Introduction to Apache ZooKeeper | Big Data Hadoop Spark Tutorial | CloudxLabCloudxLab
Big Data with Hadoop & Spark Training: http://bit.ly/2kvXlPd
This CloudxLab Introduction to Apache ZooKeeper tutorial helps you to understand ZooKeeper in detail. Below are the topics covered in this tutorial:
1) Data Model
2) Znode Types
3) Persistent Znode
4) Sequential Znode
5) Architecture
6) Election & Majority Demo
7) Why Do We Need Majority?
8) Guarantees - Sequential consistency, Atomicity, Single system image, Durability, Timeliness
9) ZooKeeper APIs
10) Watches & Triggers
11) ACLs - Access Control Lists
12) Usecases
13) When Not to Use ZooKeeper
Distributed real time stream processing- why and howPetr Zapletal
In this talk you will discover various state-of-the-art open-source distributed streaming frameworks, their similarities and differences, implementation trade-offs, their intended use-cases, and how to choose between them. Petr will focus on the popular frameworks, including Spark Streaming, Storm, Samza and Flink. You will also explore theoretical introduction, common pitfalls, popular architectures, and much more.
The demand for stream processing is increasing. Immense amounts of data has to be processed fast from a rapidly growing set of disparate data sources. This pushes the limits of traditional data processing infrastructures. These stream-based applications, include trading, social networks, the Internet of Things, and system monitoring, are becoming more and more important. A number of powerful, easy-to-use open source platforms have emerged to address this.
Petr's goal is to provide a comprehensive overview of modern streaming solutions and to help fellow developers with picking the best possible solution for their particular use-case. Join this talk if you are thinking about, implementing, or have already deployed a streaming solution.
A tutorial presentation based on storm.apache.org documentation.
I gave this presentation at Amirkabir University of Technology as Teaching Assistant of Cloud Computing course of Dr. Amir H. Payberah in spring semester 2015.
Some of the biggest issues at the center of analyzing large amounts of data are query flexibility, latency, and fault tolerance. Modern technologies that build upon the success of “big data” platforms, such as Apache Hadoop, have made it possible to spread the load of data analysis to commodity machines, but these analyses can still take hours to run and do not respond well to rapidly-changing data sets.
A new generation of data processing platforms -- which we call “stream architectures” -- have converted data sources into streams of data that can be processed and analyzed in real-time. This has led to the development of various distributed real-time computation frameworks (e.g. Apache Storm) and multi-consumer data integration technologies (e.g. Apache Kafka). Together, they offer a way to do predictable computation on real-time data streams.
In this talk, we will give an overview of these technologies and how they fit into the Python ecosystem. As part of this presentation, we also released streamparse, a new Python that makes it easy to debug and run large Storm clusters.
Links:
* http://parse.ly/code
* https://github.com/Parsely/streamparse
* https://github.com/getsamsa/samsa
Debugging Complex Systems - Erlang Factory SF 2015lpgauth
Debugging complex systems can be difficult. Luckily, the Erlang ecosystem is full of tools to help you out. With the right mindset and the right tools, debugging complex Erlang systems can be easy. In this talk, I'll share the debugging methodology I've developed over the years.
Introduction to Apache ZooKeeper | Big Data Hadoop Spark Tutorial | CloudxLabCloudxLab
Big Data with Hadoop & Spark Training: http://bit.ly/2kvXlPd
This CloudxLab Introduction to Apache ZooKeeper tutorial helps you to understand ZooKeeper in detail. Below are the topics covered in this tutorial:
1) Data Model
2) Znode Types
3) Persistent Znode
4) Sequential Znode
5) Architecture
6) Election & Majority Demo
7) Why Do We Need Majority?
8) Guarantees - Sequential consistency, Atomicity, Single system image, Durability, Timeliness
9) ZooKeeper APIs
10) Watches & Triggers
11) ACLs - Access Control Lists
12) Usecases
13) When Not to Use ZooKeeper
Distributed real time stream processing- why and howPetr Zapletal
In this talk you will discover various state-of-the-art open-source distributed streaming frameworks, their similarities and differences, implementation trade-offs, their intended use-cases, and how to choose between them. Petr will focus on the popular frameworks, including Spark Streaming, Storm, Samza and Flink. You will also explore theoretical introduction, common pitfalls, popular architectures, and much more.
The demand for stream processing is increasing. Immense amounts of data has to be processed fast from a rapidly growing set of disparate data sources. This pushes the limits of traditional data processing infrastructures. These stream-based applications, include trading, social networks, the Internet of Things, and system monitoring, are becoming more and more important. A number of powerful, easy-to-use open source platforms have emerged to address this.
Petr's goal is to provide a comprehensive overview of modern streaming solutions and to help fellow developers with picking the best possible solution for their particular use-case. Join this talk if you are thinking about, implementing, or have already deployed a streaming solution.
A tutorial presentation based on storm.apache.org documentation.
I gave this presentation at Amirkabir University of Technology as Teaching Assistant of Cloud Computing course of Dr. Amir H. Payberah in spring semester 2015.
Some of the biggest issues at the center of analyzing large amounts of data are query flexibility, latency, and fault tolerance. Modern technologies that build upon the success of “big data” platforms, such as Apache Hadoop, have made it possible to spread the load of data analysis to commodity machines, but these analyses can still take hours to run and do not respond well to rapidly-changing data sets.
A new generation of data processing platforms -- which we call “stream architectures” -- have converted data sources into streams of data that can be processed and analyzed in real-time. This has led to the development of various distributed real-time computation frameworks (e.g. Apache Storm) and multi-consumer data integration technologies (e.g. Apache Kafka). Together, they offer a way to do predictable computation on real-time data streams.
In this talk, we will give an overview of these technologies and how they fit into the Python ecosystem. As part of this presentation, we also released streamparse, a new Python that makes it easy to debug and run large Storm clusters.
Links:
* http://parse.ly/code
* https://github.com/Parsely/streamparse
* https://github.com/getsamsa/samsa
Debugging Complex Systems - Erlang Factory SF 2015lpgauth
Debugging complex systems can be difficult. Luckily, the Erlang ecosystem is full of tools to help you out. With the right mindset and the right tools, debugging complex Erlang systems can be easy. In this talk, I'll share the debugging methodology I've developed over the years.
This document discusses techniques for writing highly scalable Java programs for multi-core systems. It begins with an overview of hardware trends showing an increasing number of cores per chip. It then discusses profiling tools that can identify lock contention issues. The document provides best practices for Java programming including reducing locking scope, splitting locks, stripping locks, using atomic variables, and lock-free algorithms. It emphasizes using concurrent containers and immutable/thread-local data where possible.
This document discusses advanced Postgres monitoring. It begins with an introduction of the speaker and an agenda for the discussion. It then covers selection criteria for monitoring solutions, compares open source and SAAS monitoring options, and provides examples of collecting specific Postgres metrics using CollectD. It also discusses alerting, handling monitoring changes, and being prepared to respond to incidents outside of normal hours.
This document discusses using Grails to develop an electric vehicle charging platform. It describes how Grails was used to implement the Open Charge Point Protocol (OCPP) for communication between charging stations and a backend system. Future plans include adding additional protocol support using JSON and WebSockets for full-duplex communication. Testing and performance testing were important aspects, and plugins like RabbitMQ and CXF were leveraged. Vert.x is discussed as an option for asynchronous programming.
Introduction to DTrace (Dynamic Tracing), written by Brendan Gregg and delivered in 2007. While aimed at a Solaris-based audience, this introduction is still largely relevant today (2012). Since then, DTrace has appeared in other operating systems (Mac OS X, FreeBSD, and is being ported to Linux), and, many user-level providers have been developed to aid tracing of other languages.
This talk discusses Linux profiling using perf_events (also called "perf") based on Netflix's use of it. It covers how to use perf to get CPU profiling working and overcome common issues. The speaker will give a tour of perf_events features and show how Netflix uses it to analyze performance across their massive Amazon EC2 Linux cloud. They rely on tools like perf for customer satisfaction, cost optimization, and developing open source tools like NetflixOSS. Key aspects covered include why profiling is needed, a crash course on perf, CPU profiling workflows, and common "gotchas" to address like missing stacks, symbols, or profiling certain languages and events.
Profiling your Applications using the Linux Perf ToolsemBO_Conference
This document provides an overview of using the Linux perf tools to profile applications. It discusses setting up perf, benchmarking applications, profiling both CPU usage and sleep times, and analyzing profiling data. The document covers perf commands like perf record to collect profiling data, perf report to analyze the data, and perf script to convert it to other formats. It also discusses profiling options like call graphs and collecting kernel vs. user mode events.
Realtime Statistics based on Apache Storm and RocketMQXin Wang
This document discusses using Apache Storm and RocketMQ for real-time statistics. It begins with an overview of the streaming ecosystem and components. It then describes challenges with stateful statistics and introduces Alien, an open-source middleware for handling stateful event counting. The document concludes with best practices for Storm performance and data hot points.
Asynchronous, Event-driven Network Application Development with NettyErsin Er
"Asynchronous, Event-driven Network Application Development with Netty" presented at Ankara JUG in 2015, June.
The presentation starts with motivations for Non-Blocking I/O and continues with general overview of NIO and Netty. The actual talk was supplied with Netty's own examples.
Java In-Process Caching - Performance, Progress and Pittfallscruftex
This document discusses Java in-process caching and summarizes benchmarks of various caching libraries. It finds that Caffeine and cache2k have faster read throughput than Google Guava Cache and EHCache3 when the number of threads increases. Cache2k is the fastest overall. Benchmarking eviction quality shows Caffeine and cache2k have more efficient eviction algorithms than LRU. While Clock is O(n) in theory, cache2k optimizes it to have little increase in scan counts even for large caches. Modern caching libraries use improved algorithms over LRU to achieve better performance.
The document provides an overview of how to read and understand garbage collection (GC) log lines from different Java vendors and JVM versions. It begins by explaining the parts of a basic GC log line for the OpenJDK GC log format. It then discusses GC log lines for G1 GC and CMS GC in more detail. Finally, it shares examples of GC log formats from IBM JVMs and different levels of information provided. The document aims to help readers learn to correctly interpret GC logs and analyze GC behavior.
Linux Performance Analysis: New Tools and Old SecretsBrendan Gregg
Talk for USENIX/LISA2014 by Brendan Gregg, Netflix. At Netflix performance is crucial, and we use many high to low level tools to analyze our stack in different ways. In this talk, I will introduce new system observability tools we are using at Netflix, which I've ported from my DTraceToolkit, and are intended for our Linux 3.2 cloud instances. These show that Linux can do more than you may think, by using creative hacks and workarounds with existing kernel features (ftrace, perf_events). While these are solving issues on current versions of Linux, I'll also briefly summarize the future in this space: eBPF, ktap, SystemTap, sysdig, etc.
ZooKeeper is an open-source coordination service for distributed applications that provides common services like naming, configuration management, synchronization, and groups. It uses a hierarchical data model where data is stored as znodes that can be configured with watches, versions, access control, and more. Common uses include distributed queues, leader election, and group membership. Recipes demonstrate how to implement queues and group membership using ZooKeeper.
Infrastructure & System Monitoring using PrometheusMarco Pas
The document introduces infrastructure and system monitoring using Prometheus. It discusses the importance of monitoring, common things to monitor like services, applications, and OS metrics. It provides an overview of Prometheus including its main components and data format. The document demonstrates setting up Prometheus, adding host metrics using Node Exporter, configuring Grafana, monitoring Docker containers using cAdvisor, configuring alerting in Prometheus and Alertmanager, instrumenting application code, and integrating Consul for service discovery. Live code demos are provided for key concepts.
Anuenue is an open source Solr cluster installation tool created by mixi, Inc. to simplify deployment and operations of Solr search clusters. It provides handy configuration of search clusters with roles like master, slave and merger. It also offers commands for starting, stopping and managing indexes across clusters. Anuenue includes implementations of Japanese Did-You-Mean features like a Japanese tokenizer and mining a dictionary from query logs to suggest corrections for typos in Japanese queries.
Contiki os timer is an essential topic in contiki OS. This presentation describes the different types of timers and their API .
It is following the same explanation as contiki OS wiki.
Slides for JavaOne 2015 talk by Brendan Gregg, Netflix (video/audio, of some sort, hopefully pending: follow @brendangregg on twitter for updates). Description: "At Netflix we dreamed of one visualization to show all CPU consumers: Java methods, GC, JVM internals, system libraries, and the kernel. With the help of Oracle this is now possible on x86 systems using system profilers (eg, Linux perf_events) and the new JDK option -XX:+PreserveFramePointer. This lets us create Java mixed-mode CPU flame graphs, exposing all CPU consumers. We can also use system profilers to analyze memory page faults, TCP events, storage I/O, and scheduler events, also with Java method context. This talk describes the background for this work, instructions generating Java mixed-mode flame graphs, and examples from our use at Netflix where Java on x86 is the primary platform for the Netflix cloud."
This document discusses the development of Apache Pig on Tez, an execution engine for Pig jobs. Pig on Tez allows Pig workflows to be executed as directed acyclic graphs (DAGs) using Tez, improving performance over the default MapReduce execution. Key benefits of Tez include eliminating intermediate data writes, reducing job launch overhead, and allowing more flexible data flows. However, challenges remain around automatically determining optimal parallelism and integrating Tez with user interface and monitoring tools. Future work is needed to address these issues.
June 24, 2014. At Velocity 2014, Fastly engineer Vladimir Vuksan gave an intro to Ganglia concepts (grid, clusters, hosts) as well as an installation of a sample monitoring grid. He also goes through the following commonly used visualization tools and how they may aid in detecting issues, identifying causes, and taking corrective action:
- Cluster/Grid Views
- Aggregate graphs
- Compare Hosts
- Custom graph functionality
- Views
- Interactive graphs
- Trending
- Nagios/Alerting system integration
- How to add metrics to Ganglia
- Different export formats such as JSON, CSV, and XML
Demtech is a business development and sales company founded in 2000 that specializes in assisting companies expand into Southern Europe and Latin America markets. It offers outsourced business development, sales, and account management support. Demtech helps clients with activities from market analysis to setting up subsidiaries. It provides local sales support, leads qualification, and cultural awareness to facilitate clients' success abroad. Demtech also assists clients with exhibition support and ongoing CRM services to generate qualified sales leads and maximize their market penetration.
This document discusses techniques for writing highly scalable Java programs for multi-core systems. It begins with an overview of hardware trends showing an increasing number of cores per chip. It then discusses profiling tools that can identify lock contention issues. The document provides best practices for Java programming including reducing locking scope, splitting locks, stripping locks, using atomic variables, and lock-free algorithms. It emphasizes using concurrent containers and immutable/thread-local data where possible.
This document discusses advanced Postgres monitoring. It begins with an introduction of the speaker and an agenda for the discussion. It then covers selection criteria for monitoring solutions, compares open source and SAAS monitoring options, and provides examples of collecting specific Postgres metrics using CollectD. It also discusses alerting, handling monitoring changes, and being prepared to respond to incidents outside of normal hours.
This document discusses using Grails to develop an electric vehicle charging platform. It describes how Grails was used to implement the Open Charge Point Protocol (OCPP) for communication between charging stations and a backend system. Future plans include adding additional protocol support using JSON and WebSockets for full-duplex communication. Testing and performance testing were important aspects, and plugins like RabbitMQ and CXF were leveraged. Vert.x is discussed as an option for asynchronous programming.
Introduction to DTrace (Dynamic Tracing), written by Brendan Gregg and delivered in 2007. While aimed at a Solaris-based audience, this introduction is still largely relevant today (2012). Since then, DTrace has appeared in other operating systems (Mac OS X, FreeBSD, and is being ported to Linux), and, many user-level providers have been developed to aid tracing of other languages.
This talk discusses Linux profiling using perf_events (also called "perf") based on Netflix's use of it. It covers how to use perf to get CPU profiling working and overcome common issues. The speaker will give a tour of perf_events features and show how Netflix uses it to analyze performance across their massive Amazon EC2 Linux cloud. They rely on tools like perf for customer satisfaction, cost optimization, and developing open source tools like NetflixOSS. Key aspects covered include why profiling is needed, a crash course on perf, CPU profiling workflows, and common "gotchas" to address like missing stacks, symbols, or profiling certain languages and events.
Profiling your Applications using the Linux Perf ToolsemBO_Conference
This document provides an overview of using the Linux perf tools to profile applications. It discusses setting up perf, benchmarking applications, profiling both CPU usage and sleep times, and analyzing profiling data. The document covers perf commands like perf record to collect profiling data, perf report to analyze the data, and perf script to convert it to other formats. It also discusses profiling options like call graphs and collecting kernel vs. user mode events.
Realtime Statistics based on Apache Storm and RocketMQXin Wang
This document discusses using Apache Storm and RocketMQ for real-time statistics. It begins with an overview of the streaming ecosystem and components. It then describes challenges with stateful statistics and introduces Alien, an open-source middleware for handling stateful event counting. The document concludes with best practices for Storm performance and data hot points.
Asynchronous, Event-driven Network Application Development with NettyErsin Er
"Asynchronous, Event-driven Network Application Development with Netty" presented at Ankara JUG in 2015, June.
The presentation starts with motivations for Non-Blocking I/O and continues with general overview of NIO and Netty. The actual talk was supplied with Netty's own examples.
Java In-Process Caching - Performance, Progress and Pittfallscruftex
This document discusses Java in-process caching and summarizes benchmarks of various caching libraries. It finds that Caffeine and cache2k have faster read throughput than Google Guava Cache and EHCache3 when the number of threads increases. Cache2k is the fastest overall. Benchmarking eviction quality shows Caffeine and cache2k have more efficient eviction algorithms than LRU. While Clock is O(n) in theory, cache2k optimizes it to have little increase in scan counts even for large caches. Modern caching libraries use improved algorithms over LRU to achieve better performance.
The document provides an overview of how to read and understand garbage collection (GC) log lines from different Java vendors and JVM versions. It begins by explaining the parts of a basic GC log line for the OpenJDK GC log format. It then discusses GC log lines for G1 GC and CMS GC in more detail. Finally, it shares examples of GC log formats from IBM JVMs and different levels of information provided. The document aims to help readers learn to correctly interpret GC logs and analyze GC behavior.
Linux Performance Analysis: New Tools and Old SecretsBrendan Gregg
Talk for USENIX/LISA2014 by Brendan Gregg, Netflix. At Netflix performance is crucial, and we use many high to low level tools to analyze our stack in different ways. In this talk, I will introduce new system observability tools we are using at Netflix, which I've ported from my DTraceToolkit, and are intended for our Linux 3.2 cloud instances. These show that Linux can do more than you may think, by using creative hacks and workarounds with existing kernel features (ftrace, perf_events). While these are solving issues on current versions of Linux, I'll also briefly summarize the future in this space: eBPF, ktap, SystemTap, sysdig, etc.
ZooKeeper is an open-source coordination service for distributed applications that provides common services like naming, configuration management, synchronization, and groups. It uses a hierarchical data model where data is stored as znodes that can be configured with watches, versions, access control, and more. Common uses include distributed queues, leader election, and group membership. Recipes demonstrate how to implement queues and group membership using ZooKeeper.
Infrastructure & System Monitoring using PrometheusMarco Pas
The document introduces infrastructure and system monitoring using Prometheus. It discusses the importance of monitoring, common things to monitor like services, applications, and OS metrics. It provides an overview of Prometheus including its main components and data format. The document demonstrates setting up Prometheus, adding host metrics using Node Exporter, configuring Grafana, monitoring Docker containers using cAdvisor, configuring alerting in Prometheus and Alertmanager, instrumenting application code, and integrating Consul for service discovery. Live code demos are provided for key concepts.
Anuenue is an open source Solr cluster installation tool created by mixi, Inc. to simplify deployment and operations of Solr search clusters. It provides handy configuration of search clusters with roles like master, slave and merger. It also offers commands for starting, stopping and managing indexes across clusters. Anuenue includes implementations of Japanese Did-You-Mean features like a Japanese tokenizer and mining a dictionary from query logs to suggest corrections for typos in Japanese queries.
Contiki os timer is an essential topic in contiki OS. This presentation describes the different types of timers and their API .
It is following the same explanation as contiki OS wiki.
Slides for JavaOne 2015 talk by Brendan Gregg, Netflix (video/audio, of some sort, hopefully pending: follow @brendangregg on twitter for updates). Description: "At Netflix we dreamed of one visualization to show all CPU consumers: Java methods, GC, JVM internals, system libraries, and the kernel. With the help of Oracle this is now possible on x86 systems using system profilers (eg, Linux perf_events) and the new JDK option -XX:+PreserveFramePointer. This lets us create Java mixed-mode CPU flame graphs, exposing all CPU consumers. We can also use system profilers to analyze memory page faults, TCP events, storage I/O, and scheduler events, also with Java method context. This talk describes the background for this work, instructions generating Java mixed-mode flame graphs, and examples from our use at Netflix where Java on x86 is the primary platform for the Netflix cloud."
This document discusses the development of Apache Pig on Tez, an execution engine for Pig jobs. Pig on Tez allows Pig workflows to be executed as directed acyclic graphs (DAGs) using Tez, improving performance over the default MapReduce execution. Key benefits of Tez include eliminating intermediate data writes, reducing job launch overhead, and allowing more flexible data flows. However, challenges remain around automatically determining optimal parallelism and integrating Tez with user interface and monitoring tools. Future work is needed to address these issues.
