# Sketching Quantiles and Ranks Tutorial

Streaming quantiles algorithms, or quantiles sketches, enable us to analyze the distributions of massive data very quickly using only a small amount of space.
They allow us to compute quantile values given a desired rank, or compute a rank given a quantile value. Quantile sketches enable us to plot the CDF, PMF or histograms of a distribution.

The goal of this short tutorial it to introduce the reader to some of the basic concepts of quantiles, ranks and their functions.

## What is a rank?

### A rank identifies the numeric position of a specific value in an enumerated, ordered set of values.

The actual enumeration can be done in several ways, but for our use here we will define the two common ways that rank can be specified and that we will use.

• The natural rank is a natural number from the set of one-based, natural numbers, ℕ1, and is derived by enumerating an ordered set of values, starting with the value 1, up to n, the number of values in the original set.

• The normalized rank is a number between 0.0 and 1.0 computed by dividing the natural rank by the total number of values in the set, n. Thus, for finite sets, any normalized rank is in the range (0, 1]. Normalized ranks are often written as a percent. But don’t confuse percent with percentile! This will be explained below.
• A rank of 0, whether natural or normalized, represents the empty set.

In our sketch library and documentation, when we refer to rank, we imply normalized rank. However, in this tutorial, we will sometimes use natural ranks to simplify the examples.

### Rank and Mass

Normalized rank is closely associated with the concept of mass. The value associated with the rank 0.5 represents the median value, or the center of mass of the entire set, where half of the values are below the median and half are above. The concept of mass is important to understanding the Probability Mass Function (PMF) offered by all the quantile sketches in the library.

## What is a quantile?

### A quantile is a value that is associated with a particular rank.

Quantile is the general term that includes other terms that are also quantiles. To wit:

• A percentile is a quantile where the rank domain is divided into hundredths. For example, “An SAT Math score of 740 is at the 95th percentile”. The score of 740 is the quantile and .95 is the normalized rank.
• A decile is a quantile where the rank domain is divided into tenths. For example, “An SAT Math score of 690 is at the 9th decile (rank = 0.9).
• A quartile is a quantile where the rank domain is divided into fourths. For example, “An SAT Math score of 600 is at the third quartile (rank = 0.75).
• The median is a quantile that splits the rank domain in half. For example, “An SAT Math score of 520 is at the median (rank = 0.5).

## The simple quantile and rank functions

Let’s examine the following table:

Quantile: 10 20 30 40 50
Natural Rank 1 2 3 4 5
Normalized Rank .2 .4 .6 .8 1.0

### Note:

The term “value” can be ambiguous because items that we stream into a sketch are values and numeric ranks are also values. To avoid this ambiguity, we will use the term “quantiles” to refer to values that are streamed into a sketch even before they have been associated with a rank.

Let’s define the simple functions

### rank(quantile) or r(q) := return the rank r associated with a given quantile, q.

Using an example from the table:

• Using natural ranks:
• q(3) = 30
• r(30) = 3
• Using normalized ranks:
• q(.6) = 30
• r(30) = .6

Because of the close, two-way relationship of quantiles and ranks,
r(q) and q(r) form a 1:1 functional pair if, and only if

• q = q(r(q))
• r = r(q(r))

And this is certainly true of the table above.

## The challenge of duplicates

With real data we often encounter duplicate quantiles in the stream. Let’s examine this next table.

Quantile: 10 20 20 20 50
Natural Rank 1 2 3 4 5

As you can see q(r) is straightforward. But how about r(q)? Which of the ranks 2, 3, or 4 should the function return, given the quantile 20? Given this data, and our definitions so far, the function r(q) is ambiguous. We will see how to resolve this shortly.

## The challenge of approximation

By definition, sketching algorithms are approximate, and they achieve their high performance by discarding data. Suppose you feed n quantiles into a sketch that retains only m < n quantiles. This means n-m quantiles were discarded. The sketch must track the quantity n used for computing the rank and quantile functions. When the sketch reconstructs the relationship between ranks and quantiles, n-m quantiles are missing creating holes in the ordered sequence. For example, examine the following tables.

