The document summarizes Hadoop, a full-stack real-time monitoring framework for eBay's Hadoop clusters. It discusses eBay's large-scale Hadoop environment with over 10 clusters, 10,000 nodes, and 50,000 jobs/day. It then introduces Eagle, the uniform monitoring framework, which consists of the Eagle framework and Eagle apps. The framework provides scalable real-time monitoring capabilities and the apps provide domain-specific monitoring for Hadoop, Spark, HBase etc. It highlights two Eagle apps: JPA for job performance monitoring and DAM for security monitoring.
Next Gen Big Data Analytics with Apache Apex discusses Apache Apex, an open source stream processing framework. It provides an overview of Apache Apex's capabilities for processing continuous, real-time data streams at scale. Specifically, it describes how Apache Apex allows for in-memory, distributed stream processing using a programming model of operators in a directed acyclic graph. It also covers Apache Apex's features for fault tolerance, dynamic scaling, and integration with Hadoop and YARN.
Honu is a large scale data collection and processing pipeline built using Hadoop, Hive, and Thrift that is running in production at Netflix. It collects over a billion log events per day from applications and processes them in HDFS and Hive for querying. The pipeline includes collectors that gather application logs, a processing system that parses and loads data into structured Hive tables, and a Hive data warehouse where the data is stored. Future work includes open sourcing components, adding multiple writers, and integrating real-time with monitoring systems.
Slim Baltagi, director of Enterprise Architecture at Capital One, gave a presentation at Hadoop Summit on major trends in big data analytics. He discussed 1) increasing portability between execution engines using Apache Beam, 2) the emergence of stream analytics driven by data streams, technology advances, business needs and consumer demands, 3) the growth of in-memory analytics using tools like Alluxio and RocksDB, 4) rapid application development using APIs, notebooks, GUIs and microservices, 5) open sourcing of machine learning systems by tech giants, and 6) hybrid cloud computing models for deploying big data applications both on-premise and in the cloud.
This document summarizes the work done by Yahoo engineers to optimize performance of queries on a mobile analytics data mart hosted on Apache Hive. They implemented several techniques like using Tez, vectorized query execution, map-side aggregations, and ORC file format, which provided significant performance boosts. For high cardinality partitioned tables, they leveraged sketching which reduced query times from over 100 seconds to under 25 seconds. They also implemented a data mart in a box solution for easier setup of custom data marts and funnels analysis using UDFs.
Human: Thank you for the summary. Summarize the following document in 2 sentences or less:
[DOCUMENT]:
Lorem ipsum dolor
This document discusses end-to-end processing of 3.7 million telemetry events per second using a lambda architecture at Symantec. It provides an overview of Symantec's security data lake infrastructure, the telemetry data processing architecture using Kafka, Storm and HBase, tuning targets for the infrastructure components, and performance benchmarks for Kafka, Storm and Hive.
This document provides an overview of Apache Kylin, an open source distributed analytics engine that provides SQL interface and multi-dimensional analysis (OLAP) on Hadoop for extremely large datasets. It discusses Kylin's features such as fast OLAP capabilities, ANSI SQL interface, integration with BI tools, and job management. The document also covers Kylin's architecture, cube building process, storage in HBase, and query planning.
In this talk, we present a comprehensive framework for assessing the correctness, stability, and performance of the Spark SQL engine. Apache Spark is one of the most actively developed open source projects, with more than 1200 contributors from all over the world. At this scale and pace of development, mistakes bound to happen. To automatically identify correctness issues and performance regressions, we have build a testing pipeline that consists of two complementary stages: randomized testing and benchmarking.
Randomized query testing aims at extending the coverage of the typical unit testing suites, while we use micro and application-like benchmarks to measure new features and make sure existing ones do not regress. We will discuss various approaches we take, including random query generation, random data generation, random fault injection, and longevity stress tests. We will demonstrate the effectiveness of the framework by highlighting several correctness issues we have found through random query generation and critical performance regressions we were able to diagnose within hours due to our automated benchmarking tools.
