This document provides an overview of the Google File System (GFS). It describes the key components of GFS including the master server, chunkservers, and clients. The master manages metadata like file namespaces and chunk mappings. Chunkservers store file data in 64MB chunks that are replicated across servers. Clients read and write chunks through the master and chunkservers. GFS provides high throughput and fault tolerance for Google's massive data storage and analysis needs.
The document describes the Google File System (GFS). GFS is a distributed file system that runs on top of commodity hardware. It addresses problems with scaling to very large datasets and files by splitting files into large chunks (64MB or 128MB) and replicating chunks across multiple machines. The key components of GFS are the master, which manages metadata and chunk placement, chunkservers, which store chunks, and clients, which access chunks. The master handles operations like namespace management, replica placement, garbage collection and stale replica detection to provide a fault-tolerant filesystem.
Designed by Sanjay Ghemawat , Howard Gobioff and Shun-Tak Leung of Google in 2002-03.
Provides fault tolerance, serving large number of clients with high aggregate performance.
The field of Google is beyond the searching.
Google store the data in more than 15 thousands commodity hardware.
Handles the exceptions of Google and other Google specific challenges in their distributed file system.
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.
Google File System is a distributed file system developed by Google to provide efficient and reliable access to large amounts of data across clusters of commodity hardware. It organizes clusters into clients that interface with the system, master servers that manage metadata, and chunkservers that store and serve file data replicated across multiple machines. Updates are replicated for fault tolerance, while the master and chunkservers work together for high performance streaming and random reads and writes of large files.
The document discusses key concepts related to distributed file systems including:
1. Files are accessed using location transparency where the physical location is hidden from users. File names do not reveal storage locations and names do not change when locations change.
2. Remote files can be mounted to local directories, making them appear local while maintaining location independence. Caching is used to reduce network traffic by storing recently accessed data locally.
3. Fault tolerance is improved through techniques like stateless server designs, file replication across failure independent machines, and read-only replication for consistency. Scalability is achieved by adding new nodes and using decentralized control through clustering.
This document discusses various consistency protocols for replicated data in distributed systems. It covers primary-based protocols where each data item has a primary replica, and replication-based protocols where writes can be done at multiple replicas. For primary-based protocols, it distinguishes between remote-write protocols where operations are done remotely at the primary and local-write protocols where the primary may be copied locally. It also discusses quorum-based replication protocols that use voting to achieve consistency across replicas.
Replication in computing involves sharing information so as to ensure consistency between redundant resources, such as software or hardware components, to improve reliability, fault-tolerance, or accessibility.
The document describes Google File System (GFS), which was designed by Google to store and manage large amounts of data across thousands of commodity servers. GFS consists of a master server that manages metadata and namespace, and chunkservers that store file data blocks. The master monitors chunkservers and maintains replication of data blocks for fault tolerance. GFS uses a simple design to allow it to scale incrementally with growth while providing high reliability and availability through replication and fast recovery from failures.
The document describes the Google File System (GFS). GFS is a distributed file system that runs on top of commodity hardware. It addresses problems with scaling to very large datasets and files by splitting files into large chunks (64MB or 128MB) and replicating chunks across multiple machines. The key components of GFS are the master, which manages metadata and chunk placement, chunkservers, which store chunks, and clients, which access chunks. The master handles operations like namespace management, replica placement, garbage collection and stale replica detection to provide a fault-tolerant filesystem.
Designed by Sanjay Ghemawat , Howard Gobioff and Shun-Tak Leung of Google in 2002-03.
Provides fault tolerance, serving large number of clients with high aggregate performance.
The field of Google is beyond the searching.
Google store the data in more than 15 thousands commodity hardware.
Handles the exceptions of Google and other Google specific challenges in their distributed file system.
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.
Google File System is a distributed file system developed by Google to provide efficient and reliable access to large amounts of data across clusters of commodity hardware. It organizes clusters into clients that interface with the system, master servers that manage metadata, and chunkservers that store and serve file data replicated across multiple machines. Updates are replicated for fault tolerance, while the master and chunkservers work together for high performance streaming and random reads and writes of large files.
The document discusses key concepts related to distributed file systems including:
1. Files are accessed using location transparency where the physical location is hidden from users. File names do not reveal storage locations and names do not change when locations change.
2. Remote files can be mounted to local directories, making them appear local while maintaining location independence. Caching is used to reduce network traffic by storing recently accessed data locally.
3. Fault tolerance is improved through techniques like stateless server designs, file replication across failure independent machines, and read-only replication for consistency. Scalability is achieved by adding new nodes and using decentralized control through clustering.
This document discusses various consistency protocols for replicated data in distributed systems. It covers primary-based protocols where each data item has a primary replica, and replication-based protocols where writes can be done at multiple replicas. For primary-based protocols, it distinguishes between remote-write protocols where operations are done remotely at the primary and local-write protocols where the primary may be copied locally. It also discusses quorum-based replication protocols that use voting to achieve consistency across replicas.
