Redis Labs manages over 160k+ HA databases, 10k clustered databases, without data loss in spite of one node failure a day and one data center outage per month. Using Enterprise
Redis(RLEC), Redis Labs delivers seamless zero downtime scaling, true high availability with persistence, cross-rack/zone/
datacenter replication and instant automatic failover. Learn how. Join this session for a deep dive into how enterprise Redis makes for no-hassle Redis deployments and the roadmap for new Redis capabilities. Discover new cost savings with Redis on Flash for cost-effective high performance operations and analytics
HIgh Performance Redis- Tague Griffith, GoProRedis Labs
High Performance Redis looks at a wide range of techniques - from programming to system tuning - to deploy and maintain an extremely high performing Redis cluster. From the operational
perspective, the talk lays out multiple techniques for clustering (sharding) Redis systems and examines how the different
approaches impact performance time. The talk further examines system settings (Linux network parameters, Redis
system) and how they impact performance (both good and bad). Finally, for the developer, we look at how different ways of structuring data actually demonstrate different performance characteristics
Running Analytics at the Speed of Your BusinessRedis Labs
The speed at which you can extract insights from your data is increasingly a competitive edge for your business. Data and analytics have to be at lightning fast speeds to seriously impact your user acquisition.
Join this webinar featuring Forrester analyst Noel Yuhanna and Leena Joshi, VP Product Marketing at Redis Labs to learn how you can glean insights faster with new open source data processing frameworks like Spark and Redis.
In this webinar you will learn:
* Why analytics has to run at the real time speed of business
* How this can be achieved with next generation Big Data tools
* How data structures can optimize your hybrid transaction-analytics processing scenarios
Walmart & IBM Revisit the Linear Road Benchmark- Roger Rea, IBMRedis Labs
The Linear Road benchmark was devised in 2004 to
compare Stream Data Management Systems. Walmart selected Linear Road to compare performance of streaming analytic
offerings. IBM implemented the benchmark application using Redis to maintain state, and IBM Streams to handle the
incoming events and queries. Walmart had to completely revamp the data drivers and test verification to take advantage
of multicore multithreaded servers available today. Tests were run on Microsoft Azure cloud to ensure fair comparison of
vendors. Redis and IBM Streams handled nearly 1 billion events in a 3 hour test on a single 16 core Azure node, and 3.8 billion
when scaled out to 4 nodes. Come learn about the application and near linear scalability of Redis and IBM Streams.
HIgh Performance Redis- Tague Griffith, GoProRedis Labs
High Performance Redis looks at a wide range of techniques - from programming to system tuning - to deploy and maintain an extremely high performing Redis cluster. From the operational
perspective, the talk lays out multiple techniques for clustering (sharding) Redis systems and examines how the different
approaches impact performance time. The talk further examines system settings (Linux network parameters, Redis
system) and how they impact performance (both good and bad). Finally, for the developer, we look at how different ways of structuring data actually demonstrate different performance characteristics
Running Analytics at the Speed of Your BusinessRedis Labs
The speed at which you can extract insights from your data is increasingly a competitive edge for your business. Data and analytics have to be at lightning fast speeds to seriously impact your user acquisition.
Join this webinar featuring Forrester analyst Noel Yuhanna and Leena Joshi, VP Product Marketing at Redis Labs to learn how you can glean insights faster with new open source data processing frameworks like Spark and Redis.
In this webinar you will learn:
* Why analytics has to run at the real time speed of business
* How this can be achieved with next generation Big Data tools
* How data structures can optimize your hybrid transaction-analytics processing scenarios
Walmart & IBM Revisit the Linear Road Benchmark- Roger Rea, IBMRedis Labs
The Linear Road benchmark was devised in 2004 to
compare Stream Data Management Systems. Walmart selected Linear Road to compare performance of streaming analytic
offerings. IBM implemented the benchmark application using Redis to maintain state, and IBM Streams to handle the
incoming events and queries. Walmart had to completely revamp the data drivers and test verification to take advantage
of multicore multithreaded servers available today. Tests were run on Microsoft Azure cloud to ensure fair comparison of
vendors. Redis and IBM Streams handled nearly 1 billion events in a 3 hour test on a single 16 core Azure node, and 3.8 billion
when scaled out to 4 nodes. Come learn about the application and near linear scalability of Redis and IBM Streams.
