Chrome is the absolute worst offender here, it does not fire window.onmouseover until the mouse has been moved over the page and it does not fire window.onfocus until you switch to that tab... so if the tab was started as a foreground tab, you will not get this event. The only way I managed to detect a tab being active was to poll the window.screenX and window.screenY properties. They are always 0 for a background tab. There is an obvious false positive if the browser happens to be in the screen origin, but in that case a window.onmouseover event will fire once the mouse has moved.

Safari is slightly less bad because it will fire window.onmouseover immediately when the tab is active. This works great but was a less obvious find than window.onfocus.

Firefox and IE8 have the least surprising behavior in that they fire window.onfocus immediately whenever a tab becomes visible, even if it was the foreground tab. I think this was the very first browser compatibility experiment where I've spent less time with IE than any other browser, although to be fair I didn't test anything other than IE8.

For maximum compatibility/longevity I've just provided some raw JavaScript code here. No framework. Feel free to translate this into a snippet or plugin for the framework of your choice. I'd also be interested if someone else does the testing for other browsers such as Opera and older versions of IE.

Tested with:

• Google Chrome 5.0.342.9 (Mac)
• Safari 4.0.5 (Mac)
• Firefox 3.6.3 (Mac)
• Internet Explorer 8.0.6001.18702

var timer = null;
function tabVisible() {
    if (timer) clearInterval(timer);
    timer = null;
    window.onfocus = null;
    window.onmouseover = null;
    /* your code to dispatch event here */
}
// Firefox, IE8
window.onfocus = tabVisible;
// Safari
window.onmouseover = tabVisible;
// dirty hack for Chrome
if (navigator.userAgent.indexOf(' Chrome/') != -1) {
    function dirtyChromePoll() {
        if (window.screenX || window.screenY) tabVisible();
    }
    timer = setInterval(dirtyChromePoll, 100);
}

simplejson 2.1.1 is a minor update with several bug-fixes:

• Change how setup.py imports ez_setup.py to try and workaround old versions of setuptools.
  http://code.google.com/p/simplejson/issues/detail?id=75
• Fix compilation on Windows platform (and other platforms with very picky compilers)
  http://code.google.com/p/simplejson/issues/detail?id=74
• Corrected simplejson.__version__ and other minor doc changes.
• Do not fail speedups tests if speedups could not be built.
  http://code.google.com/p/simplejson/issues/detail?id=73

Guido recently pronounced (Mixing float and Decimal) that he'd like to consider changing the behavior to match the principle of least surprise; all operations for all of the numeric types should return correct results. One of the most difficult problems to solve with such a unification is the hash invariant:

for all a and b such that a == b: hash(a) == hash(b).

While this is relatively simple to implement for the integer cases, it's much tricker to do efficiently for Decimal and float (and Fraction!) because Decimals are base 10 and float are base 2.

Note that I started writing this post yesterday after studying version 3 of the patch. I have altered the inline quotes to reflect version 4, which contains vastly improved comments that make most of my post redundant. However, it may still help in some way because it is an independent explanation and it provides some Python code.

Mark Dickinson proposed a very clever algorithm with an efficient implementation in issue8188, which he summarized as follows in the comments of the patch:

For numeric types, the hash of a number x is based on the reduction of x modulo the prime P = 2**_PyHASH_BITS - 1. It's designed so that hash(x) == hash(y) whenever x and y are numerically equal, even if x and y have different types.

A quick summary of the hashing strategy:

(1) First define the 'reduction of x modulo P' for any rational number x; this is a standard extension of the usual notion of reduction modulo P for integers. If x == p/q (written in lowest terms), the reduction is interpreted as the reduction of p times the inverse of the reduction of q, all modulo P; if q is exactly divisible by P then define the reduction to be infinity. So we've got a well-defined map

 reduce : { rational numbers } -> { 0, 1, 2, ..., P-1, infinity }.

