Thomas Lamirault_Mohamed Amine Abdessemed -A brief history of time with Apache Flink
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Many use cases in the telecommunication industry require producing counters, quality metrics, and alarms in a streaming fashion with very low latency. Most of this metrics are only valuable when they’re made available as soon as the associated events happened. In our company we are looking for a system able to produce this kind of real-time indicator, which must handle massive amounts of data (400,000 eps) with often peak loads (like New Year’s Eve) or out-of-order events like massive network disorder. Low latency and flexible window management with specific watermark emission are also a must-haves. Heterogeneous format, multiple flow correlation, and the possibility of late data arrival are other challenges. Flink being already widely used at Bouygues Telecom for real-time data integration, its features made it the evident candidate for the future System. In this talk, we'll present a real use case of streaming analytics using Flink, Kafka & HBase along with other legacy systems.
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Flink consumers read from Kafka as a scalable, high throughput, and low latency data source. However, there are challenges in scaling out data streams where migration and multiple Kafka clusters are required. Thus, we introduced a new Kafka source to read sharded data across multiple Kafka clusters in a way that conforms well with elastic, dynamic, and reliable infrastructure. In this presentation, we will present the source design and how the solution increases application availability while reducing maintenance toil. Furthermore, we will describe how we extended the existing KafkaSource to provide mechanisms to read logical streams located on multiple clusters, to dynamically adapt to infrastructure changes, and to perform transparent cluster migrations and failover.
by
Mason Chen
Building Event-Driven (Micro) Services with Apache Kafka
This talk begins with a short recap of how we created systems over the past 20 years, up to the current idea of building systems, using a Microservices architecture. What is a Microservices architecture and how does it differ from a Service-Oriented Architecture? Should you use traditional REST APIs to integrate services with each eachother in a Microservices Architecture? Or is it better to use a more loosely-coupled protocol? Answers to these and many other questions are provided. The talk will show how a distributed log (event hub) can help to create a central, persistent history of events and what benefits we achieve from doing so. Apache Kafka is a perfect match for building such an asynchronous, loosely-coupled event-driven backbone. Events trigger processing logic, which can be implemented in a more traditional as well as in a stream processing fashion. The talk shows the difference between a request-driven and event-driven communication and answers when to use which. It highlights how a modern stream processing systems can be used to hold state both internally as well as in a database and how this state can be used to further increase independence of other services, the primary goal of a Microservices architecture.
How to build a streaming Lakehouse with Flink, Kafka, and Hudi
Flink Forward San Francisco 2022.
With a real-time processing engine like Flink and a transactional storage layer like Hudi, it has never been easier to build end-to-end low-latency data platforms connecting sources like Kafka to data lake storage. Come learn how to blend Lakehouse architectural patterns with real-time processing pipelines with Flink and Hudi. We will dive deep on how Flink can leverage the newest features of Hudi like multi-modal indexing that dramatically improves query and write performance, data skipping that reduces the query latency by 10x for large datasets, and many more innovations unique to Flink and Hudi.
by
Ethan Guo & Kyle Weller
Time to-live: How to Perform Automatic State Cleanup in Apache Flink - Andrey...
A common requirement for many stateful streaming applications is to automatically cleanup application state for effective management of state size and visibility. The state time-to-live (TTL) feature enables application state cleanup in Apache Flink.
In this talk, we will first discuss the State TTL feature and its use cases. We will then outline the semantics of the feature and provide code examples before taking a closer look at the implementation details to tackle the encountered challenges associated with the background cleanup process. Finally, we will talk about the roadmap of the TTL feature including potential improvements of the feature in future Flink releases.
Thoughts on kafka capacity planning
This talk covers Kafka cluster sizing, instance type selections, scaling operations, replication throttling and more. Don’t forget to check out the Kafka-Kit repository.
https://www.youtube.com/watch?time_continue=2613&v=7uN-Vlf7W5E
Where is my bottleneck? Performance troubleshooting in Flink
Flinkn Forward San Francisco 2022.
In this talk, we will cover various topics around performance issues that can arise when running a Flink job and how to troubleshoot them. We’ll start with the basics, like understanding what the job is doing and what backpressure is. Next, we will see how to identify bottlenecks and which tools or metrics can be helpful in the process. Finally, we will also discuss potential performance issues during the checkpointing or recovery process, as well as and some tips and Flink features that can speed up checkpointing and recovery times.
by
Piotr Nowojski
Introducing BinarySortedMultiMap - A new Flink state primitive to boost your ...
Flink Forward San Francisco 2022.
Probably everyone who has written stateful Apache Flink applications has used one of the fault-tolerant keyed state primitives ValueState, ListState, and MapState. With RocksDB, however, retrieving and updating items comes at an increased cost that you should be aware of. Sometimes, these may not be avoidable with the current API, e.g., for efficient event-time stream-sorting or streaming joins where you need to iterate one or two buffered streams in the right order. With FLIP-220, we are introducing a new state primitive: BinarySortedMultiMapState. This new form of state offers you to (a) efficiently store lists of values for a user-provided key, and (b) iterate keyed state in a well-defined sort order. Both features can be backed efficiently by RocksDB with a 2x performance improvement over the current workarounds. This talk will go into the details of the new API and its implementation, present how to use it in your application, and talk about the process of getting it into Flink.
by
Nico Kruber
YOW2018 - Events and Commands: Developing Asynchronous Microservices
The microservice architecture functionally decomposes an application into a set of services. Each service has its own private database that’s only accessible indirectly through the services API. Consequently, implementing queries and transactions that span multiple services is challenging.
In this presentation, you will learn how to solve these distributed data management challenges using asynchronous messaging. I describe how to implement transactions using sagas, which are sequences of local transactions, coordinated using messages. You will learn how to implement queries using Command Query Responsibility Segregation (CQRS), which uses events to maintain replicas. I describe how to use event sourcing, which is an event-centric approach to business logic and persistence, in a microservice architecture.
Apache Spark Data Source V2 with Wenchen Fan and Gengliang Wang
As a general computing engine, Spark can process data from various data management/storage systems, including HDFS, Hive, Cassandra and Kafka. For flexibility and high throughput, Spark defines the Data Source API, which is an abstraction of the storage layer. The Data Source API has two requirements.
1) Generality: support reading/writing most data management/storage systems.
2) Flexibility: customize and optimize the read and write paths for different systems based on their capabilities.
Data Source API V2 is one of the most important features coming with Spark 2.3. This talk will dive into the design and implementation of Data Source API V2, with comparison to the Data Source API V1. We also demonstrate how to implement a file-based data source using the Data Source API V2 for showing its generality and flexibility.
Kappa vs Lambda Architectures and Technology Comparison
Real-time data beats slow data. That’s true for almost every use case. Nevertheless, enterprise architects build new infrastructures with the Lambda architecture that includes separate batch and real-time layers.
This video explores why a single real-time pipeline, called Kappa architecture, is the better fit for many enterprise architectures. Real-world examples from companies such as Disney, Shopify, Uber, and Twitter explore the benefits of Kappa but also show how batch processing fits into this discussion positively without the need for a Lambda architecture.
The main focus of the discussion is on Apache Kafka (and its ecosystem) as the de facto standard for event streaming to process data in motion (the key concept of Kappa), but the video also compares various technologies and vendors such as Confluent, Cloudera, IBM Red Hat, Apache Flink, Apache Pulsar, AWS Kinesis, Amazon MSK, Azure Event Hubs, Google Pub Sub, and more.
Video recording of this presentation:
https://youtu.be/j7D29eyysDw
Further reading:
https://www.kai-waehner.de/blog/2021/09/23/real-time-kappa-architecture-mainstream-replacing-batch-lambda/
https://www.kai-waehner.de/blog/2021/04/20/comparison-open-source-apache-kafka-vs-confluent-cloudera-red-hat-amazon-msk-cloud/
https://www.kai-waehner.de/blog/2021/05/09/kafka-api-de-facto-standard-event-streaming-like-amazon-s3-object-storage/
Streaming Event Time Partitioning with Apache Flink and Apache Iceberg - Juli...
Netflix’s playback data records every user interaction with video on the service, from trailers on the home page to full-length movies. This is a critical dataset with high volume that is used broadly across Netflix, powering product experiences, AB test metrics, and offline insights. In processing playback data, we depend heavily on event-time partitioning to handle a long tail of late arriving events. In this talk, I’ll provide an overview of our recent implementation of generic event-time partitioning on high volume streams using Apache Flink and Apache Iceberg (Incubating). Built as configurable Flink components that leverage Iceberg as a new output table format, we are now able to write playback data and other large scale datasets directly from a stream into a table partitioned on event time, replacing the common pattern of relying on a post-processing batch job that “puts the data in the right place”. We’ll talk through what it took to apply this to our playback data in practice, as well as challenges we hit along the way and tradeoffs with a streaming approach to event-time partitioning.
Data Warehousing with Amazon Redshift
by Taz Sayed, Sr Technical Account Manager AWS and Marie Yap, Enterprise Solutions Architect AWS
AWS Data & Analytics Week is an opportunity to learn about Amazon’s family of managed analytics services. These services provide easy, scalable, reliable, and cost-effective ways to manage your data in the cloud. We explain the fundamentals and take a technical deep dive into Amazon Redshift data warehouse; Data Lake services including Amazon EMR, Amazon Athena, & Amazon Redshift Spectrum; Log Analytics with Amazon Elasticsearch Service; and data preparation and placement services with AWS Glue and Amazon Kinesis. You'll will learn how to get started, how to support applications, and how to scale.
Cosco: An Efficient Facebook-Scale Shuffle Service
Cosco is an efficient shuffle-as-a-service that powers Spark (and Hive) jobs at Facebook warehouse scale. It is implemented as a scalable, reliable and maintainable distributed system. Cosco is based on the idea of partial in-memory aggregation across a shared pool of distributed memory. This provides vastly improved efficiency in disk usage compared to Spark's built-in shuffle. Long term, we believe the Cosco architecture will be key to efficiently supporting jobs at ever larger scale. In this talk we'll take a deep dive into the Cosco architecture and describe how it's deployed at Facebook. We will then describe how it's integrated to run shuffle for Spark, and contrast it with Spark's built-in sort-based shuffle mechanism and SOS (presented at Spark+AI Summit 2018).
Practical learnings from running thousands of Flink jobs
Flink Forward San Francisco 2022.
Task Managers constantly running out of memory? Flink job keeps restarting from cryptic Akka exceptions? Flink job running but doesn’t seem to be processing any records? We share practical learnings from running thousands of Flink Jobs for different use-cases and take a look at common challenges they have experienced such as out-of-memory errors, timeouts and job stability. We will cover memory tuning, S3 and Akka configurations to address common pitfalls and the approaches that we take on automating health monitoring and management of Flink jobs at scale.
by
Hong Teoh & Usamah Jassat
Evening out the uneven: dealing with skew in Flink
Flink Forward San Francisco 2022.
When running Flink jobs, skew is a common problem that results in wasted resources and limited scalability. In the past years, we have helped our customers and users solve various skew-related issues in their Flink jobs or clusters. In this talk, we will present the different types of skew that users often run into: data skew, key skew, event time skew, state skew, and scheduling skew, and discuss solutions for each of them. We hope this will serve as a guideline to help you reduce skew in your Flink environment.
by
Jun Qin & Karl Friedrich
Real-time Streaming and Querying with Amazon Kinesis and Amazon Elastic MapRe...
Originally, Hadoop was used as a batch analytics tool; however, this is rapidly changing, as applications move towards real-time processing and streaming. Amazon Elastic MapReduce has made running Hadoop in the cloud easier and more accessible than ever. Each day, tens of thousands of Hadoop clusters are run on the Amazon Elastic MapReduce infrastructure by users of every size — from university students to Fortune 50 companies. We recently launched Amazon Kinesis – a managed service for real-time processing of high volume, streaming data. Amazon Kinesis enables a new class of big data applications which can continuously analyze data at any volume and throughput, in real-time. Adi will discuss each service, dive into how customers are adopting the services for different use cases, and share emerging best practices. Learn how you can architect Amazon Kinesis and Amazon Elastic MapReduce together to create a highly scalable real-time analytics solution which can ingest and process terabytes of data per hour from hundreds of thousands of different concurrent sources. Forever change how you process web site click-streams, marketing and financial transactions, social media feeds, logs and metering data, and location-tracking events.
What's hot (20)
Presto: Fast SQL-on-Anything (including Delta Lake, Snowflake, Elasticsearch ...
Presto: Fast SQL-on-Anything (including Delta Lake, Snowflake, Elasticsearch ...
A Thorough Comparison of Delta Lake, Iceberg and Hudi
A Thorough Comparison of Delta Lake, Iceberg and Hudi
Dynamically Scaling Data Streams across Multiple Kafka Clusters with Zero Fli...
Dynamically Scaling Data Streams across Multiple Kafka Clusters with Zero Fli...
Building Event-Driven (Micro) Services with Apache Kafka
Building Event-Driven (Micro) Services with Apache Kafka
How to build a streaming Lakehouse with Flink, Kafka, and Hudi
How to build a streaming Lakehouse with Flink, Kafka, and Hudi
Time to-live: How to Perform Automatic State Cleanup in Apache Flink - Andrey...
Time to-live: How to Perform Automatic State Cleanup in Apache Flink - Andrey...
Where is my bottleneck? Performance troubleshooting in Flink
Where is my bottleneck? Performance troubleshooting in Flink
Introducing BinarySortedMultiMap - A new Flink state primitive to boost your ...
Introducing BinarySortedMultiMap - A new Flink state primitive to boost your ...
YOW2018 - Events and Commands: Developing Asynchronous Microservices
YOW2018 - Events and Commands: Developing Asynchronous Microservices
Apache Spark Data Source V2 with Wenchen Fan and Gengliang Wang
Apache Spark Data Source V2 with Wenchen Fan and Gengliang Wang
Kappa vs Lambda Architectures and Technology Comparison
Kappa vs Lambda Architectures and Technology Comparison
Streaming Event Time Partitioning with Apache Flink and Apache Iceberg - Juli...
Streaming Event Time Partitioning with Apache Flink and Apache Iceberg - Juli...
Cosco: An Efficient Facebook-Scale Shuffle Service
Cosco: An Efficient Facebook-Scale Shuffle Service
Practical learnings from running thousands of Flink jobs
Practical learnings from running thousands of Flink jobs
Evening out the uneven: dealing with skew in Flink
Evening out the uneven: dealing with skew in Flink
Real-time Streaming and Querying with Amazon Kinesis and Amazon Elastic MapRe...
Real-time Streaming and Querying with Amazon Kinesis and Amazon Elastic MapRe...
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Márton Balassi Streaming ML with Flink-
http://flink-forward.org/kb_sessions/streaming-ml-with-flink/
As continuous big data processing is gaining popularity it naturally implies that there is a need to transition many of the distributed machine learning functionality to a streaming backend. The most common use case is to give streaming predictions based on the model learnt in batch, however in some cases it is beneficial to also update the model on the fly. It is not uncommon that streaming learners need different algorithms than their batch counterparts. The talk discusses the common use cases and the pitfalls of the streaming ML transition through the example of recommender systems. It also offer a dive into the implementation of a Scala library augmenting FlinkML with streaming predictors.
Gábor Horváth - Code Generation in Serializers and Comparators of Apache Flink
http://flink-forward.org/kb_sessions/code-generation-in-serializers-and-comparators-of-apache-flink/
Performance of Big Data applications are often limited by serializers and comparators. In the case of Flink, despite its delicate type system, hand writing serializers leads to a 10% improvement for a simple WordCount job using Pojos. There are applications where the gain can be even more, about 30%. A significant amount of the overhead is the result of using reflection to access the fields of Pojo objects. There is some additional overhead due to the fact that the JVM has a hard time to optimize the current default serializers and comparators because of their dynamic nature. In order to improve the performance, I implemented runtime code generation, to generate specialized code for each Pojo type at runtime to improve the performance. The generated code accesses fields without reflection and easier to optimize. In the talk I will give a detailed overview of the challenges of implementing code generation, the solutions, and some measurements about the performance improvements.
Eron Wright - Flink Security Enhancements
http://flink-forward.org/kb_sessions/flink-security-enhancements/
Recent security enhancements to Flink make it easy to access secure data and to protect the associated credentials. In this talk we’ll describe and demonstrate the new features, including Kerberos-based access to HDFS and Kafka, transport security (TLS), and service-level authorization which protects your Flink cluster from unauthorized access.
Zoltán Zvara - Advanced visualization of Flink and Spark jobs
http://flink-forward.org/kb_sessions/advanced-visualization-of-flink-and-spark-jobs/
Understanding the physical plan of a big data application is often crucial for tracking down bottlenecks and faulty behavior. Flink and Spark although offering useful Web UI components for monitoring and understanding the logical plan of the jobs, both lack a tool that helps to understand the physical plan of the scheduler and the possibility to monitor execution at a very low level, along with the communication that occur between parallel vertex instances. We propose a tool that allows users to real-time monitor and later to replay, examine job executions on any cluster currently supported by Flink or Spark. The tool also offers monitoring of the distribution of keys in a data stream and can lead to optimizing data partitioning across parallel subtasks in the future.
Stephan Ewen - Scaling to large State
http://flink-forward.org/kb_sessions/scaling-stream-processing-with-apache-flink-to-very-large-state/
The majority of streaming programs is ‘stateful’: Windowed Aggregations, Sessions, Joins, Complex Event Processing, Tables – they all require to keep some form of state across individual events. With the migration of more and more complex batch jobs or data processing pipelines to streaming applications, some streaming programs need to keep terabytes of state. Apache Flink implements a checkpointing-based recovery mechanism that guarantees exactly-once semantics for state also in the presence of failures. The cost of checkpointing and recovery depends on the size of the program’s state. In this talk, we will discuss the current status of stateful processing in Apache Flink, as well as the ongoing efforts to make Flink’s fault tolerance mechanism scale to very large state sizes, supporting frequent checkpoints and faster recovery of large state, without requiring excessive numbers of machines.
Step-by-Step Introduction to Apache Flink
This a talk that I gave at the 2nd Apache Flink meetup in Washington DC Area hosted and sponsored by Capital One on November 19, 2015. You will quickly learn in step-by-step way:
How to setup and configure your Apache Flink environment?
How to use Apache Flink tools?
3. How to run the examples in the Apache Flink bundle?
4. How to set up your IDE (IntelliJ IDEA or Eclipse) for Apache Flink?
5. How to write your Apache Flink program in an IDE?
Flink vs. Spark
Flink vs. Spark: this is the slide deck of my talk at the 2015 Flink Forward conference in Berlin, Germany, on October 12, 2015. In this talk, we tried to compare Apache Flink vs. Apache Spark with focus on real-time stream processing. Your feedback and comments are much appreciated.
Apache Flink community Update for March 2016 - Slim Baltagi
This Apache Flink Community Update for March 2016 was given by Slim Baltagi at the New York City Apache Flink Meetup.
Overview of Apache Fink: The 4G of Big Data Analytics Frameworks
Slides of my talk at the Hadoop Summit Europe in Dublin, Ireland on April 13th, 2016. The talk introduces Apache Flink as both a multi-purpose Big Data analytics framework and real-world streaming analytics framework. It is focusing on Flink's key differentiators and suitability for streaming analytics use cases. It also shows how Flink enables novel use cases such as distributed CEP (Complex Event Processing) and querying the state by behaving like a key value data store.
Unified Batch and Real-Time Stream Processing Using Apache Flink
This talk was given at Capital One on September 15, 2015 at the launch of the Washington DC Area Apache Flink Meetup. Apache flink is positioned at the forefront of 2 major trends in Big Data Analytics:
- Unification of Batch and Stream processing
- Multi-purpose Big Data Analytics frameworks
In these slides, we will also find answers to the burning question: Why Apache Flink? You will also learn more about how Apache Flink compares to Hadoop MapReduce, Apache Spark and Apache Storm.
Eron Wright - Introducing Flink on Mesos
http://flink-forward.org/kb_sessions/introducing-flink-on-mesos/
Apache Flink supports a variety of deployment modes, with support for Apache Mesos new to Flink 1.2. This talk will cover the benefits and major features of Flink on Mesos. We’ll demonstrate the functionality and discuss future directions.
Trevor Grant - Apache Zeppelin - A friendlier way to Flink
http://flink-forward.org/kb_sessions/apache-zeppelin-a-friendlier-way-to-flink/
In this talk we present Apache Zeppelin, notebook based web-UI for working with several big data platforms, including Flink. In this talk we will show how to leverage several of Zeppelin’s exciting features, using Flink. The talk / demo will be a series of examples of increasing complexity. Starting with the obligatory word count example, we will move on to loading additional jars to perform machine learning tasks using FlinkML, we will do some examples of graph processing in Gelly and visualizing those graphs using AngularJS and d3js (both native to Zeppelin), give examples of how Zeppelin’s “ResourcePools” can be used to share variables between interpreters, leading in to a Flink Streaming Example* where variables a “bound” and visualizations update in real time. *pre-supposes working/stable Flink Streaming in REPL, editors feel free to delete this comment once that has been achieved.
Alexander Kolb - Flinkspector – Taming the squirrel
http://flink-forward.org/kb_sessions/flinkspector-taming-the-squirrel/
The costs of logic errors in production for streaming applications are higher than for batch processing systems. Depending on the setup, errors cannot be rectified or have already influenced important decisions. The goal of Flinkspector is to improve the test process of Apache Flink streaming applications in order to detect streaming application logic errors early during development. It features dedicated mechanics for test setup, execution, and evaluation. While Flinkspector’s streamlined API keeps testing overhead small. The framework is able to handle non-terminating and parallelized data flows involving windowing. The lightweight integration-tests enabled by Flinkspector allow Flink applications to be included into the continuous integration and deployment process. The talk introduces the core functionality of Flinkspector. In addition, background concepts of the runtime and the evaluation algorithms are presented. https://github.com/ottogroup/flink-spector
Ana M Martinez - AMIDST Toolbox- Scalable probabilistic machine learning with...
http://flink-forward.org/kb_sessions/amidst-toolbox-scalable-probabilistic-machine-learning-with-flink/
In this session we would like to present our AMIDST toolbox for analysis of large-scale data sets using probabilistic machine learning models. AMIDST runs algorithms in a distributed fashion for learning and
inference in a wide spectrum of latent variable models such as Gaussian mixtures, (probabilistic) principal component analysis, Hidden Markov Models, Kalman Filters, Latent Dirichlet Allocation, etc. This toolbox is
able to perform Bayesian parameter learning on any user-defined probabilistic (graphical) model with billions of nodes using novel distributed message passing algorithms.
We plan to give an overview of the AMIDST toolbox (Java open source), some details about the API and the integration with Flink, and an analysis of the scalability of our learning algorithms. All this in the context of a real use case scenario in the financial domain (BCC group), where the profile of millions of customers is analyzed using Flink and the Amazon Web Services.
Maxim Fateev - Beyond the Watermark- On-Demand Backfilling in Flink
http://flink-forward.org/kb_sessions/beyond-the-watermark-on-demand-backfilling-in-flink/
Flink has consistency guarantees and efficient checkpointing model which make it a good fit for Uber’s money-related use cases, such as driver incentives. However, Flink’s time-progress model is built around a single watermark, which is incompatible with Uber’s business need for generating aggregates retroactively. The talk covers our solution for on-demand backfilling. It also outlines other abstractions and features we expect Flink to support as it matures.
Ted Dunning-Faster and Furiouser- Flink Drift
http://flink-forward.org/kb_sessions/faster-and-furiouser-flink-drift/
Not long ago, we had the opportunity to test Apache Flink to see just how fast it would go on a moderately realistic task with fast hardware and with a good streaming transport layer underneath. Our goal was not so much careful comparison with other software, but flat-out speed, Flink against Flink. In the process, we learned a lot about what it takes to go fast. Some of the lessons were ones that we had “learned” a number of times before: – the bottleneck isn’t where you thought it was – copying data is expensive – context switches are expensive – measure twice, cut once But there were some real surprises along the way. The really important knobs weren’t quite what people say you should turn. One of the biggest surprises was the degree to which high performance libraries have threading built into them which makes the actual concurrrency much higher than the apparent concurrency. The result was that at least one cluster parameter needed to be adjusted by 30x to get real
Julian Hyde - Streaming SQL
http://flink-forward.org/kb_sessions/streaming-sql/
Streaming is necessary to handle data rates and latency, but SQL is unquestionably the lingua franca of data. Is it possible to combine SQL with streaming, and if so, what does the resulting language look like? Apache Calcite is extending SQL to include streaming, and Apache Flink is using Calcite to support both regular and streaming SQL. In this talk, Julian Hyde describes streaming SQL in detail and shows how you can use streaming SQL in your application. He also describes how Calcite’s planner optimizes queries for throughput and latency.
Ted Dunning - Keynote: How Can We Take Flink Forward?
http://flink-forward.org/kb_sessions/keynote-tba/
Apache Flink has come a long way from its academic beginnings. It is now one of the most technically advanced solutions for streaming computation. And batch computation, too. Flink has serious technical advantages when compared with nearly every alternative system.
This success ironically means that Apache Flink is right on the cusp of a critical moment. Over the next few months it will be decided whether Flink is the Next Big Thing or if it is a fine technology with limited impact.
Right now, what you and I do can make a huge difference. But as business people like to say, what got Flink here isn’t what’s going to get it there. The challenges the Flink community faces now are different from the technical challenges it has met so far.
I will talk about what I think will help and how we can all pitch in to take Flink forward.
Sanjar Akhmedov - Joining Infinity – Windowless Stream Processing with Flink
http://flink-forward.org/kb_sessions/joining-infinity-windowless-stream-processing-with-flink/
The extensive set of high-level Flink primitives makes it easy to join windowed streams. However, use cases that don’t have windows can prove to be more complicated, making it necessary to leverage operator state and low-level primitives to manually implement a continuous join. This talk will focus on the anomalies that present themselves when performing streaming joins with infinite windows, and the problems encountered operating topologies that back user-facing data. We will describe the approach taken at ResearchGate to implement and maintain a consistent join result of change data capture streams.
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Gábor Horváth - Code Generation in Serializers and Comparators of Apache Flink
Gábor Horváth - Code Generation in Serializers and Comparators of Apache Flink
Zoltán Zvara - Advanced visualization of Flink and Spark jobs
Zoltán Zvara - Advanced visualization of Flink and Spark jobs
Apache Flink community Update for March 2016 - Slim Baltagi
Apache Flink community Update for March 2016 - Slim Baltagi
Overview of Apache Fink: The 4G of Big Data Analytics Frameworks
Overview of Apache Fink: The 4G of Big Data Analytics Frameworks
Unified Batch and Real-Time Stream Processing Using Apache Flink
Unified Batch and Real-Time Stream Processing Using Apache Flink
Trevor Grant - Apache Zeppelin - A friendlier way to Flink
Trevor Grant - Apache Zeppelin - A friendlier way to Flink
Alexander Kolb - Flinkspector – Taming the squirrel
Alexander Kolb - Flinkspector – Taming the squirrel
Ana M Martinez - AMIDST Toolbox- Scalable probabilistic machine learning with...
Ana M Martinez - AMIDST Toolbox- Scalable probabilistic machine learning with...
Maxim Fateev - Beyond the Watermark- On-Demand Backfilling in Flink
Maxim Fateev - Beyond the Watermark- On-Demand Backfilling in Flink
Ted Dunning - Keynote: How Can We Take Flink Forward?
Ted Dunning - Keynote: How Can We Take Flink Forward?
Sanjar Akhmedov - Joining Infinity – Windowless Stream Processing with Flink
Sanjar Akhmedov - Joining Infinity – Windowless Stream Processing with Flink
Similar to Thomas Lamirault_Mohamed Amine Abdessemed -A brief history of time with Apache Flink
Streaming in the Wild with Apache Flink
This talk is an application-driven walkthrough to modern stream processing, exemplified by Apache Flink, and how this enables new applications and makes old applications easier and more efficient. In this talk, we will walk through several real-world stream processing application scenarios of Apache Flink, highlighting unique features in Flink that make these applications possible. In particular, we will see (1) how support for handling out of order streams enables real-time monitoring of cloud infrastructure, (2) how the ability handle high-volume data streams with low latency SLAs enables real-time alerts in network equipment, (3) how the combination of high throughput and the ability to handle batch as a special case of streaming enables an architecture where the same exact program is used for real-time and historical data processing, and (4) how stateful stream processing can enable an architecture that eliminates the need for an external database store, leading to more than 100x performance speedup, among many other benefits.
Google Cloud and Data Pipeline Patterns
deck from talk for YOW Nights Australia, on GCP (Google Cloud Platform) and Data Pipeline patterns
Case study: How Cozy Cloud monitors every layer of its activity using OVH Met...
Find out how Cozy Cloud uses the OVH Metrics Data Platform to monitor and optimise its SaaS service for the general public. From performance data aggregation to customer usage metrics, the Cozy Cloud teams will share their data-centric collaboration experience with you.
Simply Business - Near Real Time Event Processing
A presentation explaining the Simply Business analytics stack and how they use Snowplow analytics to track near real time events
Apache Flink for IoT: How Event-Time Processing Enables Easy and Accurate Ana...
https://www.bigdataspain.org/2016/program/thu-from-data-numbers-knowledge-semantic-embeddings.html
https://www.youtube.com/watch?v=1sZFrHUgUw8&list=PL6O3g23-p8Tr5eqnIIPdBD_8eE5JBDBik&t=7s&index=36
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Presentation about a project done at a customer utilizing Kafka, Camus, and Hive.
Потоковая обработка больших данных
Эталонная архитектура сервиса из компонентов со 100% открытым исходным кодом, готового к развертыванию в облаках, с масштабируемостью и надежностью уровня предприятия.
