This presentation was inspired post read of "TimeSeries Databases" -- Ted Dunning & Ellen Friedman.
I have tried to summarize a lot of the previous bench marks. Hope others find it useful. The slides were compiled early 2015 so some of the results might have changed but the core literature should still hold.
Presentation for Pervasive Systems class lectured by prof. Ioannis Chatzigiannakis, a.y. 2015-16, about the No-SQL database InfluxDB. The course is intended for students of MS in Engineering in Computer Science at Sapienza - University of Rome.
The complete code for the demo is available on Github:
https://github.com/RobGaud/PervasiveSystemsPersonal
You can also find me on LinkedIn:
https://www.linkedin.com/in/roberto-gaudenzi-4b0422116
A quick walk through InfluxDB and TICK Stack.
Telegraf (Collect), InfluxDB (Store), Chrongraf (Visualize), and Kapacitor (Process).
- What is time series data?
- Why TICK Stack?
- Where could TICK Stack be used?
Beautiful Monitoring With Grafana and InfluxDBleesjensen
Query your data streams with the time series database InfluxDB and then visualize the results with stunning Grafana dashboards. Quick and easy to set up. Fully scalable to millions of metrics per second.
In this training webinar, we will walk you through the basics of InfluxDB – the purpose-built time series database. InfluxDB has everything you need from a time series platform in a single binary – a multi-tenanted time series database, UI and dashboarding tools, background processing and monitoring agent. This one-hour session will include the training and time for live Q&A.
What you will learn
Core concepts of time series databases
An overview of the InfluxDB platform
How to ingesting and query data in InfluxDB
This presentation was inspired post read of "TimeSeries Databases" -- Ted Dunning & Ellen Friedman.
I have tried to summarize a lot of the previous bench marks. Hope others find it useful. The slides were compiled early 2015 so some of the results might have changed but the core literature should still hold.
Presentation for Pervasive Systems class lectured by prof. Ioannis Chatzigiannakis, a.y. 2015-16, about the No-SQL database InfluxDB. The course is intended for students of MS in Engineering in Computer Science at Sapienza - University of Rome.
The complete code for the demo is available on Github:
https://github.com/RobGaud/PervasiveSystemsPersonal
You can also find me on LinkedIn:
https://www.linkedin.com/in/roberto-gaudenzi-4b0422116
A quick walk through InfluxDB and TICK Stack.
Telegraf (Collect), InfluxDB (Store), Chrongraf (Visualize), and Kapacitor (Process).
- What is time series data?
- Why TICK Stack?
- Where could TICK Stack be used?
Beautiful Monitoring With Grafana and InfluxDBleesjensen
Query your data streams with the time series database InfluxDB and then visualize the results with stunning Grafana dashboards. Quick and easy to set up. Fully scalable to millions of metrics per second.
In this training webinar, we will walk you through the basics of InfluxDB – the purpose-built time series database. InfluxDB has everything you need from a time series platform in a single binary – a multi-tenanted time series database, UI and dashboarding tools, background processing and monitoring agent. This one-hour session will include the training and time for live Q&A.
What you will learn
Core concepts of time series databases
An overview of the InfluxDB platform
How to ingesting and query data in InfluxDB
QuestDB: ingesting a million time series per second on a single instance. Big...javier ramirez
In this session I will show you the technical decisions we made when building QuestDB, the open source, Postgres compatible, time-series database, and how we can achieve a million row writes per second without blocking or slowing down the reads.
Lucene 4.0 is on its way to deliver a tremendous amount of new features and improvements. Beside Real-Time Search & Flexible Indexing DocValues aka. Column Stride Fields is one of the "next generation" features
Finite state automata and transducers made it into Lucene fairly recently, but already show a very promising impact on search performance. This data structure is rarely exploited because it is commonly (and unfairly) associated with high complexity. During the talk, I will try to show that automata and transducers are in fact very simple, their construction can be very efficient (memory and time-wise) and their field of applications very broad.
Understanding InfluxDB Basics: Tags, Fields and MeasurementsInfluxData
Is it a table? No, it is much more! Finally understand tags, fields and measurements.
In this session, you will learn how to answer your real-life questions with data stored in InfluxDB. You will see that InfluxDB is more than some tables; it is a window to the world of your data. In particular, the usage of tags, fields and measurements enhances the time series database and helps answer your questions in a convenient and fast way, if you know what to do. Discover tips and tricks to use while implementing InfluxDB.
All topics are addressed in the context of IoT monitoring, predictive maintenance and medical applications.
