Talk from QCon SF on 2018-11-05
For many years, the main goal of the Netflix personalized recommendation system has been to get the right titles in front each of our members at the right time. With a catalog spanning thousands of titles and a diverse member base spanning over a hundred million accounts, recommending the titles that are just right for each member is crucial. But the job of recommendation does not end there. Why should you care about any particular title we recommend? What can we say about a new and unfamiliar title that will pique your interest? How do we convince you that a title is worth watching? Answering these questions is critical in helping our members discover great content, especially for unfamiliar titles. One way to do this is to consider the artwork or imagery we use to visually portray each title. If the artwork representing a title captures something compelling to you, then it acts as a gateway into that title and gives you some visual “evidence” for why the title might be good for you. Selecting good artwork is important because it may be the first time a member becomes aware of a title (and sometimes the only time), so it must speak to them in a meaningful way. In this talk, we will present an approach for personalizing the artwork we show for each title on the Netflix homepage. We will look at how to frame this as a machine learning problem using contextual multi-armed bandits in a recommendation system setting. We will also describe the algorithmic and system challenges involved in getting this type of approach for artwork personalization to succeed at Netflix scale. Finally, we will discuss some of the future opportunities that we see to expand and improve upon this approach.
DockerCon 2017 - Cilium - Network and Application Security with BPF and XDPThomas Graf
This talk will start with a deep dive and hands on examples of BPF, possibly the most promising low level technology to address challenges in application and network security, tracing, and visibility. We will discuss how BPF evolved from a simple bytecode language to filter raw sockets for tcpdump to the a JITable virtual machine capable of universally extending and instrumenting both the Linux kernel and user space applications. The introduction is followed by a concrete example of how the Cilium open source project applies BPF to solve networking, security, and load balancing for highly distributed applications. We will discuss and demonstrate how Cilium with the help of BPF can be combined with distributed system orchestration such as Docker to simplify security, operations, and troubleshooting of distributed applications.
In this session, we’ll review how previous efforts, including Netfilter, Berkley Packet Filter (BPF), Open vSwitch (OVS), and TC, approached the problem of extensibility. We’ll show you an open source solution available within the Red Hat Enterprise Linux kernel, where extending and merging some of the existing concepts leads to an extensible framework that satisfies the networking needs of datacenter and cloud virtualization.
The OpenSource mORMot is a RESTful ORM / MVC / SOA framework for Delphi and FreePascal. Some of its codebase is now about 10 years old, and it was time to clean and evolve. This year 2020 triggered a deep refactoring of the whole source code, with a full rewrite of some core components (JSON, RTTI). This session will present the version 2 of mORMot it its current state, the principles behind this refactoring, and how you may find useful gems for your own Delphi applications.
Talk from QCon SF on 2018-11-05
For many years, the main goal of the Netflix personalized recommendation system has been to get the right titles in front each of our members at the right time. With a catalog spanning thousands of titles and a diverse member base spanning over a hundred million accounts, recommending the titles that are just right for each member is crucial. But the job of recommendation does not end there. Why should you care about any particular title we recommend? What can we say about a new and unfamiliar title that will pique your interest? How do we convince you that a title is worth watching? Answering these questions is critical in helping our members discover great content, especially for unfamiliar titles. One way to do this is to consider the artwork or imagery we use to visually portray each title. If the artwork representing a title captures something compelling to you, then it acts as a gateway into that title and gives you some visual “evidence” for why the title might be good for you. Selecting good artwork is important because it may be the first time a member becomes aware of a title (and sometimes the only time), so it must speak to them in a meaningful way. In this talk, we will present an approach for personalizing the artwork we show for each title on the Netflix homepage. We will look at how to frame this as a machine learning problem using contextual multi-armed bandits in a recommendation system setting. We will also describe the algorithmic and system challenges involved in getting this type of approach for artwork personalization to succeed at Netflix scale. Finally, we will discuss some of the future opportunities that we see to expand and improve upon this approach.
DockerCon 2017 - Cilium - Network and Application Security with BPF and XDPThomas Graf
This talk will start with a deep dive and hands on examples of BPF, possibly the most promising low level technology to address challenges in application and network security, tracing, and visibility. We will discuss how BPF evolved from a simple bytecode language to filter raw sockets for tcpdump to the a JITable virtual machine capable of universally extending and instrumenting both the Linux kernel and user space applications. The introduction is followed by a concrete example of how the Cilium open source project applies BPF to solve networking, security, and load balancing for highly distributed applications. We will discuss and demonstrate how Cilium with the help of BPF can be combined with distributed system orchestration such as Docker to simplify security, operations, and troubleshooting of distributed applications.
In this session, we’ll review how previous efforts, including Netfilter, Berkley Packet Filter (BPF), Open vSwitch (OVS), and TC, approached the problem of extensibility. We’ll show you an open source solution available within the Red Hat Enterprise Linux kernel, where extending and merging some of the existing concepts leads to an extensible framework that satisfies the networking needs of datacenter and cloud virtualization.
The OpenSource mORMot is a RESTful ORM / MVC / SOA framework for Delphi and FreePascal. Some of its codebase is now about 10 years old, and it was time to clean and evolve. This year 2020 triggered a deep refactoring of the whole source code, with a full rewrite of some core components (JSON, RTTI). This session will present the version 2 of mORMot it its current state, the principles behind this refactoring, and how you may find useful gems for your own Delphi applications.
