Augmentation of bids for programmatic ad auctions @ real time with the power of Reactive Architecture - Sumit srivastava (MIQ)
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We at MiQ bid for billions of impressions everyday. This comes at a cost, and a lot many technical challenges. A million+ requests per second with response time limited to less than 10ms, Reactive programming enabled us to keep the resource costs minimum; with highly scalable and efficient ecosystem. In this session we delve deeper into the multiple aspects of the challenges, alongside a brush with the architecture of the system that keeps delivering for us.
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In this talk we will see, what are the benefits we get when we follow the functional programming paradigm in Java, how it makes the developer life easy. We will discuss the comparison between the imperative code vs functional code with some examples.
We will discuss the transition from imperative coding to functional coding with some code examples. This talk will be good for the people who really want to switch to functional programming and want to know how to use that in Java.
Reactive - Is it really a Magic Pill?
Reactive - Is it really a Magic Pill?Tech Triveni Reactive stack paints a very rosy picture of the way to develop the scalable applications focusing on the 4 fundamental things that are responsive, elastic, resilient and asynchronous communication. The principles are pretty simple but there are a lot of pitfalls that nobody talks about.
The challenges are numerous to build the system in the right way and in this talk we will focus on what are these pitfalls and how can we avoid them. We will be exploring the myth that once you adapt to it the journey will be smooth and show how these distributed systems add to additional complexity but can be managed by making smart decisions. I will share our experience of building Reactive Applications and how we have overcome the difficulties that we encounter.
Let’s go reactive with JAVA
Let’s go reactive with JAVATech Triveni Reactive programming is a new concept that is catching everyone’s eyes these days. The concept has been adopted by the various aspects of software engineering be it application development or DevOps. Our software development languages are also not untouched from it.
So, how can JAVA which has such a large community refrain itself from going reactive? The foundational topics for this talk will revolve around the ideas of reactive programming and what steps JAVA has taken towards being reactive. This would involve the latest buzz words in the market: Reactive streams, Reactive stream specification, the concept of mono and flux. We will also be touching how spring has used these features of reactive streams to make a new API development framework: Spring Boot Webflux. We hope the talk will help to understand Reactive programming aspects of JAVA and it will not bewilder the users as now they know that it’s a coding style that centres on the effective management of changes with async data streams.
Tackling Asynchrony with Kotlin Coroutines
Tackling Asynchrony with Kotlin CoroutinesTech Triveni Reactive Programming is an approach to asynchronous programming. Like every other tool, reactive programming tools like RxJava or Reactor come with their own challenges. Kotlin Coroutines alleviates many of these challenges. Kotlin Coroutines, when being designed, had the advantage of being able to explore existing code that uses reactive stream.
This talk will highlight some of these practical challenges and show how Kotlin Coroutines tackle them. The challenges that will be covered are changes in interface design, programming paradigm shift, reactive type selection, exception handling, cancellation, resource leakage, and sequential vs concurrent.
Programmatic Ad Tracking: Let the power of Reactive Microservices do talking
Programmatic Ad Tracking: Let the power of Reactive Microservices do talkingTech Triveni We at MiQ bid for billions of impressions every day. This comes at a cost and many technical challenges. After the ad is served we need a mechanism to share insights with clients. We need an application to gather these insights on a periodic basis.
Building microservices is comparatively easy but the more difficult part is everything that happens between the services in distributed systems. That’s where orchestration frameworks like Kubernetes come to rescue. At the same time, microservices need t to minimize synchronous invocations for intra-microservices communications to ensure the best possible isolation. Instead, architects should consider using asynchronous communication between the services. This is why reactive libraries like the ones from the ReactiveX programming, the family has become so popular.
A well-written microservice is generally going to apply the principles of the reactive manifesto. One could assert that a microservices architecture is just an extension of the reactive manifesto that is geared towards web services. But there are related subjects of reactive programming and functional reactive programming which are related to the reactive manifesto. A system can be a reactive system and not use a reactive programming model. Reactive programming is often used to coordinate asynchronous calls to multiple services along with events and streams from clients.
Reactive microservices enabled us to keep the resource costs minimum with a highly scalable, efficient ecosystem. In this session, we dig deeper into the multiple aspects of the challenges, sneak peek with the architecture of the system that keeps delivering for our clients.
Let's refine your Scala Code
Let's refine your Scala CodeTech Triveni This talk aims to be a gentle introduction to Refinement types in Scala. The talk will also introduce many related topics in the subculture of Typelevel programming in FP like Type theory, Generics (Shapeless), Optics (Monocle) and Law-based testing.
Why refinement types?
When 'String' just doesn't cut it anymore. By encoding validation into a type at the edge-of-the-system layer of your application, you can build your core domain logic without having to worry much about type-safety.
Who is it for?
Ajay would be revisiting the necessary foundations at the beginning of the talk, so beginners are welcome. People who have worked on Scala before would be able to appreciate the power of refined types more.
Supercharged imperative programming with Haskell and Functional Programming
Supercharged imperative programming with Haskell and Functional ProgrammingTech Triveni Haskell is the world's finest imperative programming language." The above quote comes from Simon P Jones, the creator of Haskell. To newcomers to Functional programming, this may seem weird and mystifying. After all, Haskell is usually known for its advanced Functional programming features such as purity and strong static typing. While it's likely that Simon said this at least partly in jest, the fact is that those same features also make imperative programming in Haskell much more flexible and powerful than in most other languages!
In this talk, we present a view of Haskell and strongly typed functional programming from the "other side" of the prism. We discuss how you can build and compose powerful imperative abstractions with Haskell using the same FP toolset. We discuss how to write common imperative algorithms in Haskell while exploiting advanced features like first-class computations, laziness, and strong static typing. We will also touch upon interfacing with external imperative systems (such as databases, and network communication). At the end of the talk, participants should have enough groundwork to explore Haskell as a *practical* language, and have a greater appreciation of the powerful features offered by Functional Programming.
Observability at scale with Neural Networks: A more proactive approach
Observability at scale with Neural Networks: A more proactive approachTech Triveni We at Expedia work on a mission of connecting people to places through the power of technology. To accomplish this, we build and run hundreds of micro-services that provide different functionalities to serve every single customer request, which results in generating billions of events. Now, what happens when one or more services fail at the same time? Well, to improve the observability in our system, we see a need to connect these failure points across our distributed topology to reduce mean time to detect(MTTD) and know (MTTK)
In this talk, we will present the journey of distributed tracing in Expedia that started with Zipkin as a prototype and ended up building our own solution(in open source). We will do a deep dive into our architecture and demonstrate how we ingest terabytes of tracing data (around 8 TB / day) in production with a peak throughput of over 550,000 spans / second for hundreds of micro-services.
We use this data for trending service errors/latencies/rate, perform anomaly detection on the aggregated trends, build service-dependency and network-latency graphs, other than our primary use case of distributed tracing.
With this increasing number, there felt the need to have a real-time intelligent alerting and monitoring system to move towards 24/7 reliability. We will talk about how we use neural networks on trends and perform anomaly detection, including a deep dive into the architecture for the automated training pipeline and online, compute using streams in a cost-effective manner
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At Envestnet | Yodlee, we have deployed several advanced state-of-the-art Machine Learning solutions that process millions of data points on a daily basis with very stringent service level commitments. A key aspect of our Natural Language Processing solutions is Semi-supervised learning (SSL): A family of methods that also make use of unlabelled data for training – typically a small amount of labeled data with a large amount of unlabelled data. Pure supervised solutions fail to exploit the rich syntactic structure of the unlabelled data to improve decision boundaries. There is an abundance of published work in the field - but few papers have succeeded in showing significantly better results than state-of-the-art supervised learning. Often, methods have simplifying assumptions that fail to transfer to real-world scenarios. There is a lack of practical guidelines for deploying effective SSL solutions. We attempt to bridge that gap by sharing our learning from successful SSL models deployed in production
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Everyone has almost become cyborg already with mobile phones in our hands and apparently until human beings bring the AI/ML to the phones completely they are not taking any rest. We are going to see the development of different architecture and algorithms around running AI/ML on low configuration devices.
