Eagle from eBay at China Hadoop Summit 2015
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Eagle from eBay at China Hadoop Summit 2015 Shanghai. http://www.chinahadoop.com/english.html
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Eagle from eBay at China Hadoop Summit 2015
- 1. HADOOP Full-stack real-time monitoring framework for eBay Hadoop Hao Chen | 陈浩 eBay Cloud Service
- 2. $ whoami Hao Chen | 陈浩 Software Engineer Analytics Data Infrastructure, Cloud Services eBay Inc. hchen9@ebay.com linkedin.com/in/haozch twitter.com/haozch weibo.com/haochencn 2
- 3. 3 eBay’s Challenges in Monitoring 10+ large hadoop clusters 10,000+ nodes 50,000+ jobs per day 50,000,000+ tasks per day 500+ types of hadoop/hbase metrics Billions of audit events per day Large Scale in Real Time Various Business Logic Hadoop Hbase Spark Data Security Hardware Cloud Database Complex and Scalable Policy Join multiple data sources Threshold based, windows based Multiple metrics correlation Metrics pre-aggregations Machine learning based Engineering Modularization Varieties of data sources Varieties of data collectors Complex business logic Alert rules can’t be hot deployed Scalability issue with single process
- 4. What’s Eagle 4 The uniform monitoring and alerting framework to monitor large-scale distributed system like hadoop, spark, cloud, etc. in real time. Eagle = Eagle Framework + Eagle Apps
- 5. Eagle Ecosystem 5 Apps DAM JPA HBase Spark Interface Web Portal REST Services Ambari Plugin Integration Kafka Storm HBase Druid Elastic Search Eagle Framework Provide full-stack monitoring framework for efficiently developing highly scalable real-time monitoring applications. Eagle Apps Provide built-in monitoring applications for domains like hadoop, spark, hbase, storm and cloud. Eagle Integration Integrate with distributed real-time execution environment like storm, message bus like kafka and storage layer like hbase, and also support extensions. Eagle Interface Allow to access or manage eagle through REST service, web UI or Ambari plugin. Eagle Framework
- 6. 6 Eagle App Highlights JPA: Job Performance Analyzer DAM: Security Data Activity Monitoring
- 7. 7 JPA: Job Performance Analyzer Historical job analysis Running job analysis Anomaly host detection Job data skew detection Job performance suggestion Anomaly Prediction based on machine learning Monitor and analyze job performance in real-time
- 8. 8 Historical Job Analyzer • Job historical performance trend • Task and attempt distribution • Various level (cluster/job/user/host) of resource utilization • Anomaly historical performance detection • TooLowBytesConsumedPerCPUSecond • JobStatisticLongDuration • TooLargeReduceNumAlert • TooLargeShuffleSizeAlert
- 9. 9 Running Job Analyzer • Monitoring running job in real time • Minute-level job progress snapshots • Minute-level resource usage snapshots • CPU, HDFS I/O, Disk I/O, slot seconds • Roll up to user/queue/cluster level • Anomaly running status detection • TooLongJobDuration • NoProgressForLong • TooManyTaskFailure
- 10. Use Case Detect node anomaly by analyzing task failure ratio across all nodes Assumption Task failure ratio for every node should be approximately equal Algorithm Node by node compare (symmetry violation) and per node trend 10 Task Failure based Anomaly Host Detection
- 11. 11 Task Failure based Anomaly Host Detection Alerting: Anomaly Detection & Alerting Insight: Task failure drill-down Insight: Task failure drill-down
- 12. Counters & Features Use Case Detect data skew by statistics and distributions for attempt execution durations and counters Assumption Duration and counters should be in normal distribution 12 Real-time Data Skew Detection mapDuration reduceDuration mapInputRecords reduceInputRecords combineInputRecords mapSpilledRecords reduceShuffleRecords mapLocalFileBytesRead reduceLocalFileBytesRead mapHDFSBytesRead reduceHDFSBytesRead Modeling & Statistics Avg Min Max Distributions Max z-score Top-N Correlation Threshold & Detection Counters Correlation > 0.9 & Max(Z-Score) > 90%
- 13. 13 Real-time Data Skew Detection
- 14. 14 Anomaly Prediction based on Machine Learning • Anomaly Metric Predictive Detection • Offline: Analyzing and combining 500+ metrics together for causal anomaly detections (IG -> PCA -> GMM -> MCC) • Online: Predictively alert for anomaly metrics Normal (Green) and Abnormal (Red) Data and Probability Distribution and Threshold Selection PCA (Principal Component Analysis)
- 15. 15 Anomaly Prediction based on Machine Learning • Anomaly Metric Predictive Detection
- 16. 16 DAM: Data Activity Monitoring Secure hadoop in real-time Security Use Cases Security Architecture Overview Security Components Highlights Security Machine Learning Integration
- 17. 17 Security Use Cases Data Loss Prevention Get alerted and stop a malicious user trying to copy, delete, move sensitive data from the Hadoop cluster. Malicious Logins Detect login when malicious user tries to guess password. Eagle creates user profiles using machine learning algorithm to detect anomalies Unauthorized access Detect and stop a malicious user trying to access classified data without privilege. Malicious user operation Detect and stop a malicious user trying to delete large amount of data. Operation type is one parameter of Eagle user profiles. Eagle supports multiple native operation types.