Replacing iptables with eBPF in Kubernetes with CiliumMichal Rostecki
Cilium is an open source project which provides networking, security and load balancing for application services that are deployed using Linux container technologies by using the native eBPF technology in the Linux kernel. In this presentation we talked about:
- The evolution of the BPF filters and explained the advantages of eBPF Filters and its use cases today in Linux especially on how Cilium networking utilizes the eBPF Filters to secure the Kubernetes workload with increased performance when compared to legacy iptables.
- How Cilium uses SOCKMAP for layer 7 policy enforcement - How Cilium integrates with Istio and handles L7 Network Policies with Envoy Proxies.
- The new features since the last release such as running Kubernetes cluster without kube-proxy, providing clusterwide NetworkPolicies, providing fully distributed networking and security observability platform for cloud native workloads etc.
ClickHouse Monitoring 101: What to monitor and howAltinity Ltd
Webinar. Presented by Robert Hodges and Ned McClain, April 1, 2020
You are about to deploy ClickHouse into production. Congratulations! But what about monitoring? In this webinar we will introduce how to track the health of individual ClickHouse nodes as well as clusters. We'll describe available monitoring data, how to collect and store measurements, and graphical display using Grafana. We'll demo techniques and share sample Grafana dashboards that you can use for your own clusters.
Near real-time statistical modeling and anomaly detection using Flink!Flink Forward
Flink Forward San Francisco 2022.
At ThousandEyes we receive billions of events every day that allow us to monitor the internet; the most important aspect of our platform is to detect outages and anomalies that have a potential to cause serious impact to customer applications and user experience. Automatic detection of such events at lowest latency and highest accuracy is extremely important for our customers and their business. After launching several resilient and low latency data pipelines in production using Flink we decided to take it up a notch; we leveraged Flink to build statistical models in near real-time and apply them on incoming stream of events to detect anomalies! In this session we will deep dive into the design as well as discuss pitfalls and learnings while developing our real-time platform that leverages Debezium, Kafka, Flink, ElasticCache and DynamoDB to process events at scale!
by
Kunal Umrigar & Balint Kurnasz
VictoriaLogs: Open Source Log Management System - PreviewVictoriaMetrics
VictoriaLogs Preview - Aliaksandr Valialkin
* Existing open source log management systems
- ELK (ElasticSearch) stack: Pros & Cons
- Grafana Loki: Pros & Cons
* What is VictoriaLogs
- Open source log management system from VictoriaMetrics
- Easy to setup and operate
- Scales vertically and horizontally
- Optimized for low resource usage (CPU, RAM, disk space)
- Accepts data from Logstash and Fluentbit in Elasticsearch format
- Accepts data from Promtail in Loki format
- Supports stream concept from Loki
- Provides easy to use yet powerful query language - LogsQL
* LogsQL Examples
- Search by time
- Full-text search
- Combining search queries
- Searching arbitrary labels
* Log Streams
- What is a log stream?
- LogsQL examples: querying log streams
- Stream labels vs log labels
* LogsQL: stats over access logs
* VictoriaLogs: CLI Integration
* VictoriaLogs Recap
Building ClickHouse and Making Your First Contribution: A Tutorial_06.10.2021Altinity Ltd
ClickHouse is open source. You can build it yourself. What’s more, you can make it better! In this webinar, we’ll demonstrate how to pull the ClickHouse code from Github and build it. We’ll then walk through how to contribute a new feature to ClickHouse by developing, testing, and pushing a pull request through the community merge process. There will be demos and ample time for questions. Join us to get started as a ClickHouse developer!
Kernel Recipes 2019 - XDP closer integration with network stackAnne Nicolas
XDP (eXpress Data Path) is the new programmable in-kernel fast-path, which is placed as a layer before the existing Linux kernel network stack (netstack).
We claim XDP is not kernel-bypass, as it is a layer before and it can easily fall-through to netstack. Reality is that it can easily be (ab)used to create a kernel-bypass situation, where non of the kernel facilities are used (in form of BPF-helpers and in-kernel tables). The main disadvantage with kernel-bypass, is the need to re-implement everything, even basic building blocks, like routing tables and ARP protocol handling.
It is part of the concept and speed gain, that XDP allows users to avoid calling part of the kernel code. Users have the freedom to do kernel-bypass and re-implement everything, but the kernel should provide access to more in-kernel tables, via BPF-helpers, such that users can leverage other parts of the Open Source ecosystem, like router daemons etc.
This talk is about how XDP can work in-concert with netstack, and proposal on how we can take this even-further. Crazy ideas like using XDP frames to move SKB allocation out of driver code, will also be proposed.
Speaker: Jun Rao, VP of Apache Kafka and Co-founder of Confluent
The controller is the brain of Apache Kafka®. A big part of what the controller does is to maintain the consistency of the replicas and determine which replica can be used to serve the clients, especially during individual broker failure.
In this talk, Jun will outline the main data flow in the controller—in particular, when a broker fails, how the controller automatically promotes another replica as the leader to serve the clients, and when a broker is started, how the controller resumes the replication pipeline in the restarted broker. Jun will then describe recent improvements to the controller that allow it to handle certain edge cases correctly and increase its performance, which allows for more partitions in a Kafka cluster.
