Eventtypes allow you to categorize events at search time based on search definitions. For example, defining an eventtype called "problem" that includes terms like "error", would tag any events containing those terms as eventtype="problem". This provides a dynamic way to tag events without modifying the raw data. Reports in Splunk display search results in a formatted view like a table or chart and can be placed on dashboards. Apps are collections of Splunk configurations and code that allow you to customize your Splunk environment for specific use cases.
Fuze Insight is a free web based platform, used to manage real-life business processes alongside the Internet of Things (IoT). It provides many customisation opportunities and incorporates both scripting for custom logic and the facility to extend the user interface using Java and the Vaadin framework. This document contains information about the standard administration of the product after installation.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive functioning. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive function. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms for those who already suffer from conditions like anxiety and depression.
25th Bi-annual Meet held by Amity School of Distance Learning (ASoDL)
Amity School of Distance Learning held its 25th Biannual Meet at Amity Campus, Sector – 44, Noida on 11th March 2012. The meet witnessed heavy crowd which further indicated the importance of distance education not only for freshers but also for the professionals.
The occasion was graced by Prof. M Aslam, Vice Chancellor, IGNOU who expressed his happiness at the standard of presentation and meeting with the students of Amity School of Distance Learning. He gave the relevance of distance education and the necessity of hard work. Welcoming the new entrants, Maj Gen (Dr.) Surendar Kumar, Chairman-ASoDL inspired them for “aiming high in life, working hard, keeping their spirits high and never giving up”. Other dignitaries from the Industry like Mr. Adarsh Agarwal, CEO, I-energizer Noida, Mr. Sekhar Sahay, Director (HR) Ericsson and Mr. N P S Rana, Vice President (HR) Motherson Sumi System Ltd., also motivated the students to work hard and work smart for achieving success in life.
Prof. (Col.) Rajive Kohli Ph. D, Director-ASoDL, Mr. Abhinash Kumar Dy. Director, Mr. Harish Kumar Dy. Director (Academics) and Mr. Alok Awtans Asst. Director apprised the students about the conduct of distance learning programmes to enable them to successfully complete their chosen programme.
The document discusses silos in organizations and how they hinder communication and coordination. A silo refers to a management system that cannot exchange information with related internal or external systems. The document also introduces the concept of "soft wiring" which refers to connections between people through social and official networks. It notes that organizations still struggle with coordination between different divisions like functions, business units, geographic offices, and job ranks due to lingering silos.
Fuze Insight is a free web based platform, used to manage real-life business processes alongside the Internet of Things (IoT). It provides many customisation opportunities and incorporates both scripting for custom logic and the facility to extend the user interface using Java and the Vaadin framework. This document contains information about the standard administration of the product after installation.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive functioning. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive function. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms for those who already suffer from conditions like anxiety and depression.
25th Bi-annual Meet held by Amity School of Distance Learning (ASoDL)
Amity School of Distance Learning held its 25th Biannual Meet at Amity Campus, Sector – 44, Noida on 11th March 2012. The meet witnessed heavy crowd which further indicated the importance of distance education not only for freshers but also for the professionals.
The occasion was graced by Prof. M Aslam, Vice Chancellor, IGNOU who expressed his happiness at the standard of presentation and meeting with the students of Amity School of Distance Learning. He gave the relevance of distance education and the necessity of hard work. Welcoming the new entrants, Maj Gen (Dr.) Surendar Kumar, Chairman-ASoDL inspired them for “aiming high in life, working hard, keeping their spirits high and never giving up”. Other dignitaries from the Industry like Mr. Adarsh Agarwal, CEO, I-energizer Noida, Mr. Sekhar Sahay, Director (HR) Ericsson and Mr. N P S Rana, Vice President (HR) Motherson Sumi System Ltd., also motivated the students to work hard and work smart for achieving success in life.
Prof. (Col.) Rajive Kohli Ph. D, Director-ASoDL, Mr. Abhinash Kumar Dy. Director, Mr. Harish Kumar Dy. Director (Academics) and Mr. Alok Awtans Asst. Director apprised the students about the conduct of distance learning programmes to enable them to successfully complete their chosen programme.
The document discusses silos in organizations and how they hinder communication and coordination. A silo refers to a management system that cannot exchange information with related internal or external systems. The document also introduces the concept of "soft wiring" which refers to connections between people through social and official networks. It notes that organizations still struggle with coordination between different divisions like functions, business units, geographic offices, and job ranks due to lingering silos.
The document outlines the salmon fishery regulations for Bristol Bay, Alaska. It identifies the commercial fishing districts and management plans that dictate criteria like trigger points, mesh restrictions, and allocation between gear types. Specific provisions cover registration requirements, a Wood River special harvest area plan, and allowances for dual permit operation.
The document describes an Irish dancer who was initially shy but gained confidence through practice. They earned money through dancing and became close friends with another dancer named Samantha, who fell in love with them. The two dancers had the idea to perform on America's Got Talent, where they did two traditional Irish dances to loud cheers. While Samantha was talented, the crowd responded more enthusiastically to the other dancer's performance, making Samantha jealous. The judges voted in their favor, allowing the dancers to move forward in the competition.
What is Risk? - lightning talk for software testers (2011)Neil Thompson
Software Testing 21 Jun 2011
The document summarizes a presentation given at the Specialist Interest Group in Software Testing (SIGiST) on June 21, 2011 about software risk. The presentation discusses what risk is, how it can be quantified using likelihood and consequence, and that risk has other dimensions like undetectability and urgency that make it worse. It provides examples of different types of risks like project, process, and product risks. It introduces the idea of a "tunable tetrahedron" to represent the interconnected factors of quality, scope, time, and cost. Finally, it discusses using likelihood, consequence, and testability to prioritize product risks for testing.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive functioning. Exercise boosts blood flow, releases endorphins, and promotes changes in the brain which help regulate emotions and stress levels.
The document proposes a pitch for a thriller film sequence that follows conventions of the genre. It will tell the story of a woman who is abducted by a masked man demanding to know where her husband hid his property. The sequence aims to create suspense and uncertainty in the audience by entering the narrative at the point of disruption, when the victim wakes up in an unknown place. Character will be limited to the introduced victim and an antagonist whose identity is not yet revealed through the end of the proposed sequence. The sequence aims to explore themes of obsession, horror of personality, realism, and the human subconscious primarily through sound, mise-en-scene, and lighting effects.
A empresa de tecnologia anunciou um novo smartphone com câmera aprimorada, tela maior e bateria de longa duração por um preço acessível. O dispositivo tem como objetivo atrair mais consumidores em mercados emergentes com suas especificações equilibradas e preço baixo. Analistas esperam que as melhorias e o preço baixo impulsionem as vendas do novo aparelho.