June 24, 2014. At Velocity 2014, Fastly engineer Vladimir Vuksan gave an intro to Ganglia concepts (grid, clusters, hosts) as well as an installation of a sample monitoring grid. He also goes through the following commonly used visualization tools and how they may aid in detecting issues, identifying causes, and taking corrective action:
- Cluster/Grid Views
- Aggregate graphs
- Compare Hosts
- Custom graph functionality
- Views
- Interactive graphs
- Trending
- Nagios/Alerting system integration
- How to add metrics to Ganglia
- Different export formats such as JSON, CSV, and XML
Demtech is a business development and sales company founded in 2000 that specializes in assisting companies expand into Southern Europe and Latin America markets. It offers outsourced business development, sales, and account management support. Demtech helps clients with activities from market analysis to setting up subsidiaries. It provides local sales support, leads qualification, and cultural awareness to facilitate clients' success abroad. Demtech also assists clients with exhibition support and ongoing CRM services to generate qualified sales leads and maximize their market penetration.
This document provides information about passive voice including its key components and usage. Passive voice constructs sentences with the subject receiving the action of the verb rather than performing the action. It uses a form of the verb "to be" along with the past participle of the main verb. The passive voice is often used when the performer of the action is unknown, unimportant, or already obvious from context. It places emphasis on the recipient of the action rather than who or what performed it. Examples of passive voice constructions in both English and Spanish are given.
This document discusses various research tools and Web 2.0 technologies that can be used for 21st century literacy professional development. It provides descriptions of search engines like Addict-O-Matic, research resources such as SnagFilms, ways to ask questions through Answers.com, tutorial videos on Teacher Training Videos, and other tools such as Jog the Web for curating web links and DeweyDigger.com for bookmarking and retrieving resources. The document examines how these tools can support teachers in incorporating technology into their teaching and help students complete assignments.
This document compares animals and objects using comparative adjectives. It discusses how bear is more aggressive than salmon, fly is smaller than cat, and boa is longer than buffalo. It also examines differences shown by comparative adjectives like "er", showing how Madrid is bigger than Terrassa and the Sagrada Familia is bigger than the arch of triumph. Additionally, it explores the use of "more" in comparisons such as Shakira being a more important person than Porta and climbing being more dangerous than football.
Malaysia has one of the highest numbers of days off in the world, ranking number seven in the top ten countries after Thailand, Indonesia, India and Hong Kong. 14 of our holidays are national and celebrated across the nation, while some are also celebrated only in a few states or even in one state.
This document discusses parallelization and multithreading techniques in .NET. It covers multithreading using AsyncEnumerator, which simplifies asynchronous programming. It also covers the Parallel Extensions to .NET Framework, including the Task Parallel Library for implicit parallelism using Parallel.For and Parallel.ForEach, and Parallel LINQ (PLINQ) for parallel querying of data sources. The document provides examples and discusses concepts like cancellation, exceptions, and thread safety.
Copper: A high performance workflow enginedmoebius
COPPER (COmmon Persistable Process Excecution Runtime) is an open-source high performance workflow engine, that persists the workflow instances (process) state into a database. So there is no limit to the runtime of a process. It can run for weeks, month or years. In addition, this strategy leads to crash safety.
A workflow can describe business processes for example, however any kind of use case is supported. The "modelling" language is Java, that has several advantages:
* with COPPER any Java developer is able to design workflows
* all Java developers like to use Java
* many Java libs can be integrated within COPPER
* many Java tools, like IDEs, can be used
* with COPPER your productivity will be increased when using a workflow engine
* using Java solutions will protect your investment
* COPPER is OpenSource under Apache Licence 2.0
Please visit copper-engine.org for details.
Technical Overview of Apache Drill by Jacques NadeauMapR Technologies
This document provides a technical overview of Apache Drill, including:
1) The basic query processing workflow involving Drillbits, distributed caching, and query execution.
2) The core modules within each Drillbit, including the SQL parser, optimizer, storage engines, and execution components.
3) How queries progress from SQL to logical and physical plans to distributed execution plans.
4) Technologies used include Java, Netty, Zookeeper, Parquet, and others.
The document discusses Hazelcast, an in-memory data grid platform. Hazelcast provides features like scale-out computing, resilience, fast performance, and an easy programming model. It can be used for distributed caching, computing, messaging, and data storage. Hazelcast runs as a distributed system across multiple nodes and provides APIs for Java and other languages.
Hazelcast provides scale-out computing capabilities that allow cluster capacity to be increased or decreased on demand. It enables resilience through automatic recovery from member failures without data loss. Hazelcast's programming model allows developers to easily program cluster applications as if they are a single process. It also provides fast application performance by holding large data sets in main memory.
