The document discusses Raft, a consensus algorithm that provides a simpler alternative to Paxos. It decomposes consensus into three subproblems: leader election, log replication, and safety. The algorithm elects a strong leader that clients direct requests to, and the leader replicates log entries to followers to achieve consensus. If the leader fails, followers will elect a new leader to take over log replication and continue providing consensus.
Raft is a consensus algorithm that allows for a replicated log to be consistently maintained across multiple servers. It elects a single leader to be responsible for log replication. The leader appends entries to its log, and replicates these entries to a majority of servers to be committed. This ensures safety properties like state machine safety and leader completeness. Raft aims to be more understandable than alternatives like Paxos through separation of concerns and reduced state spaces.
RAFT In Search of an Understandable Consensus Algorithm Presentation PPTDiwasPandey3
In Search of an Understandable Consensus Algorithm
Leader Election
Select one of server to act as Leader (only one)
Detect crashes, starts election
Normal Operation (Log Replication)
Leader takes command from clients, appends it to log
Leader replicates logs to other servers
Leader never overwrites or deletes entries in its log; it only appends new entries
Safety
only servers with up-to-date logs can become the new leader
Producer Performance Tuning for Apache KafkaJiangjie Qin
Kafka is well known for high throughput ingestion. However, to get the best latency characteristics without compromising on throughput and durability, we need to tune Kafka. In this talk, we share our experiences to achieve the optimal combination of latency, throughput and durability for different scenarios.
DHCP Snooping allows switches to prevent man-in-the-middle attacks by DHCP spoofing. It trusts only the port connected to the DHCP server, monitors traffic for IP and MAC bindings, and uses this information for other security features. When enabled, DHCP Snooping will only forward DHCP requests to trusted ports and maintain a table of client bindings.
Ozone is an object store for Apache Hadoop that is designed to scale to trillions of objects. It uses a distributed metadata store to avoid single points of failure and enable parallelism. Key components of Ozone include containers, which provide the basic storage and replication functionality, and the Key Space Manager (KSM) which maps Ozone entities like volumes and buckets to containers. The Storage Container Manager manages the container lifecycle and replication.
Kafka is becoming an ever more popular choice for users to help enable fast data and Streaming. Kafka provides a wide landscape of configuration to allow you to tweak its performance profile. Understanding the internals of Kafka is critical for picking your ideal configuration. Depending on your use case and data needs, different settings will perform very differently. Lets walk through performance essentials of Kafka. Let's talk about how your Consumer configuration, can speed up or slow down the flow of messages to Brokers. Lets talk about message keys, their implications and their impact on partition performance. Lets talk about how to figure out how many partitions and how many Brokers you should have. Let's discuss consumers and what effects their performance. How do you combine all of these choices and develop the best strategy moving forward? How do you test performance of Kafka? I will attempt a live demo with the help of Zeppelin to show in real time how to tune for performance.
The document discusses Raft, a consensus algorithm that provides a simpler alternative to Paxos. It decomposes consensus into three subproblems: leader election, log replication, and safety. The algorithm elects a strong leader that clients direct requests to, and the leader replicates log entries to followers to achieve consensus. If the leader fails, followers will elect a new leader to take over log replication and continue providing consensus.
Raft is a consensus algorithm that allows for a replicated log to be consistently maintained across multiple servers. It elects a single leader to be responsible for log replication. The leader appends entries to its log, and replicates these entries to a majority of servers to be committed. This ensures safety properties like state machine safety and leader completeness. Raft aims to be more understandable than alternatives like Paxos through separation of concerns and reduced state spaces.
RAFT In Search of an Understandable Consensus Algorithm Presentation PPTDiwasPandey3
In Search of an Understandable Consensus Algorithm
Leader Election
Select one of server to act as Leader (only one)
Detect crashes, starts election
Normal Operation (Log Replication)
Leader takes command from clients, appends it to log
Leader replicates logs to other servers
Leader never overwrites or deletes entries in its log; it only appends new entries
Safety
only servers with up-to-date logs can become the new leader
Producer Performance Tuning for Apache KafkaJiangjie Qin
Kafka is well known for high throughput ingestion. However, to get the best latency characteristics without compromising on throughput and durability, we need to tune Kafka. In this talk, we share our experiences to achieve the optimal combination of latency, throughput and durability for different scenarios.
DHCP Snooping allows switches to prevent man-in-the-middle attacks by DHCP spoofing. It trusts only the port connected to the DHCP server, monitors traffic for IP and MAC bindings, and uses this information for other security features. When enabled, DHCP Snooping will only forward DHCP requests to trusted ports and maintain a table of client bindings.
Ozone is an object store for Apache Hadoop that is designed to scale to trillions of objects. It uses a distributed metadata store to avoid single points of failure and enable parallelism. Key components of Ozone include containers, which provide the basic storage and replication functionality, and the Key Space Manager (KSM) which maps Ozone entities like volumes and buckets to containers. The Storage Container Manager manages the container lifecycle and replication.
Kafka is becoming an ever more popular choice for users to help enable fast data and Streaming. Kafka provides a wide landscape of configuration to allow you to tweak its performance profile. Understanding the internals of Kafka is critical for picking your ideal configuration. Depending on your use case and data needs, different settings will perform very differently. Lets walk through performance essentials of Kafka. Let's talk about how your Consumer configuration, can speed up or slow down the flow of messages to Brokers. Lets talk about message keys, their implications and their impact on partition performance. Lets talk about how to figure out how many partitions and how many Brokers you should have. Let's discuss consumers and what effects their performance. How do you combine all of these choices and develop the best strategy moving forward? How do you test performance of Kafka? I will attempt a live demo with the help of Zeppelin to show in real time how to tune for performance.
Multiversion Concurrency Control TechniquesRaj vardhan
Multiversion Concurrency Control Techniques
Q. What is multiversion concurrency control technique? Explain how multiversion concurrency control can be achieved by using Time Stamp Ordering.
Watch this talk here: https://www.confluent.io/online-talks/apache-kafka-architecture-and-fundamentals-explained-on-demand
This session explains Apache Kafka’s internal design and architecture. Companies like LinkedIn are now sending more than 1 trillion messages per day to Apache Kafka. Learn about the underlying design in Kafka that leads to such high throughput.
This talk provides a comprehensive overview of Kafka architecture and internal functions, including:
-Topics, partitions and segments
-The commit log and streams
-Brokers and broker replication
-Producer basics
-Consumers, consumer groups and offsets
This session is part 2 of 4 in our Fundamentals for Apache Kafka series.
Static libraries are linked during compilation and their functions are built into the executable file, increasing its size, while dynamic libraries are linked during execution by the operating system and loaded only once into memory from multiple programs, making programs smaller and faster to execute but potentially less compatible if the library is removed or updated.
Introduction to memcached, a caching service designed for optimizing performance and scaling in the web stack, seen from perspective of MySQL/PHP users. Given for 2nd year students of professional bachelor in ICT at Kaho St. Lieven, Gent.
Clock synchronization in distributed systemSunita Sahu
This document discusses several techniques for clock synchronization in distributed systems:
1. Time stamping events and messages with logical clocks to determine partial ordering without a global clock. Logical clocks assign monotonically increasing sequence numbers.
2. Clock synchronization algorithms like NTP that regularly adjust system clocks across the network to synchronize with a time server. NTP uses averaging to account for network delays.
3. Lamport's logical clocks algorithm that defines "happened before" relations and increments clocks between events to synchronize logical clocks across processes.
The document discusses various algorithms for achieving distributed mutual exclusion and process synchronization in distributed systems. It covers centralized, token ring, Ricart-Agrawala, Lamport, and decentralized algorithms. It also discusses election algorithms for selecting a coordinator process, including the Bully algorithm. The key techniques discussed are using logical clocks, message passing, and quorums to achieve mutual exclusion without a single point of failure.
The document summarizes the Google File System (GFS). It discusses the key points of GFS's design including:
- Files are divided into fixed-size 64MB chunks for efficiency.
- Metadata is stored on a master server while data chunks are stored on chunkservers.
- The master manages file system metadata and chunk locations while clients communicate with both the master and chunkservers.
- GFS provides features like leases to coordinate updates, atomic appends, and snapshots for consistency and fault tolerance.
Distributed database system is collection of loosely coupled sites that are independeant of each other.
Distributed transaction model
Concurrency control
2 phase commit protocol
DPDK is a set of drivers and libraries that allow applications to bypass the Linux kernel and access network interface cards directly for very high performance packet processing. It is commonly used for software routers, switches, and other network applications. DPDK can achieve over 11 times higher packet forwarding rates than applications using the Linux kernel network stack alone. While it provides best-in-class performance, DPDK also has disadvantages like reduced security and isolation from standard Linux services.