Replication in computing involves sharing information so as to ensure consistency between redundant resources, such as software or hardware components, to improve reliability, fault-tolerance, or accessibility.
The document describes Google File System (GFS), which was designed by Google to store and manage large amounts of data across thousands of commodity servers. GFS consists of a master server that manages metadata and namespace, and chunkservers that store file data blocks. The master monitors chunkservers and maintains replication of data blocks for fault tolerance. GFS uses a simple design to allow it to scale incrementally with growth while providing high reliability and availability through replication and fast recovery from failures.
A Distributed Shared Memory (DSM) system provides a logical abstraction of shared memory built using interconnected nodes with distributed physical memories. There are hardware, software, and hybrid DSM approaches. DSM offers simple abstraction, improved portability, potential performance gains, large unified memory space, and better performance than message passing in some applications. Consistency protocols ensure shared data coherency across distributed memories according to the memory consistency model.
This document discusses resource management techniques in distributed systems. It covers three main scheduling techniques: task assignment approach, load balancing approach, and load sharing approach. It also outlines desirable features of good global scheduling algorithms such as having no a priori knowledge about processes, being dynamic in nature, having quick decision-making capability, balancing system performance and scheduling overhead, stability, scalability, fault tolerance, and fairness of service. Finally, it discusses policies for load estimation, process transfer, state information exchange, location, priority assignment, and migration limiting that distributed load balancing algorithms employ.
Symmetric encryption uses the same key to encrypt and decrypt data, providing confidentiality. Keys must be distributed securely between parties. Common approaches involve using a key distribution center (KDC) that shares secret keys with parties and can provide temporary session keys. Link encryption protects data as it travels over each network link, while end-to-end encryption protects data for its entire journey but leaves some header data unencrypted. Key distribution, storage, renewal and replacement are important aspects of maintaining security when using symmetric encryption.
File Replication : High availability is a desirable feature of a good distributed file system and file replication is the primary mechanism for improving file availability. Replication is a key strategy for improving reliability, fault tolerance and availability. Therefore duplicating files on multiple machines improves availability and performance.
Replicated file : A replicated file is a file that has multiple copies, with each copy located on a separate file server. Each copy of the set of copies that comprises a replicated file is referred to as replica of the replicated file.
Replication is often confused with caching, probably because they both deal with multiple copies of data. The two concepts has the following basic differences:
A replica is associated with server, whereas a cached copy is associated with a client.
The existence of cached copy is primarily dependent on the locality in file access patterns, whereas the existence of a replica normally depends on availability and performance requirements.
Satynarayanana [1992] distinguishes a replicated copy from a cached copy by calling the first-class replicas and second-class replicas respectively
The document describes Google File System (GFS), which was designed by Google to store and manage large amounts of data across thousands of commodity servers. GFS consists of a master server that manages metadata and namespace, and chunkservers that store file data blocks. The master monitors chunkservers and maintains replication of data blocks for fault tolerance. GFS uses a simple design to allow it to scale incrementally with growth while providing high reliability and availability through replication and fast recovery from failures.
Deadlocks occur when processes are waiting for resources held by other processes, resulting in a circular wait. Four conditions must be met: mutual exclusion, hold and wait, no preemption, and circular wait. Deadlocks can be handled through avoidance, prevention, or detection and recovery. Avoidance algorithms allocate resources only if it ensures the system remains in a safe state where deadlocks cannot occur. Prevention methods make deadlocks impossible by ensuring at least one condition is never satisfied, such as through collective or ordered resource requests. Detection finds existing deadlocks by analyzing resource allocation graphs or wait-for graphs to detect cycles.
Synchronization in distributed computingSVijaylakshmi
ย
Synchronization in distributed systems is achieved via clocks. The physical clocks are used to adjust the time of nodes. Each node in the system can share its local time with other nodes in the system. The time is set based on UTC (Universal Time Coordination).
This document discusses different types of communication including unicast, broadcast, multicast, and indirect communication. It provides details on multicast communication including that it allows one-to-many communication where a message is sent to multiple devices in a group. It also discusses characteristics of multicast including fault tolerance and data distribution. Examples of multicast applications like financial services and remote conferencing are provided. The document then covers various forms of indirect communication such as group communication, publish-subscribe systems, message queues, and shared memory. It provides details on topics like event filtering, routing, and subscription models for publish-subscribe systems.
1. Distributed transaction managers ensure transactions have ACID properties through implementing the 2-phase commit protocol for reliability, 2-phase locking for concurrency control, and timeouts for deadlock detection on top of local transaction managers.
2. The 2-phase commit protocol guarantees subtransactions of the same transaction will all commit or abort despite failures, while 2-phase locking requires subtransactions acquire locks in a growing phase and release in a shrinking phase.
