Latency SLOs done right
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This document discusses best practices for defining and measuring latency service level objectives (SLOs). It recommends computing SLOs directly from raw log data using histograms, which allow arbitrary percentiles to be derived and are better than averaging sample percentiles. Histograms can also be aggregated over time and used to count the number of requests above a latency threshold regardless of what the threshold was set to initially. Common histogram implementations like HDR-Histogram and t-digest are suggested.
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We present how we did some slight modifications so that we can use with Flink at even greater scale to return the TopK best matches on past data or streaming data.
Post quantum cryptography in vault (hashi talks 2020)
This talk is an introduction to quantum cryptography and cryptanalysis: the physics and mathematics behind how quantum computers provide unique opportunities and threats to traditional cryptographic systems. We will review the basics behind quantum mechanics and quantum computers, why quantum computers pose a unique threat to cryptographic systems and what secure infrastructure systems must do to protect secrets in a post-quantum world.
Naked Performance With Clojure
Clojure is a modern dynamically typed lisp. Dynamical typing is ofter associated with poor performance and runtime failures. In this talk, I'll present some of the lessons learned on building Clojure/Script systems that are both ridiculously fast and will fail fast on errors. Will compare the performance of mutable, persistent & zero-copy data structures and show how we can use interpreters and compilers to build beautiful and performant abstractions. A quick demo on how to build a simple non-blocking web server that runs idiomatic Clojure to serve millions of requests per sec.
Clojure is awesome, and it can be fast too.
Video: https://www.youtube.com/watch?v=3SSHjKT3ZmA
Welcome Webinar Slides
This document provides an agenda and overview for a Sumo Logic webinar training session. The agenda includes sections on data collection, search and analysis, and visualizing and monitoring. It discusses Sumo Logic's analytics platform and data flow. It also provides instructions for logging into a training environment and demonstrates examples of searching log data and creating dashboards and alerts.
SnappyData Ad Analytics Use Case -- BDAM Meetup Sept 14th
Presented at the BDAM meetup in Palo Alto on Sept 14th. Jags Ramnarayan, CTO, SnappyData, discusses an ad analytics use case running on SnappyData.
Mining data streams
This document discusses mining data streams. It describes how stream management is important when input is controlled externally, unlike in a DBMS where input is controlled by programming staff. It explains the stream model where tuples enter rapidly and the system cannot store the entire stream. Critical calculations must be made about the stream using limited memory. There are two forms of queries: ad-hoc queries asked once and standing queries asked continuously. The document also discusses sliding windows as a model where queries are about the most recent N elements received, where N is too large to store in memory. It provides examples of applications of the stream model like mining query streams and click streams.
Identifying and Correlating Internet-wide Scan Traffic to Newsworthy Security...
In this presentation, we will discuss using GreyNoise, a geographically and logically distributed system of passive Internet scan traffic collector nodes, to identify statistical anomalies in global opportunistic Internet scan traffic and correlate these anomalies with publicly disclosed vulnerabilities, large-scale DDoS attacks, and other newsworthy events. We will discuss establishing (and identifying any deviations away from) a “standard” baseline of Internet scan traffic. We will discuss successes and failures of different methods employed over the past six months. We will explore open questions and future work on automated anomaly detection of Internet scan traffic. Finally, we will provide raw data and a challenge as an exercise to the attendees.
Accurate and Reliable What-If Analysis of Business Processes: Is it Achievable?
This document discusses using event logs to generate business process simulation models. It describes traditional discrete event simulation approaches that discover simulation models from event logs recorded by information systems. Deep learning techniques are also discussed that can generate traces without an explicit process model. The document suggests that combining discrete event simulation and deep learning may produce more accurate simulations, but challenges remain around validating such hybrid approaches and testing them in previously unseen scenarios. More research is needed before these data-driven simulation methods can reliably predict the effects of interventions.
Boetticher Presentation Promise 2008v2
The document discusses building human-based software estimation models that are accurate, intuitive, and easy to understand. It presents an approach using correlation and scale factors between estimated and actual effort. Experiments on a dataset of 178 samples show that combining correlation and scale factors into a decision tree achieves up to 93.3% accuracy. The resulting model bridges expert and algorithmic estimation methods.
