Scaling AncestryDNA with the Hadoop Ecosystem. Presented at the San Jose Hadoop Summit on 6-5-2014
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Scaling AncestryDNA with the Hadoop Ecosystem. Presented at the San Jose Hadoop Summit on 6-5-2014

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How Ancestry supports our DNA results processing with Hadoop, HBase, and Azkaban. This shows how we evolved and learned while supporting the business

How Ancestry supports our DNA results processing with Hadoop, HBase, and Azkaban. This shows how we evolved and learned while supporting the business

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  • Critically important. In software development you must measure your performance at every step. Does not matter what you are doing, if you are not measuring your performance, you can’t improve. The last point is critical. We could determine the formula for performance of key phases (correlate this) and used that formula to predict future performance at particular DNA pool sizes. We could see the problems coming and knew when we were going to have performance issues. <br /> <br /> Story #1: Our first step that was going out of control (going quadratic), was the first implementation of the relationship calculation – happens just after matching. This step was basically two nested for loops that walked over the entire DNA pool for each input sample. Simple code, it worked with small numbers, fell over fast. Time was approaching 5 hours to run. Two of my developers rewrote this in PERL and got it down to 2 minutes 30 seconds. They were ecstatic. One of our DNA Scientists (PhD in Bioinformatics, MS in Computer Science – he knows how to code) wrote an AWK command (nasty regular expressions) that ran in less than 10 seconds. My devs were humbled. For the next week, whenever they ran into Keith, they formally bowed to his skills. (All in good nature, all fun.) <br />
  • Static by batch size (Phasing). Some steps took a long time but were very consistent. A worry but not critical to fix up front. <br /> <br /> Linear by DNA Pool size (Pipeline Initialization). Looked at ways to streamline and improve performance of these steps. <br /> <br /> Quadratic – those are the time bombs (Germline, Relationship processing, Germline results processing) <br /> The only way we knew this was coming was because we measured each step in the pipeline. <br />
  • ‘G’ Germline would have to rebuild the hash table for all samples and then re-run all comparisons. An all by all comparison <br />
  • This is where HBase shines. It is easy to add columns and rows, very efficient with empty cells (sparse matrix). Hammer HBase with multiple processes doing this at the same time. <br />

Scaling AncestryDNA with the Hadoop Ecosystem. Presented at the San Jose Hadoop Summit on 6-5-2014 Presentation Transcript

  • 1. Scaling AncestryDNA with the Hadoop Ecosystem June 5, 2014
  • 2. What Ancestry uses from the Hadoop ecosystem ● Hadoop, HDFS, and MapReduce ● HBase ● Columnar, NoSQL data store, unlimited rows and columns ● Azkaban ● Workflow 2
  • 3. What will this presentation cover? ● Describe the problem ● Discoveries with DNA ● Three key steps in the pipeline process ● Measure everything principle ● Three steps with Hadoop ● Hadoop as a job scheduler for the ethnicity step ● Scaling matching step ● MapReduce implementation of phasing ● Performance ● What comes next? 3
  • 4. Discoveries with DNA ● Autosomal DNA test that analyzes 700,000 SNPs ● Over 400,000 DNA samples in our database ● Identified 30 million relationships that connect the genotyped members through shared ancestors 4 - 5,000,000 10,000,000 15,000,000 20,000,000 25,000,000 30,000,000 - 50,000 100,000 150,000 200,000 250,000 300,000 350,000 400,000 450,000 Jan-12 Apr-12 Jul-12 Oct-12 Jan-13 Apr-13 Jul-13 Oct-13 Jan-14 Apr-14 Cousinmatches(4thorcloser) DatabaseSize(#ofsamples) DNA DB Size Cousin Matches