Slice: OpenJPA for Distributed PersistencePinaki Poddar
The document discusses Slice, an OpenJPA module that allows JPA applications to use distributed, horizontally partitioned databases in a transparent manner. It describes how Slice works under the hood to enable features like parallel query execution across database partitions, replication of master data, and distribution policies to determine which partition a given object is stored in. The document provides examples of configuring Slice and developing distribution policies.
Thinking Distributed: The Hazelcast Way document discusses Hazelcast, an in-memory data grid that provides distributed computing capabilities. It describes how Hazelcast enables scale-out computing, resilience to failures, and an easy programming model. It also outlines Hazelcast's features such as fast performance, persistence, SQL queries, and support for various APIs and languages.
Tempesta FW: a FrameWork and FireWall for HTTP DDoS mitigation and Web Applic...Alexander Krizhanovsky
Tempesta FW is an open source framework for building high performance intelligent DDoS mitigation systems and web application firewalls. It directly embeds into the Linux TCP/IP stack and uses a just-in-time domain specific language to efficiently process and filter traffic at layers 3 through 7. This allows for fine-grained rule filtering, acceleration of web applications to mitigate DDoS attacks, and caching of content for improved performance. Tempesta aims to overcome limitations of traditional web servers and firewalls through its synchronous socket processing, fast HTTP parsing, generic finite state machine, and in-memory persistent database.
This document discusses stateful streaming data pipelines using Apache Apex. It introduces Apache Apex and describes its key components like tuples, operators, and the directed acyclic graph (DAG) structure. It then discusses challenges around checkpointing large operator state and introduces managed state and spillable data structures as solutions. Managed state incrementally checkpoints state to disk and allows configuring memory thresholds. Spillable data structures decouple data from serialization and provide map, list, and set interfaces to stored data. Examples demonstrate building complex data structures on top of managed state.
The document discusses different runtime environments for programming languages:
(1) Fully static environments where all data remains fixed in memory like FORTRAN77. No dynamic allocation.
(2) Stack-based environments used by languages allowing recursion and dynamic allocation like C/C++. Activation records are allocated on a runtime stack.
(3) Dynamic environments like LISP where data is allocated on a heap.
It covers key aspects of runtime environments like memory organization, calling conventions, parameter passing, and handling local variables and procedures. Different languages require different solutions for variable-length data, nested declarations, non-local references, and procedure parameters.
Leveraging Hadoop in your PostgreSQL EnvironmentJim Mlodgenski
This talk will begin with a discussion of the strengths of PostgreSQL and Hadoop. We will then lead into a high level overview of Hadoop and its community of projects like Hive, Flume and Sqoop. Finally, we will dig down into various use cases detailing how you can leverage Hadoop technologies for your PostgreSQL databases today. The use cases will range from using HDFS for simple database backups to using PostgreSQL and Foreign Data Wrappers to do low latency analytics on your Big Data.
At Capital One, I built a small framework on top of Apache Cascading. We have found the framework can significantly reduce the effort in developing and enhance the maintainability of Cascading applications.
1) The document introduces Infinispan, an open source in-memory data grid and distributed cache. It discusses Infinispan's architecture as an embedded library or standalone server, clustering modes, persistence, querying, transactions and more.
2) Use cases for Infinispan include sharing data, high performance caching, scalability, and as a database platform in the cloud. Example applications discussed are session clustering and a data grid platform.
3) The document provides a case study of using Infinispan with Spring for HTTP session clustering, describing how to configure Infinispan, implement a custom SecurityContextDao, and integrate it with Spring Security.
The .NET Garbage Collector (GC) is really cool. It helps providing our applications with virtually unlimited memory, so we can focus on writing code instead of manually freeing up memory. But how does .NET manage that memory? What are hidden allocations? Are strings evil? It still matters to understand when and where memory is allocated. In this talk, we’ll go over the base concepts of .NET memory management and explore how .NET helps us and how we can help .NET – making our apps better. Expect profiling, Intermediate Language (IL), ClrMD and more!
Node has captured the attention of early adopters by clearly differentiating itself as being asynchronous from the ground up while remaining accessible. Now that server side JavaScript is at the cutting edge of the asynchronous, real time web, it is in a much better position to establish itself as the go to language for also making synchronous, CRUD webapps and gain a stronger foothold on the server.
This talk covers the current state of server side JavaScript beyond Node. It introduces Common Node, a synchronous CommonJS compatibility layer using node-fibers which bridges the gap between the different platforms. We look into Common Node's internals, compare its performance to that of other implementations such as RingoJS and go through some ideal use cases.