Stateful, Stateless and Serverless - Running Apache Kafka® on Kubernetesconfluent
This document provides an overview of a webinar on Kubernetes operators presented by Joe Beda. It discusses:
- Kubernetes allows for developer and operator productivity through improved workflows and better resource efficiency.
- Operators are domain-specific controllers that manage both a software application and related Kubernetes objects, and help automate common operational tasks through code.
- The Kafka operator example shows how an operator can handle tasks like identity, discovery, storage and logging/metrics for Kafka deployments on Kubernetes through a custom resource definition.
This document discusses real-time operating systems (RTOS). It defines RTOS as operating systems that are able to respond to inputs immediately within a specified time delay. It compares RTOS to general operating systems and discusses the types, characteristics, functions, and applications of RTOS. Examples of RTOS like VxWorks are provided. The key functions of an RTOS include task management, scheduling, resource allocation, and interrupt handling. RTOS are widely used in applications that require deterministic responses like avionics, medical devices, industrial automation, and more.
Parquet Strata/Hadoop World, New York 2013Julien Le Dem
Parquet is a columnar storage format for Hadoop data. It was developed collaboratively by Twitter and Cloudera to address the need for efficient analytics on large datasets. Parquet provides more efficient compression and I/O compared to row-based formats by only reading and decompressing the columns needed by a query. It has been adopted by many companies for analytics workloads involving terabytes to petabytes of data. Parquet is language-independent and supports integration with frameworks like Hive, Pig, and Impala. It provides significant performance improvements and storage savings compared to traditional row-based formats.
A Distributed File System(DFS) is simply a classical model of a file system distributed across multiple machines.The purpose is to promote sharing of dispersed files.
The top 3 challenges running multi-tenant Flink at scaleFlink Forward
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.
Cloud-Native Apache Spark Scheduling with YuniKorn SchedulerDatabricks
Kubernetes is the most popular container orchestration system that is natively designed for Cloud. At Lyft and Cloudera, we have both emerged the next-generation, cloud-native infrastructure based on Kubernetes, which supports various distributed workloads.
A brief overview of caching mechanisms in a web application. Taking a look at the different layers of caching and how to utilize them in a PHP code base. We also compare Redis and MemCached discussing their advantages and disadvantages.
Spanner is Google's globally distributed database that provides ACID transactions across data centers. It uses TrueTime to assign timestamps for distributed transactions, ensuring consistency. A Spanner deployment consists of servers organized into zones across datacenters, with a universe master and placement driver coordinating. Transactions are executed across servers and committed using time-based consensus. Evaluation shows Spanner provides low-latency reads and commits at large scale for Google applications.
Tzu-Li (Gordon) Tai - Stateful Stream Processing with Apache FlinkVerverica
As Apache Flink continues to push the boundaries of stateful stream processing as an integral part of its past releases, increasing numbers of users are starting to realize the potential of stateful stream processing as a promising paradigm for robust and reactive data analytics as well as event-driven applications.
This talk aims at covering the general idea and motivations of stateful stream processing, and how Flink enables it with its powerful set of state management features and programming APIs. In addition to that, we will also take a look at the recent advancements related to Flink's state management and large state handling that were driven by our team at data Artisans team in the latest version 1.3 (expected release by end of May / early June).
The document discusses the RAFT consensus algorithm and the Copycat framework. It provides an overview of RAFT and how it uses a replicated state machine approach with log replication to achieve consensus. Key aspects of RAFT include leader election, log replication using append entries RPC calls, and three states that nodes can be in: follower, candidate, or leader. It also describes how Copycat is an implementation of RAFT that adds features like passive followers that replicate logs asynchronously using a gossip protocol for improved scalability.
Multiversion Concurrency Control TechniquesRaj vardhan
Multiversion Concurrency Control Techniques
Q. What is multiversion concurrency control technique? Explain how multiversion concurrency control can be achieved by using Time Stamp Ordering.
Watch this talk here: https://www.confluent.io/online-talks/apache-kafka-architecture-and-fundamentals-explained-on-demand
This session explains Apache Kafka’s internal design and architecture. Companies like LinkedIn are now sending more than 1 trillion messages per day to Apache Kafka. Learn about the underlying design in Kafka that leads to such high throughput.