Jun Rao is the co-founder of Confluent, a company that provides a streaming data platform on top of Apache Kafka. Previously, Jun was a senior staff engineer at LinkedIn, where he led the development of Kafka, and a researcher at IBM's Almaden research datacenter, where he conducted research on database and distributed systems. Jun is the PMC chair of Apache Kafka and a committer of Cassandra. He writes at https://cnfl.io/blog-jun-rao.
Surge 2014: From Clouds to Roots: root cause performance analysis at Netflix. Brendan Gregg.
At Netflix, high scale and fast deployment rule. The possibilities for failure are endless, and the environment excels at handling this, regularly tested and exercised by the simian army. But, when this environment automatically works around systemic issues that aren’t root-caused, they can grow over time. This talk describes the challenge of not just handling failures of scale on the Netflix cloud, but also new approaches and tools for quickly diagnosing their root cause in an ever changing environment.
DTrace and SystemTap are dynamic tracing frameworks available for Solaris and Linux respectively. This session will give an overview of the static DTrace probes available in both Drizzle and MySQL and show numerous examples of scripts that utilize these probes. Mixing dynamic and static probes will also be discussed.
The Linux kernel is undergoing the most fundamental architecture evolution in history and is becoming a microkernel. Why is the Linux kernel evolving into a microkernel? The potentially biggest fundamental change ever happening to the Linux kernel. This talk covers how companies like Facebook and Google use BPF to patch 0-day exploits, how BPF will change the way features are added to the kernel forever, and how BPF is introducing a new type of application deployment method for the Linux kernel.
Deeper Things: How Netflix Leverages Deep Learning in Recommendations and Se...Sudeep Das, Ph.D.
In this talk, we will provide an overview of Deep Learning methods applied to personalization and search at Netflix. We will set the stage by describing the unique challenges faced at Netflix in the areas of recommendations and information retrieval. Then we will delve into how we leverage a blend of traditional algorithms and emergent deep learning methods and new types of embeddings, especially hyperbolic space embeddings, to address these challenges.
Presentation at Strata Data Conference 2018, New York
The controller is the brain of Apache Kafka. A big part of what the controller does is to maintain the consistency of the replicas and determine which replica can be used to serve the clients, especially during individual broker failure.
Jun Rao outlines the main data flow in the controller—in particular, when a broker fails, how the controller automatically promotes another replica as the leader to serve the clients, and when a broker is started, how the controller resumes the replication pipeline in the restarted broker.
Jun then describes recent improvements to the controller that allow it to handle certain edge cases correctly and increase its performance, which allows for more partitions in a Kafka cluster.
RecSys 2020 A Human Perspective on Algorithmic Similarity Schendel 9-2020Zachary Schendel
In the Netflix user interface (UI), when a row or UI element is named “Because you Watched...”, “More Like This”, or “Because you added to your list”, the overarching goal is to recommend a movie or TV show that a member might like based on the fact that they took a meaningful action on a source item. We have employed similar recommendations in many UI elements: on the homepage as a row of recommendations, after you click into a title, or as a piece of information about why a member should watch a title.
From an algorithmic perspective, there are many ways to define a “successful” similar recommendation. We sought to broaden that definition of success. To this end, the Consumer Insights team recently completed a suite of research projects to explore the intricacies of member perceptions of similar recommendations. The Netflix Consumer Insights team employs qualitative (e.g., in-depth interviews) and quantitative (e.g., surveys) research methods, interfacing directly with Netflix members to uncover pain points that can inspire new product innovation. The research concluded that, while the typical member believes movies are broadly similar when they share a common genre or theme, similarity is more complex, nuanced, and personal than we might have imagined. The vernacular we use in the UI implies that there should be at least some kind of relationship between the source item and the recommendations that follow. Many of our similar recommendations felt “out of place”, mostly because the relationship between the source item and the recommendation was unclear or absent. When similar recommendations tell a completely misleading, incorrect, or confusing story, member trust can be broken.
We will structure the presentation around three new insights that our research found to have an influence on the perception of similarity in the context of Netflix as well as the research methods used to uncover those insights. First, the reason a member loves a given movie will vary. For example, do you want to watch other baseball movies like Field of Dreams, or would you prefer other romances like Field of Dreams? Second, members are more or less flexible about how similar a recommendation actually needs to be depending on the properties of and their interactions with the canvas containing the recommendation. For example, a Because You Watched row on the homepage implies vaguer similarity while a More Like This gallery behind a click into the source item implies stricter similarity. Finally, even when we held the UI element constant, we found that similar recommendations are only valuable in some contexts. After finishing a movie, a member might prefer a similar recommendation one day and a change of pace the next. Research methods discussed will include Inverse Multi-Dimensional Scaling [1], survey experimentation, and ways to apply qualitative research to improve algorithmic recommendations.
“Alexa, be quiet!”: End-to-end near-real time model building and evaluation i...Flink Forward
Flink Forward San Francisco 2022.