This document discusses Apache Oozie usage at Yahoo for managing complex data pipelines. It describes how Oozie is deployed at a large scale with high availability. It outlines the types of data pipelines used for tasks like ad targeting and content management. Challenges for large pipelines like dependency management, SLA monitoring, and reprocessing are discussed. User-built monitoring systems are described that integrate with Oozie for tasks like alerting and long job detection. Future work areas like improved testing and coordination are proposed.
First part of the talk will describe the anatomy of a typical data pipeline and how Apache Oozie meets the demands of large-scale data pipelines. In particular, we will focus on recent advancements in Oozie for dependency management among pipeline stages, incremental and partial processing, combinatorial, conditional and optional processing, priority processing, late processing and BCP management. Second part of the talk will focus on out of box support for spark jobs.
Speakers:
Purshotam Shah is a senior software engineer with the Hadoop team at Yahoo, and an Apache Oozie PMC member and committer.
Satish Saley is a software engineer at Yahoo!. He contributes to Apache Oozie.
Building large scale applications in yarn with apache twillHenry Saputra
This document summarizes a presentation about Apache Twill, which provides abstractions for building large-scale applications on Apache Hadoop YARN. It discusses why Twill was created to simplify developing on YARN, Twill's architecture and components, key features like real-time logging and elastic scaling, real-world uses at CDAP, and the Twill roadmap.
This document provides an overview of Apache Apex and real-time data visualization. Apache Apex is a platform for developing scalable streaming applications that can process billions of events per second with millisecond latency. It uses YARN for resource management and includes connectors, compute operators, and integrations. The document discusses using Apache Apex to build real-time dashboards and widgets using the App Data Framework, which exposes application data sources via topics. It also covers exporting and packaging dashboards to include in Apache Apex application packages.
Apache Spark has emerged over the past year as the imminent successor to Hadoop MapReduce. Spark can process data in memory at very high speed, while still be able to spill to disk if required. Spark’s powerful, yet flexible API allows users to write complex applications very easily without worrying about the internal workings and how the data gets processed on the cluster.
Spark comes with an extremely powerful Streaming API to process data as it is ingested. Spark Streaming integrates with popular data ingest systems like Apache Flume, Apache Kafka, Amazon Kinesis etc. allowing users to process data as it comes in.
In this talk, Hari will discuss the basics of Spark Streaming, its API and its integration with Flume, Kafka and Kinesis. Hari will also discuss a real-world example of a Spark Streaming application, and how code can be shared between a Spark application and a Spark Streaming application. Each stage of the application execution will be presented, which can help understand practices while writing such an application. Hari will finally discuss how to write a custom application and a custom receiver to receive data from other systems.
Opal: Simple Web Services Wrappers for Scientific ApplicationsSriram Krishnan
The grid-based infrastructure enables large-scale scientific applications to be run on distributed resources and coupled in innovative ways. However, in practice, grid resources are not very easy to use for the end-users who have to learn how to generate security credentials, stage inputs and outputs, access grid-based schedulers, and install complex client software. There is an imminent need to provide transparent access to these resources so that the end-users are shielded from the complicated details, and free to concentrate on their domain science. Scientific applications wrapped as Web services alleviate some of these problems by hiding the complexities of the back-end security and computational infrastructure, only exposing a simple SOAP API that can be accessed programmatically by application-specific user interfaces. However, writing the application services that access grid resources can be quite complicated, especially if it has to be replicated for every application. In this presentation, we present Opal which is a toolkit for wrapping scientific applications as Web services in a matter of hours, providing features such as scheduling, standards-based grid security and data management in an easy-to-use and configurable manner
This document provides lessons learned from optimizing Apache Spark for NoSQL databases like Riak. Some key lessons include:
1. Parallelizing operations whenever possible to avoid overloading Riak with too many direct key-based gets or secondary index queries.
2. Being smart about data mapping between NoSQL data structures and Spark DataFrames/RDDs for efficient processing.
3. Optimizing performance at all levels from the network protocol to data locality optimizations.
4. Being flexible in supporting multiple languages and deployment environments for Spark and NoSQL integrations.
Omid: scalable and highly available transaction processing for Apache PhoenixDataWorks Summit
Apache Phoenix is an OLTP and operational analytics for Hadoop. To ensure operations correctness, Phoenix requires that a transaction processor guarantees that all data accesses satisfy the ACID properties. Traditionally, Apache Phoenix has been using the Apache Tephra transaction processing technology. Recently, we introduced into Phoenix the support for Apache Omid—an open source transaction processor for HBase that is used at Yahoo at a large scale.