Smash the Stack: Writing a Buffer Overflow Exploit (Win32)Elvin Gentiles
This document provides an overview of buffer overflow exploits on Windows 32-bit systems. It discusses the lab environment that will be used, basic assembly concepts like registers and instructions, the Windows 32 memory layout, how the stack works, and the general steps for exploit development. These include causing a crash, identifying the offset, determining bad characters, locating space for shellcode, generating shellcode, and redirecting execution to the shellcode. The document concludes by listing some hands-on exercises that will be covered, and recommending additional learning materials on exploit writing.
Near Real time Indexing Kafka Messages to Apache Blur using Spark StreamingDibyendu Bhattacharya
My presentation at recently concluded Apache Big Data Conference Europe about the Reliable Low Level Kafka Spark Consumer I developed and an use case of real time indexing to Apache Blur using this consumer
FBTFTP: an opensource framework to build dynamic tftp serversAngelo Failla
Talk given at EuroPython2016, Bilbao:
https://ep2016.europython.eu/conference/talks/fbtftp-facebooks-python3-framework-for-tftp-servers
TFTP was first standardized in ’81 (same year I was born!) and one of its primary uses is in the early stage of network booting. TFTP is very simple to implement, and one of the reasons it is still in use is that its small footprint allows engineers to fit the code into very low resource, single board computers, system-on-a-chip implementations and mainboard chipsets, in the case of modern hardware.
It is therefore a crucial protocol deployed in almost every data center environment. It is used, together with DHCP, to chain load Network Boot Programs (NBPs), like Grub2 and iPXE. They allow machines to bootstrap themselves and install operating systems off of the network, downloading kernels and initrds via HTTP and starting them up.
At Facebook, we have been using the standard in.tftpd daemon for years, however, we started to reach its limitations. Limitations that were partially due to our scale and the way TFTP was deployed in our infrastructure, but also to the protocol specifications based on requirements from the 80’s.
To address those limitations we ended up writing our own framework for creating dynamic TFTP servers in Python3, and we decided to open source it.
I will take you thru the framework and the features it offers. I’ll discuss the specific problems that motivated us to create it. We will look at practical examples of how touse it, along with a little code, to build your own server that are tailored to your own infra needs.
The document discusses Coordinated Restore at Checkpoint (CRaC), a feature of the Java Virtual Machine (JVM) that allows saving the state of a running application and restoring it later to avoid JVM startup overhead. CRaC uses the CRIU userspace checkpoint/restore mechanism and provides a simple API for applications to register resources that need to be notified during checkpoint and restore. This allows restoring application state like open files and sockets. An example demonstrates how CRaC can speed up subsequent runs of an application by restoring a pre-filled cache from a previous checkpoint.
Your One-Stop Shop for Python Success: Top 10 US Python Development Providersakankshawande
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
Things to Consider When Choosing a Website Developer for your Website | FODUUFODUU
Choosing the right website developer is crucial for your business. This article covers essential factors to consider, including experience, portfolio, technical skills, communication, pricing, reputation & reviews, cost and budget considerations and post-launch support. Make an informed decision to ensure your website meets your business goals.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
OpenID AuthZEN Interop Read Out - AuthorizationDavid Brossard
During Identiverse 2024 and EIC 2024, members of the OpenID AuthZEN WG got together and demoed their authorization endpoints conforming to the AuthZEN API
Infrastructure Challenges in Scaling RAG with Custom AI modelsZilliz
Building Retrieval-Augmented Generation (RAG) systems with open-source and custom AI models is a complex task. This talk explores the challenges in productionizing RAG systems, including retrieval performance, response synthesis, and evaluation. We’ll discuss how to leverage open-source models like text embeddings, language models, and custom fine-tuned models to enhance RAG performance. Additionally, we’ll cover how BentoML can help orchestrate and scale these AI components efficiently, ensuring seamless deployment and management of RAG systems in the cloud.
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?Speck&Tech
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.
Ocean lotus Threat actors project by John Sitima 2024 (1).pptxSitimaJohn
Ocean Lotus cyber threat actors represent a sophisticated, persistent, and politically motivated group that poses a significant risk to organizations and individuals in the Southeast Asian region. Their continuous evolution and adaptability underscore the need for robust cybersecurity measures and international cooperation to identify and mitigate the threats posed by such advanced persistent threat groups.
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
GraphRAG for Life Science to increase LLM accuracyTomaz Bratanic
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers