Monitoring Clojure Applications with Prometheus
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How do you know your Clojure web service is doing what it is supposed to do? In this talk I give a quick introduction to Metrics and Monitoring before showing how you can easily add Prometheus Metrics to Clojure applications. Just give it a try and fork: https://github.com/joachimdraeger/clojure-prometheus-demo
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Pre-computing for telemetry data on collectors, frequently referred to as edge computing. In VictoriaMetrics it is named as streaming aggregation and allows collectors to pre-compute data, reducing its resolution and cardinality before it is pushed to the database. This is especially important for Prometheus-like systems because streaming aggregation is compatible with Prometheus RemoteWrite protocol and can be used with any system which supports it.
Cardinality explorer. Interface, which provides useful insights into data stored by the TSDB. It helps to identify the most expensive metrics or labels and see how they have changed in time.
Query tracing. This feature provides details about all the stages of query execution, including time spent on index lookups, disk reads, data transfer, computation, and memory expenses. This is similar to SQL EXPLAIN feature, and helps to improve the performance of read queries.
Compute efficiency. VictoriaMetrics components for collecting and storing telemetry data consume fewer resources compared to components from Prometheus ecosystem. It may sound like competition or bragging, but this is a real reason why people migrate from Prometheus to VictoriaMetrics – to cut their infrastructure costs by 2-3 times.
All the features listed above are open-source and are available for everyone to use. The talk will be mostly concentrated on typical use cases in monitoring and elegant ways to make things more efficient.
Microservices and Prometheus (Microservices NYC 2016)
Brian Brazil is an engineer passionate about reliable systems. He has experience at Google SRE and Boxever. He is the founder of Robust Perception and a contributor to open source projects including Prometheus. Prometheus is a monitoring system designed for microservices that allows inclusive, scalable monitoring across languages and services. It uses labels, queries, and federation to provide powerful yet manageable monitoring of dynamic environments.
Scalable Realtime Analytics with declarative SQL like Complex Event Processin...
This document discusses real-time analytics and introduces WSO2 Complex Event Processing (CEP) as a SQL-like language for real-time analytics. It describes how CEP can be used to define filtering, aggregation, pattern matching, and other common operations on streaming data. It also discusses how CEP queries can be scaled out across multiple nodes by partitioning streams and queries. CEP provides an easy to use yet powerful way to perform real-time analytics on streaming data at scale.
Monitoring in Big Data Platform - Albert Lewandowski, GetInData
Did you like it? Check out our blog to stay up to date: https://getindata.com/blog
The webinar was organized by GetinData on 2020. During the webinar we explaned the concept of monitoring and observability with focus on data analytics platforms.
Watch more here: https://www.youtube.com/watch?v=qSOlEN5XBQc
Whitepaper - Monitoring ang Observability for Data Platform: https://getindata.com/blog/white-paper-big-data-monitoring-observability-data-platform/
Speaker: Albert Lewandowski
Linkedin: https://www.linkedin.com/in/albert-lewandowski/
___
Getindata is a company founded in 2014 by ex-Spotify data engineers. From day one our focus has been on Big Data projects. We bring together a group of best and most experienced experts in Poland, working with cloud and open-source Big Data technologies to help companies build scalable data architectures and implement advanced analytics over large data sets.
Our experts have vast production experience in implementing Big Data projects for Polish as well as foreign companies including i.a. Spotify, Play, Truecaller, Kcell, Acast, Allegro, ING, Agora, Synerise, StepStone, iZettle and many others from the pharmaceutical, media, finance and FMCG industries.
https://getindata.com
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In this talk I'll look at the How, Why and What of monitoring and show you how you can use Prometheus to gain insight into your Python applications.
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As distributed applications grow more complex, dynamic, and massively scalable, “observability” becomes more critical. Observability is the practice of using metrics, monitoring and distributed tracing to understand how a system works. In this presentation we’ll explore two complementary Open Source technologies: Prometheus for monitoring application metrics; and OpenTracing and Jaeger for distributed tracing. We’ll discover how they improve the observability of a massively scalable Anomaly Detection system - an application which is built around Apache Cassandra and Apache Kafka for the data layers, and dynamically deployed and scaled on Kubernetes, a container orchestration technology. We will give an overview of Prometheus and OpenTracing/Jaeger, explain how the application is instrumented, and describe how Prometheus and OpenTracing are deployed and configured in a production environment running Kubernetes, to dynamically monitor the application at scale. We conclude by exploring the benefits of monitoring and tracing technologies for understanding, debugging and tuning complex dynamic distributed systems built on Kafka, Cassandra and Kubernetes, and introduce a new use case to enable Cassandra Elastic Autoscaling, by combining Prometheus alerts, Instaclustr’s Provisioning API for Dynamic Resizing, and the new Prometheus monitoring API.