(2) Now for a rational number x, define hash(x) by:

 reduce(x)   if x >= 0
 -reduce(-x) if x < 0

If the result of the reduction is infinity (this is impossible for integers, floats and Decimals) then use the predefined hash value
_PyHASH_INF instead.
_PyHASH_INF, _PyHASH_NINF and _PyHASH_NAN are also used for the hashes of float and Decimal infinities and nans.

A selling point for the above strategy is that it makes it possible to compute hashes of decimal and binary floating-point numbers efficiently, even if the exponent of the binary or decimal number is large. The key point is that

 reduce(x * y) == reduce(x) * reduce(y) (modulo _PyHASH_MASK)

provided that {reduce(x), reduce(y)} != {0, infinity}. The reduction of a binary or decimal float is never infinity, since the denominator is a power of 2 (for binary) or a divisor of a power of 10 (for decimal). So we have, for nonnegative x,

 reduce(x * 2**e) == reduce(x) * reduce(2**e) % _PyHASH_MASK

 reduce(x * 10**e) == reduce(x) * reduce(10**e) % _PyHASH_MASK

and reduce(10**e) can be computed efficiently by the usual modular exponentiation algorithm. For reduce(2**e) it's even better: since P is of the form 2**n-1, reduce(2**e) is 2**(e mod n), and multiplication by 2**(e mod n) modulo 2**n-1 just amounts to a rotation of bits.

The choices of P for his implementation are (2**31)-1 for 32-bit platforms and (2**61)-1 for 64-bit platforms. These numbers are interesting because they are the eighth and ninth Mersenne prime numbers. I'm not entirely sure yet if these numbers being prime is essential or not, but it's definitely conventional for a hash modulus to be prime. A very important feature of these numbers is that P+1 is a power of two.

One thing that wasn't immediately obvious to me was how to define modulus of a (rational) number f such that 0 < f < 1. We know from the above that in the floating point case we can break f into its mantissa and exponent:

reduce(m * (2**e)) == reduce(reduce(m) * reduce(2**e))

but that leaves the cases where 0 < 2**e < 1. Well, because we are working with a modulus of P, we know that P+1 is the multiplicative identity, so we can find some number n such that ((P+1)**n) * (2**e) is an integer. We also know that ((P+1)**n) * (2**e) mod P must be non-zero because P is prime.

We can demonstrate that reduce(x) where x = 2**e is quite a trivial task for a typical CPU as follows (k is log2(P+1), which is 61 or 31). All of the following expressions mod P are equivalent to x mod P.

(x * (P+1)**n)              # multiplicative identity
(x * (2**k)**n)             # P+1 == 2**k
(x * (2**(k*n))             # (a**b)**c == a**(b*c)
((2**e) * (2**(k*n)))       # x == 2**e
(2**(e + (k * n)))          # (a**b)*(a**c) == a**(b+c)
(2**((e + (k * n)) % k))    # 2**k is identity, so exponent is mod k
(2**(e % k))                # (k * n) % k == 0
1 << (e % k)                # a * (2**b) == a << b

In Python the naive algorithms would be as follows (ignoring inf and NaN):

from math import frexp

# Doesn't matter whether we use 61 or 31.
HASH_SHIFT = 61
HASH_MODULUS = (2 ** HASH_SHIFT) - 1

def hash_int(n):
    if n == 0:
        return 0
    elif n < 0:
        rval = -((-n) % HASH_MODULUS)
        return -2 if rval == -1 else rval
    else:
        return n % HASH_MODULUS

def hash_float(f):
    if f == 0.0:
        return 0
    elif f < 0.0:
        rval = -hash_float(-f)
        return -2 if rval == -1 else rval
    # m = mantissa (float), e = exponent of 2 (integer)
    m, e = frexp(f)
    # "arbitrarily" process 28 bits at a time. For a normal float,
    # this loop will iterate no more than twice since the mantissa
    # is 53 bits in a 64-bit IEEE-754 double.
    # After the loop, n will be some integer such that n ** e = f
    n = 0
    BITS = 28
    while m:
        m *= (2.0 ** BITS)
        e -= BITS
        m_floor = int(m)
        n = (n << BITS) | m_floor
        m -= m_floor
    # see above "proof" for this definition of reduce(2**e)
    return hash_int(hash_int(n) << (e % HASH_SHIFT))

You might notice the strange intentional mapping of -1 to -2, the reason for this is simply that the convention of Python's C API is such that return values of -1 mean that an exception may have occurred (and a global variable must be checked). If -1 is never returned on success then there are no false positives so the general case is faster. Essentially Python is trading this known worst case for a potential hash collision, which is probably the right call.

