hash values result in a hash collision
and therefore cause a minor speed penalty for dicts and sets with mixed keys.
The cause of the collision could be removed by e.g. subtracting ``2`` from
the hash value of bytes. ``-2`` because ``hash(b"") == 0`` and ``-1`` is
reserved. The PEP doesn't change the hash value.


References
==========

* Issue 19183 [issue19183]_ contains a reference implementation.

.. [29c3] http://events.ccc.de/congress/2012/Fahrplan/events/5152.en.html

.. [fnv] http://en.wikipedia.org/wiki/Fowler-Noll-Vo_hash_function

.. [sip] https://131002.net/siphash/

.. [ocert] http://www.nruns.com/_downloads/advisory28122011.pdf

.. [ocert-2012-001] http://www.ocert.org/advisories/ocert-2012-001.html

.. [poc] https://131002.net/siphash/poc.py

.. [issue13703] http://bugs.python.org/issue13703

.. [issue14621] http://bugs.python.org/issue14621

.. [issue16427] http://bugs.python.org/issue16427

.. [issue19183] http://bugs.python.org/issue19183

.. [trie] http://en.wikipedia.org/wiki/Trie

.. [city] http://code.google.com/p/cityhash/

.. [murmur] http://code.google.com/p/smhasher/

.. [csiphash] https://github.com/majek/csiphash/

.. [pep-0393] http://www.python.org/dev/peps/pep-0393/

.. [aes-ni] http://en.wikipedia.org/wiki/AES_instruction_set

.. [pluggable] https://mail.python.org/pipermail/python-dev/2013-October/129138.html

.. [alignmentmyth] http://lemire.me/blog/archives/2012/05/31/data-alignment-for-speed-myth-or-reality/

.. [pybench] http://hg.python.org/benchmarks/

.. [benchmarks] https://bitbucket.org/tiran/pep-456-benchmarks/src

.. [pep-456-repos] http://hg.python.org/features/pep-456


Copyright
=========

This document has been placed in the public domain.



..
   Local Variables:
   mode: indented-text
   indent-tabs-mode: nil
   sentence-end-double-space: t
   fill-column: 70
   coding: utf-8
   End: