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Graph Gurus Episode 11: Accumulators for Complex Graph Analytics
1.
Graph Gurus Episode 11 Using
Accumulators in GSQL for Complex Graph Analytics
2.
© 2019 TigerGraph.
All Rights Reserved Welcome ● Attendees are muted ● If you have any Zoom issues please contact the panelists via chat ● We will have 10 min for Q&A at the end so please send your questions at any time using the Q&A tab in the Zoom menu ● The webinar will be recorded and sent via email 2
3.
© 2019 TigerGraph.
All Rights Reserved Developer Edition Available We now offer Docker versions and VirtualBox versions of the TigerGraph Developer Edition, so you can now run on ● MacOS ● Windows 10 ● Linux Developer Edition Download https://www.tigergraph.com/developer/ 3 Version 2.3 Released Today
4.
© 2019 TigerGraph.
All Rights Reserved Today's Gurus 4 Victor Lee Director of Product Management ● BS in Electrical Engineering and Computer Science from UC Berkeley, MS in Electrical Engineering from Stanford University ● PhD in Computer Science from Kent State University focused on graph data mining ● 15+ years in tech industry Gaurav Deshpande Vice President of Marketing ● Built out and positioned IBM’s Big Data and Analytics portfolio, driving 45 percent year-over-year growth. ● Led 2 startups through explosive growth - i2 Technologies (IPO) & Trigo Technologies (largest MDM acquisition by IBM) ● Big Data Analytics Veteran, 14 patents in supply chain management and big data analytics
5.
© 2019 TigerGraph.
All Rights Reserved How Can TigerGraph Help You? Improve Operational EfficiencyReduce Costs & Manage RisksIncrease Revenue • Recommendation Engine • Real-time Customer 360/ MDM • Product & Service Marketing • Fraud Detection • Anti-Money Laundering (AML) • Risk Assessment & Monitoring • Cyber Security • Enterprise Knowledge Graph • Network, IT and Cloud Resource Optimization • Energy Management System • Supply Chain Analysis Analyze all interactions in real-time to sell more Reduce costs and assess and monitor risks effectively Manage resources for maximum output Foundational Use Cases: Geospatial Analysis, Time Series Analysis, AI and Machine Learning
6.
© 2019 TigerGraph.
All Rights Reserved Driving Business Outcomes with TigerGraph Increase Revenue Grew to Billion+ in annual revenue in 5 years with TigerGraph Reduce Costs & Manage Risks Recommendation Engine Detects fraud in real-time for 300+ Million calls per day with TigerGraph Fraud Detection Improve Operational Efficiency Improve Operational Efficiency Optimizes infrastructure to minimize outages for critical workloads with TigerGraph Network & IT Resource Optimization
7.
© 2019 TigerGraph.
All Rights Reserved Accumulators 7 Example: A teacher collects test papers from all students and calculates an average score. Teacher: node with an accumulator Student: node Student-to-teacher: edge Test paper: message sent to accumulator Average Score: final value of accumulator Phase 1: teacher collects all the test papers Phase 2: teacher grades it and calculates the average score.
8.
© 2019 TigerGraph.
All Rights Reserved Example 1: Counting Movies Seen by Friends Analogy: You ask each of your friends to tally how many movies they saw last year (They work at the same time → Parallelism!) ● This is the Gathering phase ● Next, you need to Consolidate into one list. → Eliminate the duplicates Lee Bob Chris Sue If we are only counting (not recording names), How can we eliminate duplicates? P Q R S T
9.
© 2019 TigerGraph.
All Rights Reserved Eliminating Duplication: visited flag ● Traditional method: Each target Item has a true/false "flag", initialized to "false" ● If a flag is "false": ○ It may be counted; Set the flag to "true" ● If a flag is "true": ○ Skip it ● ⇒ Each item is counted once Lee Bob Chris Sue If we are only counting (not recording names), How can we eliminate duplicates?
10.
© 2019 TigerGraph.
All Rights Reserved Counting Movies with Accumulators An accumulator is a runtime, shared variable with a built-in update function. 1. Each friend's tally is a local Accumulator. 2. The visited flag is also a local Accumulator. a. Bob, Chris, and Sue all visit "R", but we don't know who will first. 3. GSQL queries are designed to let each friend work in parallel, but at its own pace, and in any order 4. Consolidate the Lists into one global Accumulator. Bob: 2 (P, Q) Chris: 1 (S) Sue: 2 (R,T)
11.
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All Rights Reserved Optimization: One global list/tally Instead of each friend keeping a separate tally… Keep only one tally. ● Eliminates the consolidate step ● Saves memory Each friend still works separately, but they record their efforts in one common place. Lee Bob Chris Sue
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All Rights Reserved friendsMovieCount GSQL Query CREATE QUERY friendsMovieCount (VERTEX me) { OrAccum @visited = false; // @ means local SumAccum<INT> @@movieCount; // @@ means global #~~~~~~~~~~~~~~ Start = {me}; Friends = SELECT f # 1. Identify my friends FROM Start:s -(friendOf:e)- Person:f; #~~~~~~~~~~~~~~ Movies = SELECT m # 2. Identify the movies they've seen FROM Friends:f -(saw:r)- Movie:m ACCUM IF m.@visited == false THEN @@movieCount += 1, m.@visited += true; # 3. Count each movie once #~~~~~~~~~~~~~~ PRINT @@movieCount; }
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All Rights Reserved Review - Accumulator Properties ● Scope: Local (per vertex) or global ● Multiple data types and functions available ○ For flag: Boolean true/false data, OR function ○ For tally: Integer data, Sum function ● Supports multi-worker processing for parallelism ○ Shared: Anyone can read ○ Shared: Anyone can update submit an update request ○ All the accumulated updates are processed at once, at the end.