If you read the actual C implementation there are a few additional math tricks at play, the most important of which is this implementation of long_hash from longobject.c:

static long
long_hash(PyLongObject *v)
{
    unsigned long x;
    Py_ssize_t i;
    int sign;

    i = Py_SIZE(v);
    switch(i) {
    case -1: return v->ob_digit[0]==1 ? -2 : -(sdigit)v->ob_digit[0];
    case 0: return 0;
    case 1: return v->ob_digit[0];
    }
    sign = 1;
    x = 0;
    if (i < 0) {
        sign = -1;
        i = -(i);
    }
    while (--i >= 0) {
        /* Here x is a quantity in the range [0, _PyHASH_MASK); we
           want to compute x * 2**PyLong_SHIFT + v->ob_digit[i] modulo
           _PyHASH_MASK.

           The computation of x * 2**PyLong_SHIFT % _PyHASH_MASK
           amounts to a rotation of the bits of x.  To see this, write

             x * 2**PyLong_SHIFT = y * 2**_PyHASH_BITS + z

           where y = x >> (_PyHASH_BITS - PyLong_SHIFT) gives the top
           PyLong_SHIFT bits of x (those that are shifted out of the
           original _PyHASH_BITS bits, and z = (x << PyLong_SHIFT) &
           _PyHASH_MASK gives the bottom _PyHASH_BITS - PyLong_SHIFT
           bits of x, shifted up.  Then since 2**_PyHASH_BITS is
           congruent to 1 modulo _PyHASH_MASK, y*2**_PyHASH_BITS is
           congruent to y modulo _PyHASH_MASK.  So

             x * 2**PyLong_SHIFT = y + z (mod _PyHASH_MASK).

           The right-hand side is just the result of rotating the
           _PyHASH_BITS bits of x left by PyLong_SHIFT places; since
           not all _PyHASH_BITS bits of x are 1s, the same is true
           after rotation, so 0 <= y+z < _PyHASH_MASK and y + z is the
           reduction of x*2**PyLong_SHIFT modulo _PyHASH_MASK. */
        x = ((x << PyLong_SHIFT) & _PyHASH_MASK) |
            (x >> (_PyHASH_BITS - PyLong_SHIFT));
        x += v->ob_digit[i];
        if (x >= _PyHASH_MASK)
            x -= _PyHASH_MASK;
    }
    x = x * sign;
    if (x == (unsigned long)-1)
        x = (unsigned long)-2;
    return (long)x;
}

In order to understand this better we'll translate this to Python first, but to do that we need to understand the layout of integers in py3k. In py3k integers are represented as a sequence of zero or more digits, where a digit is 2**sys.int_info.bits_per_digit bits wide, and the least significant digit is first in the array. I'm not aware of any Python function to see integers at this level so we'll craft our own way to "disassemble" an integer in the way that the C implementation will see it. Instead of tracking the sign and size as one integer we will track the sign on its own and use the length of the list to track size.