Антон Овчинников, Grid Dynamics
Digital Business Transformation in the Streaming Era
Enterprises are rapidly adopting stream computing backbones, in-memory data stores, change data capture, and other low-latency approaches for end-to-end applications. As businesses modernize their data architectures over the next several years, they will begin to evolve toward all-streaming architectures. In this webcast, Wikibon, Attunity, and MemSQL will discuss how enterprise data professionals should migrate their legacy architectures in this direction. They will provide guidance for migrating data lakes, data warehouses, data governance, and transactional databases to support all-streaming architectures for complex cloud and edge applications. They will discuss how this new architecture will drive enterprise strategies for operationalizing artificial intelligence, mobile computing, the Internet of Things, and cloud-native microservices.
Link to the Wikibon report - wikibon.com/wikibons-2018-big-data-analytics-trends-forecast
Link to Attunity Streaming CDC Book Download - http://www.bit.ly/cdcbook
Link to MemSQL's Free Data Pipeline Book - http://go.memsql.com/oreilly-data-pipelines
Google's Infrastructure and Specific IoT Services
We discuss Google's infrastructure and specific IoT services.
Smart building mendix azure influx / smart City / IoT
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Smart Buildings Actionable Data in Mendix with Azure & Open SourceA Trifecta of Real-Time Applications: Apache Kafka, Flink, and Druid
"The data coming into Kafka is fresh and hot. And you can deliver a new level of operational visibility and intelligence fueling applications with it. But streaming data is no longer real-time when the sink is batch. So the challenge is processing it and analyzing it at scale and extracting those insights - before they go stale.
So what’s the right architecture? Should you ingest streams into a data warehouse or data lake? Maybe use a stream processor or a database? Engineering teams love using Apache Flink, but they also love using Apache Druid, a popular real-time analytics database used by 1000s of companies like Confluent and Netflix. Do you need Flink and Druid? When does it make sense vs when does it not?
Join this session to learn about Apache Druid and why companies use it in combination with Kafka and Flink for real-time applications. Learn how Apache Druid complements Flink and Kafka - and what makes it purpose-built for analyzing streams and events. This talk shows real-world examples from companies that use Apache Druid with Kafka and Flink in production today and the best-practices that every dev can take advantage of."
Introduction to OVH Analytics Data Platform
Discover OVH Big Data solutions and learn how to deploy Analytics Data Platform it in 1 hour.
Watch our OVHmeetup on Youtube ▶︎ https://youtu.be/YiadlF5jw24 (French only)
Kostas Tzoumas - Apache Flink®: State of the Union and What's Next
Streaming data is now the new trend, and for very good reasons. Most data is produced continuously, and it makes sense that it is processed and analyzed continuously. Whether it is the need for more real-time products, adopting micro-services, or building continuous applications, stream processing technology offers to simplify the data infrastructure stack and reduce the latency to decisions. Before Apache Flink, users of stream processing frameworks had to make hard choices and trade off either latency, throughput, or result accuracy. Flink was the first open source framework (and still the only one), that has been demonstrated to deliver (1) throughput in the order of tens of millions of events per second in moderate clusters, (2) sub-second latency that can be as low as few 10s of milliseconds, (3) guaranteed exactly once semantics for application state, as well as exactly once end-to-end delivery with supported sources and sinks (e.g., pipelines from Kafka to Flink to HDFS or Cassandra), and (4) accurate results in the presence of out of order data arrival through its support for event time. In this talk, I will cover the basics on Flink: why the project exists, where it came from, what gap does it fill, how it differs from all the other stream processing projects, and what is it being used for. I will also recent developments in the Flink community, what the community is working on currently, and touch upon a longer-term vision for Flink.
How We Captured 10.3 Million Pages of Documents – and Lived to Tell the Tale
AIIM Webinar presented by Micah Smith, Tech Lead for a Fortune 100 financial services company. Sponsored by EMC.
In-Stream Processing Service Blueprint, Reference architecture for real-time ...
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Digital Business Transformation in the Streaming Era
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Smart building mendix azure influx / smart City / IoT
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A Trifecta of Real-Time Applications: Apache Kafka, Flink, and Druid
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Kostas Tzoumas - Apache Flink®: State of the Union and What's Next
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Building a fully managed stream processing platform on Flink at scale for Lin...
Apache Flink is a distributed stream processing framework that allows users to process and analyze data in real-time. At LinkedIn, we developed a fully managed stream processing platform on Flink running on K8s to power hundreds of stream processing pipelines in production. This platform is the backbone for other infra systems like Search, Espresso (internal document store) and feature management etc. We provide a rich authoring and testing environment which allows users to create, test, and deploy their streaming jobs in a self-serve fashion within minutes. Users can focus on their business logic, leaving the Flink platform to take care of management aspects such as split deployment, resource provisioning, auto-scaling, job monitoring, alerting, failure recovery and much more. In this talk, we will introduce the overall platform architecture, highlight the unique value propositions that it brings to stream processing at LinkedIn and share the experiences and lessons we have learned.
“Alexa, be quiet!”: End-to-end near-real time model building and evaluation i...
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.
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Introducing the Apache Flink Kubernetes Operator
Flink Forward San Francisco 2022.
The Apache Flink Kubernetes Operator provides a consistent approach to manage Flink applications automatically, without any human interaction, by extending the Kubernetes API. Given the increasing adoption of Kubernetes based Flink deployments the community has been working on a Kubernetes native solution as part of Flink that can benefit from the rich experience of community members and ultimately make Flink easier to adopt. In this talk we give a technical introduction to the Flink Kubernetes Operator and demonstrate the core features and use-cases through in-depth examples."
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Thomas Weise
Autoscaling Flink with Reactive Mode
Flink Forward San Francisco 2022.
Resource Elasticity is a frequently requested feature in Apache Flink: Users want to be able to easily adjust their clusters to changing workloads for resource efficiency and cost saving reasons. In Flink 1.13, the initial implementation of Reactive Mode was introduced, later releases added more improvements to make the feature production ready. In this talk, we’ll explain scenarios to deploy Reactive Mode to various environments to achieve autoscaling and resource elasticity. We’ll discuss the constraints to consider when planning to use this feature, and also potential improvements from the Flink roadmap. For those interested in the internals of Flink, we’ll also briefly explain how the feature is implemented, and if time permits, conclude with a short demo.
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Robert Metzger
One sink to rule them all: Introducing the new Async Sink
Flink Forward San Francisco 2022.
Next time you want to integrate with a new destination for a demo, concept or production application, the Async Sink framework will bootstrap development, allowing you to move quickly without compromise. In Flink 1.15 we introduced the Async Sink base (FLIP-171), with the goal to encapsulate common logic and allow developers to focus on the key integration code. The new framework handles things like request batching, buffering records, applying backpressure, retry strategies, and at least once semantics. It allows you to focus on your business logic, rather than spending time integrating with your downstream consumers. During the session we will dive deep into the internals to uncover how it works, why it was designed this way, and how to use it. We will code up a new sink from scratch and demonstrate how to quickly push data to a destination. At the end of this talk you will be ready to start implementing your own Flink sink using the new Async Sink framework.
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Steffen Hausmann & Danny Cranmer
Tuning Apache Kafka Connectors for Flink.pptx
Flink Forward San Francisco 2022.
In normal situations, the default Kafka consumer and producer configuration options work well. But we all know life is not all roses and rainbows and in this session we’ll explore a few knobs that can save the day in atypical scenarios. First, we'll take a detailed look at the parameters available when reading from Kafka. We’ll inspect the params helping us to spot quickly an application lock or crash, the ones that can significantly improve the performance and the ones to touch with gloves since they could cause more harm than benefit. Moreover we’ll explore the partitioning options and discuss when diverging from the default strategy is needed. Next, we’ll discuss the Kafka Sink. After browsing the available options we'll then dive deep into understanding how to approach use cases like sinking enormous records, managing spikes, and handling small but frequent updates.. If you want to understand how to make your application survive when the sky is dark, this session is for you!
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Flink powered stream processing platform at Pinterest
Flink Forward San Francisco 2022.
Pinterest is a visual discovery engine that serves over 433MM users. Stream processing allows us to unlock value from realtime data for pinners. At Pinterest, we adopt Flink as the unified streaming processing engine. In this talk, we will share our journey in building a stream processing platform with Flink and how we onboarding critical use cases to the platform. Pinterest has supported 90+near realtime streaming applications. We will cover the problem statement, how we evaluate potential solutions and our decision to build the framework.
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Rainie Li & Kanchi Masalia
Apache Flink in the Cloud-Native Era
Flink Forward San Francisco 2022.
This talk will take you on the long journey of Apache Flink into the cloud-native era. It started all the way from where Hadoop and YARN were the standard way of deploying and operating data applications.
We're going to deep dive into the cloud-native set of principles and how they map to the Apache Flink internals and recent improvements. We'll cover fast checkpointing, fault tolerance, resource elasticity, minimal infrastructure dependencies, industry-standard tooling, ease of deployment and declarative APIs.
After this talk you'll get a broader understanding of the operational requirements for a modern streaming application and where the current limits are.
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David Moravek
Using the New Apache Flink Kubernetes Operator in a Production Deployment
Flink Forward San Francisco 2022.
Running natively on Kubernetes, using the new Apache Flink Kubernetes Operator is a great way to deploy and manage Flink application and session deployments. In this presentation, we provide: - A brief overview of Kubernetes operators and their benefits. - Introduce the five levels of the operator maturity model. - Introduce the newly released Apache Flink Kubernetes Operator and FlinkDeployment CRs - Dockerfile modifications you can make to swap out UBI images and Java of the underlying Flink Operator container - Enhancements we're making in: - Versioning/Upgradeability/Stability - Security - Demo of the Apache Flink Operator in-action, with a technical preview of an upcoming product using the Flink Kubernetes Operator. - Lessons learned - Q&A
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James Busche & Ted Chang
The Current State of Table API in 2022
Flink Forward San Francisco 2022.
The Table API is one of the most actively developed components of Flink in recent time. Inspired by databases and SQL, it encapsulates concepts many developers are familiar with. It can be used with both bounded and unbounded streams in a unified way. But from afar it can be difficult to keep track of what this API is capable of and how it relates to Flink's other APIs. In this talk, we will explore the current state of Table API. We will show how it can be used as a batch processor, a changelog processor, or a streaming ETL tool with many built-in functions and operators for deduplicating, joining, and aggregating data. By comparing it to the DataStream API we will highlight differences and elaborate on when to use which API. We will demonstrate hybrid pipelines in which both APIs interact with one another and contribute their unique strengths. Finally, we will take a look at some of the most recent additions as a first step to stateful upgrades.
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David Andreson
Flink SQL on Pulsar made easy
Flink Forward San Francisco 2022.
Based on the new Flink-Pulsar connector, we implemented Flink's TableAPI and Catalog to help users to interact with the Pulsar cluster via Flink SQL easily. We would like to go through the design and implementation of the SQL connector in the following aspects:
1. Two different modes of use Pulsar as a metadata store
2. Data format transformation and management
3. SQL semantics support within Pulsar context
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Sijie Guo & Neng Lu
Dynamic Rule-based Real-time Market Data Alerts
Flink Forward San Francisco 2022.
At Bloomberg, we deal with high volumes of real-time market data. Our clients expect to be notified of any anomalies in this market data, which may indicate volatile movements in the markets, notable trades, forthcoming events, or system failures. The parameters for these alerts are always evolving and our clients can update them dynamically. In this talk, we'll cover how we utilized the open source Apache Flink and Siddhi SQL projects to build a distributed, scalable, low-latency and dynamic rule-based, real-time alerting system to solve our clients' needs. We'll also cover the lessons we learned along our journey.
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Ajay Vyasapeetam & Madhuri Jain
Exactly-Once Financial Data Processing at Scale with Flink and Pinot
Flink Forward San Francisco 2022.
At Stripe we have created a complete end to end exactly-once processing pipeline to process financial data at scale, by combining the exactly-once power from Flink, Kafka, and Pinot together. The pipeline provides exactly-once guarantee, end-to-end latency within a minute, deduplication against hundreds of billions of keys, and sub-second query latency against the whole dataset with trillion level rows. In this session we will discuss the technical challenges of designing, optimizing, and operating the whole pipeline, including Flink, Kafka, and Pinot. We will also share our lessons learned and the benefits gained from exactly-once processing.
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Xiang Zhang & Pratyush Sharma & Xiaoman Dong
Processing Semantically-Ordered Streams in Financial Services
Flink Forward San Francisco 2022.
What if my data is already in order? Stream Processing has given us an elegant and powerful solution for running analytic queries and logic over high volumes of continuously arriving data. However, in both Apache Flink and Apache Beam, the notion of time-ordering is baked in at a very low level, making it difficult to express computations that are interested in a semantic-, rather than time-ordering of the data. In financial services, what often matters the most about the data moving between systems is not when the data was created, but in what order, to the extent that many institutions engineer a global sequencing over all data entering and produced by their systems to achieve complete determinism. How, then, can financial institutions and others best employ Stream Processing on streams of data that are already ordered? I will cover various techniques that can make this work, as well as seek input from the community on how Flink might be improved to better support these use-cases.
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Patrick Lucas
Tame the small files problem and optimize data layout for streaming ingestion...
Flink Forward San Francisco 2022.
In modern data platform architectures, stream processing engines such as Apache Flink are used to ingest continuous streams of data into data lakes such as Apache Iceberg. Streaming ingestion to iceberg tables can suffer by two problems (1) small files problem that can hurt read performance (2) poor data clustering that can make file pruning less effective. To address those two problems, we propose adding a shuffling stage to the Flink Iceberg streaming writer. The shuffling stage can intelligently group data via bin packing or range partition. This can reduce the number of concurrent files that every task writes. It can also improve data clustering. In this talk, we will explain the motivations in details and dive into the design of the shuffling stage. We will also share the evaluation results that demonstrate the effectiveness of smart shuffling.
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Gang Ye & Steven Wu
Batch Processing at Scale with Flink & Iceberg
Flink Forward San Francisco 2022.
Goldman Sachs's Data Lake platform serves as the firm's centralized data platform, ingesting 140K (and growing!) batches per day of Datasets of varying shape and size. Powered by Flink and using metadata configured by platform users, ingestion applications are generated dynamically at runtime to extract, transform, and load data into centralized storage where it is then exported to warehousing solutions such as Sybase IQ, Snowflake, and Amazon Redshift. Data Latency is one of many key considerations as producers and consumers have their own commitments to satisfy. Consumers range from people/systems issuing queries, to applications using engines like Spark, Hive, and Presto to transform data into refined Datasets. Apache Iceberg allows our applications to not only benefit from consistency guarantees important when running on eventually consistent storage like S3, but also allows us the opportunity to improve our batch processing patterns with its scalability-focused features.
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Andreas Hailu
Welcome to the Flink Community!
Flink Forward San Francisco 2022.
At Flink Forward, we get to hear creative, unique use cases, often on the bleeding edge of some of the most exciting current technologies. This talk will give you a chance to get to open up the hood on our driven and innovative Open Source community. I will cover what our community has been working on this past year, and how this work relates to our (Ververica's) exciting new Flink engineering roadmap! I will also go through some best practices and upcoming opportunities for getting involved in this community!