- 19. 19 Security Component Highlights Policy Manager Expressive language - create and modify policies for alerting and remediation on certain data activity monitoring events. Data classification Integrate with Dataguise & Apache Ranger. Policy-based Remediation Ability to detect and stop a threat, improve operational efficiencies, and reduce regulatory compliance costs. User Profiling Based on Machine learning to automatically generate anomaly detection policy User Activity Exploration Ability to drill down into alert details to understand the data security threat
- 20. 20 Security Machine Learning Integration • User Activity Profiling • Offline: Determine bandwidth from training dataset the kernel density function parameters (KDE) • Online: If a test data point lies outside the trained bandwidth, it is anomaly (Policy) PCs(Principle Components) in EVD (Eigenvalue Value Decomposition)Kernel Density Function
- 21. 21 Security Machine Learning Integration • User Activity Profiling on Spark Historical Audit Events Real-time Audit Events Batch Preprocess User Profile Model Generation (KDE + EVD Algorithm) Eagle StorageHDFS Stream Preprocess Policy Engine Online detection on Storm Offline training on Spark Archived data Real-time stream Kafka Persist model Dynamically load models & policies Alert Consumer Persist alert Eagle Security Plugins
- 22. Eagle Monitoring Framework 22 Eagle = Eagle Framework + Eagle Apps Full-stack real time monitoring framework
- 23. 23 • Data collector -> data processing -> metric pre-agg/alert engine -> storage -> dashboards • We need create framework to cover full stack in monitoring system Monitoring Programming Paradigm
- 25. Eagle Monitoring Framework Highlights 25 Eagle = Eagle Framework + Eagle Apps Lightweight Streaming Process Framework Extensible & Scalable Policy Framework Eagle Query Framework Customizable Dashboards
- 26. 26 Step 1: Task DAG graph setup Eagle Stream Data Processing API @Override protected void buildDependency(FlowDef def, DataProcessConfig config) { Task header = Task.newTask("wordgenerator").setExecutor(source).completeBuild(); Task uppertask = Task.newTask("uppercase").setExecutor(new UppercaseExecutor()).connectFrom(header).completeBuild(); Task groupbyUppercaseTask = Task.newTask("groupby_uppercase").setExecutor(new GroupbyCountExecutor()).connectFrom(uppertask).completeBuild(); def.endBy(groupbyUppercaseTask); } Step 2: Inter-task data exchange protocol @Override protected void buildDependency(FlowDef def, DataProcessConfig config) { Task header = Task.newTask("wordgenerator").setExecutor(source).completeBuild(); Task uppertask = Task.newTask("uppercase").setExecutor(new UppercaseExecutor()).connectFrom(header).completeBuild(); Task groupbyUppercaseTask = Task.newTask("groupby_uppercase").setExecutor(new GroupbyCountExecutor()).connectFrom(uppertask).completeBuild(); def.endBy(groupbyUppercaseTask); }
- 27. 27 Execution Graph development, compile and deploy Development / Compile Phase Deployment / Runtime Phase
- 28. 28 Extensible & Scalable Policy Framework Usability • Declarative Policy Definition Syntax • Stream Metadata (event attribute name, attribute type, attribute value resolver, …) Scalability • Dynamic policy partitioning across compute nodes based on configurable partition class • Dynamic policy deployment • Event partitioning by storm and policy partitioning by Eagle (N events * M policies) Extensibility • Support new policy evaluation engine, for example Siddhi, Esper, Machine learning etc.