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analysis of applications, runtimes, operating systems, and hypervisors, in an environment of 150k cloud instances
that undergo numerous production changes each week. Apart from the developers who regularly optimize their own code
, we also have a dedicated performance team to help with any issue across the cloud, and to build tooling to aid in
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o do root cause analysis on cloud performance issues. The analysis performed may be cloud-wide, using self-service
GUIs such as our open source Atlas tool, or focused on individual instances, and use our open source Vector tool, f
lame graphs, Java debuggers, and tooling that uses Linux perf, ftrace, and bcc/eBPF. You can use these open source
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By Nic Jansma and Charles Vazac (Akamai)
Fluent 2018
http://www.youtube.com/watch?v=L3LKtFh1HkQ
Would you give the Amazon Prime delivery robot the key to your house, just because it stops by to deliver delicious packages every day? Even if you would, do you still have 100% confidence that it wouldn’t accidentally drag in some mud, let the neighbor in, steal your things, or burn your house down? Worst-case scenarios such as these are what you should be planning for when deciding whether or not to include third-party libraries and services on your website. While most libraries have good intentions, by including them on your site, you have given them complete control over the kingdom. Once on your site, they can provide all of the great services you want—or they can destroy everything you’ve worked so hard to build.
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Contact us at prometheus@robustperception.io
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Video and slides synchronized, mp3 and slide download available at URL http://bit.ly/1yyaHb8.
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As distributed cloud applications grow more complex, dynamic, and massively scalable, “observability” becomes more critical.
Observability is the practice of using metrics, monitoring and distributed tracing to understand how a system works.
We’ll explore two complementary Open Source technologies:
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Speaking from experience building a SaaS: users are insane. If you are lucky, they use your service, but in reality, they probably abuse. Crazy usage patterns resulting in more requests than expected, request bursts when users come back to the office after the weekend, and more! These all pose a potential threat to the health of our web application and may impact other users or the service as a whole. Ideally, we can apply some filtering at the front door: limit the number of requests over a given timespan, limiting bandwidth, ...
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Fluent 2018: When third parties stop being polite... and start getting real
Fluent 2018: When third parties stop being polite... and start getting realAkamai Developers & Admins
At Fluent Conference 2018, Nic Jansma and Charles Vazac perform an honest audit of several popular third-party libraries to understand their true cost to your site, exploring loading patterns, SPOF avoidance, JavaScript parsing, long tasks, runtime overhead, polyfill headaches, security and privacy concerns, and more. They also share tools to help you decide if a library’s risks and unseen costs are worth it.When Third Parties Stop Being Polite... and Start Getting Real
By Nic Jansma and Charlie Vazac (Akamai)
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Would you give the Amazon Prime delivery robot the key to your house, just because it stops by to deliver delicious packages every day? Even if you would, do you still have 100% confidence that it wouldn’t accidentally drag in some mud, let the neighbor in, steal your things, or burn your house down? Worst-case scenarios such as these are what you should be planning for when deciding whether or not to include third-party libraries and services on your website. While most libraries have good intentions, by including them on your site, you have given them complete control over the kingdom. Once on your site, they can provide all of the great services you want—or they can destroy everything you’ve worked so hard to build.
It’s prudent to be cautious: we’ve all heard stories about how third-party libraries have caused slowdowns, broken websites, and even led to downtime. But how do you evaluate the actual costs and potential risks of a third-party library so you can balance that against the service it provides? Every library requires nonzero overhead to provide the service it claims. In many cases, the overhead is minimal and justified, but we should quantify it to understand the real cost. In addition, libraries need to be carefully crafted so they can avoid causing additional pain when the stars don’t align and things go wrong.
Nic Jansma and Charles Vazac perform an honest audit of several popular third-party libraries to understand their true cost to your site, exploring loading patterns, SPOF avoidance, JavaScript parsing, long tasks, runtime overhead, polyfill headaches, security and privacy concerns, and more. From how the library is loaded, to the moment it phones home, you’ll see how third-parties can affect the host page and discover best practices you can follow to ensure they do the least potential harm.
With all of the great performance tools available to developers today, we’ve gained a lot of insight into just how much third-party libraries are impacting our websites. Nic and Charles detail tools to help you decide if a library’s risks and unseen costs are worth it. While you may not have the time to perform a deep dive into every third-party library you want to include on your site, you’ll leave with a checklist of the most important best practices third-parties should be following for you to have confidence in them.
#TwitterRealTime - Real time processing @twitter
Who: Karthik Ramasamy (@karthikz)
Date: September 20, 2016
Event: #TwitterRealTime
This slide deck consists of presentations from various teams about Twitter's real time infrastructure, the components it uses, and how they function. It includes presentations from David Rusek (@davidrusek), Maosong Fu (@Louis_Fumaosong), Sandy Strong (@st5are), and Yimin Tan (@YiminTan_Kevin).