To improve Amazon Alexa experiences and support machine learning inference at scale, we built an automated end-to-end solution for incremental model building or fine-tuning machine learning models through continuous learning, continual learning, and/or semi-supervised active learning. Customer privacy is our top concern at Alexa, and as we build solutions, we face unique challenges when operating at scale such as supporting multiple applications with tens of thousands of transactions per second with several dependencies including near-real time inference endpoints at low latencies. Apache Flink helps us transform and discover metrics in near-real time in our solution. In this talk, we will cover the challenges that we faced, how we scale the infrastructure to meet the needs of ML teams across Alexa, and go into how we enable specific use cases that use Apache Flink on Amazon Kinesis Data Analytics to improve Alexa experiences to delight our customers while preserving their privacy.
by
Aansh Shah
A Multi-Armed Bandit Framework For Recommendations at NetflixJaya Kawale
In this talk, we present a general multi-armed bandit framework for recommendations on the Netflix homepage. We present two example case studies using MABs at Netflix - a) Artwork Personalization to recommend personalized visuals for each of our members for the different titles and b) Billboard recommendation to recommend the right title to be watched on the Billboard.
Streamlio and IoT analytics with Apache PulsarStreamlio
To keep up with fast-moving IoT data, you need technology that can collect, process and store data with performance and scalability. This presentation from Data Day Texas looks at the technology requirements and how Apache Pulsar can help to meet them.
Wayfair Storefront Performance Monitoring with InfluxEnterprise by Richard La...InfluxData
In this InfluxDays NYC 2019 session, Richard Laskey from the Wayfair Storefront team will share their monitoring best practices using InfluxEnterprise. These efforts are critical and help improve the user experience by driving forward site-wide improvements, establishing best practices, and driving change through many different teams.
Replacing iptables with eBPF in Kubernetes with CiliumMichal Rostecki
Cilium is an open source project which provides networking, security and load balancing for application services that are deployed using Linux container technologies by using the native eBPF technology in the Linux kernel. In this presentation we talked about:
- The evolution of the BPF filters and explained the advantages of eBPF Filters and its use cases today in Linux especially on how Cilium networking utilizes the eBPF Filters to secure the Kubernetes workload with increased performance when compared to legacy iptables.
- How Cilium uses SOCKMAP for layer 7 policy enforcement - How Cilium integrates with Istio and handles L7 Network Policies with Envoy Proxies.
- The new features since the last release such as running Kubernetes cluster without kube-proxy, providing clusterwide NetworkPolicies, providing fully distributed networking and security observability platform for cloud native workloads etc.
ClickHouse Monitoring 101: What to monitor and howAltinity Ltd
Webinar. Presented by Robert Hodges and Ned McClain, April 1, 2020
You are about to deploy ClickHouse into production. Congratulations! But what about monitoring? In this webinar we will introduce how to track the health of individual ClickHouse nodes as well as clusters. We'll describe available monitoring data, how to collect and store measurements, and graphical display using Grafana. We'll demo techniques and share sample Grafana dashboards that you can use for your own clusters.
Near real-time statistical modeling and anomaly detection using Flink!Flink Forward
Flink Forward San Francisco 2022.
At ThousandEyes we receive billions of events every day that allow us to monitor the internet; the most important aspect of our platform is to detect outages and anomalies that have a potential to cause serious impact to customer applications and user experience. Automatic detection of such events at lowest latency and highest accuracy is extremely important for our customers and their business. After launching several resilient and low latency data pipelines in production using Flink we decided to take it up a notch; we leveraged Flink to build statistical models in near real-time and apply them on incoming stream of events to detect anomalies! In this session we will deep dive into the design as well as discuss pitfalls and learnings while developing our real-time platform that leverages Debezium, Kafka, Flink, ElasticCache and DynamoDB to process events at scale!
by
Kunal Umrigar & Balint Kurnasz
VictoriaLogs: Open Source Log Management System - PreviewVictoriaMetrics
VictoriaLogs Preview - Aliaksandr Valialkin
* Existing open source log management systems
- ELK (ElasticSearch) stack: Pros & Cons
- Grafana Loki: Pros & Cons
* What is VictoriaLogs
- Open source log management system from VictoriaMetrics
- Easy to setup and operate
- Scales vertically and horizontally
- Optimized for low resource usage (CPU, RAM, disk space)
- Accepts data from Logstash and Fluentbit in Elasticsearch format
- Accepts data from Promtail in Loki format
- Supports stream concept from Loki
- Provides easy to use yet powerful query language - LogsQL
* LogsQL Examples
- Search by time
- Full-text search
- Combining search queries
- Searching arbitrary labels
* Log Streams
- What is a log stream?
- LogsQL examples: querying log streams
- Stream labels vs log labels
* LogsQL: stats over access logs
* VictoriaLogs: CLI Integration
* VictoriaLogs Recap
Building ClickHouse and Making Your First Contribution: A Tutorial_06.10.2021Altinity Ltd
ClickHouse is open source. You can build it yourself. What’s more, you can make it better! In this webinar, we’ll demonstrate how to pull the ClickHouse code from Github and build it. We’ll then walk through how to contribute a new feature to ClickHouse by developing, testing, and pushing a pull request through the community merge process. There will be demos and ample time for questions. Join us to get started as a ClickHouse developer!
Kernel Recipes 2019 - XDP closer integration with network stackAnne Nicolas
XDP (eXpress Data Path) is the new programmable in-kernel fast-path, which is placed as a layer before the existing Linux kernel network stack (netstack).
