This document discusses using Memcached and MySQL to build scalable applications. It provides an overview of Memcached, how it works, its server and client components, example architectures, and solutions for using it with MySQL. Memcached is an open-source caching system that stores objects in memory to improve performance by reducing database queries. It works by distributing cached data across multiple servers.
This document discusses how to design and implement scalable applications using Memcached and MySQL. It provides an overview of Memcached, how it works, the Memcached server and clients. It also presents example architectures for integrating Memcached and MySQL, including caching database queries in Memcached. The document concludes that Memcached can improve application performance and scalability by caching frequently accessed data and database query results.
Cassandra can be used as an alternative to Memcache for caching data from a database in order to improve performance. Cassandra offers advantages over Memcache like persistence of data, horizontal scaling, and simpler management as a single database tier rather than separate Memcache and database servers. While Memcache is faster for in-memory operations, Cassandra can serve data both from memory using caching as well as from disk, and offers different configuration options for balancing performance, consistency and fault tolerance.
Coming with different approach, this slide will explain How we can use Memcache as Session handler in PHP? This slide will also explain, How we can keep session centralised and share it on LB? Considering that you are using linux, the commands given in slides are linux commands.
We all love Ehcache. But the rise of real-time Big Data means you want to keep larger amounts of data in memory with low, predictable latency. In this webinar,
we explain how BigMemory Go can turbocharge your Ehcache deployment.
Develop skills to prepare for installing, configuring and performing ongoing maintenance of a Microsoft Exchange Server 2013 infrastructure.
Help prepare for certification exam 70-341.
Learn best practices.
This document discusses how to design and implement scalable applications using Memcached and MySQL. It provides an overview of Memcached, how it works, the Memcached server and clients. It also presents example architectures for integrating Memcached and MySQL, including caching database queries in Memcached. The document concludes that Memcached can improve application performance and scalability by caching frequently accessed data and database query results.
Cassandra can be used as an alternative to Memcache for caching data from a database in order to improve performance. Cassandra offers advantages over Memcache like persistence of data, horizontal scaling, and simpler management as a single database tier rather than separate Memcache and database servers. While Memcache is faster for in-memory operations, Cassandra can serve data both from memory using caching as well as from disk, and offers different configuration options for balancing performance, consistency and fault tolerance.
Coming with different approach, this slide will explain How we can use Memcache as Session handler in PHP? This slide will also explain, How we can keep session centralised and share it on LB? Considering that you are using linux, the commands given in slides are linux commands.
We all love Ehcache. But the rise of real-time Big Data means you want to keep larger amounts of data in memory with low, predictable latency. In this webinar,
we explain how BigMemory Go can turbocharge your Ehcache deployment.
Develop skills to prepare for installing, configuring and performing ongoing maintenance of a Microsoft Exchange Server 2013 infrastructure.
Help prepare for certification exam 70-341.
Learn best practices.
The document proposes a computerized library management system for Quest International University Perak's Run Run Shaw Library. It details problems with the current manual system such as inefficiency and lack of centralized data control. The proposed system would use a client-server model with a centralized database server and networked client terminals. This would allow for increased accuracy, efficiency, and ease of management and expansion compared to the current manual system.
This document summarizes different caching techniques that can be used with PHP, including caching content, database caching, and memory caching using APCU, Memcached, and Redis. It provides code examples for storing, getting, and deleting values from the cache with each technique. Specifically, it shows how to cache objects in memory and check the cache before querying a database to improve performance.
NoSQL Databases: An Introduction and Comparison between Dynamo, MongoDB and C...Vivek Adithya Mohankumar
The research paper covers the consolidated interpretation of NoSQL systems, on the basis of performance, scalability and data aggregation, and compares the types of NoSQL databases based on their implementation and maintenance.
MaxScale is an open-source, highly scalable, and transparent load balancing solution for MySQL and MariaDB databases. It acts as a proxy between applications and databases, authenticating clients, routing queries, and monitoring database nodes. MaxScale supports features like read/write splitting, connection load balancing, and filtering of queries through extensible plugin modules. Typical use cases include balancing read loads across database replicas and distributing connections among nodes in a Galera cluster.
This document provides a step-by-step guide for configuring distributed data center virtualization using Windows Virtual Server 2005 R2, Sanbolic's Melio File System, and Microsoft Clustering Services. Key steps include installing required software, configuring a two-node Microsoft Clustering Services cluster, creating virtual machines and storage on a SAN, and configuring resources to allow active virtual machines to migrate across physical hosts while maintaining access to shared storage.