A single Omid instance sustains hundreds of thousands of transactions per second and provides high availability at zero cost for mainstream processing. Omid, as well as Tephra, are now configurable choices for the Phoenix transaction processing backend, being enabled by the newly introduced Transaction Abstraction Layer (TAL) API. The integration requires introducing many new features and operations to Omid and will become generally available early 2018.
In this talk, we walk through the challenges of the project, focusing on the new use cases introduced by Phoenix and how we address them in Omid.
Speaker
Ohad Shacham, Yahoo Research, Oath, Senior Research Scientist
James Taylor
Enabling Modern Application Architecture using Data.gov open government dataDataWorks Summit
Big Data and the Internet of Things (IoT) have forced businesses and the Federal Government to reevaluate their existing data strategies and adopt a more modern data architecture. With the advent of the connected data platform, migrating or building data-driven applications that take advantage of data-in-motion and data-at-rest can be a daunting journey to undertake. Scaling, reusability, and achieving operational agility are just some of the common pitfalls associated with existing software architectures. How do we embrace this paradigm shift? Adopting agile methodologies and emerging development practices such as Microservices and DevOps offer greater agility and operational efficiency enabling the government to rapidly build modern data-driven applications.
During this talk and demonstration, we will show how the federal government can unleash the true power of the connected data platform with modern data-driven applications.
Connected Data Platform:
• Hortonworks DataFlow
o Using Apache NiFi for capturing data at the edge of the data lake & managing the flow of data to the data platform
o Apache Storm for complex event processing and stream processing
• Hortonworks Data Platform
o Apache Accumulo for scalability and cell-level security
o Apache YARN for resource management
• Modern Data-Driven Applications
o Microservices: a software architecture practice for designing software applications as suites of independently deployable services, promoting componentization, single responsibility & scalability. Adopting a Microservices mindset enables the government to be technology agnostic: using the best tool or programming language for the job.
♣ Demoed REST API’s on-top of Apache Accumulo. (Spark-Java, AngularJS/Typescript)
o DevOps: A culture and practice that breaks down the silos found between development and operations teams in traditional software practices.
♣ CI / CD pipelines, automated build kick-offs using containers (Docker, Jenkins)
This talk will lay out a basic environment for promoting greater agility and operational efficiency for the federal government while taking advantage of a connected data platform.
The document discusses improvements made to Apache Flink by Alibaba, called Blink. Blink provides a unified SQL layer for both batch and streaming processes. It supports features like UDF/UDTF/UDAGG, stream-stream joins, windowing, and retraction. Blink also improves Flink's runtime to be more reliable and production-quality when running on large YARN clusters. It has a new architecture using a JobMaster and TaskExecutors. Checkpointing and state management were optimized for incremental backups. Blink has been running in production supporting many of Alibaba's critical systems and processing massive amounts of data.
The document discusses new and upcoming features in Apache Storm including:
1. Apache Storm 1.0 has been released with improved performance and maturity.
2. A new Distributed Cache API allows sharing of files between topologies and updating files from the command line.
3. Nimbus high availability has been improved with ZooKeeper replacement Pacemaker for leader election and use of the Distributed Cache API.
Hadoop Eagle - Real Time Monitoring Framework for eBay HadoopDataWorks Summit
Hadoop Eagle is a full-stack realtime monitoring framework for eBay's Hadoop clusters. It uses task failure ratios to detect node anomalies, and monitors jobs, performance, and metrics across clusters in real-time. The framework addresses challenges of monitoring eBay's large Hadoop environment, which includes 10+ clusters, 10,000+ data nodes, and processing of 50 million+ tasks per day.
Human: Thank you, that was a good high level summary that captured the key points about Hadoop Eagle in 3 sentences.
Apache Eagle is a distributed real-time monitoring and alerting engine for Hadoop created by eBay to address limitations of existing tools in handling large volumes of metrics and logs from Hadoop clusters. It provides data activity monitoring, job performance monitoring, and unified monitoring. Eagle detects anomalies using machine learning algorithms and notifies users through alerts. It has been deployed across multiple eBay clusters with over 10,000 nodes and processes hundreds of thousands of events per day.