- 29. 29 Usability of Policy Framework Case HBase Region server high call queue length Policy In the past 30 minutes, there are more than 20 times call queue length>2000 from RegionCallQueueLength[value>2000]#window.Extension:messageTimeWindow(30 min) select host, value, avg(value) as avgValue, count(*) as count group by host having count >= 20 insert into HighRegionServerCallQueueLengthStream;
- 30. 30 Scalability of Policy Evaluation Dynamic Policy Partition • N Users with 3 partitions, M policies with 2 partitions, then 3*2 physical tasks • Physical partition + Policy-level partition
- 31. 31 Extensibility of Policy Framework public interface PolicyEvaluatorServiceProvider { public String getPolicyType(); public Class<? extends PolicyEvaluator> getPolicyEvaluator(); public Class<? extends PolicyDefinitionParser> getPolicyDefinitionParser(); public Class<? extends PolicyEvaluatorBuilder> getPolicyEvaluatorBuilder(); public List<Module> getBindingModules(); } Policy Evaluator Provider use SPI to register policy engine implementations Built-in Supported Policy Engine • Siddhi Complex Event Processing Engine • Machine Learning based Policy Engine
- 32. Eagle Query Framework 32 Persistence • Metric • Event • Metadata • Alert • Log • Customized Structure • … Query • Search • Filter • Aggregation • Sort • Expression • …. The light-weight metadata-driven store layer to serve commonly shared storage & query requirements of most monitoring system
- 33. 33 • Interactive: IPython notebook-like interactive visualization analysis and troubleshooting. • Dashboard: Customizable dashboard layout and drill-down path, persist and share. Customizable Dashboard Provide real-time interactive visualization and analytics capability supporting variety of data sources like eagle, druid and so on.
- 34. 34 Eagle in Future The general monitoring platform for large-scale system of eBay
- 35. 35 Open Source First Use Case Eagle to secure Hadoop in real time based on Eagle framework External Partners Hortonworks, Dataguise, Paypal and Apache Ranger Following Components to Open Source JPA (“Job Performance Analyzer”), HBase and GC Monitoring and so on is opening source soon
- 36. 36 Reference Eagle at Hadoop Summit 2015, San Jose http://2015.hadoopsummit.org Slides | Video Eagle at Big Data Summit 2014, Shanghai http://2014ebay.csdn.net/m/zone/ebay_en Slides | Video
- 37. 37 The End & Thanks If you want to go fast, go alone. If you want to go far, go together. -- African Proverb Hao Chen hchen9@ebay.com | @haozch
- 38. 38 We are Hiring Now https://careers.ebayinc.com Or contact me: hchen9@ebay.com
Editor's Notes
- Anomaly detection algorithm Continuously crawl job history files immediately after it is completed Calculate minute level job failure ratio for each node A node is identified to be anomalous when either of the following 2 conditions happen This node continuously fails tasks within this node This node has significant higher failure ratio than rest of nodes within the cluster
- Anomaly detection algorithm Continuously crawl job history files immediately after it is completed Calculate minute level job failure ratio for each node A node is identified to be anomalous when either of the following 2 conditions happen This node continuously fails tasks within this node This node has significant higher failure ratio than rest of nodes within the cluster
- Anomaly detection algorithm Continuously crawl job history files immediately after it is completed Calculate minute level job failure ratio for each node A node is identified to be anomalous when either of the following 2 conditions happen This node continuously fails tasks within this node This node has significant higher failure ratio than rest of nodes within the cluster
- Inspired by TSDB, Ganglia, Nagios, Zabbix etc. Most of them focus on infrastructure level data collection and alert, but they don’t consider business logic complexity – how to prepare data
- Inspired by TSDB, Ganglia, Nagios, Zabbix etc. Most of them focus on infrastructure level data collection and alert, but they don’t consider business logic complexity – how to prepare data
- Inspired by TSDB, Ganglia, Nagios, Zabbix etc. Most of them focus on infrastructure level data collection and alert, but they don’t consider business logic complexity – how to prepare data
- Inspired by TSDB, Ganglia, Nagios, Zabbix etc. Most of them focus on infrastructure level data collection and alert, but they don’t consider business logic complexity – how to prepare data
- Anomaly detection algorithm Continuously crawl job history files immediately after it is completed Calculate minute level job failure ratio for each node A node is identified to be anomalous when either of the following 2 conditions happen This node continuously fails tasks within this node This node has significant higher failure ratio than rest of nodes within the cluster