Microsoft releases cumulative updates (CUs) for Exchange Server 2013 that include all installation files, allowing updates to be applied without first installing a service pack. Previous versions of Exchange required separate installation of service packs and CUs. The document discusses prerequisites, installation, and post-installation configuration tasks for Exchange Server 2013, including preparing Active Directory, installing prerequisites on the Exchange server, running Setup.exe to install Exchange roles, configuring accepted domains and email address policies, and setting up send/receive connectors and DNS records.
ActiveMQ is an open source message broker built with Java that supports Java standards like JMS and J2EE. It allows applications to communicate asynchronously by sending and receiving messages through topics and queues. Messages consist of headers and a body that can contain different types of data like XML, text, or binary objects. ActiveMQ provides features like message durability, persistence, content-based filtering using SQL, and scalability.
Performance Analysis of HBASE and MONGODBKaushik Rajan
Comparison of different NoSQL databases,
namely, HBase and MongoDB at different workloads using Yahoo Cloud Serving Benchmarking (YCSB)
Tools used
> HBase, MongoDB, Shell Scripting, YCSB, Hadoop Environment
> Tableau for Visualization
> LATEX for documentation
This document discusses WebLogic JMS system best practices. It covers topics like JMS servers, persistent stores, JMS modules, and JMS resources. A JMS server manages queue and topic resources defined in JMS modules. Persistent stores provide high-performance storage for persistent messages. File stores or JDBC stores can be used. Best practices include sharing a persistent store between subsystems and only adding new stores when scaling limits are reached.
This document provides an overview of Apache ActiveMQ, an open source message broker that implements the Java Message Service (JMS) standard. It discusses JMS concepts like clients, providers, messages, queues, topics and publish/subscribe models. It then describes what ActiveMQ is, how to install it, and how to use its JMS API to send and receive messages. Finally, it briefly mentions other JMS providers and some common use cases for ActiveMQ like transactional messaging, market data distribution, clustering, and integrating messaging with REST APIs.
HP PolyServe Software for Microsoft SQL Serverwebhostingguy
The HP PolyServe Software for Microsoft SQL Server enables consolidation of multiple SQL Server instances onto fewer servers and centralized storage. It provides high availability, virtualization-like flexibility, scalable capacity allocation, and instance mobility. Key features include an adaptive SQL platform, consolidated data management, and high availability for mission-critical applications. It goes beyond consolidation and failovers to meet Microsoft's SQL Server Always On requirements, making it ideal for database consolidation.
1) VMware Virtual SAN 6.0 combined with Diablo Memory Channel Storage allows hosting large SQL Server databases in a single virtual machine without performance degradation.
2) Testing showed the combination could support over 100,000 I/O operations per second with latency under 1 millisecond, outperforming all-flash storage arrays which bottlenecked around 40,000 IOPS.
3) The combination provides unprecedented database performance, density, and cost savings by running databases alongside other workloads on a common converged infrastructure platform.
Compaction and Splitting in Apache AccumuloHortonworks
The document discusses compaction and splitting in Apache Accumulo distributed key-value stores. It explains that Accumulo tables are divided into non-overlapping ranges called tablets, and that compaction merges sorted files within a tablet into a single file to improve read performance. Splitting divides large tablets into two in order to balance workload. The document provides details on Accumulo's and HBase's compaction algorithms and how they determine when to compact and split tablets.
Megastore is a scalable data storage system developed by Google to meet the requirements of modern interactive online services. It blends the scalability of NoSQL databases with the convenience of SQL, providing ACID transactions across entity groups. Megastore uses Bigtable for data storage and an improved Paxos algorithm to synchronously replicate transaction logs across data centers, achieving high availability even in the case of data center failures.
With MySQL being the most popular open source DBMS in the world and with an estimated growth of 16 percent anually until 2020,we can assume that sooner or later an Oracle DBA will be handling a MySQL database in their shop. This beginner/intermediate-level session will take you through my journey of an Oracle DBA and my first 100 days of starting to administer a MySQL database, show several demos and all the roadblocks and the success I had along this path.
The document provides an overview of the key components and functionality of Exchange Server mailbox databases and mailboxes. It discusses:
- The mailbox server role which hosts mailbox databases and provides transport and unified messaging services.
- Managing mailbox databases including adding databases, moving databases, enabling circular logging, setting quotas and retention policies.