import sys
# Doesn't matter whether we use 61 or 31.
HASH_SHIFT = 61
# The modulus can be used as a bit mask, all bits are set
HASH_MODULUS = (2 ** HASH_SHIFT) - 1

def disassemble_int(i):
    bits_per_digit = sys.int_info.bits_per_digit
    bit_mask = (1 << bits_per_digit) - 1
    if i < 0:
        sign = -1
        i *= -1
    else:
        sign = 1
    digits = []
    # see reassemble_int for inverse of this operation
    while i:
        digits.append(i & bit_mask)
        i >>= bits_per_digit
    return sign, digits

def reassemble_int(sign, digits):
    # demonstrate similar method for just reassembling the integer
    bits_per_digit = sys.int_info.bits_per_digit
    if sign == -1:
        return -reassemble_int(1, digits)
    size = len(digits)
    if size == 0:
        return 0
    elif size == 1:
        # just an optimization for small numbers
        return digits[0]
    x = 0
    # traverse digits from most to least significant
    # n = bits_per_digit
    # d[i] = digit with index of i
    # x = d[0] + d[1]*(2**n) + ... + d[i]*(2**(n*i))
    # x = d[0] + (2**n)*(d[1] + (2**n)*(d[2] + ...))
    # x = d[0] + (d[1] + ((d[2] + (...)) << n) << n
    for digit in reversed(digits):
        x = x << bits_per_digit
        x += digit
    return x

def hash_long(sign, digits):
    bits_per_digit = sys.int_info.bits_per_digit
    if sign == -1:
        rval = -hash_long(1, digits)
        return -2 if rval == -1 else rval
    size = len(digits)
    if size == 0:
        return 0
    elif size == 1:
        # just an optimization for small numbers
        # since we assume bits_per_digit < HASH_SHIFT
        return digits[0]
    x = 0
    # traverse digits from most to least significant
    for digit in reversed(digits):
        # rotate the bottom HASH_SHIFT bits left by bits_per_digit,
        # in effect this multiplies by 2**bits_per_digit mod HASH_MODULUS
        x = (((x << bits_per_digit) & HASH_MODULUS) |
             (x >> (HASH_SHIFT - bits_per_digit)))
        x += digit
        # If the addition overflowed we compensate by decrementing, which
        # preserves the value mod HASH_MODULUS.
        if x > HASH_MODULUS:
            x -= HASH_MODULUS
    return x

Now that we have a Python implementation the only trick left to decipher is why the heck are these equivalent for our choices of modulus P (k is log2(P+1), which is 61 or 31):

x * (2**n) % P
((x << n) & P) | (x >> (k - n))

I think that one way to "prove" that multiplying by a power of 2 in mod P is equivalent to bit rotation of a k bit integer would be to decompose x into binary digits as follows (k is log2(P+1)):

for all x such that 0 <= x < P, x == x % P
any x mod P can be decomposed into binary digits (d[0] * 2**0 + d[1] * 2**1 + ... + d[k-1] * 2**(k-1))
# 2**k is a multiplicative identity mod P
2**k mod P == 2**0 == 1
# just decompose x into binary digits
x * (2**0) mod P == (d[0] * 2**(0) + d[1] * 2**(1) + ... + d[k-1] * 2**(k-1))
# show multiply by 2 is a bit rotate left of k bits
x * (2**1) mod P == (d[0] * 2**(1) + d[1] * 2**(2) + ... + d[k-1] * 2**(0))
# generalize into n multiplications of 2
x * (2**n) mod P == (d[0] * 2**((0 + n) % k) + d[1] + 2**((1 + n) % k) + ... + d[k-1] * 2**((k - 1 + n) % k))
x * (2**n) mod P == (d[0] * 2**((0 + n) % k) + d[1] + 2**((1 + n) % k) + ... + d[k-1] * 2**((-1 + n) % k))

I'm definitely not Tim Peters or even a mathematician but I found this problem interesting enough to dive into, especially because Guido didn't find this obvious either (Objects/longobject.c). I think I've covered it in sufficient depth for me to believe that it works and the patch is good, but if I'm missing something please let me know!