3. Timeouts are used to detect and abort transactions potentially experiencing a distributed deadlock.
Key management: Introduction, How public key distribution done, Diffie Hellman Key Exchage Algorithm,Digital Certificate. Key Management using Digital certificate is done etc. wireshark screenshot showing digital cetificate.
This document discusses process migration in distributed systems. It defines process migration as relocating a process from its current node to another node, which can occur either before or during process execution. The key aspects of process migration covered include selecting processes to migrate, transferring process state such as CPU registers and address space, forwarding messages, and handling communication between related processes migrated to different nodes. Various process migration mechanisms and their tradeoffs are also summarized.
Clock synchronization in distributed systemSunita Sahu
ย
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.
This document discusses consistency models in distributed systems with replication. It describes reasons for replication including reliability and performance. Various consistency models are covered, including: strict consistency where reads always return the most recent write; sequential consistency where operations appear in a consistent order across processes; weak consistency which enforces consistency on groups of operations; and release consistency which separates acquiring and releasing locks to selectively guard shared data. Client-centric models like eventual consistency are also discussed, where updates gradually propagate to all replicas.
This document discusses message authentication codes (MACs). It explains that MACs use a shared symmetric key to authenticate messages, ensuring integrity and validating the sender. The document outlines the MAC generation and verification process, and notes that MACs provide authentication but not encryption. It then describes HMAC specifically, which applies a cryptographic hash function to the message and key to generate the MAC. The key steps of the HMAC process are detailed.
The document discusses different approaches for concept learning from examples, including viewing it as a search problem to find the hypothesis that best fits the training examples. It also describes the general-to-specific learning approach, where the goal is to find the maximally specific hypothesis consistent with the positive training examples by starting with the most general hypothesis and replacing constraints to better fit the examples. The document also discusses the version space and candidate elimination algorithms for obtaining the version space of all hypotheses consistent with the training data.
The document discusses the Google File System (GFS), which was developed by Google to handle large files across thousands of commodity servers. It provides three main functions: (1) dividing files into chunks and replicating chunks for fault tolerance, (2) using a master server to manage metadata and coordinate clients and chunkservers, and (3) prioritizing high throughput over low latency. The system is designed to reliably store very large files and enable high-speed streaming reads and writes.
Facebook uses a combination of PHP, MySQL, and Memcache (LAMP stack) for their web and application tier. They have also developed various services and tools like Thrift, Scribe, and ODS to handle tasks like logging, monitoring, and communication between systems. Their architecture is designed for scale using principles like simplicity, optimizing for performance, and distributing load. Key components include caching data in Memcache, distributing MySQL databases, and developing services in higher performing languages when needed beyond the capabilities of PHP.
A Distributed Shared Memory (DSM) system provides a logical abstraction of shared memory built using interconnected nodes with distributed physical memories. There are hardware, software, and hybrid DSM approaches. DSM offers simple abstraction, improved portability, potential performance gains, large unified memory space, and better performance than message passing in some applications. Consistency protocols ensure shared data coherency across distributed memories according to the memory consistency model.
This document discusses resource management techniques in distributed systems. It covers three main scheduling techniques: task assignment approach, load balancing approach, and load sharing approach. It also outlines desirable features of good global scheduling algorithms such as having no a priori knowledge about processes, being dynamic in nature, having quick decision-making capability, balancing system performance and scheduling overhead, stability, scalability, fault tolerance, and fairness of service. Finally, it discusses policies for load estimation, process transfer, state information exchange, location, priority assignment, and migration limiting that distributed load balancing algorithms employ.
Symmetric encryption uses the same key to encrypt and decrypt data, providing confidentiality. Keys must be distributed securely between parties. Common approaches involve using a key distribution center (KDC) that shares secret keys with parties and can provide temporary session keys. Link encryption protects data as it travels over each network link, while end-to-end encryption protects data for its entire journey but leaves some header data unencrypted. Key distribution, storage, renewal and replacement are important aspects of maintaining security when using symmetric encryption.
File Replication : High availability is a desirable feature of a good distributed file system and file replication is the primary mechanism for improving file availability. Replication is a key strategy for improving reliability, fault tolerance and availability. Therefore duplicating files on multiple machines improves availability and performance.
Replicated file : A replicated file is a file that has multiple copies, with each copy located on a separate file server. Each copy of the set of copies that comprises a replicated file is referred to as replica of the replicated file.
Replication is often confused with caching, probably because they both deal with multiple copies of data. The two concepts has the following basic differences:
A replica is associated with server, whereas a cached copy is associated with a client.
The existence of cached copy is primarily dependent on the locality in file access patterns, whereas the existence of a replica normally depends on availability and performance requirements.
