The document discusses LINE's use of Prometheus and Grafana to monitor its Hadoop and Fluentd clusters. It introduces Promgen, a tool LINE created to manage server lists and configuration for Prometheus since it operates in an on-premises environment without service discovery. It then describes how LINE exports metrics from its Hadoop components like HDFS, YARN and Hive using custom exporters as well as from Fluentd using a Prometheus plugin. These metrics are collected by Prometheus and visualized in Grafana for cluster monitoring and alerting.
GrafanaCon 2015 - http://grafanacon.org/
Tobias will be giving an overview of Prometheus, an open-source monitoring system with a multi-dimensional label system, expressive query language and dashboard editor called PromDash. Learn about the highlights and differences of PromDash compared to Grafana and discuss the options to make Grafana the primary dashboard editor of the Prometheus project.
This presentation gives an overview of the Prometheus project. It explains Prometheus in terms of it's visualisation, time series processing capabilities and architecture. It also examines it's query language PromQL.
Links for further information and connecting
http://www.amazon.com/Michael-Frampton/e/B00NIQDOOM/
https://nz.linkedin.com/pub/mike-frampton/20/630/385
https://open-source-systems.blogspot.com/
Totango is an Analytics platform for Customer Success.
Our data pipeline converts usage information into actionable analytics. The pipeline is managed using Luigi workflow engine, and data transformations are done in Spark.
In Data Engineer’s Lunch #41: Pygrametl , we discussed PygramETL, a python ETL tool in order to close out our series on them.
Accompanying Blog: https://blog.anant.us/data-engineers-lunch-41-pygrametl
Accompanying YouTube: https://youtu.be/YiPuJyYLXxs
Sign Up For Our Newsletter: http://eepurl.com/grdMkn
Join Data Engineer’s Lunch Weekly at 12 PM EST Every Monday:
https://www.meetup.com/Data-Wranglers-DC/events/
Cassandra.Link:
https://cassandra.link/
Follow Us and Reach Us At:
Anant:
https://www.anant.us/
Awesome Cassandra:
https://github.com/Anant/awesome-cassandra
Email:
solutions@anant.us
LinkedIn:
https://www.linkedin.com/company/anant/
Twitter:
https://twitter.com/anantcorp
Eventbrite:
https://www.eventbrite.com/o/anant-1072927283
Facebook:
https://www.facebook.com/AnantCorp/
Join The Anant Team:
https://www.careers.anant.us
GrafanaCon 2015 - http://grafanacon.org/
Tobias will be giving an overview of Prometheus, an open-source monitoring system with a multi-dimensional label system, expressive query language and dashboard editor called PromDash. Learn about the highlights and differences of PromDash compared to Grafana and discuss the options to make Grafana the primary dashboard editor of the Prometheus project.
This presentation gives an overview of the Prometheus project. It explains Prometheus in terms of it's visualisation, time series processing capabilities and architecture. It also examines it's query language PromQL.
Links for further information and connecting
http://www.amazon.com/Michael-Frampton/e/B00NIQDOOM/
https://nz.linkedin.com/pub/mike-frampton/20/630/385
https://open-source-systems.blogspot.com/
Totango is an Analytics platform for Customer Success.
Our data pipeline converts usage information into actionable analytics. The pipeline is managed using Luigi workflow engine, and data transformations are done in Spark.
In Data Engineer’s Lunch #41: Pygrametl , we discussed PygramETL, a python ETL tool in order to close out our series on them.
Accompanying Blog: https://blog.anant.us/data-engineers-lunch-41-pygrametl
Accompanying YouTube: https://youtu.be/YiPuJyYLXxs
Sign Up For Our Newsletter: http://eepurl.com/grdMkn
Join Data Engineer’s Lunch Weekly at 12 PM EST Every Monday:
https://www.meetup.com/Data-Wranglers-DC/events/
Cassandra.Link:
https://cassandra.link/
Follow Us and Reach Us At:
Anant:
https://www.anant.us/
Awesome Cassandra:
https://github.com/Anant/awesome-cassandra
Email:
solutions@anant.us
LinkedIn:
https://www.linkedin.com/company/anant/
Twitter:
https://twitter.com/anantcorp
Eventbrite:
https://www.eventbrite.com/o/anant-1072927283
Facebook:
https://www.facebook.com/AnantCorp/
Join The Anant Team:
https://www.careers.anant.us
In recent years, many companies have adopted service-oriented architectures by deploying tens to hundreds of small microservices. But with the increasing number of independent services, do you still know what’s going on in your infrastructure?
