Sketch of the Day: HyperLogLog — Cornerstone of a Big Data Infrastructure

Intro

In the Zipfian world of AK, the HyperLogLog distinct value (DV) sketch reigns supreme. This DV sketch is the workhorse behind the majority of our DV counters (and we’re not alone) and enables us to have a real time, in memory data store with incredibly high throughput. HLL was conceived of by Flajolet et. al. in the phenomenal paper HyperLogLog: the analysis of a near-optimal cardinality estimation algorithm. This sketch extends upon the earlier Loglog Counting of Large Cardinalities (Durand et. al.) which in turn is based on the seminal AMS work FM-85, Flajolet and Martin’s original work on probabilistic counting. (Many thanks to Jérémie Lumbroso for the correction of the history here. I am very much looking forward to his upcoming introduction to probabilistic counting in Flajolet’s complete works.) UPDATE – Rob has recently published a blog about PCSA, a direct precursor to LogLog counting which is filled with interesting thoughts. There have been a few posts on HLL recently so I thought I would dive into the intuition behind the sketch and into some of the details.

Just like all the other DV sketches, HyperLogLog looks for interesting things in the hashed values of your incoming data.  However, unlike other DV sketches HLL is based on bit pattern observables as opposed to KMV (and others) which are based on order statistics of a stream.  As Flajolet himself states:

Bit-pattern observables: these are based on certain patterns of bits occurring at the beginning of the (binary) S-values. For instance, observing in the stream S at the beginning of a string a bit- pattern O^{\rho-1}1 is more or less a likely indication that the cardinality n of S is at least 2^\rho.

Order statistics observables: these are based on order statistics, like the smallest (real) values, that appear in S. For instance, if X = min(S), we may legitimately hope that n is roughly of the order of 1/X…

In my mind HyperLogLog is really composed of two insights: Lots of crappy things are sometimes better than one really good thing; and bit pattern observables tell you a lot about a stream. We’re going to look at each component in turn.

Bad Estimator

Even though the literature refers to the HyperLogLog sketch as a different family of estimator than KMV I think they are very similar. It’s useful to understand the approach of HLL by reviewing the KMV sketch. Recall that KMV stores the smallest k values that you have seen in a stream. From these k values you get an estimate of the number of distinct elements you have seen so far. HLL also stores something similar to the smallest values ever seen. To see how this works it’s useful to ask “How could we make the KMV sketch smaller?” KMV stores the actual value of the incoming numbers. So you have to store k 64 bit values which is tiny, but not that tiny. What if we just stored the “rank” of the numbers?  Let’s say the number 94103 comes through (I’ll use base 10 here to make things easier). That number is basically 9*10^4 plus some stuff. So, let’s just store the exponent, i.e. 4. In this way I get an approximation of the size of numbers I have seen so far. That turns the original KMV algorithm into only having to store the numbers 1-19 (since 2^{64} \approx 10^{19}) which is a whole lot less than 2^{64} numbers. Of course, this estimate will be much worse than storing the actual values.

Bit Pattern Observables

In actuality HLL, just like all the other DV sketches, uses hashes of the incoming data in base 2. And instead of storing the “rank” of the incoming numbers HLL uses a nice trick of looking for runs of zeroes in the hash values. These runs of zeroes are an example of “bit pattern observables”. This concept is similar to recording the longest run of heads in a series of coin flips and using that to guess the number of times the coin was flipped. For instance, if you told me that you spent some time this afternoon flipping a coin and the longest run of heads you saw was 2 I could guess you didn’t flip the coin very many times. However, if you told me you saw a run of 100 heads in a row I would gather you were flipping the coin for quite a while. This “bit pattern observable”, the run of heads, gives me information about the stream of data it was pulled from. An interesting thing to note is just how probable long runs of heads are. As Mark Shilling points out, you can almost always tell the difference between a human generated set of coin flips and an actual one, due to humans not generating long runs. (The world of coin flipping seems to be a deep and crazy pit.) Disclaimer: The only thing I am trying to motivate here is that by keeping a very small piece of information (the longest run of heads) I can get some understanding of what has happened in a stream. Of course, you could probably guess that even though we have now reduced the storage of our sketch the DV estimate is pretty crummy. But what if we kept more than one of them?