Keynote: Building and Operating A Serverless Streaming Runtime for Apache Bea...
Apache Beam is Flink’s sibling in the Apache family of streaming processing frameworks. The Beam and Flink teams work closely together on advancing what is possible in streaming processing, including Streaming SQL extensions and code interoperability on both platforms.
Beam was originally developed at Google as the amalgamation of its internal batch and streaming frameworks to power the exabyte-scale data processing for Gmail, YouTube and Ads. It now powers a fully-managed, serverless service Google Cloud Dataflow, as well as is available to run in other Public Clouds and on-premises when deployed in portability mode on Apache Flink, Spark, Samza and other runners. Users regularly run distributed data processing jobs on Beam spanning tens of thousands of CPU cores and processing millions of events per second.
In this session, Sergei Sokolenko, Cloud Dataflow product manager, and Reuven Lax, the founding member of the Dataflow and Beam team, will share Google’s learnings from building and operating a global streaming processing infrastructure shared by thousands of customers, including:
safe deployment to dozens of geographic locations,
resource autoscaling to minimize processing costs,
separating compute and state storage for better scaling behavior,
dynamic work rebalancing of work items away from overutilized worker nodes,
offering a throughput-optimized batch processing capability with the same API as streaming,
grouping and joining of 100s of Terabytes in a hybrid in-memory/on-desk file system,
integrating with the Google Cloud security ecosystem, and other lessons.
Customers benefit from these advances through faster execution of jobs, resource savings, and a fully managed data processing environment that runs in the Cloud and removes the need to manage infrastructure.
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Pinot: Realtime OLAP for 530 Million Users - Sigmod 2018
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More from Fred Moyer
Reliable observability at scale: Error Budgets for 1,000+
This document summarizes a presentation about implementing service level objectives (SLOs) and error budgets at scale. It discusses establishing service level indicators (SLIs) to define good and bad service, setting SLOs as targets for SLIs over time periods, and calculating error budgets as the complement of SLOs. The presentation provides examples of SLIs, SLOs, and error budgets for latency and availability. It also discusses challenges including variance from real users and different stakeholders' needs, and recommends approaches like flexible latency metrics and measuring as close to users as possible.
Practical service level objectives with error budgeting
This document summarizes Fred Moyer's presentation on practical service level objectives with error budgeting. It discusses how to calculate error budgets based on service level indicators, objectives, and agreements. It presents methods to calculate error budgets using log files by counting errors and slow requests, and using metrics like counters and histograms to track errors and response time distributions over time. Maintaining an error budget allows managing risk while releasing new code by setting a target for acceptable errors or slow requests.
Comprehensive Container Based Service Monitoring with Kubernetes and Istio
This document summarizes Fred Moyer's talk on comprehensive container-based service monitoring with Kubernetes and Istio. The talk covered Istio architecture and deployment, using the Istio sample bookinfo application, and monitoring the application with Istio metrics and Grafana dashboards. It also discussed Istio Mixer metrics adapters, math and statistics concepts like histograms and quantiles, and monitoring concepts like service level objectives, indicators, and agreements. The talk provided exercises for attendees to deploy sample applications and create custom metrics adapters.
Comprehensive container based service monitoring with kubernetes and istio
The document provides an overview of using Kubernetes and Istio to monitor microservices. It discusses using Istio to collect telemetry data on requests, including rate, errors, and duration. This data can be visualized in Grafana dashboards to monitor key performance indicators. Histograms are recommended to capture request durations as they allow calculating percentiles over time for service level indicators. An Istio metrics adapter is also described that sends telemetry data to Circonus for long-term storage and alerting.
Effective management of high volume numeric data with histograms
Talk I gave at DataEngConf '18 on how we use histograms to manage high volume numeric data at Circonus
Statistics for dummies
This document provides an overview of key statistical concepts including:
1. The average (arithmetic mean) is calculated by summing all values and dividing by the number of samples.
2. The median is the middle value of a data set when values are sorted from lowest to highest.
3. The 90th percentile represents the value where 90% of values are below it.
4. Standard deviation measures how spread out values are from the average and 68% of values fall within one standard deviation of the average in a normal distribution.