Counting image views using redis clusterRedis Labs
Streaming Logs and Processing View Counts using Redis Cluster
Seandon Mooy
(Imgur)
When you browse through Imgur, you notice that each user's post includes the number of views for that particular post. Imgur processes over 3 billion views per month and powers our view count feature using Redis. In this talk, we cover our current architecture for streaming logs and processing view counts using Redis Cluster, as well as some of the alternatives we explored and why we chose Redis.
Managing 50K+ Redis Databases Over 4 Public Clouds ... with a Tiny Devops TeamRedis Labs
A presentation by Redis Labs' CTO, Yiftach Shoolman, given at the July 2nd meet up, hosted by I am OnDemand and IGT Cloud at the Microsoft ILDC Auditorium.
See the video at: https://www.youtube.com/watch?v=eymqHZaUOH4
In this In this session Yiftach shares tips on how the company manages 50,000+ scalable and highly avaliable Redis databases over the 4 largest public clouds, 8 leading Platforms-as-a-Service, and across 10 geographical regions. He explains the service's back-end architecture, the open-source projects it uses, and which tools the company builds in-house. Shoolman also shares what Redis Labs' small DevOps team does automatically, and what it still does manually. Finally, he offers advice on how to build a strong R&D team that lives and breathes DevOps.
Since the company launched its Redis Cloud service, it has dealt with 150+ node failure events and a half-dozen complete data-center outages. In addition, its team has experienced many interesting scenarios, such as hard to believe scaling patterns like 0 to a few hundreds gigabytes of in-memory data in just a few minutes, and 0 to 300K+ ops/sec in just a few seconds.
Redis Developers Day 2014 - Redis Labs TalksRedis Labs
These are the slides that the Redis Labs team had used to accompany the session that we gave during the first ever Redis Developers Day on October 2nd, 2014, London. It includes some of the ideas we've come up with to tackle operational challenges in the hyper-dense, multi-tenants Redis deployments that our service - Redis Cloud - consists of.
Timely genome analysis requires a fresh approach to platform design for big data problems. Louisiana State University has tested enterprise cluster deployments of Redis with a unique solution that allows flash memory to act as extended RAM. Learn about how this solution allows large amounts of data to be handled with a fraction of the memory needed for a typical deployment.
Back your App with MySQL & Redis, the Cloud Foundry Way- Kenny Bastani, PivotalRedis Labs
In this session, we will build a minimum viable Spring Data web service with REST API, add a MySQL backing service as the primary data store, and a Redis Labs backing service for caching. We will demonstrate performance metrics without Redis caching enabled and then with Redis caching enabled. I will also provide an intro-level explanation of the platform capabilities within Pivotal Web Services.
Redis in a Multi Tenant Environment–High Availability, Monitoring & Much More! Redis Labs
Running any
application in a multi-tenant environment poses its challenges. This talk is focused around how we at Rackspace run Redis
in a multi-tenant environment, ensuring security, performance, fault tolerance and high availability. This talk will cover: an
architecture deep dive of multi tenant Redis on the cloud, management of sentinels, monitoring and operations of a large
scale Redis deployment,introducing new Redis versions,scaling, security, some lessons learnt. The target audience for this
talk is anyone who is interested in the deployment/operational aspect of running Redis. This is relevant not only for those
who want to run Redis themselves, but also interested in how a Redis provider might be doing it for them.
Dynomite: A Highly Available, Distributed and Scalable Dynamo Layer--Ioannis ...Redis Labs
Dynomite is a
thin, distributed dynamo layer for different storage engines and protocols. Currently at Netflix, we are focusing on using
Redis as the storage engine. Dynomite supports multi-datacenter replication and is designed for high availability. In the age of high scalability and big data, Dynomite’s design goal is to turn single-server datastore solutions into peer-to-peer, linearly
scalable, clustered systems while still preserving the native client/server protocols of the datastores, e.g., Redis protocol. In this talk, we are going to present Dynomite recent features, and the Dyno client. Both projects are open source and available to the community.
