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Analytics at the Real-Time Speed of Business: Spark Summit East talk by Manish Gupta

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Redis accelerates Apache Spark execution by 45 times, when used as a shared distributed in-memory datastore for Spark in analyses like time series data range queries. With the redis module for machine learning, redis-ml, implementation of spark-ml models gains a new real time serving layer that offloads processing of models directly in Redis, allows multiple applications to reuse the same models and speeds up classification and execution of these models by 13x. Join this session to learn more about the Redis Labs’ connector for Apache Spark that enhances production implementations of real-time big data processing.

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Analytics at the Real-Time Speed of Business: Spark Summit East talk by Manish Gupta

  1. 1. Home of Redis Analytics at the Real-Time Speed of Business Manish Gupta CMO, Redis Labs February 8, 2017
  2. 2. 2 Introduction The open source home and commercial provider of Redis Enterprise (Redise) software and Redis-as-a-Service. Open source. The leading in-memory database platform, supporting any high performance operational, analytical or hybrid use case. 6,800+ enterprise customers 200+ enterprise customers 60,000+ total customers Redise Cloud PrivateRedise Cloud Redise Pack ManagedRedise Pack SERVICE SOFTWARE
  3. 3. 3 Redise Differentiators Simplicity ExtensibilityPerformance ListsSorted Sets Hashes Hyperloglog Geospatial Indexes Bitmaps SetsStrings Bit field High-Performance Key/Value Store Data Structures Engine In-Memory Database Platform
  4. 4. 4 +
  5. 5. 5 Spark is Certainly Fast But… Read to RDD Deserialization Processing Serialization Write to RDD Analytics & BI 1 2 3 4 5 6 Data SinkData Source
  6. 6. 6 Spark SQL & Data Frame When Spark Meets Redis Data Source Serving Layer Analytics & BI 1 2 Processing Spark-Redis connector Read filtered/sorted data Write filtered/sorted data
  7. 7. 7 Accelerating Spark Time-Series with Redis Redis is faster by up to 100 times compared to HDFS and over 45 times compared to Tachyon or Spark
  8. 8. 8 Redis-ML • A complex problem to solve • Crowded with smart people • Years of investment • Tons of open source project So Why ?
  9. 9. 9 Machine / Deep Learning Stages (1) Training (2) Creating a model (3) Serving the model
  10. 10. 10 Machine / Deep Learning Stages (1) Training (2) Creating a model (3) Serving the model Homegrown
  11. 11. 11 Machine / Deep Learning Stages (1) Training (2) Creating a model (3) Serving the model
  12. 12. 12 Accurate Model is Important Otherwise…. The Ads serving company example: If your model is too small, it’s not accurate; if too large, difficult to fit at app layer
  13. 13. 13 Real World Challenge • Ads serving company • Need to serve 20,000 ads/sec @ 50msec data-center latency • Runs 1k campaigns  1K random forest • Each forest has 15K trees • On average each tree has 7 levels (depth)
  14. 14. 14 Why Accurate Models are Complex to Serve ? Item Calculation Total Random forest ops/sec 20K (ads/sec) x 1K (forests) x 15K(trees) x 7 x 0.5 (levels) 1.05 trillion Max ops/sec on the strongest AWS instance vcore 2.6Ghz x 0.9 (OS overhead) x 0.1 (10 lines of code per ops) x 0.1 (Java overhead) 23.4 million # of vcores needed 2.1 trillion / 23.4 million 44,872 # of c4.8xlarge instances needed 44,872 / 36 1,247 Total cost reserved instances 1,247 x $ 9213 ~$11.5M/yr
  15. 15. 15 Ads Serving Use-Case w/ and w/o Redise + ML Homegrown 1,247 x c4.8xlarge 35 x c4.8xlarge Cut computing infrastructure by 97% Resource Savings The Redis ML module provides native data types for models like tree ensembles, linear regressions, logistic regressions, matrix and vector operations, more..
  16. 16. 16 Ads Serving Use-Case w/ and w/o Redise + ML msec msec x2,000 Faster Reduced Latency Unlike training where Spark does parallel processing, serving is a serial process, so the Redis advantage increases as number of forests increase
  17. 17. 17 Faster, resource efficient, highly available categorization for real- time interactive applications. +
  18. 18. 18 Resources Dvir Volk : Senior Architect @Redis Labs http://www.slideshare.net/SparkSummit/spark-summit-eu-talk-by-shay-nativ-and-dvir-volk Getting Started with Spark and Redis: https://redislabs.com/docs/getting-started-with-spark-and-redis/ Spark ML package: https://github.com/RedisLabs/spark-redis-ml Redis ML module: https://github.com/RedisLabsModules/redis-ml More resources at: https://redislabs.com/solutions/use-cases/spark-and-redis/
  19. 19. 19 RedisConf 2017
  20. 20. Home of Redis Thank You! email: expert@redislabs.com Twitter:@RedisLabs

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