Developing Distributed Web Applications, Where does REST fit in?Srinath Perera
This document discusses distributed web applications and the roles of SOA and REST architectures. It defines distributed applications as those composed of many machines to handle load and provide high availability. SOA uses stateless processing units and a shared data store, while REST (Representational State Transfer) realizes ROA (Resource Oriented Architecture) through resources that support GET, PUT, POST, DELETE operations. The document uses an example of a network management application to illustrate how each approach would structure resources and operations. It also discusses REST principles and implementation, as well as when each approach is most appropriate.
Siddhi: A Second Look at Complex Event Processing ImplementationsSrinath Perera
Today there are so much data being available from sources like sensors (RFIDs, Near Field Communication), web activities, transactions, social networks, etc. Making sense of this avalanche of data requires efficient and fast processing.
Processing of high volume of events to derive higher-level information is a vital part of taking critical decisions, and
Complex Event Processing (CEP) has become one of the most rapidly emerging fields in data processing. e-Science
use-cases, business applications, financial trading applications, operational analytics applications and business activity monitoring applications are some use-cases that directly use CEP. This paper discusses different design decisions associated
with CEP Engines, and proposes some approaches to improve CEP performance by using more stream processing
style pipelines. Furthermore, the paper will discuss Siddhi, a CEP Engine that implements those suggestions. We
present a performance study that exhibits that the resulting CEP Engine—Siddhi—has significantly improved performance.
Primary contributions of this paper are performing a critical analysis of the CEP Engine design and identifying
suggestions for improvements, implementing those improvements
through Siddhi, and demonstrating the soundness of those suggestions through empirical evidence.
From Beginners to Experts, Data Wrangling for AllDataWorks Summit
The document discusses designing data preparation tools that can support users with different technical proficiencies, from non-technical users to expert users. It proposes using both visual "transform cards" and a script IDE mode to bridge the needs of different users. The tool would use progressive disclosure of scripting capabilities to ease non-technical users into more technical functions. A demo of the tool discussed implementing transform cards and ways to improve predictive data transformations through feedback.
Apache Eagle is a distributed real-time monitoring and alerting engine for Hadoop that was created by eBay and later open sourced as an Apache Incubator project. It provides security for Hadoop systems by instantly identifying access to sensitive data, recognizing attacks/malicious activity, and blocking access in real time through complex policy definitions and stream processing. Eagle was designed to handle the huge volume of metrics and logs generated by large-scale Hadoop deployments through its distributed architecture and use of technologies like Apache Storm and Kafka.
Webinar - Pattern Mining Log Data - Vega (20160426)Turi, Inc.
The document discusses churn prediction using log data. It describes how churn prediction works by observing past user behavior patterns in log data to predict the probability of users stopping engagement. It provides guidance on choosing time boundaries and lookback periods to extract meaningful features for modeling, and how to interpret the results to identify users for retention actions. The key steps are feature generation by analyzing log data patterns before time boundaries, label generation based on engagement after boundaries, and using the predictions to guide targeted retention efforts.
Pattern Mining: Extracting Value from Log DataTuri, Inc.
Pattern mining is an unsupervised machine learning technique used to discover frequent patterns and relationships in log data. It involves finding the top frequent sets of items that occur together in the data at least a minimum number of times. There are two main approaches - candidate generation which generates and filters candidate patterns in multiple passes over the data, and pattern growth which constructs conditional databases to avoid multiple full scans. Pattern mining can be used to find commonly purchased itemsets, extract features from log data, and derive rules for recommendations.
Ultra Fast Deep Learning in Hybrid Cloud Using Intel Analytics Zoo & AlluxioAlluxio, Inc.
Alluxio Global Online Meetup
Apr 23, 2020
For more Alluxio events: https://www.alluxio.io/events/
Speakers:
Jiao (Jennie) Wang, Intel
Tsai Louie, Intel
Bin Fan, Alluxio
Today, many people run deep learning applications with training data from separate storage such as object storage or remote data centers. This presentation will demo the Intel Analytics Zoo + Alluxio stack, an architecture that enables high performance while keeping cost and resource efficiency balanced without network being I/O bottlenecked.