Deep Dive into the New Features of Apache Spark 3.1Databricks
Continuing with the objectives to make Spark faster, easier, and smarter, Apache Spark 3.1 extends its scope with more than 1500 resolved JIRAs. We will talk about the exciting new developments in the Apache Spark 3.1 as well as some other major initiatives that are coming in the future. In this talk, we want to share with the community many of the more important changes with the examples and demos.
The following features are covered: the SQL features for ANSI SQL compliance, new streaming features, and Python usability improvements, the performance enhancements and new tuning tricks in query compiler.
A Fast and Efficient Time Series Storage Based on Apache SolrQAware GmbH
OSDC 2016, Berlin: Talk by Florian Lautenschlager (@flolaut, Senior Software Engineer at QAware)
Abstract: How to store billions of time series points and access them within a few milliseconds? Chronix! Chronix is a young but mature open source project that allows one for example to store about 15 GB (csv) of time series in 238 MB with average query times of 21 ms. Chronix is built on top of Apache Solr a bulletproof distributed NoSQL database with impressive search capabilities. In this code-intense session we show how Chronix achieves its efficiency in both respects by means of an ideal chunking, by selecting the best compression technique, by enhancing the stored data with (pre-computed) attributes, and by specialized query functions.
Would you ever play an online game if you were not able to communicate with your teammates? Isn’t it fun if you can make new friends, arrange pre-made games and celebrate your victories with people you like to play with?
Riot Games’ League of Legends handles millions of online players at any given time. Each chat server is responsible for routing over 1 billion real time events a day. In order to support the overwhelming user base and be prepared future growth, as well as pave the road for the upcoming features, chat infrastructure had to be designed and built with the utmost care, so that it would never fail the players.
In this talk I would like to present how we achieved linear scalability, improved the overall fault tolerance, created a framework for real time code upgrades and got ready for the new features we want to ship. I will also discuss in detail why we chose to use Erlang as a foundation for the system, and why we migrated our data from MySQL to Riak.
Intro to InfluxDB 2.0 and Your First Flux Query by Sonia GuptaInfluxData
In this InfluxDays NYC 2019 talk, InfluxData Developer Advocate Sonia Gupta will provide an introduction to InfluxDB 2.0 and a review of the new features. She will demonstrate how to install it, insert data, and build your first Flux query.
Hive is a data warehousing infrastructure based on Hadoop. Hadoop provides massive scale out and fault tolerance capabilities for data storage and processing (using the map-reduce programming paradigm) on commodity hardware.
Hive is designed to enable easy data summarization, ad-hoc querying and analysis of large volumes of data. It provides a simple query language called Hive QL, which is based on SQL and which enables users familiar with SQL to do ad-hoc querying, summarization and data analysis easily. At the same time, Hive QL also allows traditional map/reduce programmers to be able to plug in their custom mappers and reducers to do more sophisticated analysis that may not be supported by the built-in capabilities of the language.
Introduction to InfluxDB, an Open Source Distributed Time Series Database by ...Hakka Labs
In this presentation, Paul introduces InfluxDB, a distributed time series database that he open sourced based on the backend infrastructure at Errplane. He talks about why you'd want a database specifically for time series and he covers the API and some of the key features of InfluxDB, including:
• Stores metrics (like Graphite) and events (like page views, exceptions, deploys)
• No external dependencies (self contained binary)
• Fast. Handles many thousands of writes per second on a single node
• HTTP API for reading and writing data
• SQL-like query language
• Distributed to scale out to many machines
• Built in aggregate and statistics functions
• Built in downsampling
QuestDB: ingesting a million time series per second on a single instance. Big...javier ramirez
In this session I will show you the technical decisions we made when building QuestDB, the open source, Postgres compatible, time-series database, and how we can achieve a million row writes per second without blocking or slowing down the reads.
Lucene 4.0 is on its way to deliver a tremendous amount of new features and improvements. Beside Real-Time Search & Flexible Indexing DocValues aka. Column Stride Fields is one of the "next generation" features
Finite state automata and transducers made it into Lucene fairly recently, but already show a very promising impact on search performance. This data structure is rarely exploited because it is commonly (and unfairly) associated with high complexity. During the talk, I will try to show that automata and transducers are in fact very simple, their construction can be very efficient (memory and time-wise) and their field of applications very broad.
Understanding InfluxDB Basics: Tags, Fields and MeasurementsInfluxData
Is it a table? No, it is much more! Finally understand tags, fields and measurements.
In this session, you will learn how to answer your real-life questions with data stored in InfluxDB. You will see that InfluxDB is more than some tables; it is a window to the world of your data. In particular, the usage of tags, fields and measurements enhances the time series database and helps answer your questions in a convenient and fast way, if you know what to do. Discover tips and tricks to use while implementing InfluxDB.