Counting image views using redis clusterRedis Labs
Streaming Logs and Processing View Counts using Redis Cluster
Seandon Mooy
(Imgur)
When you browse through Imgur, you notice that each user's post includes the number of views for that particular post. Imgur processes over 3 billion views per month and powers our view count feature using Redis. In this talk, we cover our current architecture for streaming logs and processing view counts using Redis Cluster, as well as some of the alternatives we explored and why we chose Redis.
Managing 50K+ Redis Databases Over 4 Public Clouds ... with a Tiny Devops TeamRedis Labs
A presentation by Redis Labs' CTO, Yiftach Shoolman, given at the July 2nd meet up, hosted by I am OnDemand and IGT Cloud at the Microsoft ILDC Auditorium.
See the video at: https://www.youtube.com/watch?v=eymqHZaUOH4
In this In this session Yiftach shares tips on how the company manages 50,000+ scalable and highly avaliable Redis databases over the 4 largest public clouds, 8 leading Platforms-as-a-Service, and across 10 geographical regions. He explains the service's back-end architecture, the open-source projects it uses, and which tools the company builds in-house. Shoolman also shares what Redis Labs' small DevOps team does automatically, and what it still does manually. Finally, he offers advice on how to build a strong R&D team that lives and breathes DevOps.
Since the company launched its Redis Cloud service, it has dealt with 150+ node failure events and a half-dozen complete data-center outages. In addition, its team has experienced many interesting scenarios, such as hard to believe scaling patterns like 0 to a few hundreds gigabytes of in-memory data in just a few minutes, and 0 to 300K+ ops/sec in just a few seconds.
Redis Developers Day 2014 - Redis Labs TalksRedis Labs
These are the slides that the Redis Labs team had used to accompany the session that we gave during the first ever Redis Developers Day on October 2nd, 2014, London. It includes some of the ideas we've come up with to tackle operational challenges in the hyper-dense, multi-tenants Redis deployments that our service - Redis Cloud - consists of.
Timely genome analysis requires a fresh approach to platform design for big data problems. Louisiana State University has tested enterprise cluster deployments of Redis with a unique solution that allows flash memory to act as extended RAM. Learn about how this solution allows large amounts of data to be handled with a fraction of the memory needed for a typical deployment.
Back your App with MySQL & Redis, the Cloud Foundry Way- Kenny Bastani, PivotalRedis Labs
In this session, we will build a minimum viable Spring Data web service with REST API, add a MySQL backing service as the primary data store, and a Redis Labs backing service for caching. We will demonstrate performance metrics without Redis caching enabled and then with Redis caching enabled. I will also provide an intro-level explanation of the platform capabilities within Pivotal Web Services.
Redis in a Multi Tenant Environment–High Availability, Monitoring & Much More! Redis Labs
Running any
application in a multi-tenant environment poses its challenges. This talk is focused around how we at Rackspace run Redis
in a multi-tenant environment, ensuring security, performance, fault tolerance and high availability. This talk will cover: an
architecture deep dive of multi tenant Redis on the cloud, management of sentinels, monitoring and operations of a large
scale Redis deployment,introducing new Redis versions,scaling, security, some lessons learnt. The target audience for this
talk is anyone who is interested in the deployment/operational aspect of running Redis. This is relevant not only for those
who want to run Redis themselves, but also interested in how a Redis provider might be doing it for them.
Dynomite: A Highly Available, Distributed and Scalable Dynamo Layer--Ioannis ...Redis Labs
Dynomite is a
thin, distributed dynamo layer for different storage engines and protocols. Currently at Netflix, we are focusing on using
Redis as the storage engine. Dynomite supports multi-datacenter replication and is designed for high availability. In the age of high scalability and big data, Dynomite’s design goal is to turn single-server datastore solutions into peer-to-peer, linearly
scalable, clustered systems while still preserving the native client/server protocols of the datastores, e.g., Redis protocol. In this talk, we are going to present Dynomite recent features, and the Dyno client. Both projects are open source and available to the community.