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Caito Scherr
Extending Flink SQL for stream processing use cases
Flink Forward San Francisco 2022.
Apache Flink is a powerful stream processing platform that enables users to build complex real time applications. Flink SQL provides a SQL interface that implements standard SQL. While the standard SQL provides a perfect interface for batch processing, in stream processing context, it can result is ambiguity and complex syntax. As an example, consider these three types of streams: Append-only stream, Retract stream and Upsert stream. Using standard SQL, we would represent all of these streams as Table along with the Table concept in batch processing. Such overloading of concepts can result in ambiguity in SQL statements in streaming context. In this talk, we will present extensions to the Flink SQL that simplify SQL statements in the context of stream processing. We will show how such extensions work in the context of a Flink application using different use cases. These extensions are only sugar syntax and users should be able to use Flink SQL as is if they desire.
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Hojjat Jafarpour
The top 3 challenges running multi-tenant Flink at scale
Apache Flink is the foundation for Decodable's real-time SaaS data platform. Flink runs critical data processing jobs with strong security requirements. In addition, Decodable has to scale to thousands of tenants, power various use cases, provide an intuitive user experience and maintain cost-efficiency. We've learned a lot of lessons while building and maintaining the platform. In this talk, I'll share the top 3 toughest challenges building and operating this platform with Flink, and how we solved them.
Using Queryable State for Fun and Profit
Flink Forward San Francisco 2022.
A particular feature in our system relies on a streaming 90-minute trailing window of 1-minute samples - implemented as a lookaside cache - to speed up a particular query, allowing our customers to rapidly see an overview of their estate. Across our entire customer base, there is a substantial amount of data flowing into this cache - ~1,000,000 entries/second, with the entire cache requiring ~600GB of RAM. The current implementation is simplistic but expensive. In this talk I describe a replacement implementation as a stateful streaming Flink application leveraging Queryable State. This Flink application reduces the net cost by ~90%. In this session, the implementation is described in detail, including windowing considerations, a sliding-window state buffer that avoids the sliding window replication penalty, and a comparison of queryable state and Redis queries. The talk concludes with a frank discussion of when this distinctive approach is, and is not, appropriate.
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Ron Crocker
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Exactly-Once Financial Data Processing at Scale with Flink and Pinot
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Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
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一比一原版(Adelaide毕业证书)阿德莱德大学毕业证如何办理
原版定制【微信:41543339】【(Adelaide毕业证书)阿德莱德大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
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二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Dat...
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
My burning issue is homelessness K.C.M.O.
My burning issue is homelessness in Kansas City, MO
To: Tom Tresser
From: Roger Warren
一比一原版(BCU毕业证书)伯明翰城市大学毕业证如何办理
原版定制【微信:41543339】【(BCU毕业证书)伯明翰城市大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
Unleashing the Power of Data_ Choosing a Trusted Analytics Platform.pdf
In this guide, we'll explore the key considerations and features to look for when choosing a Trusted analytics platform that meets your organization's needs and delivers actionable intelligence you can trust.
一比一原版(CBU毕业证)卡普顿大学毕业证如何办理
CBU毕业证offer【微信95270640】《卡普顿大学毕业证书》《QQ微信95270640》学位证书电子版：在线制作卡普顿大学毕业证成绩单GPA修改（制作CBU毕业证成绩单CBU文凭证书样本）、卡普顿大学毕业证书与成绩单样本图片、《CBU学历证书学位证书》、卡普顿大学毕业证案例毕业证书制作軟體、在线制作加拿大硕士学历证书真实可查.
如果您是以下情况，我们都能竭诚为您解决实际问题：【公司采用定金+余款的付款流程，以最大化保障您的利益，让您放心无忧】
1、在校期间，因各种原因未能顺利毕业，拿不到官方毕业证+微信95270640
2、面对父母的压力，希望尽快拿到卡普顿大学卡普顿大学毕业证成绩单；
3、不清楚流程以及材料该如何准备卡普顿大学卡普顿大学毕业证成绩单；
4、回国时间很长，忘记办理；
5、回国马上就要找工作，办给用人单位看；
6、企事业单位必须要求办理的；
面向美国乔治城大学毕业留学生提供以下服务:
【★卡普顿大学卡普顿大学毕业证成绩单毕业证、成绩单等全套材料，从防伪到印刷，从水印到钢印烫金，与学校100%相同】
【★真实使馆认证（留学人员回国证明），使馆存档可通过大使馆查询确认】
【★真实教育部认证，教育部存档，教育部留服网站可查】
【★真实留信认证，留信网入库存档，可查卡普顿大学卡普顿大学毕业证成绩单】
我们从事工作十余年的有着丰富经验的业务顾问，熟悉海外各国大学的学制及教育体系，并且以挂科生解决毕业材料不全问题为基础，为客户量身定制1对1方案，未能毕业的回国留学生成功搭建回国顺利发展所需的桥梁。我们一直努力以高品质的教育为起点，以诚信、专业、高效、创新作为一切的行动宗旨，始终把“诚信为主、质量为本、客户第一”作为我们全部工作的出发点和归宿点。同时为海内外留学生提供大学毕业证购买、补办成绩单及各类分数修改等服务；归国认证方面，提供《留信网入库》申请、《国外学历学位认证》申请以及真实学籍办理等服务，帮助众多莘莘学子实现了一个又一个梦想。
专业服务，请勿犹豫联系我
如果您真实毕业回国，对于学历认证无从下手，请联系我，我们免费帮您递交
诚招代理：本公司诚聘当地代理人员，如果你有业余时间，或者你有同学朋友需要，有兴趣就请联系我
你赢我赢，共创双赢
你做代理，可以帮助卡普顿大学同学朋友
你做代理，可以拯救卡普顿大学失足青年
你做代理，可以挽救卡普顿大学一个个人才
你做代理，你将是别人人生卡普顿大学的转折点
你做代理，可以改变自己，改变他人，给他人和自己一个机会道银边山娃摸索着扯了扯灯绳小屋顿时一片刺眼的亮瞅瞅床头的诺基亚山娃苦笑着摇了摇头连他自己都感到奇怪居然又睡到上午点半掐指算算随父亲进城已一个多星期了山娃几乎天天起得这么迟在乡下老家暑假五点多山娃就醒来在爷爷奶奶嘁嘁喳喳的忙碌声中一骨碌爬起把牛驱到后龙山再从莲塘里采回一蛇皮袋湿漉漉的莲蓬也才点多点半早就吃过早餐玩耍去了山娃的家在闽西山区依山傍水山清水秀门前潺潺流淌的蜿蜒小溪一直都是山娃和小伙伴们盛试
一比一原版(UIUC毕业证)伊利诺伊大学|厄巴纳-香槟分校毕业证如何办理
UIUC毕业证offer【微信95270640】☀《伊利诺伊大学|厄巴纳-香槟分校毕业证购买》GoogleQ微信95270640《UIUC毕业证模板办理》加拿大文凭、本科、硕士、研究生学历都可以做,二、业务范围：
★、全套服务：毕业证、成绩单、化学专业毕业证书伪造《伊利诺伊大学|厄巴纳-香槟分校大学毕业证》Q微信95270640《UIUC学位证书购买》
(诚招代理)办理国外高校毕业证成绩单文凭学位证,真实使馆公证（留学回国人员证明）真实留信网认证国外学历学位认证雅思代考国外学校代申请名校保录开请假条改GPA改成绩ID卡
1.高仿业务:【本科硕士】毕业证,成绩单（GPA修改）,学历认证（教育部认证）,大学Offer,,ID,留信认证,使馆认证,雅思,语言证书等高仿类证书；
2.认证服务: 学历认证（教育部认证）,大使馆认证（回国人员证明）,留信认证（可查有编号证书）,大学保录取,雅思保分成绩单。
3.技术服务：钢印水印烫金激光防伪凹凸版设计印刷激凸温感光标底纹镭射速度快。
办理伊利诺伊大学|厄巴纳-香槟分校伊利诺伊大学|厄巴纳-香槟分校毕业证offer流程：
1客户提供办理信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：我们有专业老师帮你查询）；
2开始安排制作毕业证成绩单电子图；
3毕业证成绩单电子版做好以后发送给您确认；
4毕业证成绩单电子版您确认信息无误之后安排制作成品；
5成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快读邮寄）