All topics are addressed in the context of IoT monitoring, predictive maintenance and medical applications.
Deep Dive into the New Features of Apache Spark 3.1Databricks
Continuing with the objectives to make Spark faster, easier, and smarter, Apache Spark 3.1 extends its scope with more than 1500 resolved JIRAs. We will talk about the exciting new developments in the Apache Spark 3.1 as well as some other major initiatives that are coming in the future. In this talk, we want to share with the community many of the more important changes with the examples and demos.
The following features are covered: the SQL features for ANSI SQL compliance, new streaming features, and Python usability improvements, the performance enhancements and new tuning tricks in query compiler.
A Fast and Efficient Time Series Storage Based on Apache SolrQAware GmbH
OSDC 2016, Berlin: Talk by Florian Lautenschlager (@flolaut, Senior Software Engineer at QAware)
Abstract: How to store billions of time series points and access them within a few milliseconds? Chronix! Chronix is a young but mature open source project that allows one for example to store about 15 GB (csv) of time series in 238 MB with average query times of 21 ms. Chronix is built on top of Apache Solr a bulletproof distributed NoSQL database with impressive search capabilities. In this code-intense session we show how Chronix achieves its efficiency in both respects by means of an ideal chunking, by selecting the best compression technique, by enhancing the stored data with (pre-computed) attributes, and by specialized query functions.
Would you ever play an online game if you were not able to communicate with your teammates? Isn’t it fun if you can make new friends, arrange pre-made games and celebrate your victories with people you like to play with?
Riot Games’ League of Legends handles millions of online players at any given time. Each chat server is responsible for routing over 1 billion real time events a day. In order to support the overwhelming user base and be prepared future growth, as well as pave the road for the upcoming features, chat infrastructure had to be designed and built with the utmost care, so that it would never fail the players.
In this talk I would like to present how we achieved linear scalability, improved the overall fault tolerance, created a framework for real time code upgrades and got ready for the new features we want to ship. I will also discuss in detail why we chose to use Erlang as a foundation for the system, and why we migrated our data from MySQL to Riak.
Intro to InfluxDB 2.0 and Your First Flux Query by Sonia GuptaInfluxData
In this InfluxDays NYC 2019 talk, InfluxData Developer Advocate Sonia Gupta will provide an introduction to InfluxDB 2.0 and a review of the new features. She will demonstrate how to install it, insert data, and build your first Flux query.
Hive is a data warehousing infrastructure based on Hadoop. Hadoop provides massive scale out and fault tolerance capabilities for data storage and processing (using the map-reduce programming paradigm) on commodity hardware.
Hive is designed to enable easy data summarization, ad-hoc querying and analysis of large volumes of data. It provides a simple query language called Hive QL, which is based on SQL and which enables users familiar with SQL to do ad-hoc querying, summarization and data analysis easily. At the same time, Hive QL also allows traditional map/reduce programmers to be able to plug in their custom mappers and reducers to do more sophisticated analysis that may not be supported by the built-in capabilities of the language.
Introduction to InfluxDB, an Open Source Distributed Time Series Database by ...Hakka Labs
In this presentation, Paul introduces InfluxDB, a distributed time series database that he open sourced based on the backend infrastructure at Errplane. He talks about why you'd want a database specifically for time series and he covers the API and some of the key features of InfluxDB, including:
• Stores metrics (like Graphite) and events (like page views, exceptions, deploys)
• No external dependencies (self contained binary)
• Fast. Handles many thousands of writes per second on a single node
• HTTP API for reading and writing data
• SQL-like query language
• Distributed to scale out to many machines
• Built in aggregate and statistics functions
• Built in downsampling
A presentation on our experience at Ingram Content Group with Grafana and MySQL. In an enterprise environment it is sometimes necessary to keep data in a traditional, general purpose SQL database such as MySQL or PostgreSQL. These slides explore the challenges and benefits of using Grafana with an SQL database in a large enterprise production setting.
Paul Dix (Founder InfluxDB) - Organising Metrics at #DOXLONOutlyer
Video:
Paul Dix (Founder of InfluxDB) talking about his awesome Open-Source projects for monitoring.
For more info visit: InfluxDB: www.influxdb.com
Join DevOps Exchange London here: http://www.meetup.com/DevOps-Exchange-London/
Follow DOXLON on twitter: twitter.com/doxlon
Monitoring Workshop Kiel 2016 - Performancedaten Visualisierung mit Grafana /...Philip Griesbacher
How to store Nagios/Icinga(2) performancedata in Influxdb and generate automatically Grafana dashboards. Used tools:
- https://github.com/Griesbacher/nagflux
- https://github.com/Griesbacher/histou
Technology changes and process changes in how people build and manage Internet systems have driven a need for a new approach to monitoring. We talk about why, what and how.