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Proximity Targeting at Scale using Big Data Platforms
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The growth of mobile devices has led to enormous data generation which offers tremendous potential when used effectively for business. Thus we need an efficient platform where we can process such huge data efficiently and with minimum latency and cost. This talk describes MIQ's journey into building a fast and scalable processing platform using Big Data, delivering faster and actionable insights for Proximity targeting which has empowered the creation of a product generating ~30 million dollar revenue on a year to year basis.
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Effecting Pure Change - How anything ever gets done in functional programming...Tech Triveni Newcomers to functional programming are often mystified when they encounter pure functions and immutable data structures. A mathematical function which always produces the same output for the same input is so inflexible as to be almost useless! And surely how can you even represent even the simplest of dynamic program state without variables to store it in, to say nothing of fancy user interfaces, and complex input output.
This talk will present an overview of the various techniques that are used by functional programming languages to tackle state and external "effects" which are needed for any real world program. It will cover a large landscape ranging from Monads, to Algebraic Effects, to Functional Reactive Programming. And it will show how functional programming can be as *useful* for real world programs, as it is *beautiful*.
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Live coding session on AI / ML using Google Tensorflow (Python) - Tanmoy Deb ...Tech Triveni Build your own system, extend your existing systems through ML capabilities. Machine learning is about teaching computers how to learn from data to make decisions or predictions. For true machine learning, the computer must be able to learn to identify patterns without being explicitly programmed to. The topic will give briefly cover how anybody can learn and start their journey in AI, discussing the opportunities and challenges from point of view of a student, developer, and entrepreneur. The take away from the session will be to take that first step beyond the question- "where to start? There is too much out there!". This will be given with a demo that how most popular framework for Deep Learning works.
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1). The old school way (10 Minutes)
a. Overview of how the SMACK stack used to run in a non distributed environment
b. It's advantages
c. It's disadvantages
2). Overview of running SMACK stack over DCOS (10 minutes)
a. Advantages and ease of use of SMACK stack in DCOS
b. Any disadvantage/drawback
3). Overview of running SMACK stack over Kubernetes (10 minutes)
a. Advantages and ease of use of SMACK stack in Kubernetes
b. Any disadvantage/drawback
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Key points:
1. Big data – What, Who, Why
2. Our real examples of Big data applications
3. Our UX/UI learning in last10 Years in Big data space
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Key Take away:
1. Learn about Micro Frontend
2. How to practically use them
3. Key challenges
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- 1. Augmentation of bids for programmatic ad auctions @ real time with the power of Reactive Architecture Sumit Srivastava Engineering Lead - MiQ
- 2. 2 ❑ Marketing intelligence and Analytics partner to many of the world’s most prominent brands and media agencies American Express, Avis, Lenovo, Unilever, Microsoft, GroupM, Publicis and IPG. ❑ AiQ is our technology that provides modular, API-based analytics services to rapidly build data solutions for successful real-time business outcomes. MiQ - Activating Marketing Intelligence via AiQ Data Scientists & Engineers Data Scientists & Analysts Solution Engineers & Traders
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- 4. 4 The system 35 million IP range records that we store in PostgreSQL, each consisting respective Geo information 35+ Features (parameters) each bid request is evaluated upon 100ms Average lifetime of an ad request including bidding, campaign optimization and creative delivery 7+TB Amount of compressed data generated every day 90% Gateway to the exchange for MiQ campaigns Marketing Intelligence Source to the data powering the core of data science at MiQ
- 5. 5 ❑ 1.5 million requests per second ❑ Response time below 10ms ❑ IP to Geo resolution The problem statement
- 7. 7 The journey Version One Version Two Components ● In-house Netty based server ● Off-heap pagination driven custom data structure ● Vert.x ● PostgreSQL Advantages ● Customized to have extremely low memory footprint ● Non-blocking ● Low maintenance ● Highly scalable ● Active community Disadvantages ● High maintenance ● Blocking ● Not scalable ● Complex debug process ● Readability ● No community support ● Slightly higher memory footprint
- 8. 8 Stats ❑ Requests per server: (10 connections, 10 threads) ● 200k/sec with no lookup data ● 25+k/sec with all features loaded ❑ Average response time: 4ms (95%) ❑ PostgreSQL queries response: <=2ms (99%)
- 9. 9 Q&A
- 10. 10 Thank You