Approaches for application request throttling - dotNetCologne
Speaking from experience building a SaaS: users are insane. If you are lucky, they use your service, but in reality, they probably abuse. Crazy usage patterns resulting in more requests than expected, request bursts when users come back to the office after the weekend, and more! These all pose a potential threat to the health of our web application and may impact other users or the service as a whole. Ideally, we can apply some filtering at the front door: limit the number of requests over a given timespan, limiting bandwidth, ...
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ACM DEBS 2015: Realtime Streaming Analytics Patterns
This document discusses various patterns for real-time streaming analytics. It begins by providing background on data analytics and how real-time streaming has become important for use cases where insights need to be generated very quickly. It then covers basic patterns like preprocessing, alerts and thresholds, counting, and joining event streams. Further patterns discussed include detecting trends, interacting with databases, running batch and real-time queries, and using machine learning models. The document also reviews tools for implementing real-time analytics like stream processing frameworks and complex event processing. Finally, it provides examples of implementing several patterns in Storm and WSO2 CEP.
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Monitoring Clojure Applications with Prometheus
- 1. Monitoring Clojure Applications with Prometheus Jo Draeger 18/09/2018
- 2. How do you know your Clojure web service is doing what it is supposed to do?
- 4. 1. Monitoring 2. Prometheus 3. Clojure + Prometheus LIVE! Agenda
- 5. Monitoring
- 6. What is Monitoring? Metrics Raw measurements e.g. CPU usage, disk space, requests/s, number of errors Instrumentation Adding code to components to expose metrics Monitoring Collect, store, aggregate and visualise metrics, trigger actions Alerting Automated action when metrics values leave defined boundaries
- 7. ● Reliability, Availability ● Preempt problems ● Detect problems ● Identify causes ● Optimise resource utilisation: Efficiency Why Monitoring
- 8. Monitoring @ Signal AI Text-Analytics Pipeline Summarisation Topic Classification Entity Recognition Story collation Deduplication Transformation Content Provider User Print Online Broadcast Alerts API searches, latency, memory, CPU... queue sizes, latency, msgs/s, doc sizes... article count, API health... timeliness...
- 9. Prometheus
- 10. Prometheus discovers by Scrapes by HTTP visualises alerts stores to disk Targets (tasks, VMs, services) m anaged by https://github.com/SignalMedia/prometheus-ecs-sd
- 11. curl http://my-service/metrics # HELP process_start_time_seconds Start time of the process since unix epoch in seconds. # TYPE process_start_time_seconds gauge process_start_time_seconds 1.537190369188E9 # HELP process_open_fds Number of open file descriptors. # TYPE process_open_fds gauge process_open_fds 86.0 # HELP process_max_fds Maximum number of open file descriptors. # TYPE process_max_fds gauge process_max_fds 10240.0 # HELP jvm_gc_collection_seconds Time spent in a given JVM garbage collector in seconds. # TYPE jvm_gc_collection_seconds summary jvm_gc_collection_seconds_count{gc="PS Scavenge",} 9.0 jvm_gc_collection_seconds_sum{gc="PS Scavenge",} 0.085 Prometheus Metrics Exposition
- 12. Push Pull ● Client needs to know target ● Rate controlled by client ● Can monitor single events ● Scraper needs to discover targets ● Rate controlled by scraper ● Aggregates events ● Easy to develop and test (curl!) ● HollywoodPrinciple Thomas Wolf, www.foto-tw.de / Wikimedia Commons / CC BY-SA 3.0
- 13. Gauges: Current value, for slowly changing values, e.g.: Number of workers. Counters: Sum of events, or sum of values, e.g. sum of requests, sum of time taken for requests Gauges vs Counters
- 14. Counters Example t requests requests /s avg latency request counter latency counter requests /s avg latency 0s 20s 80 4.0 15ms 80 1200 40s 90 4.5 20ms 170 3000 60s 40 2.0 5ms 210 3200 3.5 15.2 80s 30 1.5 10ms 240 3500 100s 80 4.0 25ms 320 5500 120s 20 1.0 5ms 340 4500 2.2 18.5 Scraping every 60s 210/50=3.5 (340-210) / 60 = 2.2 3200/210=15.2 (5600-3200) /( 340- 210) = 18.5
- 15. Prometheus and Grafana in Docker Compose http-kit and ring based webservice Looks up coordinates for a postcode and then looks up crime stats https://tinyurl.com/cljpromdemo Live Demo
- 16. ● Add Metrics, switch on the light! ● Pull is much, much better then Push ;-) (it depends) ● Keep an eye on JVM metrics ● Try it Summary https://tinyurl.com/cljpromdemo