Leveraging Probabilistic Data Structures for Real Time Analytics with Redis M...Itamar Haber
An introduction to Redis, Redis' new modules API and probabilistic data structures (PDS). Budding data scientists and big-data gurus use PDSs for estimating that which is difficult to be accurately counted.
Real Time Recommendations Using WebSockets and Redis - Ninad Divadkar, InuitRedis Labs
WebSockets connect the browser to your app server. But what if the processing happens on some other server? In that case you need to connect the worker process to the app process via a messaging system. After experimenting with RabbitMQ, we settled on Redis as a great pub sub and a caching system. This presentation will describe the architecture of the system and how we use spring-websockets and spring-data-Redis to power the system. As a bonus, we will show a great way to find out in real time how many users are
currently using your system.
The audience will learn how to use the power of the Redis API by composing Redis commands, data types, and data structures into powerful queries. The talk will cover
common use cases such as leaderboards, profiles/sessions, voting, latest items, followers, who’s online, and advanced topics such as secondary indexes.
A generic layer that can be used with many key-value storage engines like Redis, Memcached, LMDB, etc
Focus: performance, cross-datacenter active-active replication and high availability
Features: node warmup (cold bootstrapping), tunable consistency, S3 backups/restores
Status: Open source, fully integrated with existing NetflixOSS ecosystem
A list of all URLs in the deck is at: https://gist.github.com/itamarhaber/87e8c8c7126fbfb3f722
A lightening talk filled to the brim with knowledge and tips about Redis, data structures, performance, RAM and tips to take Redis to the max
Redis is an advanced key-value store or a data structure server. This presentation will cover the following topics:
* An overview of Redis
* Data Structures
* Basics of Setup and Installation
* Basics of Administration
* Programming with Redis
* Considerations of Running Redis in a Virtual Machine
* Redis Resources There will be a number of demonstrations to help explain some of the concepts being presented.
Build a Geospatial App with Redis 3.2- Andrew Bass, Sean Yesmunt, Sergio Prad...Redis Labs
We created an app to find nearby running partners, and to demonstrate Redis Data structures and functions. In this talk, we will review the data structures and walk through our NodeJS app that depends solely on Redis Geospatial Indexes. Functions demoed are GEOADD, ZREM, GEOHASH, GEOPOS, GEODIST, GEORADIUS, GEORADIUSBYMEMBER
Get more than a cache back! The Microsoft Azure Redis Cache (NDC Oslo)Maarten Balliauw
Serving up content on the Internet is something our web sites do daily. But are we doing this in the fastest way possible? How are users in faraway countries experiencing our apps? Why do we have three webservers serving the same content over and over again? In this session, we’ll explore the Azure Content Delivery Network or CDN, a service which makes it easy to serve up blobs, videos and other content from servers close to our users. We’ll explore simple file serving as well as some more advanced, dynamic edge caching scenarios.
Big Data Day LA 2016/ NoSQL track - Analytics at the Speed of Light with Redi...Data Con LA
Spark is in-memory, Redis is in-memery. The Spark-Redis connector gives Spark access to Redis' data structures as RDDs. Redis, with its blazing fast performance and optimized in-memory data structures, reduces Spark processing time by up to 98%. In this talk, Dave will share the top use cases for Spark-Redis such as time-series, recommendations and real-time bid management.
High performance Redis is popular among developers for its incredible performance, versatility and simplicity. The powerful combination of low cost memory and high performance Redis brings to life new next generation analytic uses - such as simultaneous real time transaction and analytics processing. With Redis Labs' RLEC Flash on AWS SSD instances, you can get fantastic performance at up to 70% lower costs. Join this session to learn how next generation Flash from leading memory provider Intel has made significant strides in performance while retaining its cost advantage to memory. Using a combination of AWS' powerful SSD instances, and Redis Labs' RLEC Flash, you can achieve up to 3M ops/sec at sub millisecond latencies, with a combination of RAM and Flash. The session will also feature customer use cases from a large university, a large customer engagement company and a pioneer of online Flash sales. Session sponsored by Redis Labs.