Traditional monitoring solutions were mostly focused on machines and fell short keeping track of infrastructures where service deployments happen multiple times per day and instances get dynamically allocated on a multitude of nodes. Prometheus is a relatively new monitoring system which has gained a lot of popularity in the last two years as it was explicitly designed for today’s needs of service monitoring and container infrastructure.
In this session, you’ll learn how to instrument a service with a Prometheus client library to provide information about its current health and state. In order to get automatically notified when the service becomes unhealthy, you’ll see how to configure alerts and notifications. Along the way, I’ll discuss a few important key metrics paramount to successfully monitor a microservice.
Prometheus Is Good for Your Small Startup - ShuttleCloud Corp. - 2016ShuttleCloud
In this talk, we'll explain our journey from having near-zero monitoring to having all of our infrastructure monitored with the necessary metrics and alerts.
We will share with the audience some of the mistakes we did and what lessons we have learned. We currently have around 200 instances monitored with a comfortable cost-effective in-house monitoring stack based on Prometheus.
We want to demonstrate that you don't need to have a big fleet to embrace Prometheus and that it is a non-expensive solution for monitoring.
----------
ShuttleCloud is a small startup specialized in email and contacts migrations. We developed a reliable migration platform in high availability used by clients like Gmail, GContacts and Comcast.
For example, Gmail alone has imported data for 3 million users with our API and we process hundreds of terabytes every month.
-------------
Follow us on Twitter:
@ShuttleCloud: https://twitter.com/ShuttleCloud
@ShuttleCloudEng: https://twitter.com/ShuttleCloudEng
ShuttleCloud.com
Quick introduction about Apache Spark and how it fits in the cognitive world, how can we use it to help cognitive solutions as well as create distributed algorithms to predict and perform other machine learning tasks.
Building a reliable pipeline of data ingress, batch computation, and data egress with Hadoop can be a major challenge. Most folks start out with cron to manage workflows, but soon discover that doesn't scale past a handful of jobs. There are a number of open-source workflow engines with support for Hadoop, including Azkaban (from LinkedIn), Luigi (from Spotify), and Apache Oozie. Having deployed all three of these systems in production, Joe will talk about what features and qualities are important for a workflow system.
In this talk, we describe the design and implementation of the Python Streaming API support that has been submitted for inclusion in mainline Flink. Python is one of the most popular programming languages for data analysis. Its readability emphasizes development productivity and as a scripting language, it does not require a compilation nor complex development environment setup. Flink already has support for Python APIs for batch programming and unfortunately, the mechanism used to support batch programs (i.e., DataSet APIs) do does not work for Streaming API. We describe the limitations with the batch implementation and provide insights into how we solved this using Jython. We will walk through some of the examples programs using the new Python API and compare programmability and performance with the Java and Scala streaming APIs.
How community software supports language documentation and data analysisPeter Bouda
Field linguists have increasingly adopted the latest technologies and tools for language documentation. Their needs have led to remarkable developments in software and archiving, exemplified by work at the MPI in Nijmegen, which leads the innovation cycles that take place in the digital working environments of field linguists. The next step in research is now the analysis and theoretical exploitation of the huge amount of data that has been collected in numerous language documentation projects that use these environments. This research will also rely on computer-based strategies, as data is instantly available in digital formats.
In this talk I will introduce some of the lesser known tools and software packages for annotation and analysis tasks. Some of these tools were created within DOBES projects and/or as community projects by small teams; they can be combined with well-known tools like ELAN or Toolbox to give researchers access to their data. I will focus on how a combination of simple, special purpose tools makes researchers more productive and how existing software libraries allow scientific projects to create their own, task-specific software tools that they can tailor to their own needs.
In recent years, many companies have adopted service-oriented architectures by deploying tens to hundreds of small microservices. But with the increasing number of independent services, do you still know what’s going on in your infrastructure?
Traditional monitoring solutions were mostly focused on machines and fell short keeping track of infrastructures where service deployments happen multiple times per day and instances get dynamically allocated on a multitude of nodes. Prometheus is a relatively new monitoring system which has gained a lot of popularity in the last two years as it was explicitly designed for today’s needs of service monitoring and container infrastructure.
In this session, you’ll learn how to instrument a service with a Prometheus client library to provide information about its current health and state. In order to get automatically notified when the service becomes unhealthy, you’ll see how to configure alerts and notifications. Along the way, I’ll discuss a few important key metrics paramount to successfully monitor a microservice.
Prometheus Is Good for Your Small Startup - ShuttleCloud Corp. - 2016ShuttleCloud
In this talk, we'll explain our journey from having near-zero monitoring to having all of our infrastructure monitored with the necessary metrics and alerts.
We will share with the audience some of the mistakes we did and what lessons we have learned. We currently have around 200 instances monitored with a comfortable cost-effective in-house monitoring stack based on Prometheus.
We want to demonstrate that you don't need to have a big fleet to embrace Prometheus and that it is a non-expensive solution for monitoring.
----------
ShuttleCloud is a small startup specialized in email and contacts migrations. We developed a reliable migration platform in high availability used by clients like Gmail, GContacts and Comcast.
For example, Gmail alone has imported data for 3 million users with our API and we process hundreds of terabytes every month.
-------------
Follow us on Twitter:
@ShuttleCloud: https://twitter.com/ShuttleCloud
@ShuttleCloudEng: https://twitter.com/ShuttleCloudEng
ShuttleCloud.com
Quick introduction about Apache Spark and how it fits in the cognitive world, how can we use it to help cognitive solutions as well as create distributed algorithms to predict and perform other machine learning tasks.
Building a reliable pipeline of data ingress, batch computation, and data egress with Hadoop can be a major challenge. Most folks start out with cron to manage workflows, but soon discover that doesn't scale past a handful of jobs. There are a number of open-source workflow engines with support for Hadoop, including Azkaban (from LinkedIn), Luigi (from Spotify), and Apache Oozie. Having deployed all three of these systems in production, Joe will talk about what features and qualities are important for a workflow system.
In this talk, we describe the design and implementation of the Python Streaming API support that has been submitted for inclusion in mainline Flink. Python is one of the most popular programming languages for data analysis. Its readability emphasizes development productivity and as a scripting language, it does not require a compilation nor complex development environment setup. Flink already has support for Python APIs for batch programming and unfortunately, the mechanism used to support batch programs (i.e., DataSet APIs) do does not work for Streaming API. We describe the limitations with the batch implementation and provide insights into how we solved this using Jython. We will walk through some of the examples programs using the new Python API and compare programmability and performance with the Java and Scala streaming APIs.
How community software supports language documentation and data analysisPeter Bouda
Field linguists have increasingly adopted the latest technologies and tools for language documentation. Their needs have led to remarkable developments in software and archiving, exemplified by work at the MPI in Nijmegen, which leads the innovation cycles that take place in the digital working environments of field linguists. The next step in research is now the analysis and theoretical exploitation of the huge amount of data that has been collected in numerous language documentation projects that use these environments. This research will also rely on computer-based strategies, as data is instantly available in digital formats.
In this talk I will introduce some of the lesser known tools and software packages for annotation and analysis tasks. Some of these tools were created within DOBES projects and/or as community projects by small teams; they can be combined with well-known tools like ELAN or Toolbox to give researchers access to their data. I will focus on how a combination of simple, special purpose tools makes researchers more productive and how existing software libraries allow scientific projects to create their own, task-specific software tools that they can tailor to their own needs.
Reproducibility and automation of machine learning processDenis Dus
A speech about organization of machine learning process in practice. Conceptual and technical aspects discussed. Introduction into Luigi framework. A short story about neural networks fitting in Flo - top-level mobile tracker of women health.
A presentation on the history, design, and use of R. The talk will focus on companies that use and support R, use cases, where it is going, competitors, advantages and disadvantages, and resources to learn more about R. Speaker Bio
Joseph Kambourakis has been the Lead Data Science Instructor at EMC for over two years. He has taught in eight countries and been interviewed by Japanese and Saudi Arabian media about his expertise in Data Science. He holds a Bachelors in Electrical and Computer Engineering from Worcester Polytechnic Institute and an MBA from Bentley University with a concentration in Business Analytics.
Talk given at first OmniSci user conference where I discuss cooperating with open-source communities to ensure you get useful answers quickly from your data. I get a chance to introduce OpenTeams in this talk as well and discuss how it can help companies cooperate with communities.
時間:2018-02-10 台灣資料工程協會 2018 第一季技術工作坊
講題:使用普羅米修斯打造全棧式監控與告警平台
Building Full Stack Monitor and Notification with Prometheus
身為管理混合式雲端基礎建設的維運人員,面對分散在不同監控平台的數據是否感到頭疼呢?身為開發者,您是否苦於欠缺歷史監控數據來除錯或排查程式效能問題呢?本次分享將從動機面開始說明為何需要全棧式監控與告警平台,接著介紹過去一季講者如何使用普羅米修斯(Prometheus)與 Grafana 針對網路層、實體機器、虛擬機器、容器、中介軟體層(Ex. Apache Cassandra、Apache Kafka、CNCF Fluentd)、應用程式層來建立資料串流(Data Pipeline)的監控儀表板。礙於無法展示真實公司的環境,本分享將使用 Docker Compose 進行全棧式監控與告警平台的概念,也藉此逐一介紹搭建全棧式監控與告警平台會用到哪些普羅米修斯(Prometheus)的各類資料蒐集器(Exporter)。
As a Hybrid Cloud Operator, are you tired of collecting monitor metrics from different monitor services? As a Developer, do you need historical application and infrastructure metrics to debug or improve application performance? In this talk, I'll first talk about why should we build Full Stack Monitor and Notification with Prometheus and Grafana. I'll share my personal experience about monitoring network devices, physical machines, virtual machines, docker containers, Middleware (Ex. Apache Cassandra, Apapche Kafka, CNCF Fluentd) and Application metrics. I'll demonstrate an End-to-End Data Pipeline Dashboard with Docker Compose examples and introduce different kinds of Prometheus Exporter used for different monitor targets.
It's been said that open source software is eating the world. In the observability space, the project making this possible is OpenTelemetry. It's quickly becoming the standard for instrumentation and data collection of observability data. Understanding what data to collect and how to collect it properly is fundamental to ensuring users can quickly address availability and performance issues. Steve Flanders, Director of Engineering at Splunk, discusses the components of the project, its current status, and how you can get started integrating it into your modern app infrastructure.
Speakers:
Steve Flanders
Planets, OPF & SCAPE - presentation of tools on digital preservationSCAPE Project
Andrew Jackson from British Library presents digital preservation tools from the EU projects Planets and SCAPE and the Open Planets Foundation which is a network providing practical solutions and expertise in digital preservation.
Presented at 'Practical Tools for Digital Preservation: A Hack-a-thon' in York, September 28, 2011.
London atlassian meetup 31 jan 2016 jira metrics-extract slidesRudiger Wolf
Slides for talk given to London Atlassian User Group Jan 2017. How to get started with Python to extract data from Jira and produce charts for your Agile team.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
6. Before Prometheus
• I have experience with other monitoring tools like
Ganglia, Nagios
• I found Prometheus
– Monitoring and alerting are unified
– There is a query feature that allows ad-hoc queries
• max disk usage: max by (instance) (100 -
(node_filesystem_free{...} / node_filesystem_size{...}) * 100)
• I want to use Prometheus
• How do we adjust Prometheus to our
environment?
7. LINE’s development environment
• We rarely use cloud service like AWS because we
are under on-premises environment
– host information doesn’t change frequently
• That’s why currently we don’t use any service
discovery system (like Consul)
– Therefore, we need to use static configuration for
Prometheus
• We wanted to manage servers through a browser
• So, we created a tool to manage server list called
promgen (https://github.com/line/promgen)
21. Alertmanager
• Alertmanager is powerful because users can
avoid a flood of alert notifications
• Deduplication and silences are useful
• Alertmanager can avoid alert fatigue
• We want to manage alert notification rules and
settings easily
– for example, we want to add HipChat room and Mail
address through browser.
• That’s why we implement webhook in promgen
23. HipChat and Mail
• User can set HipChat room and mail address
to receive alert
24. How to notify alert
• Promgen has webhook feature to send alert to
both HipChat and Mail
• If alert occurs, user can receive alert through
Alertmanager, Promgen
Prometheus Alertmanager Promgen
HipChat
Mail
26. Log analysis platform
• Access logs are sent to HDFS by fluentd. There
are more than 400 Fluentd processes and
150kmsg/sec during peak times.
• Fluentd is an OSS log collector like logstash,
flume written in ruby
• Our Hadoop cluster is medium-sized,
consisting of 40 units.
MRv2/Tez/HDFS
Hive
HDP2.4.0
access
logaccess
log
access
log
27. Monitoring of hadoop/hive cluster
• Developers normally use jmx_exporter to monitor java
middleware
• But I wanted to create exporter, so I implemented
namenode/resourcemanager/jstat exporter
• namenode_exporter uses http://namenode:50070/jmx
• resourcemanager_exporter uses
http://resourcemanager:8088/ws/v1/cluster/metrics
• jstat_exporter uses jstat command
– Honestly, current jstat_exporter implementation is not so
good because when Prometheus pulls metrics, jstat
command is always executed
– cache may be necessary
31. Fluentd buffer monitoring
• Fluentd has buffer mechanism to retry if
destination is unstable
• fluent-plugin-prometheus enables buffer
monitoring
• fluent-plugin-prometheus is fluentd plugin
and use Prometheus Ruby client
32. access log count
• fluent-plugin-prometheus enables us to count
access log but we need sampling because of
high cpu usage
• One fluentd process can‘t handle high traffic
33. HTTP status count
Although 4xx/5xx is not 0, it may become 0
because of sampling. So we will switch to Flink.