  • 5. Three key steps in the pipeline ● What is a pipeline? 1. Ethnicity (AdMixture) 2. Matching (GERMLINE and Jermline) 3. Phasing (Beagle and Underdog) ● First pipeline executed on a single, beefy box. ● Only option is to scale vertically 5 Init Results Processing Finalize “Beefy Box” 24 cores, 256G mem
  • 6. Measure everything principle ● Start time, end time, duration in seconds, and sample count for every step in the pipeline. Also the full end-to-end processing time. ● Put the data in pivot tables and graphed each step ● Normalize the data (sample size was changing) ● Use the data collected to predict future performance 6
  • 7. Challenges and pain points Performance degrades when DNA pool grows ● Static (by batch size) ● Linear (by DNA pool size) ● Quadratic (matching related steps) – time bomb 7
  • 8. Ethnicity step on Hadoop Using Hadoop as a job scheduler to scale AdMixture 8
  • 9. First step with Hadoop ● What was in place? ● Smart engineers with no Hadoop experience ● Pipeline running on a single computer that would not scale ● New business that needed a scalable solution to grow First step using Hadoop ● Run AdMixture step in parallel on Hadoop ● Self contained program with set inputs and outputs ● Simple MapReduce implementation ● Experience running jobs on Hadoop ● Freed up CPU and memory on the single computer for the other steps 9
  • 10. What did we do? (Don’t cringe…) 1. Mapper -> Key: User ID, Ethnicity Result 2. Reducer -> Key: User ID, Array [Ethnicity Result] 10 DNA Hadoop Cluster (40 nodes) Pipeline Beefy Box (Process Step Control) For a batch of 1000 Submit 40 jobs with 25 samples per job AdMixture Job #1 AdMixture Job #2 AdMixture Job #40 ... Mapper that forks AdMixture sequentually for each sample Reducer #1 Reducer #2 Reducer #3 ... Results go to a simple reducer that merges them into a single results file (Shuffle)
  • 11. Performance results ● Went from processing 500 samples in 20 hours to processing 1,000 samples in 2 ½ hours ● Reduced Beagle phasing step by 4 hours 11 0 10,000 20,000 30,000 40,000 50,000 60,000 70,000 80,000 90,000 100,000 AdMixture Time (sec) Sum of Run Size Admixture Time Provided valuable experience and bought us time H1 Release AdMixture on Hadoop
  • 12. GERMLINE to Jermline Moving the matching step to MapReduce and HBase 12
  • 13. Introducing … GERMLINE! ● GERMLINE is an algorithm that finds hidden relationships within a pool of DNA ● Also refers to the reference implementation of that algorithm written in C++. You can find it here: So what’s the problem? ● GERMLINE (the implementation) was not meant to be used in an industrial setting ● Stateless, single threaded, prone to swapping (heavy memory usage) ● GERMLINE performs poorly on large data sets ● Our metrics predicted exactly where the process would slow to a crawl ● Put simply: GERMLINE couldn't scale 13 http://www1.cs.columbia.edu/~gusev/germline/
  • 14. GERMLINE run times (in hours) 14 0 5 10 15 20 25 2,500 5,000 7,500 10,000 12,500 15,000 17,500 20,000 22,500 25,000 27,500 30,000 32,500 35,000 37,500 40,000 42,500 45,000 47,500 50,000 52,500 55,000 57,500 60,000 Hours Samples
  • 15. Projected GERMLINE run times (in hours) 15 Hours Samples 0 100 200 300 400 500 600 700 2,500 12,500 22,500 32,500 42,500 52,500 62,500 72,500 82,500 92,500 102,500 112,500 122,500 GERMLINE run times Projected GERMLINE run times 700 hours = 29+ days
  • 16. DNA matching walkthrough Simplified example of showing how the code works 16
  • 17. DNA matching : How it works 17 Cersei : ACTGACCTAGTTGAC Joffrey : TTAAGCCTAGTTGAC The Input Cersei Baratheon • Former queen of Westeros • Machiavellian manipulator • Mostly evil, but occasionally sympathetic Joffrey Baratheon • Pretty much the human embodiment of evil • Needlessly cruel • Kinda looks like Justin Bieber
  • 18. DNA matching : How it works 18 0 1 2 Cersei : ACTGA CCTAG TTGAC Joffrey : TTAAG CCTAG TTGAC Separate into words