This talk provides a comprehensive overview of Kafka architecture and internal functions, including:
-Topics, partitions and segments
-The commit log and streams
-Brokers and broker replication
-Producer basics
-Consumers, consumer groups and offsets
This session is part 2 of 4 in our Fundamentals for Apache Kafka series.
Static libraries are linked during compilation and their functions are built into the executable file, increasing its size, while dynamic libraries are linked during execution by the operating system and loaded only once into memory from multiple programs, making programs smaller and faster to execute but potentially less compatible if the library is removed or updated.
Introduction to memcached, a caching service designed for optimizing performance and scaling in the web stack, seen from perspective of MySQL/PHP users. Given for 2nd year students of professional bachelor in ICT at Kaho St. Lieven, Gent.
Clock synchronization in distributed systemSunita Sahu
This document discusses several techniques for clock synchronization in distributed systems:
1. Time stamping events and messages with logical clocks to determine partial ordering without a global clock. Logical clocks assign monotonically increasing sequence numbers.
2. Clock synchronization algorithms like NTP that regularly adjust system clocks across the network to synchronize with a time server. NTP uses averaging to account for network delays.
3. Lamport's logical clocks algorithm that defines "happened before" relations and increments clocks between events to synchronize logical clocks across processes.
The document discusses various algorithms for achieving distributed mutual exclusion and process synchronization in distributed systems. It covers centralized, token ring, Ricart-Agrawala, Lamport, and decentralized algorithms. It also discusses election algorithms for selecting a coordinator process, including the Bully algorithm. The key techniques discussed are using logical clocks, message passing, and quorums to achieve mutual exclusion without a single point of failure.
The document summarizes the Google File System (GFS). It discusses the key points of GFS's design including:
- Files are divided into fixed-size 64MB chunks for efficiency.
- Metadata is stored on a master server while data chunks are stored on chunkservers.
- The master manages file system metadata and chunk locations while clients communicate with both the master and chunkservers.
- GFS provides features like leases to coordinate updates, atomic appends, and snapshots for consistency and fault tolerance.
Distributed database system is collection of loosely coupled sites that are independeant of each other.
Distributed transaction model
Concurrency control
2 phase commit protocol
DPDK is a set of drivers and libraries that allow applications to bypass the Linux kernel and access network interface cards directly for very high performance packet processing. It is commonly used for software routers, switches, and other network applications. DPDK can achieve over 11 times higher packet forwarding rates than applications using the Linux kernel network stack alone. While it provides best-in-class performance, DPDK also has disadvantages like reduced security and isolation from standard Linux services.
Stateful, Stateless and Serverless - Running Apache Kafka® on Kubernetesconfluent
This document provides an overview of a webinar on Kubernetes operators presented by Joe Beda. It discusses:
- Kubernetes allows for developer and operator productivity through improved workflows and better resource efficiency.
- Operators are domain-specific controllers that manage both a software application and related Kubernetes objects, and help automate common operational tasks through code.
- The Kafka operator example shows how an operator can handle tasks like identity, discovery, storage and logging/metrics for Kafka deployments on Kubernetes through a custom resource definition.
This document discusses real-time operating systems (RTOS). It defines RTOS as operating systems that are able to respond to inputs immediately within a specified time delay. It compares RTOS to general operating systems and discusses the types, characteristics, functions, and applications of RTOS. Examples of RTOS like VxWorks are provided. The key functions of an RTOS include task management, scheduling, resource allocation, and interrupt handling. RTOS are widely used in applications that require deterministic responses like avionics, medical devices, industrial automation, and more.
Parquet Strata/Hadoop World, New York 2013Julien Le Dem
Parquet is a columnar storage format for Hadoop data. It was developed collaboratively by Twitter and Cloudera to address the need for efficient analytics on large datasets. Parquet provides more efficient compression and I/O compared to row-based formats by only reading and decompressing the columns needed by a query. It has been adopted by many companies for analytics workloads involving terabytes to petabytes of data. Parquet is language-independent and supports integration with frameworks like Hive, Pig, and Impala. It provides significant performance improvements and storage savings compared to traditional row-based formats.
A Distributed File System(DFS) is simply a classical model of a file system distributed across multiple machines.The purpose is to promote sharing of dispersed files.