Anwendungsfälle für Elasticsearch JAX 2015Florian Hopf
The document discusses various use cases for Elasticsearch including document storage, full text search, geo search, log file analysis, and analytics. It provides examples of installing Elasticsearch, indexing and retrieving documents, performing searches using query DSL, filtering and sorting results, and aggregating data. Popular users mentioned include GitHub, Stack Overflow, and Microsoft. The presentation aims to demonstrate the flexibility and power of Elasticsearch beyond just full text search.
This document discusses a 39-year-old patient's dental health and silver fillings. X-rays showed no decay on the patient's 3 silver fillings, though slight openings were seen at the edges. However, upon removal, decay was found underneath the fillings in all three teeth, with one tooth fractured through the base. The document warns that silver mercury fillings can cause decay underneath and stress fractures over time, compromising tooth structure, and recommends replacing fillings before symptoms occur to maintain long-term dental health.
W.M.C. Elmo Fernando is an experienced professional with over 30 years of experience in electronics engineering, training, and maintenance. He has held roles such as Director of an engineering institute, maintenance manager, and lecturer. His expertise includes quality management, curriculum development, training delivery, and maintenance of electrical and electronic equipment. He holds qualifications in electronics engineering, programming logic controllers, and is a lead auditor for quality management systems.
The document provides information about training conducted by the 2nd Battalion, 122nd Field Artillery Regiment as part of the XCTC program at Camp Ripley, Minnesota. It discusses how the battalion conducted artillery air assaults which involved sling loading Howitzer cannons onto helicopters to rapidly deploy them. It also describes fire missions conducted including different types of ammunition. Maintenance support from the 634th Brigade Support Battalion is highlighted as enabling the training to continue without disruption.
Avikalp Mishra is seeking experience working with people in need through an organization committed to social causes. He has a Master's degree in Disaster Management and a Bachelor's degree in Agricultural Science. His technical skills include proficiency in Microsoft Office, communication, and conducting surveys. He has internship experience assessing drought conditions, post-disaster needs, and industrial disaster preparedness. His dissertation examined vector-borne diseases in flood-prone border areas of India and Nepal.
More About InnoSeal Systems- Tamper Evident Bag SealerChristy_innoseal
The document provides information about Innoseal Systems' InnoSealer tamper evident bag closure system. It includes specifications for the InnoSealer machine and refill tapes/papers. It describes how the InnoSealer works to provide a tamper evident seal for bags, lists key markets it serves like produce packers and bakeries, and answers common questions about using and maintaining the InnoSealer.
This document provides information about a lesson on adjectives, including:
1. The objectives of identifying types of adjectives, constructing sentences using adjectives, and participating in class recitation.
2. Details about different types of adjectives like articles, proper adjectives, and predicate adjectives.
3. Information on the degrees of comparison for adjectives including positive, comparative, and superlative forms.
4. Exercises for students to practice using adjectives in sentences and a quiz to identify adjectives and their forms of comparison.
This document provides an overview and introduction to Splunk, an enterprise software platform for searching, monitoring, and analyzing machine-generated big data, such as logs, metrics, and events. The agenda covers what Splunk is, how to get started with Splunk including installing and licensing, basic search functionality, creating alerts and dashboards, deployment and integration options to scale Splunk across multiple sites and systems, and resources for support and the Splunk community. Key capabilities highlighted include searching and analyzing structured and unstructured machine data, indexing petabytes of data per day, role-based access controls, high availability, and integrating with third-party systems.
Splunk Training is an adaptable programming logical instrument utilized for looking, and dissecting constant machine-created huge information. It is started as a web crawler for the log documents put away in the foundation. It works with colossal volumes of information to dissect machine-created results and resolve information examination issues with any size. The sources of info are taken in any format(.csv,JSON). They give a wide assortment of administrations to clients like ordering, examining, Mapping, Scheduling. In Layman terms, Splunk can be characterized as pulling information from different frameworks and informational indexes involving keys and indexers continuously and turn machine information (organization, Smartphones, Web administrations, Security) into business values As Data Platform. It is an open stage and Extensible Architecture. Splunk is authorized on the consistent schedule of information volumes and very costly. Splunk utilizes a cloud administration variant called Splunk storm with a yearly membership.
This document provides an overview of Central Log Management at the University of Cape Town. It discusses Splunk and the ELK stack for collecting, analyzing, and monitoring machine data from various sources. Splunk is featured for its collection, search, reporting, and alerting capabilities. The ELK stack deployed at UCT includes Logstash to process logs from firewalls and send them to Elasticsearch for storage and querying in Kibana for visualization. Shipper and indexer configurations are shown for ingesting Palo Alto firewall logs into Elasticsearch.
This document provides an overview of Splunk capabilities including knowledge objects, tags, event types, saved searches, alerts, and the search pipeline. It demonstrates how to use these features to better organize and analyze IT data through examples such as monitoring server activity, detecting suspicious login attempts, and tracking software sales. Advanced searching techniques including comparison operators, stats, and transaction commands are also explained to help users leverage Splunk's powerful search language.
Getting started with Splunk - Break out SessionGeorg Knon
This document provides an overview and getting started guide for Splunk. It discusses what Splunk is for exploring machine data, how to install and start Splunk, add sample data, perform basic searches, create saved searches, alerts and dashboards. It also covers deployment and integration topics like scaling Splunk, distributing searches across data centers, forwarding data to Splunk, and enriching data with lookups. The document recommends resources like the Splunk community for further support.
This document provides an overview and getting started guide for Splunk. It discusses what Splunk is for exploring machine data, how to install and start Splunk, add sample data, perform basic searches, create saved searches, alerts and dashboards. It also covers deployment and integration topics like scaling Splunk, distributing searches across data centers, forwarding data to Splunk, and enriching data with lookups. The document recommends resources like the Splunk community for support.
This document provides an overview of complex event processing (CEP). It defines CEP as treating inputs as events to look for patterns and correlations in order to extract meaning and act on inferred events. CEP is used in logistics, stock markets, and anywhere with a need to find patterns in large amounts of time-based event data. It discusses events, patterns, time windows, temporal reasoning, event definitions, CEP libraries like Drools and Esper, and provides an example of a FedEx tracking application.
The document outlines the salmon fishery regulations for Bristol Bay, Alaska. It identifies the commercial fishing districts and management plans that dictate criteria like trigger points, mesh restrictions, and allocation between gear types. Specific provisions cover registration requirements, a Wood River special harvest area plan, and allowances for dual permit operation.
The document describes an Irish dancer who was initially shy but gained confidence through practice. They earned money through dancing and became close friends with another dancer named Samantha, who fell in love with them. The two dancers had the idea to perform on America's Got Talent, where they did two traditional Irish dances to loud cheers. While Samantha was talented, the crowd responded more enthusiastically to the other dancer's performance, making Samantha jealous. The judges voted in their favor, allowing the dancers to move forward in the competition.
What is Risk? - lightning talk for software testers (2011)Neil Thompson
Software Testing 21 Jun 2011
The document summarizes a presentation given at the Specialist Interest Group in Software Testing (SIGiST) on June 21, 2011 about software risk. The presentation discusses what risk is, how it can be quantified using likelihood and consequence, and that risk has other dimensions like undetectability and urgency that make it worse. It provides examples of different types of risks like project, process, and product risks. It introduces the idea of a "tunable tetrahedron" to represent the interconnected factors of quality, scope, time, and cost. Finally, it discusses using likelihood, consequence, and testability to prioritize product risks for testing.