Calculating dynamic pricing, estimated travel times or detecting fraud in real time. These are all the cases where realtime analytics create the differentiation between experiences. Redis comes with built in types to enable realtime processing of complex analytics with data types like sorted sets, hyperloglog, bloom and cuckoo filters and more.
LinkedIn leverages the Apache Hadoop ecosystem for its big data analytics. Steady growth of the member base at LinkedIn along with their social activities results in exponential growth of the analytics infrastructure. Innovations in analytics tooling lead to heavier workloads on the clusters, which generate more data, which in turn encourage innovations in tooling and more workloads. Thus, the infrastructure remains under constant growth pressure. Heterogeneous environments embodied via a variety of hardware and diverse workloads make the task even more challenging.
This talk will tell the story of how we doubled our Hadoop infrastructure twice in the past two years.
• We will outline our main use cases and historical rates of cluster growth in multiple dimensions.
• We will focus on optimizations, configuration improvements, performance monitoring and architectural decisions we undertook to allow the infrastructure to keep pace with business needs.
• The topics include improvements in HDFS NameNode performance, and fine tuning of block report processing, the block balancer, and the namespace checkpointer.
• We will reveal a study on the optimal storage device for HDFS persistent journals (SATA vs. SAS vs. SSD vs. RAID).
• We will also describe Satellite Cluster project which allowed us to double the objects stored on one logical cluster by splitting an HDFS cluster into two partitions without the use of federation and practically no code changes.
• Finally, we will take a peek at our future goals, requirements, and growth perspectives.
SPEAKERS
Konstantin Shvachko, Sr Staff Software Engineer, LinkedIn
Erik Krogen, Senior Software Engineer, LinkedIn
Analytics at the Real-Time Speed of Business: Spark Summit East talk by Manis...Spark Summit
Redis accelerates Apache Spark execution by 45 times, when used as a shared distributed in-memory datastore for Spark in analyses like time series data range queries. With the redis module for machine learning, redis-ml, implementation of spark-ml models gains a new real time serving layer that offloads processing of models directly in Redis, allows multiple applications to reuse the same models and speeds up classification and execution of these models by 13x. Join this session to learn more about the Redis Labs’ connector for Apache Spark that enhances production implementations of real-time big data processing.
An overview of the Amazon ElastiCache managed service, with examples of how it can be used to increase performance, lower costs and augment other database services and databases to make things faster, easier and less expensive.
This session shows an overview of the features and architecture of SQL Server on Linux and Containers. It covers install, config, performance, security, HADR, Docker containers, and tools. Find the demos on http://aka.ms/bobwardms
Hands-On with Amazon ElastiCache for Redis - Workshop (DAT309-R1) - AWS re:In...Amazon Web Services
In-memory services, such as Amazon ElastiCache for Redis, provide a number of tools to accelerate application and database performance. In this workshop, we explore those tools and features and dive deep into Redis, the ElastiCache API, and the common architecture patterns for which we see customers using in-memory services. Gain hands-on experience launching a Redis cluster through AWS CloudFormation, updating the cluster through the CLI, and working with Redis data structures.
Learn how Amazon Redshift, our fully managed, petabyte-scale data warehouse, can help you quickly and cost-effectively analyze all of your data using your existing business intelligence tools. Get an introduction to how Amazon Redshift uses massively parallel processing, scale-out architecture, and columnar direct-attached storage to minimize I/O time and maximize performance. Learn how you can gain deeper business insights and save money and time by migrating to Amazon Redshift. Take away strategies for migrating from on-premises data warehousing solutions, tuning schema and queries, and utilizing third party solutions.