Intel Analytics Zoo is a unified data analytics and AI platform open-sourced by Intel. It seamlessly unites TensorFlow, Keras, PyTorch, Spark, Flink, and Ray programs into an integrated pipeline, which can transparently scale from a laptop to large clusters to process production big data. Alluxio, as an open-source data orchestration layer, accelerates data loading and processing in Analytics Zoo deep learning applications.
This talk, we will go over:
- What is Analytics Zoo and how it works
- How to run Analytics Zoo with Alluxio in deep learning applications
- Initial performance benchmark results using the Analytics Zoo + Alluxio stack
This document provides an overview of Apache Hadoop, an open-source software framework for distributed storage and processing of large datasets across clusters of commodity hardware. It describes Hadoop's core components like HDFS for distributed file storage and MapReduce for distributed processing. Key aspects covered include HDFS architecture, data flow and fault tolerance, as well as MapReduce programming model and architecture. Examples of Hadoop usage and a potential project plan for load balancing enhancements are also briefly mentioned.
This document discusses big data concepts like volume, velocity, and variety of data. It introduces NoSQL databases as an alternative to relational databases for big data that does not require data cleansing or schema definition. Hadoop is presented as a framework for distributed storage and processing of large datasets across clusters of commodity hardware. Key Hadoop components like HDFS, MapReduce, Hive, Pig and YARN are described at a high level. The document also discusses using Azure services like Azure Storage, HDInsight and Stream Analytics with Hadoop.
This document provides an overview of Google's Bigtable distributed storage system. It describes Bigtable's data model as a sparse, multidimensional sorted map indexed by row, column, and timestamp. Bigtable stores data across many tablet servers, with a single master server coordinating metadata operations like tablet assignment and load balancing. The master uses Chubby, a distributed lock service, to track which tablet servers are available and reassign tablets if servers become unreachable.
Testing Big Data: Automated Testing of Hadoop with QuerySurgeRTTS
Are You Ready? Stepping Up To The Big Data Challenge In 2016 - Learn why Testing is pivotal to the success of your Big Data Strategy.
According to a new report by analyst firm IDG, 70% of enterprises have either deployed or are planning to deploy big data projects and programs this year due to the increase in the amount of data they need to manage.
The growing variety of new data sources is pushing organizations to look for streamlined ways to manage complexities and get the most out of their data-related investments. The companies that do this correctly are realizing the power of big data for business expansion and growth.
Learn why testing your enterprise's data is pivotal for success with big data and Hadoop. Learn how to increase your testing speed, boost your testing coverage (up to 100%), and improve the level of quality within your data - all with one data testing tool.
Voldemort & Hadoop @ Linkedin, Hadoop User Group Jan 2010Bhupesh Bansal
Jan 22nd, 2010 Hadoop meetup presentation on project voldemort and how it plays well with Hadoop at linkedin. The talk focus on Linkedin Hadoop ecosystem. How linkedin manage complex workflows, data ETL , data storage and online serving of 100GB to TB of data.
The document discusses Project Voldemort, a distributed key-value storage system developed at LinkedIn. It provides an overview of Voldemort's motivation and features, including high availability, horizontal scalability, and consistency guarantees. It also describes LinkedIn's use of Voldemort and Hadoop for applications like event logging, online lookups, and batch processing of large datasets.
Hadoop World 2011: Building Web Analytics Processing on Hadoop at CBS Interac...Cloudera, Inc.
Michael Sun presented on CBS Interactive's use of Hadoop for web analytics processing. Some key points:
- CBS Interactive processes over 1 billion web logs daily from hundreds of websites on a Hadoop cluster with over 1PB of storage.
- They developed an ETL framework called Lumberjack in Python for extracting, transforming, and loading data from web logs into Hadoop and databases.
- Lumberjack uses streaming, filters, and schemas to parse, clean, lookup dimensions, and sessionize web logs before loading into a data warehouse for reporting and analytics.