The raw data might look like this, with its associated natural ranks.

Quantile: 10 20 30 40 50 60 70 80 90 100
Natural Rank 1 2 3 4 5 6 7 8 9 10

The sketch might discard the even numbered quantiles producing something like this:

Quantile: 10 30 50 70 90
Natural Rank 2 4 6 8 10

When the sketch deletes quantiles it adjusts the associated ranks by effectively increasing the “weight” of adjacent quantiles so that they are approximately positionally correct and the top natural rank corresponds to n.

How do we resolve q(3) or r(20)?

The quantile sketch algorithms discussed in the literature primarily differ by how they choose which quantiles in the stream should be discarded. After the elimination process, all of the quantiles sketch implementations are left with the challenge of how to reconstruct the actual distribution, approximately and with good accuracy.

Given the presence of duplicates and absence of values from the stream we must redefine the above quantile and rank functions as inequalities while retaining the properties of 1:1 functions.

One can find examples of the following definitions in the research literature. All of our library quantile sketches allow the user to choose the searching criteria.

These next examples use a small data set that mimics what could be the result of both duplication and sketch data deletion.

## The rules for returned quantiles or ranks

• Rule 1: Every quantile that exists in the input stream or retained by the sketch has an associated rank.

• Rule 2: All of our quantile sketches only retain quantiles that exist in the actual input stream of quantiles.

• Rule 3: For the getQuantile(rank) queries, all of our quantile sketches only return quantiles that were retained by the sketch. (i.e, we do not interpolate between quantiles.)

• Rule 4: For the getRank(quantile) queries, all of our quantile sketches only return ranks that are associated with quantiles retained by the sketch. (i.e, we do not interpolate between ranks.)

• Rule 5: All of our quantile algorithms compensate for quantiles removed during the sketch quantile selection and compression process by increasing the weights of some of the quantiles not selected for removal, such that:

• The sum of the natural weights of all quantiles retained by the sketch equals n, the total count of all quantiles given to the sketch.
• And by corollary, the largest quantile, when sorted by cumulative rank, has a cumulative natural rank of n, or equivalently, a cumulative normalized rank of 1.0.

## The rank functions with inequalities

### rank(quantile, INCLUSIVE) or r(q, LE) :=Given q, return the rank, r, of the largest quantile that is less than or equal to q.

Implementation:

• Given q, search the quantile array until we find the adjacent pair {q1, q2} where q1 <= q < q2.
• Return the rank, r, associated with q1, the first of the pair.

Boundary Exceptions:

• Boundary Rule 1: If the given q is >= the quantile associated with the largest cumulative rank retained by the sketch, the function will return the largest cumulative rank, 1.0.
• Boundary Rule 2: If the given q is < the quantile associated with the smallest cumulative rank retained by the sketch, the function will return a rank of 0.0.

Examples using normalized ranks:

• r(30) = .786 Normal rule applies: 30 <= 30 < 40, return r(q1) = .786.
Quantile[]: 10 20 20 30 30 30 40 50
Natural Rank[]: 1 3 5 7 9 11 13 14
Normalized Rank[]: .071 .214 .357 .500 .643 .786 .929 1.0
Quantile input           30
Qualifying pair           q1 q2
Rank result           .786
• r(55) = 1.0 Use Boundary Rule 1: 50 <= 55, return 1.0.
Quantile[]: 10 20 20 30 30 30 40 50 ?
Natural Rank[]: 1 3 5 7 9 11 13 14
Normalized Rank[]: .071 .214 .357 .500 .643 .786 .929 1.0
Quantile input               55
Qualifying pair               q1 (q2)
Rank result               1.0
• r(5) = 0.0 Use Boundary Rule 2: 5 < 10, return 0.0.
Quantile[]: ? 10 20 20 30 30 30 40 50
Natural Rank[]:   1 3 5 7 9 11 13 14
Normalized Rank[]:   .071 .214 .357 .500 .643 .786 .929 1.0
Quantile input 5
Qualifying pair (q1) q2
Rank result 0

### rank(quantile, EXCLUSIVE) or r(q, LT) :=Given q, return the rank, r, of the largest quantile that is strictly Less Thanq.

Implementation:

• Given q, search the quantile array until we find the adjacent pair {q1, q2} where q1 < q <= q2.
• Return the rank, r, associated with q1, the first of the pair.

Boundary Exceptions:

• Boundary Rule 1: If the given q is > the quantile associated with the largest cumulative rank retained by the sketch, the sketch will return the the largest cumulative rank, 1.0.
• Boundary Rule 2: If the given q is <= the quantile associated with the smallest cumulative rank retained by the sketch, the sketch will return a rank of 0.0.

Examples using normalized ranks:

• r(30) = .357 Normal rule applies: 20 < 30 <= 30, return r(q1) = .357.
Quantile[]: 10 20 20 30 30 30 40 50
Natural Rank[]: 1 3 5 7 9 11 13 14
Normalized Rank[]: .071 .214 .357 .500 .643 .786 .929 1.000
Quantile input       30
Qualifying pair     q1 q2
Rank result     .357
• r(55) = 1.0 Use Boundary Rule 1: 50 < 55, return 1.0.
Quantile[]: 10 20 20 30 30 30 40 50 ?
Natural Rank[]: 1 3 5 7 9 11 13 14
Normalized Rank[]: .071 .214 .357 .500 .643 .786 .929 1.0
Quantile input                 55
Qualifying pair               q1 (q2)
Rank result               1.000
• r(5) = 0.0 Use Boundary Rule 2: 5 <= 10, return 0.
Quantile[]: ? 10 20 20 30 30 30 40 50
Natural Rank[]:   1 3 5 7 9 11 13 14
Normalized Rank[]:   .071 .214 .357 .500 .643 .786 .929 1.0
Quantile input 5
Qualifying pair (q1) q2
Rank result 0

## The quantile functions with inequalities

### quantile(rank, INCLUSIVE) or q(r, GE) :=Given r, return the quantile, q, of the smallest rank that is strictly Greater than or Equal to r.

Implementation:

• Given r, search the rank array until we find the adjacent pair {r1, r2} where r1 < r <= r2.
• Return the quantile, q, associated with r2, the second of the pair.

Boundary Exceptions:

• Boundary Rule 2: If the given normalized rank, r, is <= the smallest rank, the function will return the quantile associated with the smallest cumulative rank.

Examples using normalized ranks:

• q(.786) = 30 Normal rule applies: .643 < .786 <= .786, return q(r2) = 30.
Quantile[]: 10 20 20 30 30 30 40 50
Natural Rank[]: 1 3 5 7 9 11 13 14
Normalized Rank[]: .071 .214 .357 .500 .643 .786 .929 1.000
Rank input           .786
Qualifying pair         r1 r2
Quantile result           30
• q(1.0) = 50 Normal rule applies: .929 < 1.0 <= 1.0, return q(r2) = 50.
Quantile[]: 10 20 20 30 30 30 40 50
Natural Rank[]: 1 3 5 7 9 11 13 14
Normalized Rank[]: .071 .214 .357 .500 .643 .786 .929 1.0
Rank input               1.0
Qualifying pair             r1 r2
Quantile result               50
• q(0.0 <= .071) = 10 Use Boundary Rule 2: 0.0 <= .071, return 10.
Quantile[]: ? 10 20 20 30 30 30 40 50
Natural Rank[]:   1 3 5 7 9 11 13 14
Normalized Rank[]:   .071 .214 .357 .500 .643 .786 .929 1.0
Rank input 0.0
Qualifying pair (r1) r2
Rank result   10

### quantile(rank, EXCLUSIVE) or q(r, GT) :=Given r, return the quantile, q, of the smallest rank that is strictly Greater Than r.

Implementation:

• Given r, search the rank array until we find the adjacent pair {r1, r2} where r1 <= r < r2.
• Return the quantile, q, associated with r2, the second of the pair.

Boundary Exceptions:

• Boundary Rule 1: If the given normalized rank, r, is equal to 1.0, there is no quantile that satisfies this criterion. However, for convenience, the function will return quantile associated with the largest cumulative rank retained by the sketch.
• Boundary Rule 2: If the given normalized rank, r, is less than the smallest rank, the function will return the quantile associated with the smallest cumulative rank retained by the sketch.

Examples using normalized ranks:

• q(.357) = 30 Normal rule applies: .357 <= .357 < .500, return q(r2) = 30.
Quantile[]: 10 20 20 30 30 30 40 50
Natural Rank[]: 1 3 5 7 9 11 13 14
Normalized Rank[]: .071 .214 .357 .500 .643 .786 .929 1.000
Rank input     .357
Qualifying pair     r1 r2
Quantile result       30
• q(1.0) = 50 Use Boundary Rule 1 1.0 <= 1.0 < ?, return 50.
Quantile[]: 10 20 20 30 30 30 40 50 ?
Natural Rank[]: 1 3 5 7 9 11 13 14
Normalized Rank[]: .071 .214 .357 .500 .643 .786 .929 1.0
Rank input               1.0
Qualifying pair               r1 (r2)
Quantile result                 50
• q(0.99) = 50 Normal rule applies .929 <= .99 < 1.0, return 50.
Quantile[]: 10 20 20 30 30 30 40 50
Natural Rank[]: 1 3 5 7 9 11 13 14
Normalized Rank[]: .071 .214 .357 .500 .643 .786 .929 1.0
Rank input             .99
Qualifying pair             r1 r2
Quantile result               50
• q(0.0 <= .071) = 10 Use Boundary Rule 2: 0.0 < .071, return 10.
Quantile[]: ? 10 20 20 30 30 30 40 50
Natural Rank[]:   1 3 5 7 9 11 13 14
Normalized Rank[]:   .071 .214 .357 .500 .643 .786 .929 1.0
Rank input 0.0
Qualifying pair (r1) r2
Rank result   10

### Note: This rule is marginal in its usefulness so it is not currently implemented.

Implementation:

• Given r, search the rank array until we find the adjacent pair {r1, r2} where r1 <= r < r2.
• Return the quantile, q, associated with r2, the second of the pair.

Boundary Exceptions:

• Boundary Rule 1: If the given normalized rank, r, is equal to 1.0, there is no quantile that satisfies this criterion. Return NaN or null.
• Boundary Rule 2: If the given normalized rank, r, is less than the smallest rank, the function will return the quantile associated with the smallest cumulative rank retained by the sketch..

Examples using normalized ranks:

• q(.357) = 30 Normal rule applies: .357 <= .357 < .500, return q(r2) = 30.
Quantile[]: 10 20 20 30 30 30 40 50
Natural Rank[]: 1 3 5 7 9 11 13 14
Normalized Rank[]: .071 .214 .357 .500 .643 .786 .929 1.000
Rank input     .357
Qualifying pair     r1 r2
Quantile result       30
• q(1.0) = 50 Use Boundary Rule 1 1.0 <= 1.0 < ?, return NaN or null.
Quantile[]: 10 20 20 30 30 30 40 50 ?
Natural Rank[]: 1 3 5 7 9 11 13 14
Normalized Rank[]: .071 .214 .357 .500 .643 .786 .929 1.0
Rank input               1.0
Qualifying pair               r1 (r2)
Quantile result                 NaN or null
• q(0.99) = 50 Normal rule applies .929 <= .99 < 1.0, return 50.
Quantile[]: 10 20 20 30 30 30 40 50
Natural Rank[]: 1 3 5 7 9 11 13 14
Normalized Rank[]: .071 .214 .357 .500 .643 .786 .929 1.0
Rank input             .99
Qualifying pair             r1 r2
Quantile result               50
• q(0.0 <= .071) = 10 Use Boundary Rule 2: 0.0 < .071, return 10.
Quantile[]: ? 10 20 20 30 30 30 40 50
Natural Rank[]:   1 3 5 7 9 11 13 14
Normalized Rank[]:   .071 .214 .357 .500 .643 .786 .929 1.0
Rank input 0.0
Qualifying pair (r1) r2
Rank result   10

## Summary

The power of these inequality search algorithms is that they produce repeatable and accurate results, are insensitive to duplicates and sketch deletions, and maintain the property of 1:1 functions.