Stochastic Averaging

In order to improve the estimate, the HLL algorithm stores many estimators instead of one and averages the results. However, in order to do this you would have to hash the incoming data through a bunch of independent hash functions. This approach isn’t a very good idea since hashing each value a bunch of times is expensive and finding good independent hash families is quite difficult in practice. The work around for this is to just use one hash function and “split up” the input into m buckets while maintaining the observable (longest run of zeroes) for each bucket. This procedure is called stochastic averaging. You could do this split in KMV as well and it’s easier to visualize. For an m of 3 it would look like:

To break the input into the m buckets, Durand suggests using the first few (k) bits of the hash value as an index into a bucket and compute the longest run of zeroes (R) on what is left over. For example, if your incoming datum looks like 010100000110 and k = 3 you could use the 3 rightmost bits, 110, to tell you which register to update (110_2 = 6) and from the remaining bits, 010100000, you could take the longest run of zeroes (up to some max), which in this case is 5. In order to compute the number of distinct values in the stream you would just take the average of all of the m buckets:

\displaystyle DV_{LL} = \displaystyle\text{constant} * m*2^{\overline{R}}

Here \overline{R} is the average of the values R in all the buckets. The formula above is actually the estimator for the LogLog algorithm, not HyperLogLog. To get HLL, you need one more piece…

Harmonic Mean

A fundamental insight that Flajolet had to improve LogLog into HyperLogLog was that he noticed the distribution of the values in the m registers is skewed to the right, and there can be some dramatic outliers that really mess up the average used in LogLog (see Fig. 1 below). He and Durand knew this when they wrote LogLog and did a bunch of hand-wavey stuff (like cut off the top 30% of the register values) to create what he called the “SuperLogLog”, but in retrospect this seems kind of dumb. He fixed this in HLL by tossing out the odd rules in SuperLogLog and deciding to take the harmonic mean of the DV estimates. The harmonic mean tends to throw out extreme values and behave well in this type of environment. This seems like an obvious thing to do. I’m a bit surprised they didn’t try this in the LogLog paper, but perhaps the math is harder to deal with when using the harmonic mean vs the geometric mean.

Fig. 1:  The theoretical distribution of register values after v distinct values have been run through an HLL.

Throw all these pieces together and you get the HyperLogLog DV estimator:

\displaystyle DV_{HLL} = \displaystyle\text{constant} * m^2 *\left (\sum_{j=1}^m 2^{-R_j} \right )^{-1}

Here R_j is the longest run of zeroes in the i^{th} bucket.

Putting it All Together

Even with the harmonic mean Flajolet still has to introduce a few “corrections” to the algorithm. When the HLL begins counting, most of the registers are empty and it takes a while to fill them up. In this range he introduces a “small range correction”. The other correction is when the HLL gets full. If a lot of distinct values have been run through an HLL the odds of collisions in your hash space increases. To correct for hash collisions Flajolet introduces the “large range collection”. The final algorithm looks like (it might be easier for some of you to just look at the source in the JavaScript HLL simulation):

m = 2^b #with b in [4...16]

if m == 16:
    alpha = 0.673
elif m == 32:
    alpha = 0.697
elif m == 64:
    alpha = 0.709
else:
    alpha = 0.7213/(1 + 1.079/m)

registers = [0]*m # initialize m registers to 0

##############################################################################################
# Construct the HLL structure
for h in hashed(data):
    register_index = 1 + get_register_index( h,b ) # binary address of the rightmost b bits
    run_length = run_of_zeros( h,b ) # length of the run of zeroes starting at bit b+1
    registers[ register_index ] = max( registers[ register_index ], run_length )

##############################################################################################
# Determine the cardinality
DV_est = alpha * m^2 * 1/sum( 2^ -register )  # the DV estimate

if DV_est < 5/2 * m: # small range correction
    V = count_of_zero_registers( registers ) # the number of registers equal to zero
    if V == 0:  # if none of the registers are empty, use the HLL estimate
          DV = DV_est
    else:
          DV = m * log(m/V)  # i.e. balls and bins correction

if DV_est <= ( 1/30 * 2^32 ):  # intermediate range, no correction
     DV = DV_est
if DV_est > ( 1/30 * 2^32 ):  # large range correction
     DV = -2^32 * log( 1 - DV_est/2^32)

Rob wrote up an awesome HLL simulation for this post. You can get a real sense of how this thing works by playing around with different values and just watching how it grows over time. Click below to see how this all fits together.