The top 3 challenges running multi-tenant Flink at scaleFlink Forward
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.
Cloud-Native Apache Spark Scheduling with YuniKorn SchedulerDatabricks
Kubernetes is the most popular container orchestration system that is natively designed for Cloud. At Lyft and Cloudera, we have both emerged the next-generation, cloud-native infrastructure based on Kubernetes, which supports various distributed workloads.
A brief overview of caching mechanisms in a web application. Taking a look at the different layers of caching and how to utilize them in a PHP code base. We also compare Redis and MemCached discussing their advantages and disadvantages.
Spanner is Google's globally distributed database that provides ACID transactions across data centers. It uses TrueTime to assign timestamps for distributed transactions, ensuring consistency. A Spanner deployment consists of servers organized into zones across datacenters, with a universe master and placement driver coordinating. Transactions are executed across servers and committed using time-based consensus. Evaluation shows Spanner provides low-latency reads and commits at large scale for Google applications.
Tzu-Li (Gordon) Tai - Stateful Stream Processing with Apache FlinkVerverica
As Apache Flink continues to push the boundaries of stateful stream processing as an integral part of its past releases, increasing numbers of users are starting to realize the potential of stateful stream processing as a promising paradigm for robust and reactive data analytics as well as event-driven applications.
This talk aims at covering the general idea and motivations of stateful stream processing, and how Flink enables it with its powerful set of state management features and programming APIs. In addition to that, we will also take a look at the recent advancements related to Flink's state management and large state handling that were driven by our team at data Artisans team in the latest version 1.3 (expected release by end of May / early June).
The document discusses the RAFT consensus algorithm and the Copycat framework. It provides an overview of RAFT and how it uses a replicated state machine approach with log replication to achieve consensus. Key aspects of RAFT include leader election, log replication using append entries RPC calls, and three states that nodes can be in: follower, candidate, or leader. It also describes how Copycat is an implementation of RAFT that adds features like passive followers that replicate logs asynchronously using a gossip protocol for improved scalability.
O documento compara os algoritmos de consenso Paxos e RAFT, descrevendo suas diferenças principais, como Paxos permitir várias propostas concorrentes enquanto RAFT designa um nó único para propor valores. O documento também lista referências sobre os dois protocolos.
This document discusses elements of research design for a qualitative research project, including developing a research plan, conducting a literature review, formulating research questions and purpose, and planning for data analysis. It emphasizes that qualitative research requires thorough preparation and planning while still allowing for flexibility during the research process as understanding develops. The research plan provides structure but should not limit promising options or flexibility.
This document presents a business plan for "Petals Limited", a new company introducing chicken biscuits to the Pakistani market. The company is formed by three partners with a 10 million rupee budget. It aims to become a market leader in 5 years by developing a popular chicken biscuit brand. The plan describes the company, product details, a marketing strategy covering price, placement, promotion, and competitors. It also presents budgets, objectives, and a PEST analysis of opportunities and threats in the biscuit industry.
This document outlines 7 common mistakes people make during divorce. The first mistake is assuming litigation is necessary, when there are alternative dispute resolution options like negotiation, mediation, or collaborative lawyering. The second mistake is emptying joint bank accounts, which is seen as a declaration of war. The third mistake is trash talking the ex to others or children. The fourth is oversharing details of the divorce on social media. The fifth mistake is filing false charges against the ex. The sixth mistake is not getting a second legal opinion. And the seventh mistake is not hiring a collaborative lawyer, which can save time, money and prevent trauma compared to traditional litigation.
This document presents an investment opportunity in SunTrust 88 Gibraltar Garden Residences, a residential development in Baguio City. The minimum investment is 20,000 pesos as a reservation fee plus 25% of the total investment paid in monthly installments of 13,000 pesos over 4 years. The projected value of units could increase 32-48% in the first year and 2-3% with each block of 25 units sold, potentially reaching a value of 3.1-3.5 million pesos within 3-4 years, resulting in a net profit of 100,000 to 600,000 pesos after equity payments.
Este documento discute a criação de novos conhecimentos de design para apoiar a transição para a sustentabilidade. Apresenta uma conferência de designer-pesquisadores reunidos para debater o estado da arte e como criar conhecimentos de design, aceitando o desafio de uma transição para uma sociedade em rede e baseada no conhecimento enquanto faz a transição para a sustentabilidade. Também inclui um rascunho de uma "Agenda de Pesquisa de Design para a Sustentabilidade".