Satynarayanana [1992] distinguishes a replicated copy from a cached copy by calling the first-class replicas and second-class replicas respectively
The document describes Google File System (GFS), which was designed by Google to store and manage large amounts of data across thousands of commodity servers. GFS consists of a master server that manages metadata and namespace, and chunkservers that store file data blocks. The master monitors chunkservers and maintains replication of data blocks for fault tolerance. GFS uses a simple design to allow it to scale incrementally with growth while providing high reliability and availability through replication and fast recovery from failures.
Deadlocks occur when processes are waiting for resources held by other processes, resulting in a circular wait. Four conditions must be met: mutual exclusion, hold and wait, no preemption, and circular wait. Deadlocks can be handled through avoidance, prevention, or detection and recovery. Avoidance algorithms allocate resources only if it ensures the system remains in a safe state where deadlocks cannot occur. Prevention methods make deadlocks impossible by ensuring at least one condition is never satisfied, such as through collective or ordered resource requests. Detection finds existing deadlocks by analyzing resource allocation graphs or wait-for graphs to detect cycles.
Synchronization in distributed computingSVijaylakshmi
ย
Synchronization in distributed systems is achieved via clocks. The physical clocks are used to adjust the time of nodes. Each node in the system can share its local time with other nodes in the system. The time is set based on UTC (Universal Time Coordination).
This document discusses different types of communication including unicast, broadcast, multicast, and indirect communication. It provides details on multicast communication including that it allows one-to-many communication where a message is sent to multiple devices in a group. It also discusses characteristics of multicast including fault tolerance and data distribution. Examples of multicast applications like financial services and remote conferencing are provided. The document then covers various forms of indirect communication such as group communication, publish-subscribe systems, message queues, and shared memory. It provides details on topics like event filtering, routing, and subscription models for publish-subscribe systems.
1. Distributed transaction managers ensure transactions have ACID properties through implementing the 2-phase commit protocol for reliability, 2-phase locking for concurrency control, and timeouts for deadlock detection on top of local transaction managers.
2. The 2-phase commit protocol guarantees subtransactions of the same transaction will all commit or abort despite failures, while 2-phase locking requires subtransactions acquire locks in a growing phase and release in a shrinking phase.
3. Timeouts are used to detect and abort transactions potentially experiencing a distributed deadlock.
Key management: Introduction, How public key distribution done, Diffie Hellman Key Exchage Algorithm,Digital Certificate. Key Management using Digital certificate is done etc. wireshark screenshot showing digital cetificate.
This document discusses process migration in distributed systems. It defines process migration as relocating a process from its current node to another node, which can occur either before or during process execution. The key aspects of process migration covered include selecting processes to migrate, transferring process state such as CPU registers and address space, forwarding messages, and handling communication between related processes migrated to different nodes. Various process migration mechanisms and their tradeoffs are also summarized.
Clock synchronization in distributed systemSunita Sahu
ย
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.
This document discusses consistency models in distributed systems with replication. It describes reasons for replication including reliability and performance. Various consistency models are covered, including: strict consistency where reads always return the most recent write; sequential consistency where operations appear in a consistent order across processes; weak consistency which enforces consistency on groups of operations; and release consistency which separates acquiring and releasing locks to selectively guard shared data. Client-centric models like eventual consistency are also discussed, where updates gradually propagate to all replicas.
This document discusses message authentication codes (MACs). It explains that MACs use a shared symmetric key to authenticate messages, ensuring integrity and validating the sender. The document outlines the MAC generation and verification process, and notes that MACs provide authentication but not encryption. It then describes HMAC specifically, which applies a cryptographic hash function to the message and key to generate the MAC. The key steps of the HMAC process are detailed.
The document discusses different approaches for concept learning from examples, including viewing it as a search problem to find the hypothesis that best fits the training examples. It also describes the general-to-specific learning approach, where the goal is to find the maximally specific hypothesis consistent with the positive training examples by starting with the most general hypothesis and replacing constraints to better fit the examples. The document also discusses the version space and candidate elimination algorithms for obtaining the version space of all hypotheses consistent with the training data.
The document discusses the Google File System (GFS), which was developed by Google to handle large files across thousands of commodity servers. It provides three main functions: (1) dividing files into chunks and replicating chunks for fault tolerance, (2) using a master server to manage metadata and coordinate clients and chunkservers, and (3) prioritizing high throughput over low latency. The system is designed to reliably store very large files and enable high-speed streaming reads and writes.
Facebook uses a combination of PHP, MySQL, and Memcache (LAMP stack) for their web and application tier. They have also developed various services and tools like Thrift, Scribe, and ODS to handle tasks like logging, monitoring, and communication between systems. Their architecture is designed for scale using principles like simplicity, optimizing for performance, and distributing load. Key components include caching data in Memcache, distributing MySQL databases, and developing services in higher performing languages when needed beyond the capabilities of PHP.
