This document describes Cacheonix, an open source distributed Java cache that provides strict data consistency in distributed systems with failures. It achieves this through a replicated state machine, cluster management protocol, reliable totally-ordered multicast protocol, and state transfer on join. These components work together to ensure all members see updates in the same total order. The architecture is designed to handle failures through synchronous execution of commands and repartitioning of data as needed.
In the last few years, Apache Kafka has been used extensively in enterprises for real-time data collecting, delivering, and processing. In this presentation, Jun Rao, Co-founder, Confluent, gives a deep dive on some of the key internals that help make Kafka popular.
- Companies like LinkedIn are now sending more than 1 trillion messages per day to Kafka. Learn about the underlying design in Kafka that leads to such high throughput.
- Many companies (e.g., financial institutions) are now storing mission critical data in Kafka. Learn how Kafka supports high availability and durability through its built-in replication mechanism.
- One common use case of Kafka is for propagating updatable database records. Learn how a unique feature called compaction in Apache Kafka is designed to solve this kind of problem more naturally.
In the last few years, Apache Kafka has been used extensively in enterprises for real-time data collecting, delivering, and processing. In this presentation, Jun Rao, Co-founder, Confluent, gives a deep dive on some of the key internals that help make Kafka popular.
- Companies like LinkedIn are now sending more than 1 trillion messages per day to Kafka. Learn about the underlying design in Kafka that leads to such high throughput.
- Many companies (e.g., financial institutions) are now storing mission critical data in Kafka. Learn how Kafka supports high availability and durability through its built-in replication mechanism.
- One common use case of Kafka is for propagating updatable database records. Learn how a unique feature called compaction in Apache Kafka is designed to solve this kind of problem more naturally.
A tutorial-like technical presentation that covers fundamental approaches for replication along with their advantages, disadvantages, comparisons with each other etc.
Synchronization Pradeep K Sinha
Introduction
Issues in Synchronization
Clock synchronization
Event Ordering
Mutual Exclusion
Deadlock
Election algorithms
Clock Synchronization
How Computer Clocks are Implemented
Drifting of Clocks
Types of Clock Synchronization and issues in them
Clock Synchronization Algorithms
Distributed and Centralized Algorithms
Case Study
Event Ordering
Happened Before Relation
Logical Clocks Concept and Implementation
Mutual Exclusion
Centralized Approach, Distributed Approach, Token Passing Approach
Deadlocks
Election algorithms
this presentation explains chapter 3 of the distributed operating system book for Andrew S.tanenbaum in addition to other related topics in the synchronization of the distributed operating system
A distributed system is a network that consists of autonomous computers that are connected using a distribution middleware. They help in sharing different resources and capabilities to provide users with a single and integrated coherent network.
This ppt covers different aspects about timing issues and various algorithms involved in having better sync between different systems in a distributed environment
"Osworld scientific equipment Pvt Ltd offers a comprehensive range of services, from planning, designing and manufacturing Pharmaceutical Equipments like stability chamber, humidity control oven, B.O.D. Incubator etc."
Arrow Group’s mission is to offer its clients a broad range of services that combine technological expertise with business knowledge.
Arrow Group is a software editor, a training and a consulting firm. It provides a 360° solution in the latest technologies and open source to all the financial, insurance, mobility and asset management sectors. Discover our main solutions :
- Scub Foundation (Framework open source J2EE)
- Square (CRM for mutuality and insurance)
- Funamble (Framework cross / trans media)
- Camaris (The cloud solution for asset management)
Moreover, discover our known technical expertise with OSGi, Liferay
Pearl Waterless Car Care Finland - Customer's Vehicle, Yacht, Motorcycle Trea...Pearl Nano Promotions
Pearl Waterless products come from England.
Pearl are suitable for cars, campers, motor boats, motorcycles and even small aircraft cleaning and waxing. Our range includes the product, which is also suitable for use in all types of household surfaces.
Pesto India Services Pvt. Ltd. provides pest control services in Mumbai. We serve a large no. of customers consisting of corporate, individual residences, co-operative societies, government offices, restaurants, shopping malls, hospitals, warehouses etc.
A tutorial-like technical presentation that covers fundamental approaches for replication along with their advantages, disadvantages, comparisons with each other etc.
Synchronization Pradeep K Sinha
Introduction
Issues in Synchronization
Clock synchronization
Event Ordering
Mutual Exclusion
Deadlock
Election algorithms
Clock Synchronization
How Computer Clocks are Implemented
Drifting of Clocks
Types of Clock Synchronization and issues in them
Clock Synchronization Algorithms
Distributed and Centralized Algorithms
Case Study
Event Ordering
Happened Before Relation
Logical Clocks Concept and Implementation
Mutual Exclusion
Centralized Approach, Distributed Approach, Token Passing Approach
Deadlocks
Election algorithms
this presentation explains chapter 3 of the distributed operating system book for Andrew S.tanenbaum in addition to other related topics in the synchronization of the distributed operating system
A distributed system is a network that consists of autonomous computers that are connected using a distribution middleware. They help in sharing different resources and capabilities to provide users with a single and integrated coherent network.