El síndrome de ovario poliquístico (SOP) es un trastorno endocrino-metabólico frecuente que se caracteriza por irregularidades menstruales, manifestaciones de hiperandrogenismo y ovarios poliquísticos. El diagnóstico requiere la presencia de al menos dos de estas tres características luego de excluir otras causas. El SOP se asocia a riesgos reproductivos, metabólicos y oncológicos, por lo que es importante el diagnóstico y tratamiento oportunos de las pacientes.
This document lists 50 potential final year IEEE projects related to digital signal processing for the years 2014-2015. It provides the project codes, titles, and contact information for Praveen Kumar if interested in the projects. The projects focus on topics like wireless sensor networks, cognitive radio networks, MIMO systems, and energy efficiency in communications.
쉐어몬스터 주식회사는 전문가 공유 전문 회사입니다.
어떤 어려움을 겪고 있나요? 어떤 문제점에 봉착해 있나요? 전문가와 함께하면 어렵지 않습니다. 고민하지 마시고 지금 바로 연락주세요.
쉐어몬스터 주식회사는 경영, IT, 창업, 특허, 부동산 등 다양한 분야의 전문가를 모시고 여러분을 도와드리고자 합니다.
The Raft consensus algorithm was created to provide an understandable replication protocol as an alternative to Paxos. It uses leader election, log replication, and safety rules to ensure consistency across servers. The key aspects are: (1) electing a single leader via heartbeat messages and randomized timeouts, (2) the leader appends client commands to its log and replicates the log to followers to synchronize state, (3) only servers with up-to-date logs can become leader to ensure consistency. The goal was to create an algorithm that is easier to understand and implement in real systems compared to Paxos.
Megastore combines the scalability of NoSQL with the ACID properties of relational databases. It uses Paxos replication across data centers to provide high availability with low latency. The data is partitioned into entity groups which are replicated independently to allow for scale. Transactions within a group use multi-version concurrency control and across groups use two-phase commit. Coordinators track write ordering to prevent conflicts during reads and writes. Metrics from Google showed Megastore provided low latency access even with widespread data distribution.
This document discusses link aggregation, which involves combining multiple network connections in parallel to increase throughput beyond what a single connection would allow, and to provide redundancy in case one of the links fails. It focuses on link aggregation configuration and operation on the AT-8000S device, including defining link aggregation groups (LAGs), adding ports to LAGs, and the use of LACP (Link Aggregation Control Protocol) to automatically negotiate LAG membership and manage changes. LACP allows devices to exchange information so that links are only aggregated if both ends have identical configuration settings.
2009-03-13 Atlanda System z Council MeetingShawn Wells
The document provides an overview of current and future states of Linux on IBM System z. It summarizes Red Hat Enterprise Linux 5.3 updates, including technical highlights for System z such as support for the latest z10 instruction set and cryptographic acceleration. It also outlines works in progress for upcoming RHEL 5.4 and 6 releases, noting plans to integrate additional z/Architecture features and improve performance.
The document discusses a lab experiment on network devices and Packet Tracer. It aims to familiarize students with switches, routers, and the network simulator Packet Tracer. The document provides details on router and switch components and functions, and guides students through building a simple topology in Packet Tracer.
An update from the RabbitMQ team - Michael KlishinRabbitMQ Summit
Michael Klishin from Pivotal gave an update on RabbitMQ. He discussed the state of the 3.7.x releases which focused on deployment automation, operator friendliness, and stability. Future plans include improved scalability, reliability, and simplifying upgrades. Key projects include implementing quorum queues using Raft for improved mirroring, adding OAuth 2.0 support, developing a new schema storage system, and allowing mixed version clusters. The 3.8 release will include quorum queues, OAuth 2.0, and initial mixed version cluster support.
Microservices add complexity to monitoring that was not present with monolithic architectures. While microservices provide benefits, they also introduce significant monitoring challenges around communication between services. Prometheus has emerged as a powerful open source solution for monitoring microservices as it was designed to address issues of scale and flexibility that monitoring microservices requires.