The Google File System (GFS) is a distributed file system designed to provide efficient, reliable access to data stored on commodity hardware. It consists of a single master node that manages metadata and chunk replication, and multiple chunkserver nodes that store file data in chunks. The master maintains metadata mapping files to variable-sized chunks, which are replicated across servers for fault tolerance. It performs tasks like chunk creation, rebalancing, and garbage collection to optimize storage usage and availability. Data flows linearly through servers to minimize latency. The system provides high throughput, incremental growth, and fault tolerance for Google's large-scale computing needs.
The Google File System (GFS) is designed to provide reliable, scalable storage for large files on commodity hardware. It uses a single master server to manage metadata and coordinate replication across multiple chunk servers. Files are split into 64MB chunks which are replicated across servers and stored as regular files. The system prioritizes high throughput over low latency and provides fault tolerance through replication and checksumming to detect data corruption.
The document provides an overview of key concepts in system design including:
1) Breaking problems into modules using a top-down approach and discussing trade-offs.
2) Architectural components like load balancing, databases, caching, and data partitioning that are important to consider in system design.
3) Database types like SQL and NoSQL and when each is best suited based on factors like data structure, scalability needs, and development agility.
Amazon Aurora adds PostgreSQL compatibility to its cloud-optimized relational database. With PostgreSQL compatibility, customers can now choose to use Amazon's database with the performance and availability of commercial databases and the simplicity and cost-effectiveness of open source databases. Amazon Aurora provides high performance, durability, availability and automatic scaling capabilities for PostgreSQL workloads.
Andreas Grabner maintains that most performance and scalability problems donโt need a large or long running performance test or the expertise of a performance engineering guru. Donโt let anybody tell you that performance is too hard to practice because it actually is not. You can take the initiative and find these often serious defects. Andreas analyzed and spotted the performance and scalability issues in more than 200 applications last year. He shares his performance testing approaches and explores the top problem patterns that you can learn to spot in your apps. By looking at key metrics found in log files and performance monitoring data, you will learn to identify most problems with a single functional test and a simple five-user load test. The problem patterns Andreas explains are applicable to any type of technology and platform. Try out your new skills in your current testing project and take the first step toward becoming a performance diagnostic hero.
AWS re:Invent 2016: Amazon CloudFront Flash Talks: Best Practices on Configur...Amazon Web Services
ย
In this series of 15-minute technical flash talks you will learn directly from Amazon CloudFront engineers and their best practices on debugging caching issues, measuring performance using Real User Monitoring (RUM), and stopping malicious viewers using CloudFront and AWS WAF.
KSQL Performance Tuning for Fun and Profit ( Nick Dearden, Confluent) Kafka S...confluent
ย
Ever wondered just how many CPU cores of KSQL Server you need to provision to handle your planned stream processing workload ? Or how many GBits of aggregate network bandwidth, spread across some number of processing threads, you'll need to deal with combined peak throughput of multiple queries ? In this talk we'll first explore the basic drivers of KSQL throughput and hardware requirements, building up to more advanced query plan analysis and capacity-planning techniques, and review some real-world testing results along the way. Finally we will recap how and what to monitor to know you got it right!
GFS is a file system designed by Google to share data across large clusters of commodity servers and PCs. It uses a master server to manage metadata and chunk servers to store and serve file data in 64MB chunks. The design aims to detect and recover from failures automatically while supporting large files, streaming reads and writes, and concurrent appends across multiple clients. The client API mimics UNIX but provides only append semantics without consistency guarantees between clients.
Performance Optimization in Large Systems - Cusec 2019Pierre-Luc Maheu
ย
This document discusses performance optimization in large systems. It defines what constitutes a "large" system as having multiple servers, services, users, and over 100,000 lines of code. It explains that performance can be measured in terms of latency, CPU usage, memory usage, and other factors. The document discusses common approaches like guessing, profiling code, and using tools like code profilers, ANTS, and MiniProfiler to identify hot spots and bottlenecks. It also covers application performance monitoring tools to monitor performance at a system level. Key takeaways are to measure before and after optimizing, understand root causes, establish clear goals, and learn relevant tooling.
EVCache: Lowering Costs for a Low Latency Cache with RocksDBScott Mansfield
ย
EVCache is a distributed, sharded, replicated key-value store optimized for Netflix's use cases on AWS. It is based on Memcached but uses RocksDB for persistent storage, lowering costs compared to storing all data in memory. Moneta is the next generation EVCache server, using Rend and Mnemonic libraries to intelligently manage data placement in RAM and SSD. This provides high performance for both volatile and batch workloads while reducing costs by 70% compared to the original Memcached-based design.
The document discusses troubleshooting performance issues for SQL Server. It begins with an introduction and case study on the MS Society of Canada's website. It then discusses optimizing the environment, using Performance Monitor (PerfMon) to monitor performance, and concludes with recommendations to address issues like high CPU usage, slow disk speeds, and insufficient memory.