- Anomaly detection algorithm Continuously crawl job history files immediately after it is completed Calculate minute level job failure ratio for each node A node is identified to be anomalous when either of the following 2 conditions happen This node continuously fails tasks within this node This node has significant higher failure ratio than rest of nodes within the cluster
- Anomaly detection algorithm Continuously crawl job history files immediately after it is completed Calculate minute level job failure ratio for each node A node is identified to be anomalous when either of the following 2 conditions happen This node continuously fails tasks within this node This node has significant higher failure ratio than rest of nodes within the cluster
- IG: Information Gain， 信息增益, 概率分布或者信息论，是非对称的，用以度量两种概率分布P和Q的差异。信息增益描述了当使用Q进行编码时，再使用P进行编码的差异。通常P代表样本或观察值的分布，也有可能是精确计算的理论分布。Q代表一种理论，模型，描述或者对P的近似。目的： 特征选择 PCA: 主成分分析（Principal Component Analysis，PCA）， 将多个变量通过线性变换以选出较少个数重要变量的一种多元统计分析方法。又称主分量分析。http://baike.baidu.com/view/45376.htm?fromtitle=principal+component+analysis&type=syn GMM: 高斯混合模型（或者混合高斯模型），也可以简写为MOG（Mixture of Gaussian）。用高斯概率密度函数（正态分布曲线）精确地量化事物，将一个事物分解为若干的基于高斯概率密度函数（正态分布曲线）形成的模型。http://baike.baidu.com/view/3767607.htm MCC: 马修相关系数，http://baike.baidu.com/view/3767607.html
- IG: Information Gain， 信息增益, 概率分布或者信息论，是非对称的，用以度量两种概率分布P和Q的差异。信息增益描述了当使用Q进行编码时，再使用P进行编码的差异。通常P代表样本或观察值的分布，也有可能是精确计算的理论分布。Q代表一种理论，模型，描述或者对P的近似。目的： 特征选择 PCA: 主成分分析（Principal Component Analysis，PCA）， 将多个变量通过线性变换以选出较少个数重要变量的一种多元统计分析方法。又称主分量分析。http://baike.baidu.com/view/45376.htm?fromtitle=principal+component+analysis&type=syn GMM: 高斯混合模型（或者混合高斯模型），也可以简写为MOG（Mixture of Gaussian）。用高斯概率密度函数（正态分布曲线）精确地量化事物，将一个事物分解为若干的基于高斯概率密度函数（正态分布曲线）形成的模型。http://baike.baidu.com/view/3767607.htm MCC: 马修相关系数，http://baike.baidu.com/view/3767607.html
- Data loss prevention Get alerted and stop a malicious user trying to copy, delete, move sensitive data from the hadoop cluster. Malicious Logins Detect login when malicious user tries to guess password. Eagle creates user profiles using machine learning algorithm to detect anomalies. This anomaly detection together with the policy for user logins would trigger an alert and block this user from accessing sensitive datasets. Unauthorized access Detect and stop a malicious user trying to access classified data without privilege. Unauthorized access is one fact of Eagle user profiles, and machine learning algorithm will detect anomaly. This anomaly detection together with the policy of user access to classified data without authorization would trigger an alert to user's manager. Malicious user operation Detect and stop a malicious user trying to delete large amount of data. Operation type is one parameter of Eagle user profiles. Eagle supports multiple native operation types. Dataguise delivers data privacy protection and risk assessment analytics that allow organizations to safely leverage and share enterprise data. Our solutions simplify governance as they proactively locate sensitive data, automatically protect it with appropriate remediation polices, and provide actionable compliance intelligence to decision makers, in real-time. In Hadoop deployments, our solutions inspect incoming data and protect it before it is stored. These capabilities simplify risk management, improve operational efficiencies, and reduce regulatory compliance costs.
- Data loss prevention Get alerted and stop a malicious user trying to copy, delete, move sensitive data from the hadoop cluster. Malicious Logins Detect login when malicious user tries to guess password. Eagle creates user profiles using machine learning algorithm to detect anomalies. This anomaly detection together with the policy for user logins would trigger an alert and block this user from accessing sensitive datasets. Unauthorized access Detect and stop a malicious user trying to access classified data without privilege. Unauthorized access is one fact of Eagle user profiles, and machine learning algorithm will detect anomaly. This anomaly detection together with the policy of user access to classified data without authorization would trigger an alert to user's manager. Malicious user operation Detect and stop a malicious user trying to delete large amount of data. Operation type is one parameter of Eagle user profiles. Eagle supports multiple native operation types. Dataguise delivers data privacy protection and risk assessment analytics that allow organizations to safely leverage and share enterprise data. Our solutions simplify governance as they proactively locate sensitive data, automatically protect it with appropriate remediation polices, and provide actionable compliance intelligence to decision makers, in real-time. In Hadoop deployments, our solutions inspect incoming data and protect it before it is stored. These capabilities simplify risk management, improve operational efficiencies, and reduce regulatory compliance costs.