This ppt covers different aspects about timing issues and various algorithms involved in having better sync between different systems in a distributed environment
"Osworld scientific equipment Pvt Ltd offers a comprehensive range of services, from planning, designing and manufacturing Pharmaceutical Equipments like stability chamber, humidity control oven, B.O.D. Incubator etc."
Arrow Group’s mission is to offer its clients a broad range of services that combine technological expertise with business knowledge.
Arrow Group is a software editor, a training and a consulting firm. It provides a 360° solution in the latest technologies and open source to all the financial, insurance, mobility and asset management sectors. Discover our main solutions :
- Scub Foundation (Framework open source J2EE)
- Square (CRM for mutuality and insurance)
- Funamble (Framework cross / trans media)
- Camaris (The cloud solution for asset management)
Moreover, discover our known technical expertise with OSGi, Liferay
Pearl Waterless Car Care Finland - Customer's Vehicle, Yacht, Motorcycle Trea...Pearl Nano Promotions
Pearl Waterless products come from England.
Pearl are suitable for cars, campers, motor boats, motorcycles and even small aircraft cleaning and waxing. Our range includes the product, which is also suitable for use in all types of household surfaces.
Pesto India Services Pvt. Ltd. provides pest control services in Mumbai. We serve a large no. of customers consisting of corporate, individual residences, co-operative societies, government offices, restaurants, shopping malls, hospitals, warehouses etc.
Splunk Sales Presentation Imagemaker 2014Urena Nicolas
Splunk provee Inteligencia operativa para todos
Splunk es la plataforma de inteligencia operativa en tiempo real líder del sector. Es una forma fácil, rápida y segura de buscar, analizar y visualizar los grandes flujos de datos de máquina generados por sus sistemas de TI e infraestructura tecnológica (físicos, virtuales y en la nube).
Splunk Enterprise 6 es la versión más reciente y proporciona:
- Análisis potente para todos los usuarios a velocidades sorprendentes
- Experiencia de usuario completamente rediseñada
- Entorno del desarrollador más enriquecido para una ampliación fácil de la plataforma
Splunk Enterprise 6 ya está disponible. Descárguelo ahora y pruébelo usted mismo.
AppDynamics Sales Presentation Imagemaker 2014Urena Nicolas
AppDynamics es una nueva clase de software para gestión de rendimiento de aplicaciones (APM o Aplication Performance Management). AppDynamics ha sido diseñado para resolver las problemáticas de las plataformas modernas basadas en JEE y .NET, donde la combinación de servicios y componentes están sometidos a condiciones dinámicas y cambios frecuentes. Y ahora también tiene monitoreo dinámico de la experiencia de usuarios finales (EUM)
The Power of Determinism in Database SystemsDaniel Abadi
Slides for Daniel Abadi talk at UC Berkeley on 10/22/2014. Discusses the problems with traditional database systems, especially around modularity and horizontal scalability, and shows how deterministic database systems can help.
Exactly-once Stream Processing Done Right with Matthias J SaxHostedbyConfluent
Exactly-once semantics is the holy grail in data stream processing, and Apache Kafka (including its stream processing library Kafka Streams) supports it. However, there is a lot of misunderstanding what exactly-once really is, what Kafka technically offers, where the limitations are, and how to use it correctly.
In this talk, we will dive into technical details to shed some light on the above questions. We approach the topic from a conceptual point of view, explain the challenges Kafka Connect faces when it comes to exactly-once, discuss how external source and sink systems can be integrated, and provide practical guidelines for implementing end-to-end exactly-once data pipelines correctly.
Consensus Algorithms: An Introduction & AnalysisZak Cole
When evaluating blockchain networks, consensus mechanism and design are an imperative aspect of system function. While effective implementations should meet technical criteria specific to distributed computational environments, use case should also be taken into consideration.
This webinar will focus on illustrating these concepts while providing a high-level overview of popular consensus algorithms such as Paxos, Aura, Clique, Proof-of-Work, and Proof-of-Stake. Please join us in the discussion – whether you wish to learn about the basics of consensus algorithms or if you have deeper questions on performance, security, or suitability.
Real-time, Exactly-once Data Ingestion from Kafka to ClickHouse at eBayAltinity Ltd
LIVE WEBINAR: October 21, 2021 | 10 am PT
SPEAKERS: Jun Li, Principal Architect, eBay & Robert Hodges, CEO, Altinity
eBay depends on Kafka to solve the impedance mismatch between rapidly arriving messages in event streams and efficient block insert into ClickHouse clusters. Naïve loading procedures from Kafka to ClickHouse generate non-deterministic blocks, which can lead to data loss and incorrect results in applications. The eBay team solved this problem with a block aggregator that leverages Kafka to store message processing metadata as well as ClickHouse deduplication to ensure blocks being loaded to ClickHouse exactly once. The block aggregator allows eBay to support a sharded ClickHouse architecture across multiple data centers that can tolerate failures in any individual part of the system. Join us to learn how eBay developed this unique architecture and how they use it to deliver low-latency analytics to users.