MongoDB World 2019: Finding the Right MongoDB Atlas Cluster Size: Does This I...MongoDB
ย
How do you determine whether your MongoDB Atlas cluster is over provisioned, whether the new feature in your next application release will crush your cluster, or when to increase cluster size based upon planned usage growth? MongoDB Atlas provides over a hundred metrics enabling visibility into the inner workings of MongoDB performance, but how do apply all this information to make capacity planning decisions? This presentation will enable you to effectively analyze your MongoDB performance to optimize your MongoDB Atlas spend and ensure smooth application operation into the future.
Software architecture for data applicationsDing Li
ย
The document provides an overview of software architecture considerations for data applications. It discusses sample data system components like Memcached, Redis, Elasticsearch, and Solr. It covers topics such as service level objectives, data models, query languages, graph models, data warehousing, machine learning pipelines, and distributed systems. Specific frameworks and technologies mentioned include Spark, Kafka, Neo4j, PostgreSQL, and ZooKeeper. The document aims to help understand architectural tradeoffs and guide the design of scalable, performant, and robust data systems.
No C-QL (Or how I learned to stop worrying, and love eventual consistency) (N...Brian Brazil
ย
Traditional relational databases focus on ACID, providing strong semantics that require careful synchronisation between actors that limit scalability. NoSQL Column Stores such as Cassandra, Riak and Dynamo offer another way, by eschewing strong consistency you can meet your application's needs while also increasing scalability and reliability. This talk will cover how and where to use eventual consistency.
Top Java Performance Problems and Metrics To Check in Your PipelineAndreas Grabner
ย
Why is Performance Important? What are the most common reasons applications dont scale and perform well. Which technical metrics to look at. How to check it automated in the pipeline
Cluster based storage - Nasd and Google file system - advanced operating syst...Antonio Cesarano
ย
This is a seminar at the Course of Advanced Operating Systems at University of Salerno which shows the first cluster based storage technology (NASD) and its evolution till the development of the new Google File System.
The document discusses testing the performance of several computers using Windows Performance Monitor and TreeSize software. It includes details of tests run on one computer to check processor and memory usage under normal browsing conditions. The results found the processor peaked at 66% during Photoshop use but was generally lower than expected, while memory increases were small. Upgrading the computer's memory and processor is recommended over buying a new system based on the lower cost of upgrading. Regular performance monitoring is advised to optimize computer usage and efficiency.
- The document describes installing Oracle Real Application Clusters (RAC) and Cluster Ready Services (CRS) on a two-node Windows cluster.
- It involves a two phase installation - first installing and configuring CRS, then installing the Oracle Database with RAC.
- Key steps include configuring shared disks and partitions for the Oracle Cluster Registry, voting disk, and Automatic Storage Management; installing and configuring CRS; and then installing Oracle Database with RAC.
Cross-Cultural Leadership and CommunicationMattVassar1
ย
Business is done in many different ways across the world. How you connect with colleagues and communicate feedback constructively differs tremendously depending on where a person comes from. Drawing on the culture map from the cultural anthropologist, Erin Meyer, this class discusses how best to manage effectively across the invisible lines of culture.
Get Success with the Latest UiPath UIPATH-ADPV1 Exam Dumps (V11.02) 2024yarusun
ย
Are you worried about your preparation for the UiPath Power Platform Functional Consultant Certification Exam? You can come to DumpsBase to download the latest UiPath UIPATH-ADPV1 exam dumps (V11.02) to evaluate your preparation for the UIPATH-ADPV1 exam with the PDF format and testing engine software. The latest UiPath UIPATH-ADPV1 exam questions and answers go over every subject on the exam so you can easily understand them. You won't need to worry about passing the UIPATH-ADPV1 exam if you master all of these UiPath UIPATH-ADPV1 dumps (V11.02) of DumpsBase. #UIPATH-ADPV1 Dumps #UIPATH-ADPV1 #UIPATH-ADPV1 Exam Dumps
Decolonizing Universal Design for LearningFrederic Fovet
ย
UDL has gained in popularity over the last decade both in the K-12 and the post-secondary sectors. The usefulness of UDL to create inclusive learning experiences for the full array of diverse learners has been well documented in the literature, and there is now increasing scholarship examining the process of integrating UDL strategically across organisations. One concern, however, remains under-reported and under-researched. Much of the scholarship on UDL ironically remains while and Eurocentric. Even if UDL, as a discourse, considers the decolonization of the curriculum, it is abundantly clear that the research and advocacy related to UDL originates almost exclusively from the Global North and from a Euro-Caucasian authorship. It is argued that it is high time for the way UDL has been monopolized by Global North scholars and practitioners to be challenged. Voices discussing and framing UDL, from the Global South and Indigenous communities, must be amplified and showcased in order to rectify this glaring imbalance and contradiction.