HyperLogLog Simulation

Click above to run the HyperLogLog simulation

Unions

Unions are very straightforward to compute in HLL and, like KMV, are lossless. All you need to do to combine the register values of the 2 (or n) HLL sketches is take the max of the 2 (or n) register values and assign that to the union HLL. With a little thought you should realize that this is the same thing as if you had fed in the union stream to begin with. A nice side effect about lossless unions is that HLL sketches are trivially parallelizable. This is great if, like us, you are trying to digest a firehose of data and need multiple boxes to do summarization. So, you have:

for i in range(0, len(R_1)):
     R_new[i] = max( R_1[i], R_2[i] )

To combine HLL sketches that have differing sizes read Chris’s blog post about it.

Wrapping Up

In our research, and as the literature says, the HyperLogLog algorithm gives you the biggest bang for the buck for DV counting. It has the best accuracy per storage of all the DV counters to date. The biggest drawbacks we have seen are around intersections. Unlike KMV, there is no explicit intersection logic, you have to use the inclusion/exclusion principle and this gets really annoying for anything more than 3 sets. Aside from that, we’ve been tickled pink using HLL for our production reporting. We have even written a PostgreSQL HLL data type that supports cardinality, union, and intersection. This has enabled all kinds of efficiencies for our analytics teams as the round trips to Hadoop are less and most of the analysis can be done in SQL. We have seen a massive increase in the types of analytics that go on at AK since we have adopted a sketching infrastructure and I don’t think I’m crazy saying that many big data platforms will be built this way in the future.

P.S.  Sadly, Philippe Flajolet passed away in March 2011. It was actually a very sad day for us at Aggregate Knowledge because we were so deep in our HLL research at the time and would have loved to reach out to him, he seems like he would have been happy to see his theory put to practice. Based on all I’ve read about him I’m very sorry to have not met him in person. I’m sure his work will live on but we have definitely lost a great mind both in industry and academia. Keep counting Philippe!

Photo courtesy of http://www.ae-info.org/

Doubling the Size of an HLL Dynamically

Introduction

In my last post, I explained how to halve the number of bins used in an HLL as a way to allow set operations between that HLL and smaller HLLs.  Unfortunately, the accuracy of an HLL is tied to the number of bins used, so one major drawback with this “folding” method is that each time you have the number of bins, you reduce that accuracy by a factor of \sqrt{2}.

In this series of posts I’ll focus on the opposite line of thinking: given an HLL, can one double the number of bins, assigning the new bins values according to some strategy, and recover some of the accuracy that a larger HLL would have had?  Certainly, one shouldn’t be able to do this (short of creating a new algorithm for counting distinct values) since once we use the HLL on a dataset the extra information that a larger HLL would have gleaned is gone.  We can’t recover it and so we can’t expect to magically pull a better estimate out of thin air (assuming Flajolet et al. have done their homework properly and the algorithm makes the best possible guess with the given information – which is a pretty good bet!).  Instead, in this series of posts, I’ll focus on how doubling plays with recovery time and set operations.  By this, I mean the following:  Suppose we have an HLL of size 2n and while its running, we double it to be an HLL of size 2n+1. Initially, this may have huge error, but if we allow it to continue running, how long will it take for its error to be relatively small?  I’ll also discuss some ways of modifying the algorithm to carry slightly more information.

The Candidates

Before we begin, a quick piece of terminology.  Suppose we have an HLL of size 2n and we double it to be an HLL of size 2 n+1.  We consider two bins to be partners if their bin numbers differ by 2n.  To see why this is important – check the post on HLL folding.

Colin and I did some thinking and came up with a few naive strategies to fill in the newly created bins after the doubling. I’ve provided a basic outline of the strategies below.