Atlanta was a fantastic location for PyCon 2010, and I look forward to returning next year. Hopefully if I give another talk I'll be able to put a little more time into it :)

As per usual, I've been incredibly lazy about updating this blog, so you're much better off following @etrepum on Twitter.

simplejson 2.1.0 is a major update with several new features and bug-fixes:

• Decimal serialization officially supported for encoding with use_decimal=True. For encoding this encodes Decimal objects and for decoding it implies parse_float=Decimal
• Python 2.4 no longer supported (may still work, but no longer tested)
• Decoding performance and memory utilization enhancements http://bugs.python.org/issue7451
• JSONEncoderForHTML class for escaping &, <, > http://code.google.com/p/simplejson/issues/detail?id=66
• Memoization of object keys during encoding (when using speedups)
• Encoder changed to use PyIter_Next for list iteration to avoid potential threading issues
• Encoder changed to use iteritems rather than PyDict_Next in order to support dict subclasses that have a well defined ordering http://bugs.python.org/issue6105
• indent encoding parameter changed to be a string rather than an integer (integer use still supported for backwards compatibility) http://code.google.com/p/simplejson/issues/detail?id=56
• Test suite (python setup.py test) now automatically runs with and without speedups http://code.google.com/p/simplejson/issues/detail?id=55
• Fixed support for older versions of easy_install (e.g. stock Mac OS X config) http://code.google.com/p/simplejson/issues/detail?id=54
• Fixed str/unicode mismatches when using ensure_ascii=False http://code.google.com/p/simplejson/issues/detail?id=48
• Fixed error message when parsing an array with trailing comma with speedups http://code.google.com/p/simplejson/issues/detail?id=46
• Refactor decoder errors to raise JSONDecodeError instead of ValueError http://code.google.com/p/simplejson/issues/detail?id=45
• New ordered_pairs_hook feature in decoder which makes it possible to preserve key order. http://bugs.python.org/issue5381
• Fixed containerless unicode float decoding (same bug as 2.0.4, oops!) http://code.google.com/p/simplejson/issues/detail?id=43
• Share PosInf definition between encoder and decoder
• Minor reformatting to make it easier to backport simplejson changes to Python 2.7/3.1 json module

I'm getting increasingly lazy about updating my blog these days, probably best to follow me on twitter: http://twitter.com/etrepum

Anyway, I gave a talk at PyCon 2009 in Rosemont ("Chicago") last week called Drop ACID and think about data. Basically it is a survey of some of the various kinds of non-traditional database technologies I've been looking at the past few years. Notable technologies NOT talked about are object databases and graph databases. *UPDATE* Video available here: http://blip.tv/file/1949416

Slides are on BitBucket for now: Drop ACID and think about data

I'll be giving a (hopefully updated) version of this talk at OpenSourceBridge, which is in Portland, OR June 17-19.

If you're interested in the content of this talk there is far more insightful information on Jonathan Ellis' Programming Blog, one of the developers working on Cassandra.

simplejson (documentation) is a simple, fast, complete, correct and extensible JSON (RFC 4627) encoder/decoder for Python 2.4+. It is pure Python code with no dependencies, but features an optional C extension for speed-ups.

simplejson 2.0.9 is a major bug-fix update:

• Adds cyclic GC to the Encoder and Scanner speedups, which could've caused uncollectible cycles in some cases when using custom parser or encoder functions

simplejson (documentation) is a simple, fast, complete, correct and extensible JSON (RFC 4627) encoder/decoder for Python 2.4+. It is pure Python code with no dependencies, but features an optional C extension for speed-ups.

simplejson 2.0.8 is a minor bug-fix update:

• Documentation fixes
• Fixes encoding True and False as keys
• Fixes checking for True and False by identity for several parameters

simplejson (documentation) is a simple, fast, complete, correct and extensible JSON (RFC 4627) encoder/decoder for Python 2.4+. It is pure Python code with no dependencies, but features an optional C extension for speed-ups.

simplejson 2.0.7 is a minor bug-fix update:

• Documentation fixes
• C extension now always returns unicode strings when the input string is unicode, even for empty strings

simplejson (documentation) is a simple, fast, complete, correct and extensible JSON (RFC 4627) encoder/decoder for Python 2.4+. It is pure Python code with no dependencies, but features an optional C extension for speed-ups.

simplejson 2.0.6 is a minor bug-fix update:

• Windows build fixes