This session represents an opportunity for the author to reflect on a volume he has just finished editing entitled Decolonizing UDL and to highlight and share insights into the key innovations, promising practices, and calls for change, originating from the Global South and Indigenous Communities, that have woven the canvas of this book. The session seeks to create a space for critical dialogue, for the challenging of existing power dynamics within the UDL scholarship, and for the emergence of transformative voices from underrepresented communities. The workshop will use the UDL principles scrupulously to engage participants in diverse ways (challenging single story approaches to the narrative that surrounds UDL implementation) , as well as offer multiple means of action and expression for them to gain ownership over the key themes and concerns of the session (by encouraging a broad range of interventions, contributions, and stances).
The Science of Learning: implications for modern teachingDerek Wenmoth
ย
Keynote presentation to the Educational Leaders hui Koฬkiritia Marautanga held in Auckland on 26 June 2024. Provides a high level overview of the history and development of the science of learning, and implications for the design of learning in our modern schools and classrooms.
(๐๐๐ ๐๐๐) (๐๐๐ฌ๐ฌ๐จ๐ง 3)-๐๐ซ๐๐ฅ๐ข๐ฆ๐ฌ
Lesson Outcomes:
- students will be able to identify and name various types of ornamental plants commonly used in landscaping and decoration, classifying them based on their characteristics such as foliage, flowering, and growth habits. They will understand the ecological, aesthetic, and economic benefits of ornamental plants, including their roles in improving air quality, providing habitats for wildlife, and enhancing the visual appeal of environments. Additionally, students will demonstrate knowledge of the basic requirements for growing ornamental plants, ensuring they can effectively cultivate and maintain these plants in various settings.
Environmental science 1.What is environmental science and components of envir...Deepika
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Environmental science for Degree ,Engineering and pharmacy background.you can learn about multidisciplinary of nature and Natural resources with notes, examples and studies.
1.What is environmental science and components of environmental science
2. Explain about multidisciplinary of nature.
3. Explain about natural resources and its types
Brand Guideline of Bashundhara A4 Paper - 2024khabri85
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It outlines the basic identity elements such as symbol, logotype, colors, and typefaces. It provides examples of applying the identity to materials like letterhead, business cards, reports, folders, and websites.
Information and Communication Technology in EducationMJDuyan
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(๐๐๐ ๐๐๐) (๐๐๐ฌ๐ฌ๐จ๐ง 2)-๐๐ซ๐๐ฅ๐ข๐ฆ๐ฌ
๐๐ฑ๐ฉ๐ฅ๐๐ข๐ง ๐ญ๐ก๐ ๐๐๐ ๐ข๐ง ๐๐๐ฎ๐๐๐ญ๐ข๐จ๐ง:
Students will be able to explain the role and impact of Information and Communication Technology (ICT) in education. They will understand how ICT tools, such as computers, the internet, and educational software, enhance learning and teaching processes. By exploring various ICT applications, students will recognize how these technologies facilitate access to information, improve communication, support collaboration, and enable personalized learning experiences.
๐๐ข๐ฌ๐๐ฎ๐ฌ๐ฌ ๐ญ๐ก๐ ๐ซ๐๐ฅ๐ข๐๐๐ฅ๐ ๐ฌ๐จ๐ฎ๐ซ๐๐๐ฌ ๐จ๐ง ๐ญ๐ก๐ ๐ข๐ง๐ญ๐๐ซ๐ง๐๐ญ:
-Students will be able to discuss what constitutes reliable sources on the internet. They will learn to identify key characteristics of trustworthy information, such as credibility, accuracy, and authority. By examining different types of online sources, students will develop skills to evaluate the reliability of websites and content, ensuring they can distinguish between reputable information and misinformation.
2. Contents
๏ Introduction to GFS
๏ System Architecture
๏ System Features
๏ Working of GFS
๏ Latest advancement
๏ Conclusion
๏ Questions
3. Introduction
๏ More than 15,000 commodity-class PC's.
๏ Multiple clusters distributed worldwide.
๏ Thousands of queries served per second.
๏ One query reads 100's of MB of data.
๏ One query consumes 10's of billions of CPU
cycles.
๏ Google stores dozens of copies of the entire Web!
Conclusion: Need large, distributed, highly
fault tolerant file system.
4. System Architecture
A GFS cluster consists of a single master and multiple
chunk-servers and is accessed by multiple clients.
5. Large Chunk
๏ GFS uses large chunk: 64MB (1G = 1024 MB = 16 chunks)
๏ Stored as a plain Linux file, which will be lazily extended up to 64MB.
๏ Opt to many read and write on a given chunk
๏ Reduces network overhead by keeping a connection to the chunk
server.
๏ See also Map-Reduce, Big-Table.