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive functioning. Exercise boosts blood flow, releases endorphins, and promotes changes in the brain which help regulate emotions and stress levels.
The document proposes a pitch for a thriller film sequence that follows conventions of the genre. It will tell the story of a woman who is abducted by a masked man demanding to know where her husband hid his property. The sequence aims to create suspense and uncertainty in the audience by entering the narrative at the point of disruption, when the victim wakes up in an unknown place. Character will be limited to the introduced victim and an antagonist whose identity is not yet revealed through the end of the proposed sequence. The sequence aims to explore themes of obsession, horror of personality, realism, and the human subconscious primarily through sound, mise-en-scene, and lighting effects.
A empresa de tecnologia anunciou um novo smartphone com câmera aprimorada, tela maior e bateria de longa duração por um preço acessível. O dispositivo tem como objetivo atrair mais consumidores em mercados emergentes com suas especificações equilibradas e preço baixo. Analistas esperam que as melhorias e o preço baixo impulsionem as vendas do novo aparelho.
Anwendungsfälle für Elasticsearch JAX 2015Florian Hopf
The document discusses various use cases for Elasticsearch including document storage, full text search, geo search, log file analysis, and analytics. It provides examples of installing Elasticsearch, indexing and retrieving documents, performing searches using query DSL, filtering and sorting results, and aggregating data. Popular users mentioned include GitHub, Stack Overflow, and Microsoft. The presentation aims to demonstrate the flexibility and power of Elasticsearch beyond just full text search.
This document discusses a 39-year-old patient's dental health and silver fillings. X-rays showed no decay on the patient's 3 silver fillings, though slight openings were seen at the edges. However, upon removal, decay was found underneath the fillings in all three teeth, with one tooth fractured through the base. The document warns that silver mercury fillings can cause decay underneath and stress fractures over time, compromising tooth structure, and recommends replacing fillings before symptoms occur to maintain long-term dental health.
W.M.C. Elmo Fernando is an experienced professional with over 30 years of experience in electronics engineering, training, and maintenance. He has held roles such as Director of an engineering institute, maintenance manager, and lecturer. His expertise includes quality management, curriculum development, training delivery, and maintenance of electrical and electronic equipment. He holds qualifications in electronics engineering, programming logic controllers, and is a lead auditor for quality management systems.
The document provides information about training conducted by the 2nd Battalion, 122nd Field Artillery Regiment as part of the XCTC program at Camp Ripley, Minnesota. It discusses how the battalion conducted artillery air assaults which involved sling loading Howitzer cannons onto helicopters to rapidly deploy them. It also describes fire missions conducted including different types of ammunition. Maintenance support from the 634th Brigade Support Battalion is highlighted as enabling the training to continue without disruption.
Avikalp Mishra is seeking experience working with people in need through an organization committed to social causes. He has a Master's degree in Disaster Management and a Bachelor's degree in Agricultural Science. His technical skills include proficiency in Microsoft Office, communication, and conducting surveys. He has internship experience assessing drought conditions, post-disaster needs, and industrial disaster preparedness. His dissertation examined vector-borne diseases in flood-prone border areas of India and Nepal.
More About InnoSeal Systems- Tamper Evident Bag SealerChristy_innoseal
The document provides information about Innoseal Systems' InnoSealer tamper evident bag closure system. It includes specifications for the InnoSealer machine and refill tapes/papers. It describes how the InnoSealer works to provide a tamper evident seal for bags, lists key markets it serves like produce packers and bakeries, and answers common questions about using and maintaining the InnoSealer.
This document provides information about a lesson on adjectives, including:
1. The objectives of identifying types of adjectives, constructing sentences using adjectives, and participating in class recitation.
2. Details about different types of adjectives like articles, proper adjectives, and predicate adjectives.
3. Information on the degrees of comparison for adjectives including positive, comparative, and superlative forms.
4. Exercises for students to practice using adjectives in sentences and a quiz to identify adjectives and their forms of comparison.
This document provides an overview and introduction to Splunk, an enterprise software platform for searching, monitoring, and analyzing machine-generated big data, such as logs, metrics, and events. The agenda covers what Splunk is, how to get started with Splunk including installing and licensing, basic search functionality, creating alerts and dashboards, deployment and integration options to scale Splunk across multiple sites and systems, and resources for support and the Splunk community. Key capabilities highlighted include searching and analyzing structured and unstructured machine data, indexing petabytes of data per day, role-based access controls, high availability, and integrating with third-party systems.
Splunk Training is an adaptable programming logical instrument utilized for looking, and dissecting constant machine-created huge information. It is started as a web crawler for the log documents put away in the foundation. It works with colossal volumes of information to dissect machine-created results and resolve information examination issues with any size. The sources of info are taken in any format(.csv,JSON). They give a wide assortment of administrations to clients like ordering, examining, Mapping, Scheduling. In Layman terms, Splunk can be characterized as pulling information from different frameworks and informational indexes involving keys and indexers continuously and turn machine information (organization, Smartphones, Web administrations, Security) into business values As Data Platform. It is an open stage and Extensible Architecture. Splunk is authorized on the consistent schedule of information volumes and very costly. Splunk utilizes a cloud administration variant called Splunk storm with a yearly membership.
This document provides an overview of Central Log Management at the University of Cape Town. It discusses Splunk and the ELK stack for collecting, analyzing, and monitoring machine data from various sources. Splunk is featured for its collection, search, reporting, and alerting capabilities. The ELK stack deployed at UCT includes Logstash to process logs from firewalls and send them to Elasticsearch for storage and querying in Kibana for visualization. Shipper and indexer configurations are shown for ingesting Palo Alto firewall logs into Elasticsearch.
This document provides an overview of Splunk capabilities including knowledge objects, tags, event types, saved searches, alerts, and the search pipeline. It demonstrates how to use these features to better organize and analyze IT data through examples such as monitoring server activity, detecting suspicious login attempts, and tracking software sales. Advanced searching techniques including comparison operators, stats, and transaction commands are also explained to help users leverage Splunk's powerful search language.
Getting started with Splunk - Break out SessionGeorg Knon
This document provides an overview and getting started guide for Splunk. It discusses what Splunk is for exploring machine data, how to install and start Splunk, add sample data, perform basic searches, create saved searches, alerts and dashboards. It also covers deployment and integration topics like scaling Splunk, distributing searches across data centers, forwarding data to Splunk, and enriching data with lookups. The document recommends resources like the Splunk community for further support.
This document provides an overview and getting started guide for Splunk. It discusses what Splunk is for exploring machine data, how to install and start Splunk, add sample data, perform basic searches, create saved searches, alerts and dashboards. It also covers deployment and integration topics like scaling Splunk, distributing searches across data centers, forwarding data to Splunk, and enriching data with lookups. The document recommends resources like the Splunk community for support.