Add Redis to Postgres to Make Your Microservices Go Boom!Dave Nielsen
Slides for talk delivered at PostgresOpen 2018 in San Francisco https://postgresql.us/events/pgopen2018/schedule/session/538-add-redis-to-postgres-to-make-your-microservice-go-boom/
10 Ways to Scale with Redis - LA Redis Meetup 2019Dave Nielsen
Redis has 10 different data structures (String, Hash, List, Set, Sorted Set, Bit Array, Bit Field, Hyperloglog, Geospatial Index, Streams) plus Pub/Sub and many Redis Modules. In this talk, Dave will give 10 examples of how to use these data structures to scale your website. I will start with the basics, such as a cache and User session management. Then I demonstrate user generated tags, leaderboards and counting things with hyberloglog. I will with a demo of Redis Pub/Sub vs Redis Streams which can be used to scale your Microservices-based architecture.
Have you heard that all in-memory databases are equally fast but unreliable, inconsistent and expensive? This session highlights in-memory technology that busts all those myths.
Redis, the fastest database on the planet, is not a simply in-memory key-value data-store; but rather a rich in-memory data-structure engine that serves the world’s most popular apps. Redis Labs’ unique clustering technology enables Redis to be highly reliable, keeping every data byte intact despite hundreds of cloud instance failures and dozens of complete data-center outages. It delivers full CP system characteristics at high performance. And with the latest Redis on Flash technology, Redis Labs achieves close to in-memory performance at 70% lower operational costs. Learn about the best uses of in-memory computing to accelerate everyday applications such as high volume transactions, real time analytics, IoT data ingestion and more.
Similar to What's new with enterprise Redis - Leena Joshi, Redis Labs (20)
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
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.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
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
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
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.
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
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.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
5. 5
Which In Turn Means:
Infinite Seamless Scaling
True High Availability
Top notch expert support
6. 6
Redis Labs Enhances OSS Redis
Redis Labs Node
Open Source
Zero latency proxy Cluster Manager
REST API
Odd number of
nodes needed to
handle network
splits- not three
copies of data
Redis Labs Cluster
• Shared nothing cluster
architecture
• Fully compatible with open
source commands & data
structures
Proprietary
7. 7
The Same Technology Runs Redis Cloud
Cluster
Management Path
Proxies
Node Watchdog
Cluster Watchdog
Node 1 Node 2 Node N (uneven number)…
Redis
Shards
Unique multi-tenant “Docker” like architecture enables running hundreds of databases over a single,
average cloud instance without performance degradation and with maximum security provisions
Data Path
Distributed Proxies
Single or Multiple Endpoints
50,000+ Customers
8. 8
Tremendous Customer Traction
Redis Cloud
Available since mid-2013
6000+ enterprise customers
Redis Labs Enterprise Cluster (RLEC)
Available since early-2015
100+ enterprise customers
9. 9
Always On - Highly
Available & Persistent
Simple, Seamless
Clustering. Linear
Scalability.
Enterprise-Class
Management and
Support
Enterprise-Class Redis – The Benefits
Stable & Predictable
Top Performance
Operational Cost
Savings
10. 10
Simple, Seamless
Scaling and Clustering
Auto- scaling/re-sharding/re-balancing
No downtime while scaling
Supports cross-shard operations
Simple, Seamless Clustering. Linear Scalability.
Linear Scalability
11. 11
Always On - Highly
Available & Persistent
Seamless cross
datacenter/region/cloud
replication
Instant auto-failover
Persistence, backups and DR
Always On - Highly-Available & Persistent
12. 12
Stable & Predictable
Top Performance
Consistent high performance
achieved under any load or
cluster size
Database processed by multiple
cores
Built-in performance
enhancement techniques
Stable & Predictable Top Performance
13. 13
Operational Cost Savings
OSS Redis Redis Labs
More efficient hardware utilization: fewer servers,
lower power & cooling and operational costs
Reduced manual labor through automation -
reduced time writing scripts, scaling,
configuration, monitoring, re-balancing and more
Run Redis on flash memory as RAM extender –
up to 10 times cheaper
Reduced downtime incidents
Shorten time to deploy Redis by over 50%
14. 14
Enterprise Management
and Support
UI, CLI, REST API -based
management & alerting
Proven technology supporting
thousands of customers
24x7 enterprise support,
top notch Redis expertise
Enterprise-Class Management & Support
15. 15
Redis Labs: Fastest Recovery, No Data Loss
%oftimesdatawaslost
Averagetimetorecoverinseconds
Redis Labs recovers in 5 seconds and does not lose data.