As always the conference was opened with a speech by Alexei Vladishev, the creator of Zabbix, glancing over the accomplishments Zabbix made during the past year, mostly focusing on the features and improvements that await us all in the Zabbix 3.0 release.
Zabbix Conference 2015
Most often Zabbix users will monitor Linux hosts using the Zabbix agent, however SNMP is not only an option, it's actually a very viable one. Andrew Nelson will describe his experience configuring Zabbix to monitor a Linux environment of over 500 systems using only SNMP.
Zabbix Conference 2015
KVM and docker LXC Benchmarking with OpenStackBoden Russell
Passive benchmarking with docker LXC and KVM using OpenStack hosted in SoftLayer. These results provide initial incite as to why LXC as a technology choice offers benefits over traditional VMs and seek to provide answers as to the typical initial LXC question -- "why would I consider Linux Containers over VMs" from a performance perspective.
Results here provide insight as to:
- Cloudy ops times (start, stop, reboot) using OpenStack.
- Guest micro benchmark performance (I/O, network, memory, CPU).
- Guest micro benchmark performance of MySQL; OLTP read, read / write complex and indexed insertion.
- Compute node resource consumption; VM / Container density factors.
- Lessons learned during benchmarking.
The tests here were performed using OpenStack Rally to drive the OpenStack cloudy tests and various other linux tools to test the guest performance on a "micro level". The nova docker virt driver was used in the Cloud scenario to realize VMs as docker LXC containers and compared to the nova virt driver for libvirt KVM.
Please read the disclaimers in the presentation as this is only intended to be the "chip of the ice burg".
Scaling up Near Real-time Analytics @Uber &LinkedInC4Media
Video and slides synchronized, mp3 and slide download available at URL http://bit.ly/2nSvlYI.
Chinmay Soman and Yi Pan discuss how Uber and LinkedIn use Apache Samza, Apache Calcite and Pinot. They talk about their analytics platform AthenaX used by data scientists and engineers for specifying data transformations and make it available for querying by real-time dashboards & maps within minutes. Then they focus on what happens under the hood and challenges faced with respect to scale. Filmed at qconsf.com.
Yi Pan is a Distributed Systems Engineer at Linkedin. He joined Linkedin in 2014 and has quickly become the lead of Samza team in LinkedIn and a Committer and PMC member in Apache Samza. Chinmay Soman is a software engineer at Uber, where he builds a self-service platform for doing near real-time analytics. His areas of interest include distributed systems and security.
Architecting peta-byte-scale analytics by scaling out Postgres on Azure with ...Citus Data
A story about powering a 1.5 petabyte internal analytics application at Microsoft with 2816 cores and 18.7 TB of memory in the Citus cluster.
The internal RQV analytics dashboard at Microsoft helps the Windows team to assess the quality of upcoming Windows releases. The system tracks 20,000 diagnostic and quality metrics, digests data from 800 million Windows devices and currently supports over 6 million queries per day, with hundreds of concurrent users. The RQV analytics dashboard relies on Postgres—along with the Citus extension to Postgres to scale out horizontally—and is deployed on Microsoft Azure.
Talk about the InfluxData TICK Stack.
Demo source code can be found here: https://github.com/wilk/tick-golang-meetup
Here the live streaming recorded (IT): https://www.youtube.com/watch?v=5KI6Bv_alK8
What's New in Apache Spark 2.3 & Why Should You CareDatabricks
The Apache Spark 2.3 release marks a big step forward in speed, unification, and API support.
This talk will quickly walk through what’s new and how you can benefit from the upcoming improvements:
* Continuous Processing in Structured Streaming.
* PySpark support for vectorization, giving Python developers the ability to run native Python code fast.
* Native Kubernetes support, marrying the best of container orchestration and distributed data processing.
SQL Performance Tuning and New Features in Oracle 19cRachelBarker26
What's new in Oracle 19c (and CMiC R12) and the reporting software Jaspersoft Studios. If you are not interested in Jasper go ahead and skip to page 26. Explains how to read an execution plan and what to look for in an optimized execution plan.
Keynote of HadoopCon 2014 Taiwan:
* Data analytics platform architecture & designs
* Lambda architecture overview
* Using SQL as DSL for stream processing
* Lambda architecture using SQL
AWS November Webinar Series - Advanced Analytics with Amazon Redshift and the...Amazon Web Services
Amazon Machine Learning is a service that makes it easy for developers of all skill levels to use machine learning technology and Amazon Redshift is a fast, fully managed, petabyte-scale data warehouse that makes it simple and cost-effective to efficiently analyze all your data using your existing business intelligence tools. The combination of the two can provide a solution to power advanced analytics for not only what has happened in the past, but make intelligent predictions about the future. Please join this webinar to learn how get the most value from your data for your data driven business.