GrafanaCon EU 2018
Fred Moyer from Circonus presented on IRONdb and Grafana. IRONdb is a time series database that can replace existing TSDBs without changes to ingestion or visualizations. It provides scale, reliability, and ease of operations. IRONdb is distributed, replicated across multiple datacenters for reliability, and can store years of high-cardinality histogram and metric data. The upcoming IRONdb data source for Grafana will support histograms, stream tags, and Prometheus storage. Attendees could sign up for early access and preview accounts.
Fredmoyer postgresopen 2017
Lightning talk I gave at #pgopen2017 on collecting postgresql query performance data with wirelatency and Circonus.com
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Slides from my presentation of 'Better service monitoring through histograms' which was presented to Silicon Valley Perl on 9/1/2016
Better service monitoring through histograms
This document discusses using histograms and percentiles to better monitor service performance. It begins by noting the limitations of synthetic monitoring and outlines how real user data can provide a more accurate picture. Percentiles like the median and 90th percentile are explained as useful metrics for understanding performance. Histograms of request latency data over time are presented as a way to detect non-normal distributions that could indicate issues. Calculating alerting thresholds based on percentiles rather than averages is advocated to avoid missing multiple high samples. Examples are given of how percentile-based alerting can more effectively detect performance problems and avoid unnecessary alerts.
The Breakup - Logically Sharding a Growing PostgreSQL Database
The document discusses the process of logically sharding a growing PostgreSQL database. It describes the stages involved: diagnosing which tables are largest; evaluating options like account, geographic or hardware-based sharding; scoping the solution by separating tables between a main and marks database; implementing changes including managing transactions and configuration across databases; releasing the changes; and cleaning up afterwards. It emphasizes testing rollback processes, managing technical debt, and bringing empathy to understanding legacy code and configurations.
Learning go for perl programmers
The document discusses differences between Perl and Go for Perl programmers. It covers Go topics like goroutines (threads), channels (queues), formatting code with gofmt, defining structs instead of hashes/objects, using slices instead of arrays, maps instead of hashes, error handling, importing packages instead of using Perl modules, writing tests with godoc instead of perldoc, and getting code with go get instead of cpanminus. It also provides Golang web resources for learning more.
Surge 2012 fred_moyer_lightning
Netfilter was used to solve performance and scalability issues with an existing captive portal solution. A netfilter module was developed that removed port numbers from HTTP requests, allowing most static content to be fetched directly from origin servers rather than through a proxy. This avoided proxying all traffic and achieved better performance than alternatives like Tinyproxy. The netfilter solution worked well technically but did not prove viable long-term for business reasons.
Qpsmtpd
The talk I gave on writing applications with Qpsmtpd at the Nordic Perl Workshop in 2006 at Oslo, Norway.
Apache Dispatch
This document discusses Apache::Dispatch, a lightweight abstraction layer for mod_perl applications. It maps URIs to application resources via method handlers, providing the power of mod_perl handlers with a painless migration. The document reviews how Apache::Dispatch works, provides examples of configuration, method handlers, and testing with Apache::Test. It also covers additional Apache::Dispatch features like pre/post-dispatch handlers, inheritance, autoloading, and filtering.
Ball Of Mud Yapc 2008
Or, what to do when your organically grown Perl application grows too big for its (and your) own good.
Data::FormValidator Simplified
This document discusses the Data::FormValidator module, which provides a simplified way to validate form data in Perl. It allows defining validation profiles that specify required and optional fields, as well as custom and built-in constraint methods. The module takes request parameters, runs validation according to the profile, and returns results that can be easily integrated into templates to display error messages.