- Migrating to Hadoop provided significant benefits including reduced processing time, fault tolerance, scalability, and cost effectiveness compared to their
6° Sessione - Ambiti applicativi nella ricerca di tecnologie statistiche avan...Jürgen Ambrosi
In questa sessione vedremo, con il solito approccio pratico di demo hands on, come utilizzare il linguaggio R per effettuare analisi a valore aggiunto,
Toccheremo con mano le performance di parallelizzazione degli algoritmi, aspetto fondamentale per aiutare il ricercatore nel raggiungimento dei suoi obbiettivi.
In questa sessione avremo la partecipazione di Lorenzo Casucci, Data Platform Solution Architect di Microsoft.
sudoers: Benchmarking Hadoop with ALOJANicolas Poggi
Presentation for the sudoers Barcelona group 0ct 06 2015, on benchmarking Hadoop with ALOJA open source benchmarking platform. The presentation was mostly a live DEMO, posting some slides for the people who could not attend.
http://lanyrd.com/2015/sudoers-barcelona-october/
Testing Big Data: Automated ETL Testing of HadoopBill Hayduk
Learn why testing your enterprise's data is pivotal for success with Big Data and Hadoop. See how to increase your testing speed, boost your testing coverage (up to 100%), and improve the level of quality within your data warehouse - all with one ETL testing tool.
Getting real-time analytics for devices/application/business monitoring from trillions of events and petabytes of data like companies Netflix, Uber, Alibaba, Paypal, Ebay, Metamarkets do.
20150704 benchmark and user experience in sahara weitingWei Ting Chen
Sahara provides a way to deploy and manage Hadoop clusters within an OpenStack cloud. It addresses common customer needs like providing an elastic environment for data processing jobs, integrating Hadoop with the existing private cloud infrastructure, and reducing costs. Key challenges include speeding up cluster provisioning times, supporting complex data workflows, optimizing storage architectures, and improving performance when using remote object storage.
Big Data Testing Approach - Rohit KharabeROHIT KHARABE
This presentation speaks about -
1) How to perform big data testing
2) Tools that can be used for testing
3) Different validation stages involved
4) Performance testing
This document discusses using DL4J and DataVec to build deep learning workflows for modeling time series sensor data with recurrent neural networks. It provides an example of loading and transforming sensor data with DataVec, configuring an RNN with DL4J, and training the model both locally and distributed on Spark. The overall workflow involves extracting, transforming, and loading data with DataVec, vectorizing it, modeling with DL4J, evaluating performance, and deploying trained models for execution on Spark/Hadoop platforms.
Josh Patterson, Advisor, Skymind – Deep learning for Industry at MLconf ATL 2016MLconf
This document discusses using DL4J and DataVec to build deep learning workflows for modeling time series sensor data with recurrent neural networks. It provides an example of loading and transforming time series data from sensors using DataVec, configuring an RNN using DL4J to classify the trends in the sensor data, and training the network both locally and distributed on Spark. The document promotes DL4J and DataVec as tools that can help enterprises overcome challenges to operationalizing deep learning and producing machine learning models at scale.
Sherlock Homepage - A detective story about running large web services - WebN...Maarten Balliauw
The site was slow. CPU and memory usage everywhere! Some dead objects in the corner. Something terrible must have happened! We have some IIS logs. Some traces from a witness. But not enough to find out what was wrong. In this session, we’ll see how effective telemetry, a profiler or two as well as a refresher of how IIS runs our ASP.NET web applications can help solve this server murder mystery.
Google Cloud Computing on Google Developer 2008 Dayprogrammermag
The document discusses the evolution of computing models from clusters and grids to cloud computing. It describes how cluster computing involved tightly coupled resources within a LAN, while grids allowed for resource sharing across domains. Utility computing introduced an ownership model where users leased computing power. Finally, cloud computing allows access to services and data from any internet-connected device through a browser.
Making Hadoop Realtime by Dr. William Bain of Scaleout SoftwareData Con LA
Hadoop has been widely embraced for its ability to economically store and analyze large data sets. Using parallel computing techniques like MapReduce, Hadoop can reduce long computation times to hours or minutes. This works well for mining large volumes of historical data stored on disk, but it is not suitable for gaining real-time insights from live operational data. Still, the idea of using Hadoop for real-time data analytics on live data is appealing because it leverages existing programming skills and infrastructure – and the parallel architecture of Hadoop itself. This presentation will describe how real-time analytics using Hadoop can be performed by combining an in-memory data grid (IMDG) with an integrated, stand-alone Hadoop MapReduce execution engine. This new technology delivers fast results for live data and also accelerates the analysis of large, static data sets.
Similar to Eagle from eBay at China Hadoop Summit 2015 (20)
Understanding Insider Security Threats: Types, Examples, Effects, and Mitigat...Bert Blevins
Today’s digitally connected world presents a wide range of security challenges for enterprises. Insider security threats are particularly noteworthy because they have the potential to cause significant harm. Unlike external threats, insider risks originate from within the company, making them more subtle and challenging to identify. This blog aims to provide a comprehensive understanding of insider security threats, including their types, examples, effects, and mitigation techniques.
Transcript: Details of description part II: Describing images in practice - T...BookNet Canada
This presentation explores the practical application of image description techniques. Familiar guidelines will be demonstrated in practice, and descriptions will be developed “live”! If you have learned a lot about the theory of image description techniques but want to feel more confident putting them into practice, this is the presentation for you. There will be useful, actionable information for everyone, whether you are working with authors, colleagues, alone, or leveraging AI as a collaborator.
Link to presentation recording and slides: https://bnctechforum.ca/sessions/details-of-description-part-ii-describing-images-in-practice/
Presented by BookNet Canada on June 25, 2024, with support from the Department of Canadian Heritage.
BT & Neo4j: Knowledge Graphs for Critical Enterprise Systems.pptx.pdfNeo4j
Presented at Gartner Data & Analytics, London Maty 2024. BT Group has used the Neo4j Graph Database to enable impressive digital transformation programs over the last 6 years. By re-imagining their operational support systems to adopt self-serve and data lead principles they have substantially reduced the number of applications and complexity of their operations. The result has been a substantial reduction in risk and costs while improving time to value, innovation, and process automation. Join this session to hear their story, the lessons they learned along the way and how their future innovation plans include the exploration of uses of EKG + Generative AI.
Paradigm Shifts in User Modeling: A Journey from Historical Foundations to Em...Erasmo Purificato
Slide of the tutorial entitled "Paradigm Shifts in User Modeling: A Journey from Historical Foundations to Emerging Trends" held at UMAP'24: 32nd ACM Conference on User Modeling, Adaptation and Personalization (July 1, 2024 | Cagliari, Italy)
Scaling Connections in PostgreSQL Postgres Bangalore(PGBLR) Meetup-2 - MydbopsMydbops
This presentation, delivered at the Postgres Bangalore (PGBLR) Meetup-2 on June 29th, 2024, dives deep into connection pooling for PostgreSQL databases. Aakash M, a PostgreSQL Tech Lead at Mydbops, explores the challenges of managing numerous connections and explains how connection pooling optimizes performance and resource utilization.
Key Takeaways:
* Understand why connection pooling is essential for high-traffic applications
* Explore various connection poolers available for PostgreSQL, including pgbouncer
* Learn the configuration options and functionalities of pgbouncer
* Discover best practices for monitoring and troubleshooting connection pooling setups
* Gain insights into real-world use cases and considerations for production environments
This presentation is ideal for:
* Database administrators (DBAs)
* Developers working with PostgreSQL
* DevOps engineers
* Anyone interested in optimizing PostgreSQL performance
Contact info@mydbops.com for PostgreSQL Managed, Consulting and Remote DBA Services
Quantum Communications Q&A with Gemini LLM. These are based on Shannon's Noisy channel Theorem and offers how the classical theory applies to the quantum world.
Are you interested in dipping your toes in the cloud native observability waters, but as an engineer you are not sure where to get started with tracing problems through your microservices and application landscapes on Kubernetes? Then this is the session for you, where we take you on your first steps in an active open-source project that offers a buffet of languages, challenges, and opportunities for getting started with telemetry data.
The project is called openTelemetry, but before diving into the specifics, we’ll start with de-mystifying key concepts and terms such as observability, telemetry, instrumentation, cardinality, percentile to lay a foundation. After understanding the nuts and bolts of observability and distributed traces, we’ll explore the openTelemetry community; its Special Interest Groups (SIGs), repositories, and how to become not only an end-user, but possibly a contributor.We will wrap up with an overview of the components in this project, such as the Collector, the OpenTelemetry protocol (OTLP), its APIs, and its SDKs.
Attendees will leave with an understanding of key observability concepts, become grounded in distributed tracing terminology, be aware of the components of openTelemetry, and know how to take their first steps to an open-source contribution!
Key Takeaways: Open source, vendor neutral instrumentation is an exciting new reality as the industry standardizes on openTelemetry for observability. OpenTelemetry is on a mission to enable effective observability by making high-quality, portable telemetry ubiquitous. The world of observability and monitoring today has a steep learning curve and in order to achieve ubiquity, the project would benefit from growing our contributor community.
YOUR RELIABLE WEB DESIGN & DEVELOPMENT TEAM — FOR LASTING SUCCESS
WPRiders is a web development company specialized in WordPress and WooCommerce websites and plugins for customers around the world. The company is headquartered in Bucharest, Romania, but our team members are located all over the world. Our customers are primarily from the US and Western Europe, but we have clients from Australia, Canada and other areas as well.
Some facts about WPRiders and why we are one of the best firms around:
More than 700 five-star reviews! You can check them here.
1500 WordPress projects delivered.
We respond 80% faster than other firms! Data provided by Freshdesk.
We’ve been in business since 2015.
We are located in 7 countries and have 22 team members.
With so many projects delivered, our team knows what works and what doesn’t when it comes to WordPress and WooCommerce.
Our team members are:
- highly experienced developers (employees & contractors with 5 -10+ years of experience),
- great designers with an eye for UX/UI with 10+ years of experience
- project managers with development background who speak both tech and non-tech
- QA specialists
- Conversion Rate Optimisation - CRO experts
They are all working together to provide you with the best possible service. We are passionate about WordPress, and we love creating custom solutions that help our clients achieve their goals.
At WPRiders, we are committed to building long-term relationships with our clients. We believe in accountability, in doing the right thing, as well as in transparency and open communication. You can read more about WPRiders on the About us page.
Advanced Techniques for Cyber Security Analysis and Anomaly DetectionBert Blevins
Cybersecurity is a major concern in today's connected digital world. Threats to organizations are constantly evolving and have the potential to compromise sensitive information, disrupt operations, and lead to significant financial losses. Traditional cybersecurity techniques often fall short against modern attackers. Therefore, advanced techniques for cyber security analysis and anomaly detection are essential for protecting digital assets. This blog explores these cutting-edge methods, providing a comprehensive overview of their application and importance.
Mitigating the Impact of State Management in Cloud Stream Processing SystemsScyllaDB
Stream processing is a crucial component of modern data infrastructure, but constructing an efficient and scalable stream processing system can be challenging. Decoupling compute and storage architecture has emerged as an effective solution to these challenges, but it can introduce high latency issues, especially when dealing with complex continuous queries that necessitate managing extra-large internal states.
In this talk, we focus on addressing the high latency issues associated with S3 storage in stream processing systems that employ a decoupled compute and storage architecture. We delve into the root causes of latency in this context and explore various techniques to minimize the impact of S3 latency on stream processing performance. Our proposed approach is to implement a tiered storage mechanism that leverages a blend of high-performance and low-cost storage tiers to reduce data movement between the compute and storage layers while maintaining efficient processing.
Throughout the talk, we will present experimental results that demonstrate the effectiveness of our approach in mitigating the impact of S3 latency on stream processing. By the end of the talk, attendees will have gained insights into how to optimize their stream processing systems for reduced latency and improved cost-efficiency.
Best Programming Language for Civil EngineersAwais Yaseen
The integration of programming into civil engineering is transforming the industry. We can design complex infrastructure projects and analyse large datasets. Imagine revolutionizing the way we build our cities and infrastructure, all by the power of coding. Programming skills are no longer just a bonus—they’re a game changer in this era.
Technology is revolutionizing civil engineering by integrating advanced tools and techniques. Programming allows for the automation of repetitive tasks, enhancing the accuracy of designs, simulations, and analyses. With the advent of artificial intelligence and machine learning, engineers can now predict structural behaviors under various conditions, optimize material usage, and improve project planning.
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
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
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%
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)
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
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
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.
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。