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All Rights Reserved Types of Accumulators The GSQL language provides many different accumulators, which follow the same rules for receiving and accessing data. However each of them has their unique way of aggregating values. 14 Old Value: 2 New Value: 11 1, 3, 5 1 3 5 SumAccum<int> Old Value: 2 New Value: 5 1, 3, 5 1 3 MaxAccum<int> Old Value: 2 New Value: 1 1, 3, 5 1 3 MinAccum<int> Old Value: 2 New Value: 2.75 1, 3, 5 1 3 AvgAccum 5 5 5 Computes and stores the cumulative sum of numeric values or the cumulative concatenation of text values. Computes and stores the cumulative maximum of a series of values. Computes and stores the cumulative mean of a series of numeric values. Computes and stores the cumulative minimum of a series of values.
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All Rights Reserved The GSQL language provides many different accumulators, which follow the same rules for receiving and accessing data. However each of them has their unique way of aggregating values. 15 Old Value: [2] New Value: [2,1,3,5] 1, 3, 3, 5 1 3 5 SetAccum<int> Old Value: [2] New Value: [2,1,5,3,3] 1, 5, 3, 3 1 3 ListAccum<int> Old Value: [1->1] New Value: 1->6 5->2 1->2 1->3 5->2 1->2 1->3 MapAccum<int,SumAccum<int>> Old Value: [userD,150] New Value: [userC,300, UserD,150, userA,100] userC,300 userA,100 (“userA”, 100) HeapAccum<Tuple> 5 5->23 3 (“userC”, 300) Maintains a collection of unique elements. Maintains a sequential collection of elements. Maintains a collection of (key → value) pairs. Maintains a sorted collection of tuples and enforces a maximum number of tuples in the collection Types of Accumulators, continued
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All Rights Reserved Use Case: Graph Algorithms PageRank analyzes the WHOLE graph ● For each node V: PR(V) = sum [ PR(A) /deg(A) + PR(B) /deg(B) + PR(C) /deg(C) ] ○ Summing contributions from several independent nodes → Ideal task for accumulators and parallel processing ● Compute a score for each node → parallelism ● Iterative method: Keep recalculating PR(v) for the full graph, unless the max change in PR < threshold → MaxAccum V A B C
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All Rights Reserved 17 Input: A person p, two integer parameters k1 and k2 Algorithm steps: 1. Find all movies p has rated; 2. Find all persons rated same movies as p; 3. Based on the movie ratings, find the k1 persons that have most similar tastes with p; 4. Find all movies these k1 persons rated that p hasn’t rated yet; 5. Recommend the top k2 movies with highest average rating by the k1 persons; Output: At most k2 movies to be recommended to person p. p …... rate rate rate PRatedMovies PSet …... PeopleRatedSameMovies rate rate rate rate …... rate rate rate rate Implementing Movie Recommendation Algorithm
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All Rights Reserved More Use Cases: Anti-Fraud TestDrive Anti-Fraud: https://testdrive.tigergraph.com/main/dashboard CIrcle Detection: ● Is money passing from A → B → C → … → back to A? See Graph Gurus #4 on our YouTube Channel: https://www.youtube.com/tigergraph
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All Rights Reserved Demo 19
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All Rights Reserved Summary • Graph Traversal and Graph Analytics can be complex, but also amenable to parallelism. • Accumulators, coupled with the TigerGraph MPP engine, provide simple and efficient parallelized aggregation. • Accumulators come in a variety of shapes and sizes, to fit all different needs. 20
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Q&A Please send your
questions via the Q&A menu in Zoom 21
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All Rights Reserved NEW! Graph Gurus Developer Office Hours 22 The Graph Gurus Webinar in late March or catch up on previous episodes: https://www.tigergraph.com/webinars-and-events/ Every Thursday at 11:00 am Pacific Talk directly with our engineers every week. During office hours, you get answers to any questions pertaining to graph modeling and GSQL programming. https://info.tigergraph.com/officehours
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All Rights Reserved Additional Resources 23 New Developer Portal https://www.tigergraph.com/developers/ Download the Developer Edition or Enterprise Free Trial https://www.tigergraph.com/download/ Guru Scripts https://github.com/tigergraph/ecosys/tree/master/guru_scripts Join our Developer Forum https://groups.google.com/a/opengsql.org/forum/#!forum/gsql-users @TigerGraphDB youtube.com/tigergraph facebook.com/TigerGraphDB linkedin.com/company/TigerGraph
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