All other vendors lose data and take many minutes to recover
Vendors evaluated include
(not in order)
• Heroku Redis
• AWS ElastiCache
• Microsoft AzureCache
• Compose.io
16. 16
Redis Labs: The Only True HA Redis
16
Failure Event In-memory
Replication
Multi-DC/Zone
replication
Auto-failover AOF Data
Persistence
Backup (using
snapshots)
Multi-
region/Cloud
replication
Process failure Instant recovery* Slow recovery
Node failure Instant recovery* Slow recovery
Multi-node failure Instant recovery*
Network split Instant recovery*
Zone/Rack failure Instant recovery* Slow recovery Fast recovery
Region/Cloud failure Slow recovery Fast recovery
Typeofoutage
Essential features for high availability
*Auto-failover should run on same nodes as Redis deployment
Redis Labs provides all the essential HA features that protect against every type of outage
18. 18
Why Analyze Data In-Memory?
“Information is the oil of 21st century, and analytics is the combustion engine”- Peter
Sondergaard, Gartner Analyst
19. 19
Decision Speeds Are Accelerating..
“Big Data” gains
popularity as tools
become available to
harness it
Batch insights start to
drive business
Real time insights
automate decision-
making
2005 2012 - 2015 2016…
THE DATA REVOLUTION IS MATURING..
20. 20
The Race Is On..
INSIGHTS FROM YOUR DATA NEED TO BE
INSTANTANEOUS
COST EFFECTIVE
23. 23
Redis on Flash Concepts
• Flash used as a RAM extender (NOT as a persistent storage)
• Global key list in RAM; ‘hot’ values in RAM; ‘cold’ values on Flash.
• Multi-threaded & async Redis when accessing objects on Flash.
Utilizes multi-core and Flash concurrency architecture
• 100% compatibility with Redis
24. 24
How to Achieve Optimal Price/Performance
By dynamically setting RAM/Flash ratio
26. 26
A Real Life Example With Redis On Flash
Customer Scenario:
• Genome dataset
• Key sizes: 32B, value sizes : 5-12B
• No of keys: 250 x109… 250 x1012
Key1: AAAAAAAAAAAAAAAAAAAAAAAACCCCAAA Value1 = Freq=4 IE=A OE=A
Key2: AAAAAAAAAAAAAAAAAAAAAAAAACAACCC Value2 = Freq=7 IE=A,C,T,G OE=A,C,T,G
*IE – Inside End sequences
*OE – Outside End sequences
27. 27
Optimizing Redis Usage
RAW
• # of keys 250x10^9
• Key size = 32B
• Value size = 8B (average)
• Overhead per object (key+value) = ±61B (key) + 9 (value) = 70
• Internal fragmentation per object = 14B
• RAM size = ±31TB
28. 28
Further Optimizations
Encoding keys and values to
compress sizes
• # of keys 1.25x10^9
• Key size = 4B
• Value size = 3612B
• RAM overhead per object (key+value) = ±40B
• RAM size = ±55GB // for optimal performance we used 500GB to keep 10/90 RAM/Flash ratio
• Flash size = ±4.5TB
Using Redis Hashes to store
compressed keys/values (200
keys and values per hash)
1 2
29. 29
Memory Usage and Cost Comparison
Redis on RAM
Strings
Redis on Flash
Hashes
RAM size 31TB 0.5 TB
Flash size - 4.5TB
EC2 instances 155 x r3.8xlarge 2 x i2.8xlarge
1yr costs
(reserved
instances)
$2,017,325 $49,862
Savings with Flash
& Hashes %
97.6%
31. 31
Spark & Redis - Connector & Service Layer
Data Source
Serving Layer
Spark SQL &
Data Frame
RDD,
Data Source,
Data Set
RDD,
Data Source,
Data Set
Analytics & BI
32. 32
Spark & Redis – Internal Accelerator
Data Source
RDD,
Data Source,
Data Set
RDD,
Data Source,
Data Set
Spark SQL &
Data Frame
Analytics & BI
RDD,
Data Source,