This document provides an overview of complex event processing (CEP). It defines CEP as treating inputs as events to look for patterns and correlations in order to extract meaning and act on inferred events. CEP is used in logistics, stock markets, and anywhere with a need to find patterns in large amounts of time-based event data. It discusses events, patterns, time windows, temporal reasoning, event definitions, CEP libraries like Drools and Esper, and provides an example of a FedEx tracking application.
This document provides an overview of Splunk Enterprise, including what it is, how it deploys and integrates, and its capabilities around real-time search, alerting, and reporting. Splunk Enterprise is an industry-leading platform for machine data that allows users to search, monitor, and analyze machine data from any source, location, or volume in real-time or historically. It deploys easily in 4 steps and scales to handle hundreds of terabytes of data per day from diverse sources like servers, applications, sensors, and more.
This document provides an introduction and overview of Apache UIMA (Unstructured Information Management Architecture).
Apache UIMA is an open source framework for analyzing unstructured information like text, audio, and video. It allows defining type systems and building analysis pipelines using components called annotators that can extract metadata from unstructured data.
The document outlines some key aspects of Apache UIMA including its goals of supporting a community around analyzing unstructured content, how it can bridge different domains, and provides an example scenario of using it to extract metadata from articles about movies.
May 2012 JaxDUG presentation by Zachary Gramana on using the Lucene.NET library to add search functionality to .NET applications. Contains an overview of search/information retrieval concepts and highlights some common use-cases.
This slide deck talks about Elasticsearch and its features.
When you talk about ELK stack it just means you are talking
about Elasticsearch, Logstash, and Kibana. But when you talk
about Elastic stack, other components such as Beats, X-Pack
are also included with it.
what is the ELK Stack?
ELK vs Elastic stack
What is Elasticsearch used for?
How does Elasticsearch work?
What is an Elasticsearch index?
Shards
Replicas
Nodes
Clusters
What programming languages does Elasticsearch support?
Amazon Elasticsearch, its use cases and benefits
Proposed Event Processing Definitions, DRAFT Work in Progress, September 20, 2006 4th Draft, Tim Bass, CISSP, Principal Global Architect, Director, TIBCO Software Inc.
Big Data Security Analytic Solution using SplunkIJERA Editor
Over the past decade, usage of online applications is experiencing remarkable growth. One of the main reasons for the success of web application is its “Ease of Access” and availability on internet. The simplicity of the HTTP protocol makes it easy to steal and spoof identity. The business liability associated with protecting online information has increased significantly and this is an issue that must be addressed. According to SANSTop20, 2013 list the number one targeted server side vulnerability are Web Applications. So, this has made detecting and preventing attacks on web applications a top priority for IT companies. In this paper, a rational solution is brought to detect events on web application and provides Security intelligence, log management and extensible reporting by analyzing web server logs.
Splunk produces software for searching, monitoring, and analyzing machine-generated big data. It turns machine data into valuable insights. With the Splunk log file generated using the Splunk cloud product, helps you to not only track your data over the Splunk cloud environment but also analyze and visualize the data as well.
The document provides information about Hibernate and Object Relational Mapping (ORM). It defines what hibernation is for animals and discusses if humans can hibernate. It then explains that Hibernate is a popular ORM framework for Java and defines what an ORM is and why they are used. The document goes on to describe typical ORM flows, other ORM options besides Hibernate, and compares Hibernate to JDBC. It provides details on Hibernate configuration, mapping data types, and entity relationships.
FlinkForward Asia 2019 - Evolving Keystone to an Open Collaborative Real Time...Zhenzhong Xu
Netflix is obsessed with customer joy, we relentlessly focus on product experience and high-quality content. In recent years, we have been making heavy investments in the tech-driven studio and content production. As a result, a lot of unique challenges arise in the real-time data infrastructure space. For example, in a microservices architecture, domain entities are spread in different applications and persistence storages, this made low latency consistent operational reporting and entity searching especially challenging.
In this talk, we’ll talk about some interesting use cases, the various challenges lay in the fundamentals of distributed systems, and how did we solve them. We will also discuss the learnings, things we could’ve done differently, and the new vision towards an open self-serving Data Mesh platform that empowers our partners and users to build flexible real-time data pipelines.
The document discusses digital forensics techniques for investigating incidents on Windows systems. It covers examining memory dumps, processes, services, drivers, ports, file systems, and other artifacts to determine what occurred. Specific techniques include comparing memory data to self-reported information and disk sources, identifying unknown files, examining auto-start points and jump lists, analyzing prefetch and event logs, and reviewing internet history and cache files. The goal is to discern how and when a system may have been compromised through analyzing changing system states and artifacts left by activities on the system.
This document provides an introduction to running, reusing, and sharing workflows with Taverna. It describes how Taverna can be used to analyze gene lists from experiments by finding existing workflows on myExperiment that enrich data with pathway information, gene functions, and literature evidence. It then demonstrates combining multiple workflows to analyze a chip-seq gene list, including extracting the gene list, converting identifiers, identifying pathways, and gene ontology terms. Finally, it discusses using text mining workflows to search literature.
Este documento descreve o uso da Splunk na empresa VTEX para gerenciar logs e métricas de mais de 1000 clientes. A VTEX começou usando a Splunk para armazenar 2GB de dados, e agora armazena 65GB para fornecer insights que melhoram a tomada de decisão. A Splunk permite monitorar o desempenho, identificar anormalidades e aumentar a conversão.
About ExxonMobil and Geoffrey Martins
Why Shared Service?
The Four Major Challenges
Final Unified Network
Next Steps
Takeouts on how to build a successful Shared Service
Q&A
O documento discute como a 99Taxis usa o Splunk para agregar logs de sistemas, permitindo buscas entre sistemas, monitoramento em tempo real de métricas chave e análises que melhoram a agilidade e tomada de decisões. Isso superou desafios de visibilidade e troubleshooting em um ambiente complexo com dezenas de sistemas e 100GB de logs diários.
O documento discute como a VTEX usa o Splunk para coletar e analisar logs, métricas e dados de máquinas para monitoramento e fornecer insights de negócios aos clientes. Antes do Splunk, a VTEX enfrentava desafios para centralizar e analisar grandes volumes de dados gerados. O Splunk permitiu a criação de um ambiente centralizado para logs e o desenvolvimento de aplicativos para análises específicas.
Splunk live! São Paulo 2014 - Edenred-TicketSplunk
O documento descreve como a Edenred, líder mundial em cartões e vouchers de serviços pré-pagos, implementou o Splunk para centralizar logs e melhorar a visibilidade e análise de segurança e desempenho de redes e sistemas. Antes do Splunk, a Edenred enfrentava desafios como demora na análise de incidentes, falta de histórico e métricas em tempo real. Ao implementar o Splunk, a empresa passou a centralizar logs de Active Directory, projetos PCI e firewalls, entre outros, para agilizar respostas e auditorias.