-办理真实使馆公证（即留学回国人员证明）
-办理各国各大学文凭（世界名校一对一专业服务,可全程监控跟踪进度）
-全套服务：毕业证成绩单真实使馆公证真实教育部认证。让您回国发展信心十足！
（详情请加一下 文凭顾问+微信:95270640）欢迎咨询！的鬼地方父亲的家在高楼最底屋最下面很矮很黑是很不显眼的地下室父亲的家安在别人脚底下须绕过高楼旁边的垃圾堆下八个台阶才到父亲的家很狭小除了一张单人床和一张小方桌几乎没有多余的空间山娃一下子就联想起学校的男小便处山娃很想笑却怎么也笑不出来山娃很迷惑父亲的家除了一扇小铁门连窗户也没有墓穴一般阴森森有些骇人父亲的城也便成了山娃的城父亲的家也便成了山娃的家父亲让山娃呆在屋里做作业看电视最多只能在门口透透气间
哪里卖(usq毕业证书)南昆士兰大学毕业证研究生文凭证书托福证书原版一模一样
原版定制【Q微信:741003700】《(usq毕业证书)南昆士兰大学毕业证研究生文凭证书》【Q微信:741003700】成绩单 、雅思、外壳、留信学历认证永久存档查询，采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【Q微信741003700】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信741003700】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
Adjusting primitives for graph : SHORT REPORT / NOTES
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
一比一原版(Deakin毕业证书)迪肯大学毕业证如何办理
原版定制【微信:41543339】【(Deakin毕业证书)迪肯大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
一比一原版(UofS毕业证书)萨省大学毕业证如何办理
原版定制【微信:41543339】【(UofS毕业证书)萨省大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
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Learn SQL from basic queries to Advance queries
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
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Thomas Lamirault_Mohamed Amine Abdessemed -A brief history of time with Apache Flink
- 1. A Brief History of Time with Apache Flink Real time monitoring and analysis with Flink, Kafka and HBase Flink Forward 2016 Berlin, September, 12th, 2016 1 Thomas LAMIRAULT Mohamed Amine ABDESSEMED
- 2. The speakers • Software engineer & solution architect @ Bouygues Telecom since 2013 • A Flinker since the early beginnings @AminouvicTweets 2 Mohamed Amine ABDESSEMED • Bigdata Software engineer @ Bouygues Telecom since 2015 • A Flink master enthusiast @thomaslamirault Thomas LAMIRAULT
- 3. Outline • Who is Bouygues Telecom • Data Value and Streaming Analytics • Use case • Challenges • Streaming analytics with Flink • Results 3
- 4. 15M Clients 12,1M Mobile subscriber 2,9M Fixed customer WHO IS BOUYGUES TELECOM ? Mobile . Fixed . TV . Internet . Cloud 4 First Android TV BOX Leader 4G/4G+ VoLTE UHSM A very Innovative company
- 5. WHO IS BOUYGUES TELECOM ? 5
- 6. LUX: Logged User eXperience Mobile QoE • Our Big Data platform • Produce Mobile QoE indicators based on massive network equipment’s event logs (Billions event/day). • Goals: – QoE (User) instead of QoS (Machine) – Real-time Diagnostic (<60’ end-to-end latency) – Business Intelligence – Reporting – Real-time alarming 6
- 7. LUX In Numbers 7 ~300 users 30 Flink production apps 10 Billions raw event/day 2 Po storage ~120 users 5 Flink production apps 4 Billions raw event/day 750 To storage 2015 Today
- 8. Analytic Data value 8 Important event occurrence Predictive analytics Advanced analytics Time Data Value Streaming analytics T0 T1 T2 TyT-x Before important event NOW After important event
- 10. Analytic Data value • Data is most valuable when made available as soon as important events occur. • Get the most of Data – Collect data fast. – (Pre)Process it fast. – Analyze it and create added value to act faster! 10
- 11. The use case 11 GNOC Identify Define ExploreAction Look back Specific Numbers Emergency Numbers Call Center Numbers Something wrong? The BIG picture isn’t always significant Global counts have no sense !
- 12. The use case • A simple and valuable use case • Need to analyze the entire call traffic : –Considering multiple aggregation axes –Fine grained analysis –Detect when something is happening somewhere in real-time –Compare with historical values trends 12
- 13. Challenges • Low latency & streaming fashion counters • Quickly available KPIs = value • Massive amounts of data + peak loads • Reliability • Multiple flow correlation • Time management: – Out of order & late events our worst enemies – Flexible window management – Specific watermark emission 13
- 14. Streaming Analytics Time Management 14 8h00 8h30 9h00 9h30 Processing Time
- 15. Streaming Analytics Time Management 15 8h00 8h30 9h00 9h30 Processing Time 8h09 8h14 8h03 8h00 8h12 8h43 8h45 8h48 9h10 8h00 9h12 8h13 8h47 9h32 9h35 9h14 9h30 8h02 8h03 Event Time Window FIRE
- 16. Streaming Analytics with Flink Built-in windowing functionalities • Custom Watermark extractor • Custom Triggers for lateness management • Custom Key extractor Stateful Streaming • Checkpointing • Fault tolerance • Savepoints • Update without data loss 16
- 17. Streaming analytics with Flink Performance • High throughput • Low latency • Excellent memory management Flexible window management • Tumbling • Sliding • Session 17
- 18. High Level Architecture 18 Queue Streaming Processing Windowing Computation Collect Storage Dataviz In-Memory Lookups Alarming Queue Queue Streaming Streaming
- 19. Architectural details 19 Storage Kafkacluster T’1 P1 Px .. T’n P1 Px .. T1 P1 Px .. Tn P1 Px .. Tm P1 Px .. T’m P1 Px .. Analytic APP State Backend Historical Data Monitoring Alarming
- 20. Streaming application Details 20 TnTnTn Multiple Input Topics filterfilterfilter Keep only records that interest us Extract timestamp Extract relevant event timestamp, with custom watermark emission keyBy Group by considered aggregation axes window Tumbling windows trigger Custom trigger, allow configurable late data and manage lagged data sink Write Data into HBase reduce Produce KPIs UnionUnion Correlate input flows
- 21. The results Production metrics • Low latency (<100ms) • Input : Up to ~80.000 events/sec • Output : Produce ~40.000 KPI/window 21
- 23. Benefits 24 • Monitor and improve customer experience • Reduced incident detection time • Help GNOC alarm prioritization based on customer experience • Reduced operating costs
- 24. Difficulties • Massive amounts of data in both input and output • Savepoint/Checkpoint cost • HBase analytic limitations –Read vs Write –Long Scan • Massive out of order events 25
- 25. What’s Next 26 • Flink + Kudu • Async/Incremental checkpoints • Flink CEP • Streaming SQL • Flink applications monitoring & industrialization.
- 26. 27 Questions ?