Data Set,
Redis API
Data Sink
33. 33
Accelerate Spark Time-Series with Redis
Redis sorted sets accelerate time series data
processing by 100 times compared to other in-
memory K/V stores
Example time series data: Stock prices for 1024
stocks over 32 years
34. 34
Spark-Redis Package : The Results
Redis faster by upto 100 times compared to HDFS
and over 45 times compared to Tachyon or Spark
36. 36
36
Modules Extend Redis’ Use Case Coverage
MongoDB
Cassandra/
Datastax
Couchbase Redis (original) Redis + Modules
Single View Coming Soon
Personalization
Catalog Coming Soon
IoT
Real-Time Analytics
Content Management Coming Soon
Messaging
Fraud Detection Coming Soon
Graph Coming Soon
Time Series
Caching
Text Search Coming Soon
Image Processing Coming Soon
Machine Learning Coming Soon
Linear Algebra Coming Soon
Probabilistic data structures
for processing continuous,
Coming Soon
More
37. 37
Modules Turn Redis into a Multi-Model Database
37
MongoDB
Cassandra/
Datastax
Couchbase Redis (original) Redis + Modules
Document-Based Coming Soon
Column-Based Coming Soon
Key-Value Data Structures Data Structures
Graph Coming Soon
38. 38
Redis Module Hub
• A Redis Module Marketplace – for everyone
• Every problem a developer solves with Redis – now extended to the
Enterprise
• Will help developers monetize their work and reach enterprise
Redis users
• Will give enterprise Redis users the confidence and peace of mind
to easily deploy modules
www.redismodules.com
40. 40
40
3.15
2.40
21.00
8.70
24.57
10.61
0.00
5.00
10.00
15.00
20.00
25.00
30.00
Full text search Prefix search
Average Latency (msec)
RLEC Elasticsearch Solr
20,045
6,831
690
3,686
621
3,133
0
5,000
10,000
15,000
20,000
25,000
Full text search Prefix search
Ops/sec
RLEC Elasticsearch Solr
85% higher
32x higher
7.8x faster 4.1x faster
redisearch
The world fastest text search engine
41. 41
What Can Modules Do
41
• All modules are certified by Redis Labs for full compliance with OSS
Redis, Redis Cloud and Redis Labs Enterprise Cluster (RLEC)
Full Text Search Enhanced JSON Graph Operations Secondary Indexes
Linear Algebra SQL Support Image Processing
N-Dimension
Queries …
DRAM prices have been relatively stable over the years – and it continues to be expensive. Technologies such as Flash offer performance that is 3-4 orders of magnitude slower but 10 times cheaper. Emerging technologies such as Flash offer performance that is only an order of magnitude slower at 3 times lower cost. This makes for quite an attractive cost-performance tradeoff!
We extended Redis to take advantage of the multithreaded and asyn nature of Flash/other slower memory. Not only that, we added the capability to recognize “fast” and “slow” memory – with a configurable ratio so that all keys and hot values can be stored in the fast memory and cold values in slow memory such as Flash, 3 D Cross point or Storage Class memory for optimum performance.
NVMe – is easily x40 the throughput of SATA based Flash
We encoded and compressed the keys and values–keys were 31 bytes of string of 4 nucleobases. We encoded so that they could be represented with 2 bits per nucleobase – 62 bits and values were similarly compressed.
Flash works at 4KB blocks size, so hash sizes < 4KB.
Limited hashes to 200 entries to achieve the 4kb size per hash