Approximate Algorithms and Sketches in Druid

Many exact queries require computation and storage that scale linearly or superlinearly in the data. There exist many classes of problems for which exact results are not necessary. Druid has long provided support for various approximate algorithms and sketches, and recently Yahoo has been doing some work to integrate new sketch algorithms directly into Druid. We will talk about the theta sketch algorithm that Yahoo has developed and how it can be leveraged for analytics in Druid.

Transcript

1. Summarization, Approximation, Sampling: Improving Query Times on Big Data OR Approximate Algorithms and Sketches In Druid Lee Rhodes, Eric Tschetter, Much debt owed to Kevin Lang Hadoop Summit, 2015; Druid Meetup Aug 2015
2. 2 Yahoo Analyzes Big Data •  Tens of billions of user events / day •  Complex behaviors and interactions of User Groups
3. 3 Thus, Much Of Our Analysis Is Keyed From Unique Identifiers … That Appear Many Times •  B-Cookies •  Device-IDs •  YUIDs / SIDs •  Session IDs •  Advertiser IDs •  Etc. Big Data
4. 4 Arithmetic Operations possible with additive metrics … (e.g., # views, # clicks …) C i ∑ = C j ∑ C i ∑ C j C i C i ∑ Merging, Partitioning C = C i, j,k Δi,Δj,Δk ∑ i j k Dimensional Summing t1 t2 t3 C t ∑ Summing over Time Differencing - C 2 ∑ C 1 ∑ C 1 ∑ − C 2 ∑
5. 5 C i ∑ = C j ∑ C i ∑ C j C i C i ∑ Merging, Partitioning C = C i, j,k Δi,Δj,Δk ∑ i j k Dimensional Summing t1 t2 t3 C t ∑ Summing over Time Called unique, or “non-additive” metrics. Difficult computational challenge at large scale Differencing - C 2 ∑ C 1 ∑ C 1 ∑ − C 2 ∑ … Are Not Possible with Metrics that have Duplicates (e.g., # B-Cookies, # Session-IDs, …)
6. 6 Proposition… If An Approximate Answer Is Acceptable … There Is Likely A Much More Efficient Solution! But How Approximate? How Efficient?
7. 7 Welcome to the New Science of Approximate Computing For Big Data * •  Sampling Oldest: CS ~1986 Stats: 1700’s •  Histograms •  Wavelets •  Sketches Newest: CS ~2008, Theory ~1984 * Graham Cormode, et al. “Synopses for Massive Data: Samples, Histograms, Wavelets, Sketches”, 2011
8. 8 “Sketch” Applies to a Broad Range of Algorithms Result +/- ε Big Data Stream •  Small, fast, single pass, and approximate •  With mathematically proven error bounds* * The mathematical proofs are complex! “Stochastic Streaming Algorithms” “Approximate Query Processing” Theor. Math Comp. Sci ‘70s – D. Knuth ‘85 – P. Flajolet Explosion of Papers Since ‘90s-’00s –A. Broder, R. Kumar, E. Cohen Data Structure Transform Estimator White Noise
9. 9 Long City Block: 1/5 Mile = 1056 Ft d = 100% You @ 2 ft From Curb d =2 / 1056 = 0.19% Couples Closer “Dreamers” Farther Apart 1st Estimate: 1/d 1 person/ (2 ft /1056 ft ) = 528 People Sample contains 1 value Curb @ 0 ft The Concert Line How Long?
10. 10 11th in line @ 30 ft d = 30 / 1056 = 2.84% Long City Block: 1/5 Mile = 1056 Ft d = 100% You @ 2 ft From Curb d =2 / 1056 = 0.19% Couples Closer “Dreamers” Farther Apart 10 Sidewalk Cracks = 30 feet 1st Estimate: 1/d 1 person/ (2 ft /1056 ft ) = 528 People Sample contains 1 value Better Estimate: 11/d 11 people / (30 ft / 1056 ft ) = 387 People Sample contains 11 values Curb @ 0 ft The Concert Line How Long?
11. 11 Ordered List 1.0 Uniform Random Hash è (0,1) Big Data 0.0 k Minimum Value (KMV) Sketch for Counting Uniques •  Maintain ordered list of hash values
12. 12 Ordered List 1.0 Uniform Random Hash è (0,1) Big Data Data as “White Noise” 0.0 0.922 0.873 0.822 0.758 0.610 0.437 0.386 0.195 0.145 0.008 d = 0.191 Est = 1 0.191≈ 5 d = 0.008 Est = 1 0.008 ≈125 k Minimum Value (KMV) Sketch for Counting Uniques •  Maintain ordered list of hash values •  1st Estimate = 1/d but NOISY!
13. 13 Ordered List Uniform Random Hash è (0,1) Big Data KMV Data as “White Noise” 0.922 0.873 0.822 0.758 0.610 0.437 0.386 0.195 0.145 0.008 1.0 0.0 d V(kth) = 0.195 •  Maintain ordered list of hash values •  Choose k Min Values •  Better Est =(k-1) / V(kth) = 2 / 0.195 = 10.26 k Minimum Value (KMV) Sketch for Counting Uniques
14. 14 Ordered List 1.0 Uniform Random Hash è (0,1) Big Data KMV Data as “White Noise” 0.0 0.195 0.145 0.008 V(kth) = 0.195 •  Maintain ordered list of hash values •  Choose Min k Values •  Better Est =(k-1)/V(kth) •  Reject hash ≥ V(kth) •  Reject duplicates k Minimum Value (KMV) Sketch for Counting Uniques
15. 15 •  Maintain ordered list of hash values •  Choose Min k Values •  Better Est =(k-1)/V(kth) •  Reject hash ≥ kth min •  Reject duplicates •  Otherwise insert in order, toss the top value, track V(kth). Ordered List 1.0 Uniform Random Hash è (0,1) Big Data KMV 0.0 0.145 0.100 0.008 V(kth) = 0.145 Note that RSE is independent of n ! Unbiased Est(n): ˆ n = (k −1) /V(kth) Relative Standard Error, RSE = σ ˆ n n < 1 k − 2 k Minimum Value (KMV) Sketch for Counting Uniques
16. 16 Relative Error Distribution, k = 4K Sketch, Synthesized Data 5th %ile 95th %ile Mean, Median 25th %ile Act RSE Th. RSE 75th %ile
17. 17 Yep, It Scales!
18. 18 Relative Error Distribution, k = 64K Sketch, Real Data Sketch: Words = 64K Std Err: <0.4% Max size = 512KB Input: (many dimensions) 5.2TB ~20B events 5.2M Total Rows = # dim comb 4M Sketches for Viewing BC RESULTS: Avg Size ~2.5KB / sketch 25,604 (0.64%) had any error at all Error Feather Duster Plot Cum Error Distribution Measured Theoretical Upper Bound Zero Error
19. 19 Sketch Update Speed, 64K Sketch, 64-bit Long Inputs 45M 142M Updates/sec 20M
20. 20 Sketch Merge Time / Query 14.5M Sk / Sec / Proc
21. 21 θ Sketches Enable Full Set Operations Example Queries Easily Answered using Sketches How many users visited both Sports and Finance within the last day? How many non-US users visited Yahoo.com in January that also visited in February? A∪B ( )∩ C∪D ( )\ E x y z Sketch Mart Big Data Stream And Real-time Set Expessions On Streams Set Operations Result S Sportst ( )∩S Financet ( ) S Jan ( )∩S Feb ( ) ( )\ S US ( ) Batch
22. 22 Hyper-Log Log Sketches §  2008 Theoretical Paper by Philippe Flajolet §  Smallest Space Sketch for a Given Counting Accuracy §  Outstanding for Simple Counting and Simple Merging §  Unsuitable for Set Intersection or Difference Ø  Generally relies on “Include / Exclude” approach => Huge Error §  All source and target sketches in Unions must be the same size, k. §  Typically slower to update and merge than Theta Sketches
23. 23 Example θ Sketch System Flow Architecture Sketch Data Σ Sketch Data Sketch Sketch Data Σ Sketch Data Sketch Sketch Σ Sketch Sketch Sketch Σ Sketch Sketch SetOp Sketch Result Sketch Result Σ Hadoop Grid (Pig) Query Engine (Druid, Java) The Power of “Additive” Intermediates! ∩, \
24. 24 θ Sketch Advantages for Analysis of Uniques §  Scalable ›  User specifies Upper Bound Size / Accuracy Trade-off. Example: 16K Sketch: 2RSE <= 1.56% @ 95% confidence, max size ~ 131KB = 8*k ›  Sketch Upper Bound Size is Independent of Input Stream Size §  “Additive”, Order Insensitive, Duplicate Insensitive, and Parallelizable ›  Merge across arbitrary dimensions or time. ›  Enables ad-hoc queries of unique counts from intermediate sketch data ›  Enables simplified processing of late data ›  Simplifies processing pipeline architectures
25. 25 §  Fast ›  Obtain results from a single pass of identity-grain data => Real-Time ›  Updates in 10s of nSec, millions of Sketch Merges per Sec. §  Analysis of Set Expressions (Union, Intersection, Difference) ›  Accuracy superior to traditional Include / Exclude approach (and HLL). ›  The result of a Set Operation is Another Sketch ›  Can accommodate source and target sketches of different size, k, and different θ-Sketch type. §  Well Defined (Mathematically Proven) Error Properties ›  The width of the error distribution is a trade-off with sketch size ›  The theoretical error bounds are real-time queryable θ Sketch Advantages for Analysis of Uniques
26. 26 Tuple Sketches for User Behavior Modeling & Analysis Benefits •  Ideal for Analysis of Sets of Users •  Enables Accurate Estimation and Set Ops •  High Performance for both Real-time and Batch •  Standardized Library Will Promote Data Sharing Across Platforms Tuple Sketch: b1 Object b2 Object b3 Object Real-Time Data Stream Extendable For User Behavior Analysis θ Under Development •  Frequency Cap Modeling •  Stratified Sampling
27. 27 Off-Heap Sketches & Swim Lanes Sketch JVM Sketch Data Sketch Sketch Data 4GB 10GB 10GB Off-Java Heap “Swim Lanes” 1 N Thread / CPU Managed via “Memory” Package
28. Thank You! Questions?
29. •  Enables many sketch algorithms •  Enables sketches as results •  Simplifies implementation of set expressions 29 Theta (θ) Sketch Framework •  Create variable θ = 1.0 •  Configure k •  Define: Theta Choosing Function (TCF) •  Example: θ = last tossed. •  k can be stochastic k values 0.195 0.145 0.100 0.008 θ Unbiased Estimate ˆ n = S θ S = cache (Set) cardinality Relative Standard Error, RSE < 1 k −1 Theta is the probability that the next value will modify the sketch •  Estimate & RSE become very simple
30. 30 •  Set Theoretic Include / Exclude Equation is based on cardinalities: •  Unfortunately, this results in large relative errors when the result is small compared to the largest set: A∩B = A + B − A∪B A B A∩B ±εr = A ±εA + B ±εB − A∪B ±εA∪B Set Intersection and Difference Error Because the error components ADD! The smaller the Relative Result the worse the Relative Error
31. 31 Δ =∩, \ = Intersection or Difference F = A∪B AΔB = Inverse "Broder Rule" RE = Measured Truth −1 RE θ = Relative error for θ Sketch Intersection ≈ F 1 k RE IE = Relative error for HLL using IE ≈ F 1 k RE IE RE θ = F F = F