  • Zeroes – Fill in with zeroes.
  • Concatenate – Fill in each bin with the value of its partner.
  • MinusTwo – Fill in each bin with the value of its partner minus two. Two may seem like an arbitrary amount, but quick look at the formulas involved in the algorithm show that this leaves the cardinality estimate approximately unchanged.
  • RandomEstimate (RE) – Fill in each bin according to its probability distribution. I’ll describe more about this later.
  • ProportionDouble (PD) – This strategy is only for use with set operations. We estimate the number of bins in the two HLLs which should have the same value, filling in the second half so that that proportion holds and the rest are filled in according to RE.

Nitty Gritty of RE

The first three strategies given above are pretty self-explanatory, but the last two are a bit more complicated. To understand these, one needs to understand the distribution of values in a given bin.  In the original paper, Flajolet et al. calculate the probability that a given bin takes the value k to be given by (1 - 0.5^k)^v - (1 - 0.5^{k-1})^v where v is the number of keys that the bin has seen so far. Of course, we don’t know this value (v) exactly, but we can easily estimate it by dividing the cardinality estimate by the number of bins. However, we have even more information than this. When choosing a value for our doubled HLL, we know that that value cannot exceed its partner’s value. To understand why this is so, look back at my post on folding, and notice how the value in the partner bins in a larger HLL correspond to the value in the related bin in the smaller HLL.

Hence, to get the distribution for the value in a given bin, we take the original distribution, chop it off at the relevant value, and rescale it to have total area 1. This may seem kind of hokey but let’s quickly look at a toy example. Suppose you ask me to guess a number between 1 and 10, and you will try to guess which number I picked. At this moment, assuming I’m a reasonable random number generator, there is a 1/10 chance that I chose the number one, a 1/10 chance that I chose the number two, etc. However, if I tell you that my guess is no larger than two, you can now say there there is a 1/2 chance that my guess is a one, a 1/2 chance that my guess is a two, and there is no chance that my guess is larger. So what happened here? We took the original probability distribution, used our knowledge to cut off and ignore the values above the maximum possible value, and then rescaled them so that the sum of the possible probabilities is equal to zero.

RE consists simply of finding this distribution, picking a value according to it, and placing that value in the relevant bin.

Nitty Gritty of PD

Recall that we only use PD for set operations. One thing we found was that the accuracy of doubling with set operations according to RE is highly dependent on the the intersection size of the two HLLs. To account for this, we examine the fraction of bins in the two HLLs which contain the same value, and then we force the doubled HLL to preserve this fraction

So how do we do this? Let’s say we have two HLLs: H and G. We wish to double H before taking its union with G. To estimate the proportion of their intersection, make a copy of G and fold it to be the same size as H. Then count the number of bins where G and H agree, call this number a. Then if m is the number of bins in H, we can estimate that H and G should overlap in about a/m bins. Then for each bin, with probability a/m we fill in the bin with the the minimum of the relevant bin from G and that bin’s partner in G. With probability 1 - a/m we fill in the bin according to the rules of RE.

The Posts

(Links will be added as the posts are published. Keep checking back for updates!)

Set Operations On HLLs of Different Sizes

Introduction

Here at AK, we’re in the business of storing huge amounts of information in the form of 64 bit keys. As shown in other blog posts and in the HLL post by Matt, one efficient way of getting an estimate of the size of the set of these keys is by using the HyperLogLog (HLL) algorithm.  There are two important decisions one has to make when implementing this algorithm.  The first is how many bins one will use and the second is the maximum value one allows in each bin.  As a result, the amount of space this will take up is going to be the number of bins times the log of the maximum value you allow in each bin.  For this post we’ll ignore this second consideration and focus instead on the number of bins one uses.  The accuracy for an estimate is given approximately by 1.04/√b, where b is the number of bins.  Hence there is a tradeoff between the accuracy of the estimate and the amount of space you wish to dedicate to this estimate. Certainly, projects will have various requirements that call for different choices of number of bins.

The HLL algorithm natively supports the union operation.  However, one requirement for this operation is that the HLLs involved are of the same size, i.e. have the same number of bins.  In practice, there’s no guarantee that HLLs will satisfy this requirement.  In this post, I’ll outline the method by which we transform an HLL with a certain number of bins to one with a fewer number of bins, allowing us to perform set operations on any two HLLs, regardless of size.