The document discusses the evolution of a SPARQL benchmarking framework from version 1.x to 2.x. Version 1.x had several limitations, such as only supporting SPARQL queries and using a hardcoded methodology. Version 2.x addressed these limitations by supporting different types of operations, separating the test methodology, and making the framework more customizable and extensible. Examples are given of how the framework is used internally and how others can further customize it for their needs.
Slides from my presentation about Shopzilla's concurrency strategies to the Pasadena Java User's Group on April 26, 2010. This is essentially the same material as covered by my colleague Rodney Barlow in an earlier presentation http://www.slideshare.net/rodneypbarlow/shopzilla-on-concurrency, with a few minor tweaks.
M|18 Choosing the Right High Availability Strategy for YouMariaDB plc
This document discusses MariaDB high availability strategies including replication, failover, and clustering. It defines key HA terminology and describes different replication topologies like asynchronous, semi-synchronous, and synchronous replication using Galera cluster. Use cases provided show how geographically distributed and production control systems benefit from MariaDB HA features.
Squash Those IoT Security Bugs with a Hardened System ProfileSteve Arnold
Although the tools and documentation have been around a long time, the industry as a whole has been woefully slow at taking security engineering seriously (even more so in the embedded world). The current mainline kernel includes several access control systems that reduce the risk of bugs escalating into high-level security compromises, such as the venerable SELinux (which is enabled by default in Android 4.4 and several "enterprise" Linux distributions). This presentation focuses on a complementary set of security mechanisms that work independently from the overlying frameworks: PIE toolchain hardening, PAX kernel hardening, and the PAX userland tools. These technologies work together to demote whole classes of bugs from headline-grabbing remote compromise and/or data theft exploits to "mere" DoS annoyances.
Presto is an open source distributed SQL query engine for running interactive analytic queries against data sources of all sizes ranging from gigabytes to petabytes.
The document discusses some key challenges with achieving high availability and scalability in distributed systems based on the CAP theorem. It explains that consistency, availability, and partition tolerance cannot all be guaranteed simultaneously. It then provides examples of how this manifests in real database systems like MySQL, PostgreSQL, Redis, and RabbitMQ. It discusses strategies for improving availability and scalability in systems like PostgreSQL clusters and MySQL Galera clusters, but also limitations and complexity involved.
The document provides an overview of data engineering concepts for data scientists. It discusses the CAP theorem, which states that a distributed system cannot simultaneously provide consistency, availability, and partition tolerance. It describes various data store types and architectures that provide different balances of these properties, such as leader-follower systems that prioritize availability and consistency over partition tolerance. The document also summarizes reference architectures like Lambda and Kappa and discusses the concept of a data lake.
Choosing the right high availability strategyMariaDB plc
- MariaDB provides several high availability options including asynchronous replication, semi-synchronous replication, Galera synchronous replication, and MaxScale for load balancing and failover.
- Asynchronous replication allows for read scaling but carries a risk of data loss during failover. Semi-synchronous replication reduces this risk by ensuring data is written to at least one slave before confirming to the client.
- Galera synchronous multi-master replication ensures all nodes remain in sync with no data loss but can impact performance. MaxScale helps manage replication topology and perform automated failovers.
Fine-tuning Group Replication for PerformanceVitor Oliveira
This presentation is an overview of Group Replication from the perspective of performance optimization. It shows the main moving parts, the available options and how they can be used to tune its behaviour, and also a few significant benchmark results.
java.util.concurrent for Distributed Coordination, Riga DevDays 2019Ensar Basri Kahveci
The evolution of distributed coordination tools shows that high-level APIs ease implementation of coordination tasks, such as leader election, locking, synchronized actions. For instance, the Chubby paper highlights familiarity of lock-based interfaces. Similarly, Apache Curator hides complexity of ZooKeeper recipes behind Java APIs, while etcd and Consul implement concurrency primitives on their own.
A different path in this journey would be extending the long-lasting java.util.concurrent APIs, such as Lock, Semaphore, etc. Simplicity of these APIs makes them very useful in distributed coordination use cases.
Join this talk to explore Hazelcast’s brand new implementation of java.util.concurrent APIs on top of the Raft consensus algorithm. I will walk through code samples to demonstrate how Java locks, semaphores, etc. can be used in distributed environments that involve partial failures. I will also share our experience of how we coped with several challenges we faced while developing and testing our Raft implementation.
