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Stsg17 speaker yousunjeong

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Veröffentlicht am

Strata Singapore 2017 business use case section
"Big Telco Real-Time Network Analytics"
https://conferences.oreilly.com/strata/strata-sg/public/schedule/detail/62797

Veröffentlicht in: Technologie
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Stsg17 speaker yousunjeong

  1. 1. Big Telco 
 Real-Time Network Analytics Yousun Jeong
  2. 2. Who am I? § Senior Software Engineer of SK Telecom, South Korea’s largest wireless communications provider § Work on commercial products (~ ’17) - She worked with Big Data Solution - She worked with IaaS(OpenStack) - She worked with PaaS(CloudFoundry)
 § Mail to : jerryjung@apache.org 22
  3. 3. Table of Contents § Big Data in SK Telecom § History of SKT's big data § Overall Architecture § Use case: Real-Time Network Analytics 3
  4. 4. Big Data in SKT in a Nutshell § Data Size - Currently collecting 100 TB/day § Big Data Management Infrastructure - Hadoop cluster (1400+ nodes); migrated from MPP RDBMS § Overall Architecture - Spark - Druid § Real-Time Network Analytics - Real-Time Processing - Hadoop DW - Big Data Discovery 4
  5. 5. 5
  6. 6. History of SKT’s Big Data 6 § Batch Processing(Daily) § Map-Reduce Programming § Hadoop HDFS 2013 § Batch Processing(Hourly, Daily) § SQL on Hadoop § Hive(UDF, UDAF) 2014 § Real-time Processing (Near real-time) § Hadoop DW § Spark(Streaming, SQL) 2015 § Big Data OLAP cube § Self Data Discovery § Druid Now
  7. 7. Overall Architecture § Designed to handle both real-time & batch data processing and high level analysis using Spark and Druid as a core technology 7 BatchInterface Layer Flume Kafka HDFS oozie (workflow) Spark (ETL) Analytics Layer 1 2 Spark SQL Spark MlLib Jupyter(R,Python) Kubernetes YARN (Unified Resource Manager) Real-Time Layer NoSQL Elastic
 Search HDFS Data Service Layer Legacy App 3 Analytics Layer Batch Processing Layer Hadoop EDW Real-Time Layer Real-Time analysis 3 1 2 【 Components 】 Spark Streaming H/W Accelerator (SSD, FPGA) Provisioning PXEBoot/chef 4 5 Druid (Mart) Metatron(BI)
  8. 8. Benefits of Spark § Spark help us to have the gains in processing speed and implement various big data applications easily and speedily § Why SKT use Spark… - Support for Event Stream Processing - Fast Data Queries in Real Time - Improved Programmer Productivity - Fast Batch Processing of Large Data Set 8
  9. 9. Benchmark - SQL on Hadoop § Spark vs Hive 9 Table 1 Query
 ID Q01 Q02 Q03 Q04 Q05 Q06 Q07 Q08 Q09 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 Q19 Q20 Q21 Q22 Spark 47s 16s 47s 61s 62s 50s 72s 107s 133s 57s 191s 59s 25s 50s 56s 40s 143s 147s 60s 81s 228s 21s Hive
 (tez) 68s 62s 190s 122s 115s 61s 207s 133s 390s 110s 47s 70s 54s 54s 69s 81s 139s 195s 85s 114s 232s 91s
  10. 10. Benefits of Druid § Druid is a distributed in-memory OLAP data store. It has features of timestamp-based sharding, columnar index & compression, and pre-aggregation on the metric § Why SKT use Druid… - Sub-second processing capability - Stores aggregated summary data 
 for time-series data - Separated processing engine
 (Real-time and historical engine) 
 support analytics at the same time 10 Deep Storage (HDFS/S3) Realtime Nodes Hand off Data Historical Nodes Broker Coordinator MetaData Streaming Data Batch Data Indexing Data segments Queries Queries
  11. 11. Druid vs Spark Performance Comparison § Druid and Spark have different results depending on the nature of the engine. § Druid vs Spark - Druid converts data into OLAP 
 optimized pre-aggregated, indexed, 
 columnar structures - Druid has separate ingestion overhead - Excellent in terms of memory and 
 disk I/O compared to Spark - Spark is able to process all TPC-H queries 11 https://github.com/jaehc/tpch-spark/tree/feature-run-multiple-queries
 http://druid.io/blog/2014/03/17/benchmarking-druid.html
  12. 12. Druid vs Spark Performance Comparison § SUM_ALL_YEAR - SELECT YEAR(L_SHIPDATE), SUM(L_EXTENDEDPRICE), SUM(L_DISCOUNT),SUM(L_TAX), SUM(L_QUANTITY) FROM LINEITEM GROUP BY YEAR(L_SHIPDATE) § TOP_100_PARTS_DETAILS - SELECT L_PARTKEY, SUM(L_QUANTITY), SUM(L_EXTENDEDPRICE),MIN(L_DISCOUNT), MAX(L_DISCOUNT) FROM LINEITEM GROUP BY L_PARTKEY ORDER BY SUM(L_QUANTITY) DESC LIMIT 100 12
  13. 13. Use cases : Summary 13 TANGO-D APOLLO • TANGO(T Advanced Next Generation OSS)-D(Data warehouse) • End-to-end network quality assurance and fault analysis in a timely manner • APOLLO(Analytics PlatfOrm for inteLLigent Operation) • Real-time analysis of radio access network to improve operation efficiency Real-Time Network analytics 1 2 Metatron 
 Discovery 3 • Metatron(Development by SKT big data discovery & analytics solution) • Interactive Analysis for network engineer & operator & data scientist
  14. 14. Use Case 1: Apollo Real-Time Analytics § APOLLO aims to improve mobile user experience, reduce operation cost, and improve operation efficiency by analyzing radio access networks 14 Analytics Output Root Cause Finding Anomaly Detection Optimization Resource Monitoring Call Data RF Signal Customer/Service Device Data A/F/S Real-Time Analytics Platform Data Collecting Analytics based Control OAM Operator Predictive Analysis Service Analysis Real-time Monitoring & OptimizationEngineering 
 Optimization NetworkIntelligence KPI Detection * APOLLO : Analytics PlatfOrm for inteLLigent Operation
  15. 15. Use Case 1: Apollo Real-Time Analytics § APOLLO collects and analyzes raw data from base stations in real time to optimize the service performance § Spark Streaming - Processes raw data to obtain statistics 
 every 10 seconds - Automatically detects abnormality § Real-Time User/Service Level Optimization - Predict traffic variation and base 
 station performance - Minimize degradation in base
 station and user performance 15 Base Station Storage Spark Dashboard Spark Streaming Data Parsing Real time Processing Kafka Data Converting RDD Elastic
 Search [ Real-Time Analytics]
  16. 16. Use case 2: TANGO-D § TANGO-D is a Hadoop DW that can handle big telco data with scalability & cost efficiency 16 “Hadoop S/W and Commodity H/W Based Cost-effective IT Infrastructure System” 【 SKT DW Infrastructure】 “High-price, High-performance Proprietary IT Infrastructure System” 【 Legacy IT Infrastructure 】 ※ MPP Massively Parallel Processing, SAN Storage Area Network, NAS Network Attached Storage, RDBMS Relational DB Management System Structured/Un-structured Data Scale-out Structure (Petabyte, Exabyte) Data Structured Data Scale-up Structure (Terabyte) Commodity H/W (x86 Server)H/W High Performance H/W (MPP, Fabric Switch, etc.) Hadoop Architecture SQL on Hadoop S/W Proprietary S/W
 (RDBMS, etc.) Transaction/Batch Processing (SQL) Hadoop File System ※ MPP Massively Parallel Processing
  17. 17. Use case 2: TANGO-D § Data scientists need unified platform to collect data from all network equipment for management and analysis purpose § Expected advantages - Unification of 130+ legacy DMBSs, each of which was managing separate network monitoring system, 
 enabling thorough analysis over the entire network - Quick and accurate identification of root causes of network failure 17 NMS#1 DBMS … NMS#1 DBMS NMS#N-1 DBMS [ AS-WAS ]
 Siloed Data & IT Management Access NW Core NW Transport NMS
 #1 … NMS
 #2 NMS
 #N-1 Legacy NMS
 #N Hadoop DW DW Legacy NEW
 NMS#1 … NEW
 NMS#N BI &
 Analytic… [ AS-IS ] Network Enterprise DW
  18. 18. Use case 2: TANGO-D § TANGO-D is a Hadoop-based data warehouse built on Spark for various network statistics or raw data § User Benefits - End-to-End quality assurance,
 Fault analysis - Reduces analysis lead time
 (days → minutes) - Saves TCO (1/5 less than legacy DW) § Hadoop DW - Spark-SQL functions and query optimizer - Bulk-loading and timely processing 
 of large data 
 (processing 2,500 table per hour) 18 Acess Core Transport EMS EMS T-Pani EMS Hadoop DW DW Data Data Mart SQL on Hadoop
 (Spark SQL) IP EMS AnalyticsSQL ETL ETL O D S MQE
 (Meta Query
 Engine) BI
  19. 19. Use case 3: Metatron Discovery § We developed the Metatron Discovery solution for quick and easy data analysis and we applied it in-house big data system 19 Analysis & Analytics tools (Jupyter, Prediction, Clustering) Application (Visualization, Data Preparation, Workbench) Big Data Storage File system Key FeaturesArchitecture It easy to analyze big data with end-to-end functionality from data preparation to analysis charts. Intuitive Analysis Minimize ETL cost, speed up, and support schema changes by creating a single Big Mart by combining various dimension data based on large-capacity Big OLAP Cube By transferring data to In-memory, Local Storage, and Deep Storage over time, it is possible to respond quickly to large- capacity data over TB. Sub-second Processing Advanced Analytics Provides analysis function in conjunction with jupyter, Provides fast time series forecasting, clustering with embedded analytics. Data Processing Engine (OLAP Engine) Complex to analysis separated various SWs needed for each step of data discovery Too slow for big data not support real-time analysis Lack of analytics functions and visualization charts for telecom analysis Challenges
  20. 20. Use case 3: Metatron Discovery § Metatron Discovery enables E2E analysis to perform on a unified analytics platform § User Benefits - Operational BI using 
 network engineer and operator - Work with Jupyter to perform 
 Advanced Analysis - Drill-Down search 
 by Drag and Drop interface easily 20 Executive Officer Network Operator Field Engineer Biz. Partner TANGO-D Access Transport Core/ICT Planning and Investment Strategy Engineering Construction Operation Work & TT Management Network Monitoring N/W Data Repository Analytics PlatformE2E Inventory Operational BI Advanced
 Analytics Data Discovery
  21. 21. Use case 3: Metatron Discovery § Metatron's core engine is that Druid can query quickly by time granularity using a cache 21 Historical Nodes Broker Zookeeper Coordinator Nodes Druid Cluster 
 HDFS metastore Oozie Hadoop Cluster(DW) HDFS(Deep Storage) Segment Memory Segment Disk Cache
 Entries Segment Metadata Data/segment Queries Querying
 2017-01-03 ~
 2017-01-08 Cache (Broker Nodes) Result segment 2017-01-03/2017-01-04 Result segment 2017-01-07/2017-01-08 Querying
 (Not in Cache) Historical Node Segment 2017-01-04/2017-01-05 Segment 2017-01-07/2017-01-08 Druid
 Query Process TANGO-D (Hadoop DW) 1 3 4 2
  22. 22. Use case 3: Metatron Discovery § Metatron Discovery composes to 3 Parts (Workspace, Workbench, Jupyter). Each user can experience various analysis environments. § Workspace - General Network Engineer 
 & Operator § Workbench - Advanced Analyst § Jupyter - Statistical Analyst 22 Direct Query TANGO-D(Hadoop DW Cluster) Oozie Spark
 SQL Thrift Server Yarn SparkSQL HDFS Druid Cluster Deep Storage Historical Nodes Real-Time Nodes Broker Nodes Zookeeper Coordinator Nodes Workbench Workspace Data Analytics (SQL) 특수지역 동기화 (Sqoop) Fixed Report Dynamic Report DW/Mart Data Batch Data 
 Analytics
 Ad-hoc Jupyter R/Python Metatron Discovery Direct Query 1 2 3
  23. 23. Containerized Environment of Analytics(Ongoing) § The analysis environment can deploy as a docker, configured for individual analysis environments, and managed container resources as needed using by Kubernetes, GlusterFS 23 K8S Master K8S Master K8S Node#1 K8S Node#N K8S Node#N Nginx GlusterFS GlusterFS GlusterFS private shared [Container] [Provisioning] Admin User Docker Registry
  24. 24. Self-Data Preparation(Ongoing) § Data preparation makes it easy for anyone to do tedious and repetitive ETL tasks that preprocessing for visualizing and analyzing data 24
  25. 25. Self-Data Analytics(Ongoing) § Data analysts can interact with Metatron Discovery to run analytics and create Rest API directly from jupyter 25 1 2 3 4
  26. 26. Metatron § If you have any questions, please visit here - https://metatron.sktelecom.com/ 26
  27. 27. THANK YOU

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