  • 19. DNA matching : How it works 19 0 1 2 Cersei : ACTGA CCTAG TTGAC Joffrey : TTAAG CCTAG TTGAC ACTGA_0 : Cersei TTAAG_0 : Joffrey CCTAG_1 : Cersei, Joffrey TTGAC_2 : Cersei, Joffrey Build the hash table
  • 20. DNA matching : How it works 20 0 1 2 Cersei : ACTGA CCTAG TTGAC Joffrey : TTAAG CCTAG TTGAC ACTGA_0 : Cersei TTAAG_0 : Joffrey CCTAG_1 : Cersei, Joffrey TTGAC_2 : Cersei, Joffrey Iterate through genome and find matches Cersei and Joffrey match from position 1 to position 2
  • 21. 21 Does that mean they’re related? ...maybe
  • 22. IBD to relationship estimation ● We use the total length of all shared segments to estimate the relationship between two genetic relatives ● This is basically a classification problem 22 5 10 20 50 100 200 500 1000 5000 0.000.010.020.030.040.05 ERSA total_IBD(cM) probability m1 m2 m3 m4 m5 m6 m7 m8 m9 m10 m11
  • 23. But wait...what about Jaime? 23 Jaime : TTAAGCCTAGGGGCG Jaime Lannister • Kind of a has-been • Killed the Mad King • Has the hots for his sister, Cersei
  • 24. The way 24 Step one: Update the hash table Cersei Joffrey 2_ACTGA_0 1 2_TTAAG_0 1 2_CCTAG_1 1 1 2_TTGAC_2 1 1 Already stored in HBase Jaime : TTAAG CCTAG GGGCG New sample to add Key : [CHROMOSOME]_[WORD]_[POSITION] Qualifier : [USER ID] Cell value : A byte set to 1, denoting that the user has that word at that position on that chromosome
  • 25. The way 25 Step two: Find matches, update the results table Already stored in HBase Jaime and Joffrey match from position 0 to position 1 Jaime and Cersei match at position 1 New matches to add Key : [CHROMOSOME]_[USER ID] Qualifier : [CHROMOSOME]_[USER ID] Cell value : A list of ranges where the two users match on a chromosome 2_Cersei 2_Joffrey 2_Cersei { (1, 2), ...} 2_Joffrey { (1, 2), ...}
  • 26. The way 26 Results Table 2_Cersei 2_Joffrey 2_Jaime 2_Cersei { (1, 2), ...} { (1), ...} 2_Joffrey { (1, 2), ...} { (0,1), ...} 2_Jaime { (1), ...} { (0,1), ...} Hash Table Cersei Joffrey Jaime 2_ACTGA_0 1 2_TTAAG_0 1 1 2_CCTAG_1 1 1 1 2_TTGAC_2 1 1 2_GGGCG_2 1
  • 27. 27 But wait...what about Daenerys, Tyrion, Arya, and Jon Snow?
  • 28. Run them in parallel with Hadoop! 28
  • 29. Parallelism with Hadoop ● Batches are usually about a thousand people ● Each mapper takes a single chromosome for a single person ● MapReduce jobs: ● Job #1: Match words - Updates the hash table ● Job #2: Match segments - Identifies areas where the samples match 29
  • 30. Matching steps on Hadoop 1. Hash Table Mapper -> Breaks input into words and fills the hash table (HBase Table #1) 2. Hash Table Reducer -> default reducer (does nothing) 3. Results Mapper -> For each new user, read hash table, fill in the results table (HBase Table #2) 4. Results Reducer -> Key: Object(User ID #1, User ID #2), Array[Object(chrom + pos, Matching DNA Segment)] 30 DNA Hadoop Cluster (40 nodes) Pipeline Beefy Box (Process Step Control) Hash Table Mapper #1 Hash Table Mapper #2 Hash Table Mapper #N ... Hash Table Reducer #1 Hash Table Reducer #2 Hash Table Reducer #N ... Results Mapper #1 Results Mapper #2 Results Mapper #N ... Results Reducer #1 Results Reducer #2 Results Reducer #N ...1 2 3 4 Input: User ID, Phased DNA
  • 31. Run times for matching with 31 Hours Samples A 1700% performance improvement over GERMLINE! 0 5 10 15 20 25 2,500 7,500 12,500 17,500 22,500 27,500 32,500 37,500 42,500 47,500 52,500 57,500 62,500 67,500 72,500 77,500 82,500 87,500 92,500 97,500 102,500 107,500 112,500 117,500
  • 32. Run times for matching (in hours) 32 Hours Samples 0 20 40 60 80 100 120 140 160 180 2,500 7,500 12,500 17,500 22,500 27,500 32,500 37,500 42,500 47,500 52,500 57,500 62,500 67,500 72,500 77,500 82,500 87,500 92,500 97,500 102,500 107,500 112,500 117,500 GERMLINE run times Jermline run times Projected GERMLINE run times
  • 33. performance ● Science team is sure the Jermline algorithm is linear ● Improving the accuracy ● Found a bug in original C++ reference code ● Balancing false positives and false negatives ● Binary version of Jermline ● Use less memory and improve speed ● Paper submitted describing the implementation ● Releasing as an Open Source project soon 33