Learning Objectives:
How to scale your Redshift queries with user-defined functions (UDFs)
How to apply Machine learning to historical data in Amazon Redshift
How to visualize your data with Amazon QuickSight
Present a reference architecture for advanced analytics
Who Should Attend:
Application developers looking to add UDFs, or predictive analytics to their applications, database administrators that need to meet the demand of data driven organizations, decision makers looking to derive more insight from their data
Big Data Web applications for Interactive Hadoop by ENRICO BERTI at Big Data...Big Data Spain
This talk describes how open source Hue [1] was built in order to provide a better Hadoop User Experience. The underlying technical details of its architecture, the lessons learned and how it integrates with Impala, Search and Spark under the cover will be explained.
Jump Start into Apache® Spark™ and DatabricksDatabricks
These are the slides from the Jump Start into Apache Spark and Databricks webinar on February 10th, 2016.
---
Spark is a fast, easy to use, and unified engine that allows you to solve many Data Sciences and Big Data (and many not-so-Big Data) scenarios easily. Spark comes packaged with higher-level libraries, including support for SQL queries, streaming data, machine learning, and graph processing. We will leverage Databricks to quickly and easily demonstrate, visualize, and debug our code samples; the notebooks will be available for you to download.
Elasticsearch + Cascading for Scalable Log ProcessingCascading
Supreet Oberoi's presentation on "Large scale log processing with Cascading & Elastic Search". Elasticsearch is becoming a popular platform for log analysis with its ELK stack: Elasticsearch for search, Logstash for centralized logging, and Kibana for visualization. Complemented with Cascading, the application development platform for building Data applications on Apache Hadoop, developers can correlate at scale multiple log and data streams to perform rich and complex log processing before making it available to the ELK stack.
A presentation about deploy, scaling and the coordination problem. We will focus on redis as a coordination system in order to simplify the migration to ETCd as coordination system
Realtime and remote service integration into the our AngularJS Application, a travel around the best servies to build a serverless application. AWS Lambda, DynamoDB.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
1. TSDB
INFLUXDB A TIME SERIES DATABASE
Created by Gianluca Arbezzano / @GianArb
2. TSDB
TIME SERIES DATABASE
It is a software system that is optimized for handling time series
data, arrays of numbers indexed by time (a datetime or a
datetime range)
3. LEARN STARTUP
Eric Ries
The Lean Startup: How Today's Entrepreneurs Use Continuous
Innovation to Create Radically Successful Businesses
4. TIME SERIES DATA
A time series is a sequence of data points, measured typically at
successive points in time spaced at uniform time intervals
12. MORE POINTS FOR INSERT
[
{
"name": "log_lines",
"columns": ["time", "sequence_number", "line"],
"points": [
[1400425947368, 1, "this line is first"],
[1400425947368, 2, "and this is second"]
]
}
]
13. IMPLEMENT UDP
PROTOCOL
[input_plugins.udp]
enabled = true
port = 4444
database = "search"
InfluxDB is down? Your APP works!
14. BENCHMARK UDP VS TCP
CorleyBenchmarksInfluxDBAdapterEvent
Method Name Iterations Average Time Ops/second
------------------------ ------------ -------------- -------------
sendDataUsingHttpAdapter: [1,000 ] [0.0026700308323] [374.52751]
sendDataUsingUdpAdapter : [1,000 ] [0.0000436344147] [22,917.69026]
18. GRAFANA
Is an Javascript OpenSource Dashboard
Drag and drop panels
Click and select region to zoom
Bars, Lines, Points
Mix lines, bars and points
InfluxDB query editor
Annotation lines
25. MARK YOUR EVENT
$client->mark("error.404", ["page" => "/a-missing-page"]);
$client->mark("app.search", $points, "s");
26. QUERY
$influx->query("select * from mine");
$influx->query("select * from mine", "s");
$client->setFilter(new ColumnsPointsFilter());
$data = $client->query("select * from hd_used");
27. THE KEY OF MEASURE
GitHub - Making MySql Better at GitHub
28. RICHARD FEYNMAN - THE KEY TO SCIENCE
1. Guess
2. Compute Consequences
3. Compare with experiment/experience
"IF IT DISAGREES WITH EXPERIMENT, IT’S WRONG"
29. FUTURE
Star 12 Star this project
Use it and help us with your issues & PR