More from Fred Moyer (17)
Reliable observability at scale: Error Budgets for 1,000+
Reliable observability at scale: Error Budgets for 1,000+
Practical service level objectives with error budgeting
Practical service level objectives with error budgeting
Comprehensive Container Based Service Monitoring with Kubernetes and Istio
Comprehensive Container Based Service Monitoring with Kubernetes and Istio
Comprehensive container based service monitoring with kubernetes and istio
Comprehensive container based service monitoring with kubernetes and istio
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Effective management of high volume numeric data with histograms
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Better service monitoring through histograms sv perl 09012016
The Breakup - Logically Sharding a Growing PostgreSQL Database
The Breakup - Logically Sharding a Growing PostgreSQL Database
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Hand Rolled Applicative User Validation Code Kata
Could you use a simple piece of Scala validation code (granted, a very simplistic one too!) that you can rewrite, now and again, to refresh your basic understanding of Applicative operators <*>, <*, *>?
The goal is not to write perfect code showcasing validation, but rather, to provide a small, rough-and ready exercise to reinforce your muscle-memory.
Despite its grandiose-sounding title, this deck consists of just three slides showing the Scala 3 code to be rewritten whenever the details of the operators begin to fade away.
The code is my rough and ready translation of a Haskell user-validation program found in a book called Finding Success (and Failure) in Haskell - Fall in love with applicative functors.
Atelier - Innover avec l’IA Générative et les graphes de connaissances
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Allez au-delà du battage médiatique autour de l’IA et découvrez des techniques pratiques pour utiliser l’IA de manière responsable à travers les données de votre organisation. Explorez comment utiliser les graphes de connaissances pour augmenter la précision, la transparence et la capacité d’explication dans les systèmes d’IA générative. Vous partirez avec une expérience pratique combinant les relations entre les données et les LLM pour apporter du contexte spécifique à votre domaine et améliorer votre raisonnement.
Amenez votre ordinateur portable et nous vous guiderons sur la mise en place de votre propre pile d’IA générative, en vous fournissant des exemples pratiques et codés pour démarrer en quelques minutes.
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The UI5 tooling is the development and build tooling of UI5. It is built in a modular and extensible way so that it can be easily extended by your needs. This session will showcase various tooling extensions which can boost your development experience by far so that you can really work offline, transpile your code in your project to use even newer versions of EcmaScript (than 2022 which is supported right now by the UI5 tooling), consume any npm package of your choice in your project, using different kind of proxies, and even stitching UI5 projects during development together to mimic your target environment.
Odoo ERP Vs. Traditional ERP Systems – A Comparative Analysis
When it is all about ERP solutions, companies typically meet their needs with common ERP solutions like SAP, Oracle, and Microsoft Dynamics. These big players have demonstrated that ERP systems can be either simple or highly comprehensive. This remains true today, but there are new factors to consider, including a promising new contender in the market that’s Odoo. This blog compares Odoo ERP with traditional ERP systems and explains why many companies now see Odoo ERP as the best choice.
What are ERP Systems?
An ERP, or Enterprise Resource Planning, system provides your company with valuable information to help you make better decisions and boost your ROI. You should choose an ERP system based on your company’s specific needs. For instance, if you run a manufacturing or retail business, you will need an ERP system that efficiently manages inventory. A consulting firm, on the other hand, would benefit from an ERP system that enhances daily operations. Similarly, eCommerce stores would select an ERP system tailored to their needs.
Because different businesses have different requirements, ERP system functionalities can vary. Among the various ERP systems available, Odoo ERP is considered one of the best in the ERp market with more than 12 million global users today.
Odoo is an open-source ERP system initially designed for small to medium-sized businesses but now suitable for a wide range of companies. Odoo offers a scalable and configurable point-of-sale management solution and allows you to create customised modules for specific industries. Odoo is gaining more popularity because it is built in a way that allows easy customisation, has a user-friendly interface, and is affordable. Here, you will cover the main differences and get to know why Odoo is gaining attention despite the many other ERP systems available in the market.
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Drona Infotech is a premier mobile app development company in Noida, providing cutting-edge solutions for businesses.