6. Architecture (contโd)
Chunkserver
Files are divided into fixed-size chunks (64 MB)
Each chunk is identified by an immutable and globally
unique 64 bit chunkhandle assigned by the master at the
time of chunkcreation
Chunkservers store chunks on local disks as Linux files
and read or write chunk data specified by a chunkhandle
For reliability, each chunk is replicated on multiple
chunkservers. (default 3 replicas)
GFS Client
GFS client code linked into each application implements
the file system API and communicates with the master
and chunkservers to read or write data on behalf of the
7. System Metadata
The master stores three major types of metadata:
The file and chunk namespaces
The mapping from files to chunks
The locations of each chunkโs replicas
All metadata is kept in the masterโs memory
The first two types are also kept persistent by logging
mutations to an operation log stored on the masterโs local disk
and replicated on remote machines.
The master does not store third type persistently. Instead, it
asks each chunkserver about its chunks at master startup
8. System Features
๏Page Rank- Probability that a random surfer visits the site
Citations (Back links)
โข
How is Page Rank calculated??
โข
PR(A) = (1-d) + d (PR(T1)/C(T1) + ... + PR(Tn)/C(Tn))
where,
PR -> Page Rank of a page
T1โฆ.Tn -> Pages that point to Page A (citations)
d -> Damping Factor (0<d<1)
C(A) -> No. of Links going out from A
Page Rank of a page depends on-
๏ง Number of pages pointing to it.
๏ง Page Rank of the page that points to it.
9. System Features
๏ Anchor Text- text associated with the link
Association with the page the link is on
โข
โข Association with the page the link points to( unique
to Google)
Advantages:
โข Anchors contain more information than the pages
themselves
โข Documents that cannot be indexed can be displayed
๏ Other Features:
Proximity of location information in search for all hits
โข
Track of visual presentation details
โข
12. Google Query Evaluation
Parse the query.
1.
Convert words into wordIDs.
2.
Seek to the start of the doclist in the short barrel for every word.
3.
Scan through the doclists until there is a document that matches all the
4.
search terms.
Compute the rank of that document for the query.
5.
If in the short barrels and at the end of any doclist, seek to the start of
6.
the doclist in the full barrel for every word and go to step 4.
If we are not at the end of any doclist go to step 4.
7.
Sort the documents that have matched by rank and return the top k.
13. Client Read
๏ Client sends master:
๏ read(file name, chunk index)
๏ Masterโs reply:
๏ chunk ID, chunk version number, locations of
replicas
๏ Client sends โclosestโ chunkserver w/replica:
๏ read(chunk ID, byte range)
๏ โClosestโ determined by IP address on simple rack-
based network topology
๏ Chunkserver replies with data
14. Client Write
๏ Some chunkserver is primary for each chunk
๏ Master grants lease to primary (typically for 60 sec.)
๏ Leases renewed using periodic heartbeat messages
between master and chunkservers
๏ Client asks server for primary and secondary replicas for
each chunk
๏ Client sends data to replicas in daisy chain
๏ Pipelined: each replica forwards as it receives
๏ Takes advantage of full-duplex Ethernet links
15. Client Write (2)
๏ All replicas acknowledge data write to client
๏ Client sends write request to primary
๏ Primary assigns serial number to write
request, providing ordering
๏ Primary forwards write request with same serial
number to secondaries
๏ Secondaries all reply to primary after completing
write
๏ Primary replies to client
17. What Happen If the Master Reboots?
๏ Replays log from disk
๏ Recovers namespace (directory) information
๏ Recovers file-to-chunk-ID mapping
๏ Asks chunkservers which chunks they hold
๏ Recovers chunk-ID-to-chunkserver mapping
๏ If chunk server has older chunk, itโs stale
๏ Chunk server down at lease renewal
๏ If chunk server has newer chunk, adopt its version
number
๏ Master may have failed while granting lease
18. What Happen if Chunkserver Fails?
๏ Master notices missing heartbeats
๏ Master decrements count of replicas for all chunks on
dead chunkserver
๏ Master re-replicates chunks missing replicas in
background
๏ Highest priority for chunks missing greatest
number of replicas
19. Latest Advancement
๏ gMail - An easily configurable email
service with 1GB of web space.
๏ Blogger- A free web-based service that
helps consumers publish on the
web without writing code or installing
software.
๏ Google โnext generation corporate s/wโ
- A smaller version of the google
software, modified for private use.
20. Conclusion
๏ Success: used actively by Google to support search service and
other applications
๏ Availability and recoverability on cheap hardware
๏ High throughput by decoupling control and data
๏ Supports massive data sets and concurrent appends
๏ Semantics not transparent to apps
๏ Must verify file contents to avoid inconsistent
regions, repeated appends (at-least-once semantics)
๏ Performance not good for all apps
๏ Assumes read-once, write-once workload (no client
caching!)
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