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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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
this analysis. In this session we will summarize the Netflix environment, procedures, and tools we use and build t
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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When third parties stop being polite... and start getting real
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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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.
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Prometheus is a next-generation monitoring system with a time series database at it's core. Once you have a time series database, what do you do with it though? This talk will look at getting data in, and more importantly how to use the data you collect productively.
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
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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.
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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.
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Who: Karthik Ramasamy (@karthikz)
Date: September 20, 2016
Event: #TwitterRealTime
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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, ...
In this talk, we’ll explore the simple yet complex realm of rate limiting. We’ll go over how to decide on which resources to limit, what the limits should be and where to enforce these limits – in our app, on the server, using a reverse proxy like Nginx or even an external service like CloudFlare or Azure API management. The takeaway? Know when and where to enforce rate limits so you can have both a happy application as well as happy customers.
ACM DEBS 2015: Realtime Streaming Analytics Patterns
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- 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