Visit Us For : https://www.dronainfotech.com/mobile-application-development/
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原版定制美国纽约州立大学奥尔巴尼分校毕业证学位证书原版一模一样
原版一模一样【微信：741003700 】【美国纽约州立大学奥尔巴尼分校毕业证学位证书】【微信：741003700 】学位证，留信认证（真实可查，永久存档）offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原海外各大学 Bachelor Diploma degree, Master Degree Diploma
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留信网认证的作用:
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2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
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Do you want Software for your Business? Visit Deuglo
Deuglo has top Software Developers in India. They are experts in software development and help design and create custom Software solutions.
Deuglo follows seven steps methods for delivering their services to their customers. They called it the Software development life cycle process (SDLC).
Requirement — Collecting the Requirements is the first Phase in the SSLC process.
Feasibility Study — after completing the requirement process they move to the design phase.
Design — in this phase, they start designing the software.
Coding — when designing is completed, the developers start coding for the software.
Testing — in this phase when the coding of the software is done the testing team will start testing.
Installation — after completion of testing, the application opens to the live server and launches!
Maintenance — after completing the software development, customers start using the software.
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socradar-q1-2024-aviation-industry-report.pdf
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The aviation industry has always been a prime target for cybercriminals due to its critical infrastructure and high stakes. In the first quarter of 2024, the sector faced an alarming surge in cybersecurity threats, revealing its vulnerabilities and the relentless sophistication of cyber attackers.
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Atelier - Innover avec l’IA Générative et les graphes de connaissances
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Neo4j - Product Vision and Knowledge Graphs - GraphSummit Paris
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Neo4j - Product Vision and Knowledge Graphs - GraphSummit Paris
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Latency SLOs done right
- 1. Latency SLOs Done Right by @phredmoyer, Developer Evangelist @Circonus
- 2. Service Level Objectives SREcon 2018 SLO workshop (Google) Latency SLI The proportion of valid requests served faster than a threshold Which requests are valid? What is the threshold? @phredmoyer
- 3. Service Level Objectives SREcon 2018 SLO workshop (Google) Latency SLO 99% of home page requests in the past 28 days served in < 100ms @phredmoyer
- 4. Service Level Objectives SREcon 2018 SLO workshop (Google) What is the p90 computed over the full 28 days? @phredmoyer
- 5. Service Level Objectives SREcon 2018 SLO workshop (Google) Hint - it’s not the average of each p90 sample shown @phredmoyer
- 6. A more dramatic example Calculated p90 (10-100ms) != averaged p90 (36ms)
- 7. Rethinking computing SLO latency 1) Compute the SLO from stored raw data (logs) 2) Count the number of bad requests 3) Use histograms to store latency distribution @phredmoyer
- 8. 1) Compute the SLO from stored data Possible with many tools (Splunk, ELK, awk/grep) Not always tenable for large volumes of data Tough to do in real time @phredmoyer
- 9. 1) Compute the SLO from stored data An example of calculating percentiles w/ Splunk mydata | stats perc90(responsetime) as response90, perc99(responsetime) as response96 by ApplicationName @phredmoyer
- 10. 2) Count the number of bad requests
- 11. 2) Count the number of bad requests Percent good = 100 - (2262/60124)*100 = 96.238% Problem - you have to choose latency threshold up front If your SLO changes, you can’t analyze historical data @phredmoyer
- 12. 3) Using histogram latency data Histograms can be aggregated across time Histograms can be used to derive arbitrary percentiles Bin (bucket) choices should span sample data @phredmoyer
- 13. 3) Using histogram latency data HDR-Histogram – https://HDrhistogram.org Circllhist – https://github.com/circonus- labs/libcircllhist/ t-digest – https://github.com/tdunning/t-digest @phredmoyer
- 14. 3) Using histogram latency data
- 15. 3) Using histogram latency data
- 16. 3) Using histogram latency data 99% of home page requests in the past 28 days served in < 100ms % requests = count_below(100ms) / total_count * 100 ex: 99.484 percent faster than 100ms @phredmoyer
- 17. 3) Using histogram latency data
- 18. 3) Using histogram latency data
- 19. 30 seconds for questions, go! Tweet me @phredmoyer Ask us anything on Slack at http://slack.s.circonus.com/
- 20. 30 seconds for questions, go! Tweet me @phredmoyer Ask us anything on Slack at http://slack.s.circonus.com/