Splunk live! Inteligência operacional em um mundo de bigdataSplunk
This document discusses big data and machine data analytics. It describes how machine data from various sources like servers, security devices, sensors, and mobile devices can provide valuable insights but is challenging to manage and analyze at scale. The document promotes Splunk software's capabilities for ingesting, indexing, and analyzing large volumes of machine data from any source in real-time to provide operational intelligence and turn machine data into business value across use cases like IT operations, security, and analytics. It also advertises Splunk's upcoming annual user conference to showcase new capabilities for machine data analytics.
O documento descreve como o Universo Online usa o Splunk para monitorar transações de e-commerce, tomar decisões de negócios em tempo real e medir o retorno de investimento em mídia online. O Splunk fornece dashboards centralizados que melhoraram a visibilidade entre as equipes de monitoramento, P&D e negócios.
SplunkLive! São Paulo 2014 - Overview by markus zirnSplunk
1. The document discusses how Splunk software provides operational intelligence by collecting data from anywhere, allowing users to search and analyze everything, and gain real-time operational insights.
2. It highlights several Splunk customers and how they use Splunk across various industries and use cases such as IT operations, security, application management, and business analytics.
3. The document promotes Splunk's 5th Annual Worldwide User Conference in October 2014 with sessions, speakers, and opportunities to learn about Splunk's platform and ecosystem.
Este documento é a agenda de um evento da Splunk que apresentará casos de sucesso de clientes como Produban, Vtex, PagSeguro e Edenred. A agenda inclui boas-vindas, uma visão geral da Splunk, quatro apresentações de casos de sucesso de clientes, coffee breaks e um happy hour no final.
O documento discute a implementação do Splunk na Produban para melhorar a detecção e resposta a incidentes de segurança. Inicialmente, o SIEM anterior não atendia mais as necessidades em termos de volume de dados, disponibilidade e customização. Após testes, o Splunk mostrou-se muito mais rápido, requerendo menos hardware. O Splunk permite a automação de respostas a ameaças, integrando diversas fontes de inteligência e aplicando ações diretamente nos dispositivos de segurança. Isso agilizou a resposta a incidentes e evitou novos
Segue um material interessante do que a Vodafone está fazendo com o Splunk.
Esse em especial foi apresentado no .conf2013, convenção mundial da Splunk e teremos o .conf2014 em Outubro desse ano - programem-se e participem, vale cada centavo!
Lembrando, o .conf2014 já está com as inscrições abertas e em preço promocional.
Mais informações, aqui: http://conf.splunk.com/?r=homepage
Deploying Splunk. Arquitetura e dimensionamento do SplunkSplunk
The document discusses architecting and sizing a Splunk deployment. It covers key factors to consider like data volume, search volume, and roles of servers in a distributed Splunk topology. Recommendations are provided around server configurations based on roles like indexer, search head, and forwarder. A reference server specification is also outlined for estimating hardware needs.
Este documento fornece um resumo de como vários clientes no Brasil estão usando a Splunk para obter visibilidade operacional em tempo real e insights de negócios. Alguns clientes mencionados incluem PagSeguro, BM&F Bovespa, e Experian.
O Splunk é o mecanismo para os dados gerados por máquina
Sua infraestrutura de TI gera enormes quantidades de dados. Dados gerados por máquina - gerados por sites, aplicativos, servidores, redes, dispositivos móveis e afins. Ao monitorar e analisar tudo, de clickstreams e transações de clientes à atividade de rede para registrar chamadas, o Splunk transforma seus dados de máquina em percepções valiosas.
Solucione problemas e investigue incidentes de segurança em minutos (não horas ou dias). Monitore sua infraestrutura de ponta a ponta para evitar a degradação ou interrupções de serviço. E obtenha visibilidade em tempo real sobre a experiência, transações e comportamento dos clientes
A BM&FBOVESPA centraliza logs de suas plataformas de negociação no Splunk para monitoramento em tempo real e geração de relatórios. O Splunk permite filtrar e otimizar dados para aplicações que monitoram servidores, mensagens e o próprio ambiente Splunk. Essas aplicações foram desenvolvidas pela CME, Splunk e Silverlink Technologies para atender aos desafios de monitoramento da BM&FBOVESPA.
Coletamos os dados gerados por máquina e damos sentido a eles.
Sentido de TI. Sentido de segurança. Sentido empresarial. Bom senso. O Splunk oferece visibilidade e percepção em tempo realpara TI e os negócios.
1) O documento descreve o Splunk, uma ferramenta para coletar, indexar e analisar dados gerados por máquinas.
2) O Splunk permite monitoramento em tempo real, pesquisa histórica, criação de painéis e visualizações personalizadas.
3) O Splunk oferece inteligência operacional para TI e negócios, incluindo visibilidade, monitoramento e investigação de problemas.
O documento descreve como o Splunk pode ser usado para melhorar o gerenciamento de aplicativos através de (1) coleta e correlação de dados de múltiplas camadas para diagnóstico rápido de problemas, (2) fornecimento de visibilidade operacional em tempo real para tomada de decisões e (3) capacitação de equipes para redução de tempo de inatividade.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
AI 101: An Introduction to the Basics and Impact of Artificial IntelligenceIndexBug
Imagine a world where machines not only perform tasks but also learn, adapt, and make decisions. This is the promise of Artificial Intelligence (AI), a technology that's not just enhancing our lives but revolutionizing entire industries.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
Webinar: Designing a schema for a Data WarehouseFederico Razzoli
Are you new to data warehouses (DWH)? Do you need to check whether your data warehouse follows the best practices for a good design? In both cases, this webinar is for you.
A data warehouse is a central relational database that contains all measurements about a business or an organisation. This data comes from a variety of heterogeneous data sources, which includes databases of any type that back the applications used by the company, data files exported by some applications, or APIs provided by internal or external services.
But designing a data warehouse correctly is a hard task, which requires gathering information about the business processes that need to be analysed in the first place. These processes must be translated into so-called star schemas, which means, denormalised databases where each table represents a dimension or facts.
We will discuss these topics:
- How to gather information about a business;
- Understanding dictionaries and how to identify business entities;
- Dimensions and facts;
- Setting a table granularity;
- Types of facts;
- Types of dimensions;
- Snowflakes and how to avoid them;
- Expanding existing dimensions and facts.
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfChart Kalyan
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
Introduction of Cybersecurity with OSS at Code Europe 2024Hiroshi SHIBATA
I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
Main news related to the CCS TSI 2023 (2023/1695)Jakub Marek
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
Deep Dive: AI-Powered Marketing to Get More Leads and Customers with HyperGro...
Guia de referência Splunk
1. Eventtypes
Quick Reference Guide Eventtypes are cross-referenced searches that categorize events at search time.
For example, if you have defined an eventtype called "problem" that has a search
definition of "error OR warn OR fatal OR fail", any time you do a search where a result
CONCEPTS contains error, warn, fatal, or fail, the event will have an eventtype field/value with
eventtype=problem. So, for example, if you were searching for "login", the logins
Overview that had problems would get annotated with eventtype=problem. Eventtypes
are essentially dynamic tags that get attached to an event if it matches the search
Index-time Processing: Splunk reads data from a source, such as a file or port, on definition of the eventtype.
a host (e.g. "my machine"), classifies that source into a sourcetype (e.g., "syslog",
"access_combined", "apache_error", ...), then extracts timestamps, breaks up the Reports/Dashboards
source into individual events (e.g., log events, alerts, …), which can be a single-line
or multiple lines, and writes each event into an index on disk, for later retrieval with Search results with formatting information (e.g., as a table or chart) are informally
a search. referred to as reports, and multiple reports can be placed on a common page, called
a dashboard.
Search-time Processing: When a search starts, matching indexed events are
retrieved from disk, fields (e.g., code=404, user=david,...) are extracted from
the event's text, and the event is classified by matching against eventtype definitions
Apps Go to splunkbase.com/apps to download apps
(e.g., 'error', 'login', ...). The events returned from a search can then be Apps are collections of Splunk configurations, objects, and code, allowing you to
powerfully transformed using Splunk's search language to generate reports that live build different environments that sit on top of Splunk. You can have one app for
on dashboards. troubleshooting email servers, one app for web analysis, and so on.
Events Permissions/Users/Roles
An event is a single entry of data. In the context of log file, this is an event in a Web Saved Splunk objects, such as savedsearches, eventtypes, reports, and tags,
activity log: enrich your data, making it easier to search and understand. These objects have
permissions and can be kept private or shared with other users, via roles (e.g.,
173.26.34.223 - - [01/Jul/2009:12:05:27 -0700] "GET / "admin", "power", "user"). A role is a set of capabilities that you can define, like
trade/app?action=logout HTTP/1.1" 200 2953 whether or not someone is allowed to add data or edit a report. Splunk with a Free
License does not support user authentication.
More specifically, an event is a set of values associated with a timestamp. While
many events are short and only take up a line or two, others can be long, such as a
whole text document, a config file, or whole java stack trace. Splunk uses line- Transactions
breaking rules to determine how it breaks these events up for display in the search
A transaction is a set of events grouped into one event for easier analysis. For
results.
example, given that a customer shopping at an online store would generate web
access events with each click that each share a SessionID, it could be convenient to
Sources/Sourcetypes group all of his events together into one transaction. Grouped into one transaction
event, it’s easier to generate statistics like how long shoppers shopped, how many
A source is the name of the file, stream, or other input from which a particular event items they bought, which shoppers bought items and then returned them, etc.
originates – for example, /var/log/messages or UDP:514. Sources are classified into
sourcetypes, which can either be well known, such as access_combined (HTTP Web
server logs), or can be created on the fly by Splunk when it sees a source with data Forwarder/Indexer
and formatting it hasn’t seen before. Events with the same sourcetype can come
A forwarder is a version of Splunk that allows you to send data to a central Splunk
from different sources—events from the file /var/log/messages and from a syslog
indexer or group of indexers. An indexer provides indexing capability for local and
input on udp:514 can both have sourcetype=linux_syslog.
remote data.
Hosts
A host is the name of the physical or virtual device where an event originates. Host
provides an easy way to find all data originating from a given device.
Indexes
When you add data to Splunk, Splunk processes it, breaking the data into individual
events, timestamps them, and then stores them in an index, so that it can be later
searched and analyzed. By default, data you feed to Splunk is stored in the "main"
index, but you can create and specify other indexes for Splunk to use for different
data inputs.
Fields
Fields are searchable name/value pairings in event data. As Splunk processes events
at index time and search time, it automatically extracts fields. At index time, Splunk
extracts a small set of default fields for each event, including host, source, and
sourcetype. At search time, Splunk extracts what can be a wide range of fields from
the event data, including user-defined patterns as well as obvious field name/value
pairs such as user_id=jdoe.
Tags
Tags are aliases to field values. For example, if there are two host names that refer
to the same computer, you could give both of those host values the same tag (e.g.,
"hall9000"), and then if you search for that tag (e.g., "hal9000"), Splunk will return
events involving both host name values.
2. SEARCH LANGUAGE COMMON SEARCH COMMANDS
A search is a series of commands and arguments, each chained together with "|" COMMAND DESCRIPTION
(pipe) character that takes the output of one command and feeds it into the next
command on the right. chart/
timechart Returns results in a tabular output for (time-series) charting.
search-args | cmd1 cmd-args | cmd2 cmd-args | ...
dedup Removes subsequent results that match a specified criterion.
Search commands are used to take indexed data and filter unwanted information,
extract more information, calculate values, transform, and statistically analyze. The
search results retrieved from the index can be thought of as a dynamically created eval Calculates an expression. (See EVAL FUNCTIONS table.)
table. Each search command redefines the shape of that table. Each indexed event
is a row, with columns for each field value. Columns include basic information about
the data as well as columns that are dynamically extracted at search-time.
fields Removes fields from search results.
At the head of each search is an implied search-the-index-for-events command,
which can be used to search for keywords (e.g., error), boolean expressions
(e.g., (error OR failure) NOT success), phrases (e.g., "database head/tail Returns the first/last N results.
error"), wildcards (e.g., fail* will match fail, fails, failure, etc.), field values (e.g.,
code=404), inequality (e.g., code!=404 or code>200), a field having any value
or no value (e.g., code=* or NOT code=*). For example, the search: lookup Adds field values from an external source.
sourcetype="access_combined" error | top 10 uri
rename Renames a specified field; wildcards can be used to specify
multiple fields.
will retrieve indexed access_combined events from disk that contain the term
"error" (ANDs are implied between search terms), and then for those events,
report the top 10 most common URI values. replace Replaces values of specified fields with a specified new value.
Subsearches rex Specifies regular expression named groups to extract fields.
A subsearch is an argument to a command that runs its own search, returning those
results to the parent command as the argument value. Subsearches are contained
in square brackets. For example, finding all syslog events from the user that had the search Filters results to those that match the search expression.
last login error:
sourcetype=syslog [search login error | return user] sort Sorts search results by the specified fields.
Note that the subsearch returns one user value, because by default the "return"
command just returns one value, but there are options for more (e.g., | return stats Provides statistics, grouped optionally by fields.
5 user).
Relative Time Modifiers top/rare Displays the most/least common values of a field.
Besides using the custom-time ranges in the user-interface, you can specify in
your search the time ranges of retrieved events with the latest and earliest transaction Groups search results into transactions.
search modifiers. The relative times are specified with a string of characters that
indicate amount of time (integer and unit) and, optionally, a "snap to" time unit:
[+|-]<time_integer><time_unit>@<snap_time_unit>
Optimizing Searches
For example: "error earliest=-1d@d latest=-1h@h" will retrieve events The key to fast searching is to limit the data that needs to be pulled off disk to an
containing "error" that occurred from yesterday (snapped to midnight) to the absolute minimum, and then to filter that data as early as possible in the search so
last hour (snapped to the hour). that processing is done on the minimum data necessary.