Everything you always wanted to know about Distributed databases, at devoxx l...javier ramirez
Everything you always wanted to know about Distributed databases, at devoxx london, by javier ramirez, teowaki.
Basic concepts of distributed systems, such as consensus, gossip and infection protocols, vector clocks, sharding storage, so you can create highly available distributed systems
In this lecture we analyze key-values databases. At first we introduce key-value characteristics, advantages and disadvantages.
Then we analyze the major Key-Value data stores and finally we discuss about Dynamo DB.
In particular we consider how Dynamo DB: How is implemented
1. Motivation Background
2. Partitioning: Consistent Hashing
3. High Availability for writes: Vector Clocks
4. Handling temporary failures: Sloppy Quorum
5. Recovering from failures: Merkle Trees
6. Membership and failure detection: Gossip Protocol
Distributed shared memory
General architecture
Design and Implementation of issues of DSM
Granularity
Factors Influencing Block size Selection
Consistency Model
Replacement strategy
Which block be replace
where to place a replace block
thrashing
heterogeneous DSM
Issues
Deadlock
Modern processors are faster than memory
So Processors may waste time for accessing memory
Its purpose is to make the main memory appear to the processor to be much faster than it actually is
Real-time Inverted Search in the Cloud Using Lucene and Stormlucenerevolution
Building real-time notification systems is often limited to basic filtering and pattern matching against incoming records. Allowing users to query incoming documents using Solr's full range of capabilities is much more powerful. In our environment we needed a way to allow for tens of thousands of such query subscriptions, meaning we needed to find a way to distribute the query processing in the cloud. By creating in-memory Lucene indices from our Solr configuration, we were able to parallelize our queries across our cluster. To achieve this distribution, we wrapped the processing in a Storm topology to provide a flexible way to scale and manage our infrastructure. This presentation will describe our experiences creating this distributed, real-time inverted search notification framework.
Similar to Strict-Data-Consistency-in-Distrbuted-Systems-With-Failures (20)
1. Cacheonix: !
Architecture for !
Strict Data Consistency !
in Distributed Systems !
with Failures!
Slava Imeshev!
simeshev@cacheonix.org!
July 29, 2015!
4. Introductions
Slava Imeshev:
• Management style: my team is my family
• For fun: sci-fi, hard rock, hiking, camping
• Hobbies: software development, ham radio
• E-mail: simeshev@cacheonix.org
5.
6. Cacheonix!
https://github.com/cacheonix/cacheonix-core
Cacheonix Open Source distributed Java
cache:
– Strict data consistency
– Horizontal scalability
– Fault-tolerance
– Concurrency
– Distributed state sharing
– Coherent front cache
– Distributed locks
– Compute grid with data affinity
– Load balancing
7. Strict Data Consistency
• A guaranty that once an update to the key
happened, all members of the cluster will
see the new value
• Knowing where the key value is at all times
12. Architecture for Strict Data
Consistency
These key components working together…
• Replicated state machine
• Cluster management protocol
• Reliable totally-ordered multicast protocol
• State transfer on join
• P2P protocol with re-transmits
… allow to know EXACTLY where the data
in the cluster is.
13. Replicated State Machine
• Maintains a consistent replicated configuration
of the cluster by:
• Executing cluster, cache and partition configuration
events
– On all members of the cluster
– In the same total order
14.
15. Cluster Management Protocol
• Detects nodes joining and failing
• Maintains replicated cluster view
• Feeds the cluster events in total order to
reliable totally ordered multicast protocol
16. Reliable Totally Ordered
Multicast
• Carries cache member events (leave/join)
• Carries partition configuration messages
• Executes replicated bucket ownership
assignment table part of the replicated state
machine
17.
18. State Transfer on Join
• When a node joins a cluster, it receives a
replicated state machine from its join
coordinator
• Total order of events including join / leave
guarantees that events are executed in this
order on all members of the cluster:
• At t0 there is no new node
• At t1 there is new member fully aware of cluster
topology, data bucket locations and ready to
operate
• At t2 replicated state machine begin to execute
repartitioning protocol to move data to the new
member of the cluster
19. P2P Protocol With Retransmits
• Carries data modification messages in the
cluster (get, put, execute etc)
• Automatically resends messages if a partition
undergoing re-configuration (move, replicate,
restore etc)
• Ensures that reads and writes to a key served
one and only by a guaranteed owner of the
key.
20. Member Failure Example
1. Member fails, then, on all nodes, synchronously:
2. Cluster management protocol executes command Node
Left of the state machine ClusterView
3. ClusterView executes Remove Node command of the
state machine BucketOwnershipAssingmentTable
4. BucketOwnershipAssingmentTable executes the
repartitioning algorithm
5. Repartitioning algorithm marks buckets as
reconfiguring and sends P2P messages to move
buckets around
6. P2P messages send a reliable mcast message Move
Complete
7. BucketOwnershipAssingmentTable marks buckets as
operational
8. All members of the cluster in the same state.
21. Lessons Learned
• Tackle hard problems first:
– Hard problems define the architecture
– Hard problems drive the schedule
– Start with handling failure modes
• Make unknowns known, do research