- Data loss prevention Get alerted and stop a malicious user trying to copy, delete, move sensitive data from the hadoop cluster. Malicious Logins Detect login when malicious user tries to guess password. Eagle creates user profiles using machine learning algorithm to detect anomalies. This anomaly detection together with the policy for user logins would trigger an alert and block this user from accessing sensitive datasets. Unauthorized access Detect and stop a malicious user trying to access classified data without privilege. Unauthorized access is one fact of Eagle user profiles, and machine learning algorithm will detect anomaly. This anomaly detection together with the policy of user access to classified data without authorization would trigger an alert to user's manager. Malicious user operation Detect and stop a malicious user trying to delete large amount of data. Operation type is one parameter of Eagle user profiles. Eagle supports multiple native operation types. Dataguise delivers data privacy protection and risk assessment analytics that allow organizations to safely leverage and share enterprise data. Our solutions simplify governance as they proactively locate sensitive data, automatically protect it with appropriate remediation polices, and provide actionable compliance intelligence to decision makers, in real-time. In Hadoop deployments, our solutions inspect incoming data and protect it before it is stored. These capabilities simplify risk management, improve operational efficiencies, and reduce regulatory compliance costs.
- Histogram Density Estimation: 直方密度估计 Kernel density estimation-核密度估计 EVD: 线性代数，特征值分解，矩阵之集，Eigenvalue Value Decomposition, http://www.stats.ox.ac.uk/~sejdinov/teaching/HT15_lecture2-nup.pdf 高斯混合模型更多的用于分类，Parzen等KDE方法更多的用于概率密度的估计 http://blog.sina.com.cn/s/blog_6923201d01010tjo.html
- Histogram Density Estimation: 直方密度估计 Kernel density estimation-核密度估计 EVD: 线性代数，特征值分解，矩阵之集，Eigenvalue Value Decomposition, http://www.stats.ox.ac.uk/~sejdinov/teaching/HT15_lecture2-nup.pdf 高斯混合模型更多的用于分类，Parzen等KDE方法更多的用于概率密度的估计 http://blog.sina.com.cn/s/blog_6923201d01010tjo.html
- Anomaly detection algorithm Continuously crawl job history files immediately after it is completed Calculate minute level job failure ratio for each node A node is identified to be anomalous when either of the following 2 conditions happen This node continuously fails tasks within this node This node has significant higher failure ratio than rest of nodes within the cluster
- As a framework, Eagle does not assume : Data source (where, what) Business logic execution path (how) Policy engine implementation (how) Data sink (where, what) As a framework, Eagle does the following: SQL-like service API High-performing query framework Lightweight streaming process java API Extensible policy engine implementation Scalable and distributed rule evaluation Metadata driven stream processing Data source extensibility Data sink extensibility Interactive dashboard
- Anomaly detection algorithm Continuously crawl job history files immediately after it is completed Calculate minute level job failure ratio for each node A node is identified to be anomalous when either of the following 2 conditions happen This node continuously fails tasks within this node This node has significant higher failure ratio than rest of nodes within the cluster
- Supports syntax: Search Aggregate Time Series Histogram Expression Filter Paginations Metadata definition ORM High performance RESTful API SQL-like declarative query syntax Supporting HBase and RDBMS as storage Logically partition by tags defined in annotation Co-processor support Secondary index support Generic service client library Supports syntax: Search Aggregate Time Series Histogram Expression Filter Paginations Metadata definition ORM High performance RESTful API SQL-like declarative query syntax Supporting HBase and RDBMS as storage Logically partition by tags defined in annotation Co-processor support Secondary index support Generic service client library
- eBay内部，随着越来越多的大型分布式系统在企业级平台中部署，monitoring for large-scale 分布式系统的需求尤其强烈，eagle 将给予eagle framework 为核心基础，不断结合business logic特性逐渐壮大其Eagle Apps的生态圈，同时不断优化核心框架本身。 同时我们相信不止是ebay，大部分企业级平台，部署和维护这些大型分布式系统时，都会遇到共同的问题，集群越大，各方面监控所面临的挑战也越大，我们相信Eagle这针对于大型分布式系统监控的优势也会越突出。我们也一直非常期待同大家进行相关的交流和探讨，因此作为抛砖引玉，我们会以开源的形式开放eagle的代码，一方面ebay在这方面的大型分布式系统监控方面的努力可以对那些需要解决类似的公司有所帮助或者参考，同时也希望得到业界的反馈，对于我们的解决方式上进行深入交流，我们自己也可以从中有所收获，甚至，大家可以一起合作创建一个定位与大型分布式系统的开源监控平台。