Time Units: specified as second (s), minute(m), hour(h), day(d), week(w), Partition data into separate indexes, if you’ll rarely perform searches across multiple
month(mon), quarter(q), year(y). "time_integer" defaults to 1 (e.g., "m" is the same as types of data. For example, put web data in one index, and firewall data in another.
"1m").
Snapping: indicates the nearest or latest time to which your time amount rounds • Search as specifically as you can (e.g. fatal_error, not *error*)
down. Snaps rounds down to the latest time not after the specified time. For
example, if it is 11:59:00 and you "snap to" hours (@h), you will snap to 11:00 not • Limit the time range to only what’s needed (e.g., -1h not -1w)
12:00. You can "snap to" a specific day of the week: use @w0 for Sunday, @w1 for
Monday, etc. • Filter out unneeded fields as soon as possible in the search.
• Filter out results as soon as possible before calculations.
•
Community For report generating searches, use the Advanced Charting view, and
not the Flashtimeline view, which calculates timelines.
ask questions, find answers. • On Flashtimeline, turn off ‘Discover Fields’ when not needed.
download apps, share yours.
• Use summary indexes to pre-calculate commonly used values.
splunkbase.com
• Make sure your disk I/O is the fastest you have available.
3. SEARCH EXAMPLES
Filter Results Add Fields
Filter results to only include those with … | eval velocity=distance/
… | search fail status=0 Set velocity to distance / time.
"fail" in their raw text and status=0. time
Remove duplicates of results with the Extract "from" and "to" fields using
… | dedup host … | rex field=_raw "From:
same host value. regular expressions. If a raw event
contains "From: Susan To: David", then (?<from>.*) To: (?<to>.*)"
Keep only search results whose "_raw" … | regex _raw="(?<!d)10. from=Susan and to=David.
field contains IP addresses in the non- d{1,3}.d{1,3}.d{1,3}
routable class A (10.0.0.0/8). (?!d)" Save the running total of "count" in a … | accum count as total_
field called "total_count". count
Group Results
For each event where 'count' exists,
Cluster results together, sort by their … | cluster t=0.9
compute the difference between count
"cluster_count" values, and then return showcount=true | sort … | delta count as countdiff
and its previous value and store the
the 20 largest clusters (in data size). limit=20 -cluster_count
result in 'countdiff'.
Group results that have the same "host" Filter Fields
and "cookie", occur within 30 seconds
… | transaction host cookie
of each other, and do not have a pause Keep the "host" and "ip" fields, and
maxspan=30s maxpause=5s … | fields + host, ip
greater than 5 seconds between each display them in the order: "host", "ip".
event into a transaction.
Remove the "host" and "ip" fields. … | fields - host, ip
Group results with the same IP address
… | transaction clientip
(clientip) and where the first result
startswith="signon" Modify Fields
contains "signon", and the last result
endswith="purchase"
contains "purchase".
Rename the "_ip" field as "IPAddress". … | rename _ip as IPAddress
Order Results
Return the first 20 results. … | head 20 Change any host value that ends with … | replace *localhost with
"localhost" to "mylocalhost". mylocalhost in host
Reverse the order of a result set. … | reverse
Multi-Valued Fields
Sort results by "ip" value (in ascending
Combine the multiple values of the
order) and then by "url" value … | sort ip, -url … | nomv recipients
recipients field into a single value
(in descending order).
Separate the values of the "recipients"
Return the last 20 results … | makemv delim=","
… | tail 20 field into multiple field values,
(in reverse order). recipients | top recipients
displaying the top recipients
Reporting Create new results for each value of the
… | mvexpand recipients
… | anomalousvalue multivalue field "recipients"
Return events with uncommon values.
action=filter pthresh=0.02
For each result that is identical except … | fields EventCode,
for that RecordNumber, combine them, Category, RecordNumber
Return the maximum "delay" by "size",
… | chart max(delay) by size setting RecordNumber to be a multi- | mvcombine delim=","
where "size" is broken down into a
bins=10 valued field with all the varying values. RecordNumber
maximum of 10 equal sized buckets.
… | eval to_count =
Return max(delay) for each value of foo … | chart max(delay) over Find the number of recipient values
mvcount(recipients)
split by the value of bar. foo by bar
Find the first email address in the … | eval recipient_first =
… | chart max(delay) over recipient field mvindex(recipient,0)
Return max(delay) for each value of foo.
foo
… | eval netorg_recipients
Remove all outlying numerical values. … | outlier Find all recipient values that end in .net = mvfilter(match(recipient,
or .org ".net$") OR match(recipient,
Remove duplicates of results with the ".org$"))
same "host" value and return the total … | stats dc(host)
count of the remaining results. Find the combination of the values of … | eval newval =
foo, "bar", and the values of baz mvappend(foo, "bar", baz)
Return the average for each hour, of any
… | stats avg(*lay) by date_ … | eval orgindex =
unique field that ends with the string Find the index of the first recipient value
hour mvfind(recipient, ".org$")
"lay" (e.g., delay, xdelay, relay, etc). match ".org$"
Calculate the average value of "CPU" … | timechart span=1m Lookup Tables
each minute for each "host". avg(CPU) by host
Lookup the value of each event's 'user'
… | lookup usertogroup user
Create a timechart of the count of from field in the lookup table usertogroup,
… | timechart count by host output group
"web" sources by "host" setting the event's 'group' field.
Return the 20 most common values of Write the search results to the lookup
… | top limit=20 url … | outputlookup users.csv
the "url" field. file "users.csv".
Return the least common values of the Read in the lookup file "users.csv" as
… | rare url … | inputlookup users.csv
"url" field. search results.
4. The eval command calculates an expression and puts the resulting value into a field (e.g. "...| eval force = mass * acceleration").
EVAL FUNCTIONS The following table lists the functions eval understands, in addition to basic arithmetic operators (+ - * / %), string concatenation
(e.g., '...| eval name = last . ", " . last'), boolean operations (AND OR NOT XOR < > <= >= != = == LIKE).
FUNCTION DESCRIPTION EXAMPLES
abs(X) Returns the absolute value of X. abs(number)
Takes pairs of arguments X and Y, where X arguments are Boolean case(error == 404, "Not found", error ==
case(X,"Y",…) expressions that, when evaluated to TRUE, return the corresponding Y 500,"Internal Server Error", error ==
argument. 200, "OK")
ceil(X) Ceiling of a number X. ceil(1.9)
cidrmatch("X",Y) Identifies IP addresses that belong to a particular subnet. cidrmatch("123.132.32.0/25",ip)
coalesce(X,…) Returns the first value that is not null. coalesce(null(), "Returned val", null())
Evaluates an expression X using double precision floating point
exact(X) arithmetic.
exact(3.14*num)
exp(X) Returns eX. exp(3)
floor(X) Returns the floor of a number X. floor(1.9)
If X evaluates to TRUE, the result is the second argument Y. If X
if(X,Y,Z) evaluates to FALSE, the result evaluates to the third argument Z.
if(error==200, "OK", "Error")
isbool(X) Returns TRUE if X is Boolean. isbool(field)
isint(X) Returns TRUE if X is an integer. isint(field)
isnotnull(X) Returns TRUE if X is not NULL. isnotnull(field)
isnull(X) Returns TRUE if X is NULL. isnull(field)
isnum(X) Returns TRUE if X is a number. isnum(field)
isstr() Returns TRUE if X is a string. isstr(field)
len(X) This function returns the character length of a string X. len(field)
like(X,"Y") Returns TRUE if and only if X is like the SQLite pattern in Y. like(field, "foo%")
ln(X) Returns its natural log. ln(bytes)
Returns the log of the first argument X using the second argument Y as
log(X,Y) the base. Y defaults to 10.
log(number,2)
lower(X) Returns the lowercase of X. lower(username)
Returns X with the characters in Y trimmed from the left side. Y defaults
ltrim(X,Y) to spaces and tabs.
ltrim(" ZZZabcZZ ", " Z")
match(X,Y) Returns if X matches the regex pattern Y. match(field, "^d{1,3}.d$")
max(X,…) Returns the max. max(delay, mydelay)
md5(X) Returns the MD5 hash of a string value X. md5(field)
min(X,…) Returns the min. min(delay, mydelay)
mvcount(X) Returns the number of values of X. mvcount(multifield)
mvfilter(X) Filters a multi-valued field based on the Boolean expression X. mvfilter(match(email, "net$"))
Returns a subset of the multivalued field X from start position (zero-
mvindex(X,Y,Z) based) Y to Z (optional).
mvindex( multifield, 2)
Given a multi-valued field X and string delimiter Y, and joins the
mvjoin(X,Y) individual values of X using Y.
mvjoin(foo, ";")
now() Returns the current time, represented in Unix time. now()
null() This function takes no arguments and returns NULL. null()
Given two arguments, fields X and Y, and returns the X if the arguments
nullif(X,Y) are different; returns NULL, otherwise.
nullif(fieldA, fieldB)
pi() Returns the constant pi. pi()
pow(X,Y) Returns X .
Y pow(2,10)
random() Returns a pseudo-random number ranging from 0 to 2147483647. random()
relative_time Given epochtime time X and relative time specifier Y, returns the
relative_time(now(),"-1d@d")
(X,Y) epochtime value of Y applied to X.
Returns date with the month and day
numbers switched, so if the input was
Returns a string formed by substituting string Z for every occurrence of
replace(X,Y,Z) regex string Y in string X.
1/12/2009 the return value would be
12/1/2009: replace(date, "^(d{1,2})/
(d{1,2})/", "2/1/")
Returns X rounded to the amount of decimal places specified by Y. The
round(X,Y) default is to round to an integer.
round(3.5)
Returns X with the characters in Y trimmed from the right side.
rtrim(X,Y) If Y is not specified, spaces and tabs are trimmed.
rtrim(" ZZZZabcZZ ", " Z")
5. EVAL FUNCTIONS (continued)
FUNCTION DESCRIPTION EXAMPLES
searchmatch(X) Returns true if the event matches the search string X. searchmatch("foo AND bar")
split(X,"Y") Returns X as a multi-valued field, split be delimiter Y. split(foo, ";")
sqrt(X) Returns the square root of X. sqrt(9)
strftime(X,Y) Returns epochtime value X rendered using the format specified by Y. strftime(_time, "%H:%M")
Given a time represented by a string X, returns value parsed from
strptime(X,Y) format Y.
strptime(timeStr, "%H:%M")
Returns a substring field X from start position (1-based) Y for Z substr("string", 1, 3)
substr(X,Y,Z) (optional) characters. +substr("string", -3)
time() Returns the wall-clock time with microsecond resolution. time()
Converts input string X to a number, where Y (optional, defaults to 10)
tonumber(X,Y) defines the base of the number to convert to.
tonumber("0A4",16)
Returns a field value of X as a string. If the value of X is a number, it
reformats it as a string; if a Boolean value, either "True" or "False". If X is This example returns:
a number, the second argument Y is optional and can either be "hex" foo=615 and foo2=00:10:15:
tostring(X,Y) (convert X to hexadecimal), "commas" (formats X with commas and … | eval foo=615 | eval foo2 = tostring(foo,
2 decimal places), or "duration" (converts seconds X to readable time "duration")
format HH:MM:SS).
Returns X with the characters in Y trimmed from both sides.
trim(X,Y) If Y is not specified, spaces and tabs are trimmed.
trim(" ZZZZabcZZ ", " Z")
This example returns:
"NumberStringBoolInvalid":
typeof(X) Returns a string representation of its type.
typeof(12)+ typeof("string")+ typeof(1==2)+
typeof(badfield)
upper(X) Returns the uppercase of X. upper(username)
urldecode("http%3A%2F%2Fwww.splunk.
urldecode(X) Returns the URL X decoded.
com%2Fdownload%3Fr%3Dheader")
Given pairs of arguments, Boolean expressions X and strings Y, returns validate(isint(port), "ERROR: Port is not
validate(X,Y,…) the string Y corresponding to the first expression X that evaluates to an integer", port >= 1 AND port <= 65535,
False and defaults to NULL if all are True. "ERROR: Port is out of range")
COMMON STATS FUNCTIONS Common statistical functions used with the chart, stats, and timechart commands. Field names
can be wildcarded, so avg(*delay) might calculate the average of the delay and xdelay fields.
FUNCTION DESCRIPTION
avg(X) Returns the average of the values of field X.
count(X) Returns the number of occurrences of the field X. To indicate a specific field value to match, format X as eval(field="value").
dc(X) Returns the count of distinct values of the field X.
first(X) Returns the first seen value of the field X. In general, the first seen value of the field is the chronologically most recent instance of field.
last(X) Returns the last seen value of the field X.
list(X) Returns the list of all values of the field X as a multi-value entry. The order of the values reflects the order of input events.
max(X) Returns the maximum value of the field X. If the values of X are non-numeric, the max is found from lexicographic ordering.
median(X) Returns the middle-most value of the field X.
min(X) Returns the minimum value of the field X. If the values of X are non-numeric, the min is found from lexicographic ordering.
mode(X) Returns the most frequent value of the field X.
perc<X>(Y) Returns the X-th percentile value of the field Y. For example, perc5(total) returns the 5th percentile value of a field "total".
range(X) Returns the difference between the max and min values of the field X.
stdev(X) Returns the sample standard deviation of the field X.
stdevp(X) Returns the population standard deviation of the field X.
sum(X) Returns the sum of the values of the field X.
sumsq(X) Returns the sum of the squares of the values of the field X.
values(X) Returns the list of all distinct values of the field X as a multi-value entry. The order of the values is lexicographical.
var(X) Returns the sample variance of the field X.