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Presented by Vladimir Iglovikov:
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Presentation of the OECD Artificial Intelligence Review of Germany
Introduction to Raft algorithm
1. Raft
Understandable Consensus Algorithm
Presentation for Seminar
Muayyad Saleh Alsadi
(20158043)
Based on Raft Paper
“In Search of an Understandable
Consensus Algorithm”
Diego Ongaro and John Ousterhout, Stanford
University
2. Raft
Introduction
Introduction
Raft is a consensus algorithm for managing a
replicated log. It produces a result equivalent
to multi-Paxos, and it is as efficient as Paxos
but with completely different structure meant
to be understandable as the most key
objective.
3. Raft
Introduction to consensus algorithms
What is a consensus
algorithm?
“Consensus algorithms allow a collection of
machines to work as a coherent group that
can survive the failures of some of its
members. Because of this, they play a
key role in building reliable large-scale
software systems.”
4. Raft
Introduction to consensus algorithms
Replicated State
Machine
a collection of servers
compute identical copies
of the same state and can
continue operating even if
some of the servers are
down. Replicated state
machines are used to
solve a variety of fault
tolerance problems in
distributed systems.
5. Raft
Introduction to consensus algorithms
Why not Paxos?
“There are significant gaps between the
description of the Paxos algorithm and the
needs of a real-world system… the final
system will be based on an unproven
protocol”
Quoted from Google's Chubby implementers
6. Raft
Introduction to consensus algorithms
Why not Paxos?
#1 Single-decree Paxos is dense and subtle:
it is divided into two stages that do not have
simple intuitive explanations and cannot be
understood independently. Because of this, it
is difficult to develop intuitions about why the
single-decree protocol works. The
composition rules for multi-Paxos add
significant additional complexity and subtlety.
7. Raft
Introduction to consensus algorithms
Why not Paxos?
#2 it does not provide a good foundation for
building practical implementations. One
reason is that there is no widely agreed-upon
algorithm for multi-Paxos. Lamport’s
descriptions are mostly about single-decree
Paxos; he sketched possible approaches to
multi-Paxos, but many details are missing.
8. Raft
Properties and key features
Novel features of Raft:
● Strong leader: Log entries only flow from the leader to
other servers. This simplifies the management of the
replicated log and makes Raft easier to understand.
● Leader election: beside heartbeats required in any
consensus algorithm, Raft uses randomized timers to elect
leaders. This helps resolving conflicts simply and rapidly.
● Membership changes: changing the set of servers in the
cluster uses a new joint consensus approach where the
majorities of two different configurations overlap during
transitions. This allows the cluster to continue operating
normally during configuration changes.
9. Raft
Properties and key features
consensus algorithm properties
● Safety: never returning an incorrect result in any condition
despite network delays, partitions, and packet loss,
duplication, and reorder
● Fault-tolerant: the system would be available and fully-
functional in case of failure of some nodes.
● Does not depend on time consistency:
● Performance is not affected by minority of slow nodes
10. Raft
Sub-problems
● Leader election: system should continue to work even if
its leader fails, by electing a new leader
● Log replication: leader takes operations from clients and
replicate them into all nodes. Forcing them all to agree.
● Safety: assert that the 5 guarantees (next slide) are all
satisfied at any time
11. Raft
The 5 guarantees
1)Election Safety: at most one leader can be elected in a
given term.
2)Leader Append-Only: a leader never overwrites or
deletes entries in its log; it only appends new entries.
3)Log Matching: if two logs contain an entry with the same
index and term, then the logs are identical in all entries up
through the given index.
4)Leader Completeness: if a log entry is committed in a
given term, then that entry will be present in the logs of
the leaders for all higher-numbered terms.
5)State Machine Safety: if a server has applied a log
entry at a given index to its state machine, no other server
will ever apply a different log entry for the same index.
17. Raft
Partition tolerance
Node B consider itself a lead (from old term) but it can reach
Consensus so it won't affect consistency
18. Raft
Follower Cases
A follower might have
missed some entries
as in a-b.
Or have extra
“uncommitted”
entries as in c-f (that
should be removed)
F was a leader of
term 2 that
committed some
changes then failed
22. Raft
Resources
● The Paper
● Visualization of Raft operations
● Etcd - implementation
● Consul – another implementation
● http://raftconsensus.github.io/
● CoreOS/Fleet uses Etcd
● Google Kubernetes - a distributed system
● Mesos / Mesosphere– a distributed system