Key Processing

As discussed in the HyperLogLog paper, to get a cardinality estimate with an HLL with 2n bins on a data set we pass over each key, using the placement of the rightmost “1” to determine the value of the key and the next n digits to the left to determine in which bin to place that value.  In each bin, we only store the maximum value that that bin has “seen.”

Below I’ve shown how two HLLs (one of size 23 and one of size 24) process two different keys.  Here, the keys have the same value, because the purpose of this example is to illustrate how the location in which we place the key changes when the HLL has twice the number of bins.

HLL Folding: Simple Bin/Reg Example

Above, the keys which are attributed to the fifth and thirteenth bins in the larger HLL would both have been attributed to the fifth bin in the smaller HLL.  Hence, unraveling the algorithm a bit, we see that the values which are seen by the fifth and thirteenth bins in the larger HLL would have been seen by the fifth bin in the smaller HLL had they run on the same dataset.  Because of this, in the case where the two algorithms estimate the same dataset, the value stored in the fifth bin in the smaller HLL is the maximum of the values stored in the fifth and thirteenth bins in the larger HLL.

Folding HLLs

What happened above is not an isolated phenomenon.  In general, if one uses the HLL algorithm twice on a dataset, once with 2n+1 bins and once with 2n bins, the value in the kth bin in the smaller HLL will be the maximum of the values in the kth and (k + 2n)th bins of the larger HLL.  As a result, if given an HLL of size 2n+1 that one wishes to transform to an HLL of size 2n, one can simply fold the HLL by letting the value of the kth bin in the folded HLL be given by the maximum of the values in the kth and (k + 2n)th bins of the original HLL.

In fact, we can fold any HLL an arbitrary number of times.  Repeating this process, we can take an HLL of size 2n to an HLL of size 2m for any m which is less than or equal to n.  Hence if we wish to perform a set operation on two HLLs of different sizes, we can simply fold the larger HLL repeatedly until it is the same size as the smaller HLL.  After this, we can take unions and intersections as we wish.

Folding – An Example

Below, we show a simple example of how folding works.  Here we have an HLL with 23 bins which we fold to be an HLL with 22 bins.  In the diagram, I’ve torn an HLL of size 23 in half and placed the strips side by side to emphasize how we line up bins and take maximums in the folding process.  Notice that the values in the folded the bins of the folded HLL are the maximum of the relevant bins in the larger HLL.

HLL Folding Example

Advantages and Drawbacks

This technique gives us the flexibility to be able to perform set operations on any two HLLs regardless of the number of bins used in the algorithms.  It’s usefulness in this regard is a bit offset by the fact that the accuracy of the estimate on these is limited by the accuracy of the least accurate HLL.  For example, an HLL of size 210 will have accuracy roughly 23 times better than an HLL of size 2 (to see where I’m getting these numbers from, you’ll have to read the paper!).  Unfortunately, if we combine these with a set operation, our resulting estimate will have the same accuracy as the smaller HLL with short term loans being taken from the RAM of the machine.

Summary

The HyperLogLog algorithm supports set operations in a nice way only if the number of bins used is fixed.  Using folding, one can correct for this by reducing the size of the larger HLL to the size of the smaller.  The cost of this convenience is in the accuracy of the estimates after the folding process.  In my next post, I’ll explore some methods of performing the set operations without this loss of accuracy.

K-Minimum Values: Sketching Error, Hash Functions, and You

Introduction

“All known efficient cardinality estimators rely on randomization, which is ensured by the use of hash functions.”
–Flajolet, et al

Recalling the KMV algorithm Matt presented in his last post, one will note that every stream element is passed to a hash function as part of the processing step. This is meant to transform the data being operated on from its native distribution into something uniformly distributed. Unfortunately, we don’t live in a perfect world, and since all of the algorithm’s analysis assumes that this hash function does its job well, we wanted to get some sense of how it behaves under less friendly conditions. The first half of this post will investigate the algorithm’s performance when we artificially introduce bias, and the second half will look at its behavior with a handful of real hash functions.

A Simple Error Model

The first hash function error model that came to mind is admittedly unrealistic and ham-fisted, but hopefully illustrative. Suppose you have a stream of fixed sized, an ideal hash function, and from these you produce a distinct value estimate using the KMV algorithm. Now suppose that for some unlucky reason, one bit from your hash function is stuck; it’s always a zero or a one, but the other 31 bits are free of this curse.

Biased hash schematic

There’s nothing to stop you from computing a distinct value estimate using this janky hash with KMV, but your intuition suggests that it shouldn’t be very good. We went through this exact process with various choices of k, using a random number generator to simulate a perfect hash function.

Before we look at the data, let’s think about what we should expect. From the perspective of KMV, it shouldn’t make a whole lot of difference if your kth smallest element is odd or even (for instance, in a case where the lowest order bit always/never set, respectively). It does, however, make a difference if you’re actually incapable of seeing values smaller than 231, which is what happens when the highest order bit is always set. Thus, in both the 0-biasing and 1-biasing cases, we should expect that higher order bits have a much more dramatic effect on error than lower order bits.

Error from setting bits in KMV

Notice how the performance degradation follows two different patterns. When we are fixing bits as ones, the observed error increases fairly smoothly, and tends to result in under estimates. In contrast, setting bits to zeros results in no change until the error increases producing catastrophic over estimates. Additionally, larger values of k have protective effects against these biases.

A Somewhat Less Simple Error Model

Now that we have some intuition for the problem, let’s get a little more subtle. Instead of always setting the nth bit as a 0 or 1, let’s add a probabilistic element. We’ll do the same experiment as before, except we will now fix the nth bit with probability p. Thus, when p = 0 we have a perfectly well behaved KMV, and when p = 1, we have the experiment we just finished discussing. In the following diagram, each tile represents the average error across several experiments in which a stream of 1,000,000 unique elements was fed to a KMV sketch (k = 1024) which was rigged to modify the nth bit with probability p.

Heatmap of KMV error

Many of the same lessons can be seen here — high order bits matter more, downward biasing degrades performance sharply, upward biasing degrades more smoothly. Additionally, as we’d expect, within a given bit, more bias means more error.

Send in the Hash Functions

All of the experiments to this point have involved using a random number generator instead of hashing real data. I think it’s time that we took a look at what happens when we drop in a few real hash functions with real data. For the following experiments, I’m using four 32-bit hashes — Murmur3, SDBM, Arash Partow’s hash, and one of the old Donald Knuth hashes. You may recall these from our series on choosing a good hash function (although 64-bit versions were used there). I chose four text corpuses:

  • Romeo and Juliet, stripped of all punctuation and converted to lower case (3794 words)
  • /usr/share/dict/words (99171 words)
  • 1,000,000 random 12 character long strings, each sharing the same suffix: “123456”
  • 1,000,000 random 12 character long strings, each sharing the same prefix: “123456”

Using formulas from this paper, we can compute the relative error that 99% of KMV estimates should theoretically fall within. This turns out to depend on k and the stream size.

To make these pictures, I chose random values of k within each hash/document pair at which I evaluate the cardinality estimate and compute the relative error. The lines are linear interpolations between sampled points and are shown solely for clarity. The y-axis scale is adjusted on a per-picture basis to best display the theoretical envelope within which we expect our errors to lie.

Now that we’ve gotten through all the necessary preamble, let’s take a look at the results!

KMV error for Romeo and Juliet

One picture in and we’ve already learned a lesson: choice of hash function seriously matters! SDBM and DEK cause the algorithm to perform well below its capabilities. DEK’s error is actually off the charts for most of this graph, which is why it does not appear until k > 3,000.

KMV error for /usr/bin/dict/words

On a bigger corpus with tighter theoretical error bounds, Murmur3 and AP are still doing quite well. Do note, however, that AP dips outside the envelope for a while at k = 70,000 or so.

KMV error for suffixKMV error for shared-prefix strings

With the random strings, SDBM performs much better than it did on English words. DEK, however, is still hopeless. It’s a little tough to see on these pictures, but at high k, AP starts to fall off the wagon, and even Murmur3 dips outside the envelope, though not beyond what we’d expect, statistically speaking. Honestly, I was hoping for some fireworks here, but they didn’t materialize. I was wondering if we might see some hashes break on one version of these strings, and do fine on the other due to the location of the varying key bits (high order/low order). Sadly, that didn’t happen, but a negative result is a result none the less.

To summarize these, I made the following table, which shows us the percentage of time that an one of the samples falls outside the theoretical envelope. In this view, Murmur3’s superiority is clear.

AP DEK Murmur3 SDBM
Romeo and Juliet 0.00% 100.00% 0.00% 61.54%
/usr/share/dict/words
10.76% 100.00% 0.00% 68.46%
Common Suffix 7.22% 99.11% 1.10% 0.27%
Common Prefix 3.33% 100.00% 0.22% 0.0001%

Fin

KMV is a very nice little algorithm that is incredibly simple to understand, implement, and use. That said, if you’re going to make use of it, you really do need to practice some due diligence when choosing your hash function. With packages like smhasher available, trying out multiple hash functions is a cinch, and a little legwork at the start of a project can save you from confusion and despair later on!

Sketch of the Day: K-Minimum Values

Intro

We’ve been talking about probabilistic distinct value counting with sketches (DV sketches) for a while now and have had some fun experiences implementing them into our production environment. In this post I want to talk about a DV sketch that is very intuitive and easy to implement, the K-minimum Values sketch (KMV). While KMV sketches are relatively lightweight and accurate, they are not the best of breed when it comes to DV counting. They are useful in two ways to me though, for exposition and multi-set operations.

History

KMV seems to have been first introduced in 2002 by Ziv Bar-Yossef et. al. in the great paper Counting distinct elements in a data stream. In this paper they talk about improving on the basic intuition by the seminal DV sketch papers of Flajolet and Martin and Alon, Matias, and Szegedy (AMS) (AMS put some formality around the frequency moment problems, bounds of algorithms etc.) Flajolet and Martin’s paper is in turn based upon work from Morris 1978 (looking for streaks of right-most zeroes i.e. the predecessor to LogLog and HyperLogLog). These are fun to read (although they admittedly get pretty mathy) and it’s cool to see the progression of knowledge, accuracy, and efficiency as these guys do their work. You can almost imagine the fist fights that happen during their meet-ups! The final detailed work on KMV is by Beyer et. al. in On Synopses for Distinct-Value Estimation Under Multiset Operations.

How it works

The intuition behind KMV is straightforward. Supposing you have a good hash function, i.e. hash values are evenly distributed over the hash space (I will normalize the hash space output to [0-1] for the rest of this), then you could estimate the number of distinct values you have seen by knowing the average spacing between values in the hash space. If I see 10 distinct values, I would expect them on average to be spaced about 1/10th apart from each other. We could do this cheaply by keeping track of, say, the smallest value you have ever seen. If the values are indeed uniformly distributed and provided you’ve thrown a decent amount of data through it, you could guess that the smallest value you have seen is a decent estimate of the average spacing of hash values in your space.

Of course, this doesn’t have a lot of “nice” properties. Taking only one value opens you up to a ton of variance and you are fairly dependent on the “goodness” of your hash. In order to improve upon this Bar-Yossef suggests keeping the k smallest values you have ever seen around. The algorithm becomes:

Initialize KMV with first k values
for all h(n):
     if h(n) &lt; max(KMV):
          insert h(n) into KMV set
          remove largest value from KMV

Cardinality(KMV):
     return: (k-1)/max(KMV)

For a KMV sketch of size k=3, graphically you have:

A very straightforward approach. Note that the “-1” in the numerator comes from a bias correction in the estimate. You’re going to have to read the paper for that. So, the size of the sketch is basically k 64bit values large. Click below to run a KMV simulation:

Click above to run the KMV simulation

Set Operations

Performing set operations with KMV’s is also incredibly straightforward. The intuition around unions is that there is no difference between combining 2 KMV sketches and keeping the k minimum values in both versus just keeping one to start with, so unions are “lossless”. To perform union, you merely take 2 sketches and combine their values and keep the k smallest ones (if the 2 sketches are of different sizes, k and k’, then you keep the min(k,k’) values in order to keep the lowest resolution).

Union(A,B):
     k = min( |A|, |B|)
     return: min_k( A U B )

For intersections you use the KMV to estimate the Jaccard coefficient for the 2 (or n) sets. Basically, you treat the 2 KMV sketches for each set as a random uniform sample and intersect these to estimate Jaccard. So, you assemble the k minimum values of the two sets (as you did in union above), and intersect this result with the original sketches to obtain an estimate of the overlap of the 2 sets. The steps are:

IntersectionCard(A,B):
     L = UnionSet(A,B)  # the set this time, not just the cardinality
     k = min( |A|, |B|)
     K = | L ∩ A ∩ B |
     return: K/k * Cardinality(L)

One of the nice features of KMV which is different than say HyperLogLog, is that you can take n-way intersections by extending the algorithm above. To do this with HyperLogLog you actually need to compute the n-way algebra for set intersection i.e.

|A ∩ B| = |A| + |B| - |A U B|

However, in our experience of using KMV for set operations on Zipfian data, KMV’s still don’t perform as well HyperLogLog sketches for computing n-way intersections using the same amount of memory.

Expansion to Multisets

One of the nice features of KMV sketches is their expansion to supporting multiset operations, dubbed the AKMV sketch. This is great if you are using them for document representations and want to support document similarity operations like tf-idf (or any other multiset operation). In order to expand the basic KMV structure to support multisets (described here) you just add a counter on top of the values you are storing. In this way you get a decent sample of the counts of things in the stream/document to use for multiset operations. Most other DV sketches, HyperLogLog in particular, don’t support these types of queries.

To see how well this might work in practice, I took a look at some simple tf-idf similarity against the 20 news groups data set. This data set contains about 1000 news group emails on various topics such as atheism and motorcycles (woo!). For each article I constructed an AKMV sketch of the words in it and used this representation as the basis for tf-idf.  I cleaned up the data marginally by limiting my analysis to the 5000 most common words in the corpus (as seems to be the norm) and only considered alpahnumeric “words”.   Additionally, I cherry picked only a few newsgroups from the set that showed “nice” separation in the SVD.  You can think of the documents looking a bit like this where the red dots are the entries in the AKMV and the green dots are not (as above):

Once I created the tf-idf matrix, I SVD-ed it and plotted each newsgroup against the second and third singular vectors (the first vector in this case contained mostly information about the mean of the document vectors and contained little real information for classification).  The intermediate singular vectors for differing k were projected onto the actual singular vectors from the complete matrix (k = Inf).  Running through increasing k, the newsgroups look like this (click on the graphic to restart the animation):

Click image to restart animation

You can see the structure start to appear relatively quickly for small k and you can also see how some of the articles “stick” to their final spots due to them having less than k words.  Clearly you would have to do more work and testing if you wanted to implement something like this in a real classifier or search engine but it seems to be a promising approach.

Here is the same thing for a corpus composed of 23 articles about the Tom Cruise/Katie Holmes divorce and 20 articles about the Higgs boson.

Click image to restart animation

Using document sketches as a basis for a recommender system/search engine or any other application that requires similarity metrics seems like a promising avenue.  It would be very interesting indeed to run some real tests of precision/recall and memory footprint for sketch based recommenders/classifiers against other more standard approaches.

Disclaimer:

I make no claims about having built a classifier of any sort here. A lot of work and decisions would be necessary to move from these ideas to a useful classification scheme in a real environment. I was interested in how much of the flavor of a document would be retained in an AKMV sketch. Based on the above results, I think that the answer is “quite a bit,” even for modest values of k. I don’t think it would be out of the question to try to build a system that allowed you to compute similarities or apply classification tools after the sampling process inherent in the construction of these sketches.

Compression

An interesting thing to notice is that as your DV count gets larger, your max value of the k items is getting smaller. What this means is a simple compression algorithm that works is to just throw away the higher order unused bits of all the k values. Oddly, as the DV count gets larger your KMV will get smaller without losing accuracy.

Summary

There are many DV sketches in the world and KMV is one of the most interesting due to how easy it is to comprehend and implement. I particularly enjoy using KMV as a pedagogical tool and a solid jumping off point for DV sketching. The fact that KMV is so straightforward makes it stand out in a world of more confusing math and complicated sketching algorithms. In the right context it very well could be the right solution for your sketching needs, especially given the multiset support.