We claim XDP is not kernel-bypass, as it is a layer before and it can easily fall-through to netstack. Reality is that it can easily be (ab)used to create a kernel-bypass situation, where non of the kernel facilities are used (in form of BPF-helpers and in-kernel tables). The main disadvantage with kernel-bypass, is the need to re-implement everything, even basic building blocks, like routing tables and ARP protocol handling.
It is part of the concept and speed gain, that XDP allows users to avoid calling part of the kernel code. Users have the freedom to do kernel-bypass and re-implement everything, but the kernel should provide access to more in-kernel tables, via BPF-helpers, such that users can leverage other parts of the Open Source ecosystem, like router daemons etc.
This talk is about how XDP can work in-concert with netstack, and proposal on how we can take this even-further. Crazy ideas like using XDP frames to move SKB allocation out of driver code, will also be proposed.
Speaker: Jun Rao, VP of Apache Kafka and Co-founder of Confluent
The controller is the brain of Apache Kafka®. A big part of what the controller does is to maintain the consistency of the replicas and determine which replica can be used to serve the clients, especially during individual broker failure.
In this talk, Jun will outline the main data flow in the controller—in particular, when a broker fails, how the controller automatically promotes another replica as the leader to serve the clients, and when a broker is started, how the controller resumes the replication pipeline in the restarted broker. Jun will then describe recent improvements to the controller that allow it to handle certain edge cases correctly and increase its performance, which allows for more partitions in a Kafka cluster.
Jun Rao is the co-founder of Confluent, a company that provides a streaming data platform on top of Apache Kafka. Previously, Jun was a senior staff engineer at LinkedIn, where he led the development of Kafka, and a researcher at IBM's Almaden research datacenter, where he conducted research on database and distributed systems. Jun is the PMC chair of Apache Kafka and a committer of Cassandra. He writes at https://cnfl.io/blog-jun-rao.
Surge 2014: From Clouds to Roots: root cause performance analysis at Netflix. Brendan Gregg.
At Netflix, high scale and fast deployment rule. The possibilities for failure are endless, and the environment excels at handling this, regularly tested and exercised by the simian army. But, when this environment automatically works around systemic issues that aren’t root-caused, they can grow over time. This talk describes the challenge of not just handling failures of scale on the Netflix cloud, but also new approaches and tools for quickly diagnosing their root cause in an ever changing environment.
DTrace and SystemTap are dynamic tracing frameworks available for Solaris and Linux respectively. This session will give an overview of the static DTrace probes available in both Drizzle and MySQL and show numerous examples of scripts that utilize these probes. Mixing dynamic and static probes will also be discussed.
The Linux kernel is undergoing the most fundamental architecture evolution in history and is becoming a microkernel. Why is the Linux kernel evolving into a microkernel? The potentially biggest fundamental change ever happening to the Linux kernel. This talk covers how companies like Facebook and Google use BPF to patch 0-day exploits, how BPF will change the way features are added to the kernel forever, and how BPF is introducing a new type of application deployment method for the Linux kernel.
Deeper Things: How Netflix Leverages Deep Learning in Recommendations and Se...Sudeep Das, Ph.D.
In this talk, we will provide an overview of Deep Learning methods applied to personalization and search at Netflix. We will set the stage by describing the unique challenges faced at Netflix in the areas of recommendations and information retrieval. Then we will delve into how we leverage a blend of traditional algorithms and emergent deep learning methods and new types of embeddings, especially hyperbolic space embeddings, to address these challenges.
Presentation at Strata Data Conference 2018, New York
The controller is the brain of Apache Kafka. A big part of what the controller does is to maintain the consistency of the replicas and determine which replica can be used to serve the clients, especially during individual broker failure.
Jun Rao outlines the main data flow in the controller—in particular, when a broker fails, how the controller automatically promotes another replica as the leader to serve the clients, and when a broker is started, how the controller resumes the replication pipeline in the restarted broker.
Jun then describes recent improvements to the controller that allow it to handle certain edge cases correctly and increase its performance, which allows for more partitions in a Kafka cluster.
RecSys 2020 A Human Perspective on Algorithmic Similarity Schendel 9-2020Zachary Schendel
In the Netflix user interface (UI), when a row or UI element is named “Because you Watched...”, “More Like This”, or “Because you added to your list”, the overarching goal is to recommend a movie or TV show that a member might like based on the fact that they took a meaningful action on a source item. We have employed similar recommendations in many UI elements: on the homepage as a row of recommendations, after you click into a title, or as a piece of information about why a member should watch a title.
From an algorithmic perspective, there are many ways to define a “successful” similar recommendation. We sought to broaden that definition of success. To this end, the Consumer Insights team recently completed a suite of research projects to explore the intricacies of member perceptions of similar recommendations. The Netflix Consumer Insights team employs qualitative (e.g., in-depth interviews) and quantitative (e.g., surveys) research methods, interfacing directly with Netflix members to uncover pain points that can inspire new product innovation. The research concluded that, while the typical member believes movies are broadly similar when they share a common genre or theme, similarity is more complex, nuanced, and personal than we might have imagined. The vernacular we use in the UI implies that there should be at least some kind of relationship between the source item and the recommendations that follow. Many of our similar recommendations felt “out of place”, mostly because the relationship between the source item and the recommendation was unclear or absent. When similar recommendations tell a completely misleading, incorrect, or confusing story, member trust can be broken.
We will structure the presentation around three new insights that our research found to have an influence on the perception of similarity in the context of Netflix as well as the research methods used to uncover those insights. First, the reason a member loves a given movie will vary. For example, do you want to watch other baseball movies like Field of Dreams, or would you prefer other romances like Field of Dreams? Second, members are more or less flexible about how similar a recommendation actually needs to be depending on the properties of and their interactions with the canvas containing the recommendation. For example, a Because You Watched row on the homepage implies vaguer similarity while a More Like This gallery behind a click into the source item implies stricter similarity. Finally, even when we held the UI element constant, we found that similar recommendations are only valuable in some contexts. After finishing a movie, a member might prefer a similar recommendation one day and a change of pace the next. Research methods discussed will include Inverse Multi-Dimensional Scaling [1], survey experimentation, and ways to apply qualitative research to improve algorithmic recommendations.
“Alexa, be quiet!”: End-to-end near-real time model building and evaluation i...Flink Forward
Flink Forward San Francisco 2022.
To improve Amazon Alexa experiences and support machine learning inference at scale, we built an automated end-to-end solution for incremental model building or fine-tuning machine learning models through continuous learning, continual learning, and/or semi-supervised active learning. Customer privacy is our top concern at Alexa, and as we build solutions, we face unique challenges when operating at scale such as supporting multiple applications with tens of thousands of transactions per second with several dependencies including near-real time inference endpoints at low latencies. Apache Flink helps us transform and discover metrics in near-real time in our solution. In this talk, we will cover the challenges that we faced, how we scale the infrastructure to meet the needs of ML teams across Alexa, and go into how we enable specific use cases that use Apache Flink on Amazon Kinesis Data Analytics to improve Alexa experiences to delight our customers while preserving their privacy.
by
Aansh Shah
A Multi-Armed Bandit Framework For Recommendations at NetflixJaya Kawale
In this talk, we present a general multi-armed bandit framework for recommendations on the Netflix homepage. We present two example case studies using MABs at Netflix - a) Artwork Personalization to recommend personalized visuals for each of our members for the different titles and b) Billboard recommendation to recommend the right title to be watched on the Billboard.
Streamlio and IoT analytics with Apache PulsarStreamlio
To keep up with fast-moving IoT data, you need technology that can collect, process and store data with performance and scalability. This presentation from Data Day Texas looks at the technology requirements and how Apache Pulsar can help to meet them.
Wayfair Storefront Performance Monitoring with InfluxEnterprise by Richard La...InfluxData
In this InfluxDays NYC 2019 session, Richard Laskey from the Wayfair Storefront team will share their monitoring best practices using InfluxEnterprise. These efforts are critical and help improve the user experience by driving forward site-wide improvements, establishing best practices, and driving change through many different teams.
Big Data Day LA 2016/ Big Data Track - Portable Stream and Batch Processing w...Data Con LA
This talk explores deploying a series of small and large batch and streaming pipelines locally, to Spark and Flink clusters and to Google Cloud Dataflow services to give the audience a feel for the portability of Beam, a new portable Big Data processing framework recently submitted by Google to the Apache foundation. This talk will look at how the programming model handles late arriving data in a stream with event time, windows, and triggers.
After publishing my first terraform module I shared some details of how I used AWS services and leveraged serverless concepts and provided a demo to the Melbourne AWS User Group meetup https://www.meetup.com/aws-aus/events/291459709/
Latency is one of the most common Service Level Indicators (SLI), but where should it be measured from?
There are three main ways to measure latency:
•Server-side latency: Precise and high cardinality but missing the big picture
•Client-side latency: Big picture but noisy
•Blackbox monitoring latency: Good trade-off between the other two
In this talk, we will dive deeper into each perspective and how all of them can be leveraged. We will use Criteo’s large-scale key/value infrastructure as a case study
OSMC 2018 | Learnings, patterns and Uber’s metrics platform M3, open sourced ...NETWAYS
At Uber we use high cardinality monitoring to observe and detect issues with our 4,000 microservices running on Mesos and across our infrastructure systems and servers. We’ll cover how we put the resulting 6 billion plus time series to work in a variety of different ways, auto-discovering services and their usage of other systems at Uber, setting up and tearing down alerts automatically for services, sending smart alert notifications that rollup different failures into individual high level contextual alerts, and more. We’ll also talk about how we accomplish all this with a global view of our systems with M3, our open source metrics platform. We’ll take a deep dive look at how we use M3DB, now available as an open source Prometheus long term storage backend, to horizontally scale our metrics platform in a cost efficient manner with a system that’s still sane to operate with petabytes of metrics data.
Adventures in Observability - Clickhouse and InstanaMarcel Birkner
Monitoring is a hot topic for ClickHouse users. Joann Buch and Marcel Birkner of Instana and Robert Hodges of Altinity discuss how to create a complete monitoring solution for ClickHouse using Instana. We'll start with an overview of the Instana product, followed by a review of important metrics you should track on ClickHouse. We'll then walk through how to set up Instana on ClickHouse. We'll finish with a discussion of how Instana itself uses ClickHouse internally.
Using Apache Pulsar to Provide Real-Time IoT Analytics on the EdgeDataWorks Summit
The business value of data decreases rapidly after it is created, particularly in use cases such as fraud prevention, cybersecurity, and real-time system monitoring. The high-volume, high-velocity datasets used to feed these use cases often contain valuable, but perishable, insights that must be acted upon immediately.
In order to maximize the value of their data enterprises must fundamentally change their approach to processing real-time data to focusing reducing their decision latency on the perishable insights that exist within their real-time data streams. Thereby enabling the organization to act upon them while the window of opportunity is open.
Generating timely insights in a high-volume, high-velocity data environment is challenging for a multitude of reasons. As the volume of data increases, so does the amount of time required to transmit it back to the datacenter and process it. Secondly, as the velocity of the data increases, the faster the data and the insights derived from it lose value.
In this talk, we will present a solution based on Apache Pulsar Functions that significantly reduces decision latency by using probabilistic algorithms to perform analytic calculations on the edge.
Hadoop Summit SJ 2016: Next Gen Big Data Analytics with Apache ApexApache Apex
This is an overview of architecture with use cases for Apache Apex, a big data analytics platform. It comes with a powerful stream processing engine, rich set of functional building blocks and an easy to use API for the developer to build real-time and batch applications. Apex runs natively on YARN and HDFS and is used in production in various industries. You will learn more about two use cases: A leading Ad Tech company serves billions of advertising impressions and collects terabytes of data from several data centers across the world every day. Apex was used to implement rapid actionable insights, for real-time reporting and allocation, utilizing Kafka and files as source, dimensional computation and low latency visualization. A customer in the IoT space uses Apex for Time Series service, including efficient storage of time series data, data indexing for quick retrieval and queries at high scale and precision. The platform leverages the high availability, horizontal scalability and operability of Apex.
Provisioning and Capacity Planning Workshop (Dogpatch Labs, September 2015)Brian Brazil
If you’ve ever worried that you may have an outage someday due to your production servers not being able to handle increased user traffic, then this workshop will help put you at ease. Learn the foundations and how to apply it to your services.
Contact me at brian.brazil@robustperception.io if you'd like to learn more.
Ensuring Performance in a Fast-Paced Environment (CMG 2014)Martin Spier
Netflix accounts for more than a third of all traffic heading into American homes at peak hours. Making sure users are getting the best possible experience at all times is no simple feat and performance is at the core of this experience. In order to ensure performance and maintain development agility in a highly decentralized environment/(organization?), Netflix employs a multitude of strategies, such as production canary analysis, fully automated performance tests, simple zero-downtime deployments and rollbacks, auto-scaling clusters and a fault-tolerant stateless service architecture. We will present a set of use cases that demonstrate how and why different groups employ different strategies to achieve a common goal, great performance and stability, and detail how these strategies are incorporated into development, test and DevOps with minimal overhead.
Jeremy Edberg (MinOps ) - How to build a solid infrastructure for a startup t...Startupfest
You're building your startup and you know it will be big. You don't want to spend a lot of time on infrastructure, but you also don't want to be putting out fires after you get mentioned on Hacker News. In this session, we will give you real practical tips that you can take home with you on building an infrastructure that will scale quickly with minimal up front work on your part, using time tested techniques in infrastructure as code, SaaS, and Serverless, among other things.
Keynote: Building and Operating A Serverless Streaming Runtime for Apache Bea...Flink Forward
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.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
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Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Democratizing Fuzzing at Scale by Abhishek Aryaabh.arya
Presented at NUS: Fuzzing and Software Security Summer School 2024
This keynote talks about the democratization of fuzzing at scale, highlighting the collaboration between open source communities, academia, and industry to advance the field of fuzzing. It delves into the history of fuzzing, the development of scalable fuzzing platforms, and the empowerment of community-driven research. The talk will further discuss recent advancements leveraging AI/ML and offer insights into the future evolution of the fuzzing landscape.
Courier management system project report.pdfKamal Acharya
It is now-a-days very important for the people to send or receive articles like imported furniture, electronic items, gifts, business goods and the like. People depend vastly on different transport systems which mostly use the manual way of receiving and delivering the articles. There is no way to track the articles till they are received and there is no way to let the customer know what happened in transit, once he booked some articles. In such a situation, we need a system which completely computerizes the cargo activities including time to time tracking of the articles sent. This need is fulfilled by Courier Management System software which is online software for the cargo management people that enables them to receive the goods from a source and send them to a required destination and track their status from time to time.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Vaccine management system project report documentation..pdfKamal Acharya
The Division of Vaccine and Immunization is facing increasing difficulty monitoring vaccines and other commodities distribution once they have been distributed from the national stores. With the introduction of new vaccines, more challenges have been anticipated with this additions posing serious threat to the already over strained vaccine supply chain system in Kenya.
4. ● Global Internet:
● faster (better networking)
● slower (broader reach, congestion)
● Don't wait for it, measure it and deal
● Working app > Feature rich app
Making the Internet fast
is slow.
5. We need to know what the Internet looks like,
without averages, seeing the full distribution.
6. ● Sampling
○ Missed data
○ Rare events
○ Problems aren’t equal in
Population
● Averages
○ Can't see the distribution
○ Outliers heavily distort
∞, 0, negatives, errors
Logging Anti-Patterns
Instead, use the client as a map-reducer and send up aggregated
data, less often.
10. ● Calculate the inverse empirical cumulative
distribution function by math.
Get median, 95th, etc.
> library(HistogramTools)
> iecdf <- HistToEcdf(histogram,
method='linear’, inverse=TRUE)
> iecdf(0.5)
[1] 0.7975309 # median
> iecdf(0.95)
[1] 4.65 # 95th
percentile
o ...or just use R which is free and knows how
to do it already
14. Better than debating opinions.
Architecture is hard. Make it cheap to experiment where your users really are.
"There's no way that the
client makes that many
requests.”
"No one really minds the
spinner."
"Why should we spend
time on that instead of
COOLFEATURE?"
"We live in a
50ms world!"
16. ● Visual → Numerical, need the IECDF for
Percentiles
○ ƒ(0.50) = 50th
(median)
○ ƒ(0.95) = 95th
● Cluster to get pretty colors similar experiences.
(k-means, hierarchical, etc.)
Interpret the data
17.
18.
19.
20.
21. ● Go there!
● Abstract analysis - hard
● Feeling reality is much simpler than looking at graphs. Build!
Practical Teleportation.
24. Don't guess.
Developing a model based on
production data, without missing the
distribution of samples (network, render,
responsiveness) will lead to better
software.
Global reach doesn't need to be scary. @gcirino42 http://blogofsomeguy.com
27. Problem & Motivation
● Real-user performance monitoring solution
● More insight into the App performance
(as perceived by real users)
● Too many variables to trust synthetic
tests and labs
● Prioritize work around App performance
● Track App improvement progress over time
● Detect issues, internal and external
28. Device Diversity
● Netflix runs on all sorts of devices
● Smart TVs, Gaming Consoles, Mobile Phones, Cable TV boxes, ...
● Consistently evaluate performance
29.
30. What are we monitoring?
● User Actions
(or things users do in the App)
● App Startup
● User Navigation
● Playing a Title
● Internal App metrics
31. What are we measuring?
● When does the timer start and stop?
● Time-to-Interactive (TTI)
○ Interactive, even if
some items were not fully
loaded and rendered
● Time-to-Render (TTR)
○ Everything above the fold
(visible without scrolling)
is rendered
● Play Delay
● Meaningful for what we are monitoring
32. High-dimensional Data
● Complex device categorization
● Geo regions, subregions, countries
● Highly granular network
classifications
● High volume of A/B tests
● Different facets of the same user action
○ Cold, suspended and backgrounded
App startups
○ Target view/page on App startup
33.
34.
35.
36. Data Sketches
● Data structures that approximately
resemble a much larger data set
● Preserve essential features!
● Significantly smaller!
● Faster to operate on!
37. t-Digest
● t-Digest data structure
● Rank-based statistics
(such as quantiles)
● Parallel friendly
(can be merged!)
● Very fast!
● Really accurate!
https://github.com/tdunning/t-digest
48. ● Mid-tier stateless services are ~2/3rd of the total
● Savings - 30% of mid-tier footprint (roughly 30K instances)
○ Higher savings if we break it down by region
○ Even higher savings on services that scale well
Savings!
51. Should you autoscale?
Benefits
● On-demand capacity: direct $$ savings
● RI capacity: re-purposing spare capacity
However, for each server group, beware of
● Uneven distribution of traffic
● Sticky traffic
● Bursty traffic
● Small ASG sizes (<10)
52. Autoscaling impacts availability - true or false?
* If done correctly
Under-provisioning, however, can impact availability
● Autoscaling is not a problem
● The real problem is not knowing performance characteristics of the
service
53. AWS autoscaling mechanics
CloudWatch alarm ASG scaling policy
Aggregated metric feed
Notification
Tunables
Metric ● Threshold
● # of eval periods
● Scaling amount
● Warmup time
54. What metric to scale on?
Pros
● Tracks a direct measure of work
● Linear scaling
● Predictable
● Requires less adjustment over time
Cons
● Thresholds tend to drift over time
● Prone to changes in request mixture
● Less predictable
● More oscillation / jitter
Throughput
Resource
utilization
55. Autoscaling on multiple metrics
Proceed with caution
● Harder to reason about scaling behavior
● Different metrics might contradict each
other, causing oscillation
Typical Netflix configuration:
● Scale-up policy on throughput
● Scale-down policy on throughput
● Emergency scale-up policy on CPU, aka
“the hammer rule”
57. Common mistakes - “no rush” scaling
Problem: scaling amounts too
small, cooldown too long
Effect: scaling lags behind the
traffic flow. Not enough
capacity at peak, capacity
wasted in trough
Remedy: increase scaling
amounts, migrate to step
policies
58. Common mistakes - twitchy scaling
Problem: Scale-up policy is
too aggressive
Effect: unnecessary
capacity churn
Remedy: reduce scale-up
amount, increase the # of
eval periods
59. Common mistakes - should I stay or should I go
Problem: -up and -down
thresholds are too close to each
other
Effect: constant capacity
oscillation
Remedy: move -up and -down
thresholds farther apart
60. AWS target tracking - your best bet!
● Think of it as a step policy with auto-steps
● You can also think of it as a thermostat
● Accounts for the rate of change in monitored metric
● Pick a metric, set the target value and warmup time - that’s it!
Step Target-tracking