  • 34. Beagle to Underdog Moving phasing step from a single process to MapReduce 34
  • 35. Phasing goes to the dogs ● Beagle ● Open source, freely available program ● Multi-threaded process that runs on one computer ● More accurate with a large sample set ● Underdog ● Does the same statistical calculations with a larger reference set, which increases accuracy ● Carefully split into a MapReduce implementation that allows parallel processing ● Collaboration between the DNA Science and Pipeline Developer Teams 35
  • 36. What did we do? 1. Window Mapper -> Key: Window ID, Object(User ID, DNA) 2. Window Reducer -> Key: Window ID, Array[Object(User ID, DNA)] 3. Phase Mapper (loads window data) -> Key: User ID, Object(Window ID, Phased DNA) 4. Phase Reducer -> Key: User ID, Array[Object(Window ID, Phased DNA)] 36 DNA Hadoop Cluster (40 nodes) Pipeline Beefy Box (Process Step Control) Input is per User: ATGCATCGTACGGACT... Window Mapper #1 Window Mapper #2 Window Mapper #N ... Window Reducer #1 Window Reducer #2 Window Reducer #N ... Phase Mapper #1 Phase Mapper #2 Phase Mapper #N ... Phase Reducer #1 Phase Reducer #2 Phase Reducer #N ...1 2 3 4 Load Window Reference Data Once
  • 37. Underdog performance ● Went from 12 hours to process 1,000 samples to under 25 minutes with a MapReduce implementation 37With improved accuracy! Underdog replaces Beagle 0 10,000 20,000 30,000 40,000 50,000 60,000 70,000 80,000 Total Run Size Total Beagle-Underdog Duration
  • 38. Performance and next steps Incremental change 38
  • 39. Pipeline steps and incremental change… ● Incremental change over time ● Supporting the business in a “just in time” Agile way 39 0 50000 100000 150000 200000 250000 500 5618 9615 14446 19522 24820 31172 38307 45252 52232 61675 73407 84337 93937 104684 115758 127194 138601 149988 161616 173719 185701 197853 209575 221290 232673 243550 255111 267266 279210 291017 302658 314662 326704 338813 350854 362954 375161 387334 399512 Beagle-Underdog Phasing Pipeline Finalize Relationship Processing Germline-Jermline Results Processing Germline-Jermline Processing Beagle Post Phasing Admixture Plink Prep Pipeline Initialization Jermline replaces Germline Ethnicity V2 Release Underdog Replaces Beagle AdMixture on Hadoop
  • 40. …while the business continues to grow rapidly 40 - 50,000 100,000 150,000 200,000 250,000 300,000 350,000 400,000 450,000 Jan-12 Apr-12 Jul-12 Oct-12 Jan-13 Apr-13 Jul-13 Oct-13 Jan-14 Apr-14 #ofprocessedsamples) DNA Database Size
  • 41. What’s next? Building different pipelines ● Azkaban ● Allows us to easily tie together steps on Hadoop ● Drop different steps in/out and create different pipelines ● Significant improvement over a hand coded pipeline ● Cloud ● New algorithm changes will force a complete re-run of the entire DNA pool ● Best example: New matching or ethnicity algorithm will force us to reprocess 400K+ samples ● Solution: Use the cloud for this processing while the current pipeline keeps chugging along 41
  • 42. What’s next? Other areas for improvement ● Admixture as a MapReduce implementation ● Last major algorithm that needs to be addressed ● Expect to get performance improvements similar to Underdog ● Matching growth will cause problems ● Matches per run increasing ● Change the handoff 42Keep measuring everything and adjust 0 1,000,000 2,000,000 3,000,000 4,000,000 5,000,000 6,000,000 7,000,000 8,000,000 9,000,000 84337 92130 101143 109887 119669 129170 138601 148166 157710 167551 177654 187745 197853 207799 217516 226958 236516 245548 255111 265284 275335 285149 295020 312617 322655 332777 342854 352904 362954 373109 383312 393394 Jermline Processing Duration Batch Run Size Total Matches Per Run
  • 43. Questions? Ancestry is hiring for the DNA Pipeline Team! Tech Roots Blog: http://blogs.ancestry.com/techroots byetman@ancestry.com Special thanks to the DNA Science and Pipeline Development Teams at Ancestry