- The structure and caching of mailbox databases using transaction log files, checkpoint files and the information store process.
- Managing mailboxes such as creating new user mailboxes, assigning existing users mailboxes, and different mailbox types.
Memcached is an in-memory key-value store used to improve performance by caching data and database queries. It uses a simple get/set interface and stores data in memory for low latency access. Memcached is commonly used to cache data from dynamic database-driven websites to reduce the load on databases and improve response time. It provides basic caching functions but has no persistence or security features. Improving areas include networking, parallel access, data structures and memory management.
This document provides an overview of NoSQL databases and key-value stores. It discusses why NoSQL databases were created, examples of different NoSQL categories like key-value stores and document stores. It then focuses on key-value stores like Memcached and MemcacheDB. Memcached is an in-memory key-value store while MemcacheDB provides persistence. Both use the BerkeleyDB for storage with MemcacheDB.
The document proposes a computerized library management system for Quest International University Perak's Run Run Shaw Library. It details problems with the current manual system such as inefficiency and lack of centralized data control. The proposed system would use a client-server model with a centralized database server and networked client terminals. This would allow for increased accuracy, efficiency, and ease of management and expansion compared to the current manual system.
This document summarizes different caching techniques that can be used with PHP, including caching content, database caching, and memory caching using APCU, Memcached, and Redis. It provides code examples for storing, getting, and deleting values from the cache with each technique. Specifically, it shows how to cache objects in memory and check the cache before querying a database to improve performance.
NoSQL Databases: An Introduction and Comparison between Dynamo, MongoDB and C...Vivek Adithya Mohankumar
The research paper covers the consolidated interpretation of NoSQL systems, on the basis of performance, scalability and data aggregation, and compares the types of NoSQL databases based on their implementation and maintenance.
MaxScale is an open-source, highly scalable, and transparent load balancing solution for MySQL and MariaDB databases. It acts as a proxy between applications and databases, authenticating clients, routing queries, and monitoring database nodes. MaxScale supports features like read/write splitting, connection load balancing, and filtering of queries through extensible plugin modules. Typical use cases include balancing read loads across database replicas and distributing connections among nodes in a Galera cluster.
This document provides a step-by-step guide for configuring distributed data center virtualization using Windows Virtual Server 2005 R2, Sanbolic's Melio File System, and Microsoft Clustering Services. Key steps include installing required software, configuring a two-node Microsoft Clustering Services cluster, creating virtual machines and storage on a SAN, and configuring resources to allow active virtual machines to migrate across physical hosts while maintaining access to shared storage.
Microsoft releases cumulative updates (CUs) for Exchange Server 2013 that include all installation files, allowing updates to be applied without first installing a service pack. Previous versions of Exchange required separate installation of service packs and CUs. The document discusses prerequisites, installation, and post-installation configuration tasks for Exchange Server 2013, including preparing Active Directory, installing prerequisites on the Exchange server, running Setup.exe to install Exchange roles, configuring accepted domains and email address policies, and setting up send/receive connectors and DNS records.
ActiveMQ is an open source message broker built with Java that supports Java standards like JMS and J2EE. It allows applications to communicate asynchronously by sending and receiving messages through topics and queues. Messages consist of headers and a body that can contain different types of data like XML, text, or binary objects. ActiveMQ provides features like message durability, persistence, content-based filtering using SQL, and scalability.
Performance Analysis of HBASE and MONGODBKaushik Rajan
Comparison of different NoSQL databases,
namely, HBase and MongoDB at different workloads using Yahoo Cloud Serving Benchmarking (YCSB)
Tools used
> HBase, MongoDB, Shell Scripting, YCSB, Hadoop Environment
> Tableau for Visualization
> LATEX for documentation
This document discusses WebLogic JMS system best practices. It covers topics like JMS servers, persistent stores, JMS modules, and JMS resources. A JMS server manages queue and topic resources defined in JMS modules. Persistent stores provide high-performance storage for persistent messages. File stores or JDBC stores can be used. Best practices include sharing a persistent store between subsystems and only adding new stores when scaling limits are reached.
This document provides an overview of Apache ActiveMQ, an open source message broker that implements the Java Message Service (JMS) standard. It discusses JMS concepts like clients, providers, messages, queues, topics and publish/subscribe models. It then describes what ActiveMQ is, how to install it, and how to use its JMS API to send and receive messages. Finally, it briefly mentions other JMS providers and some common use cases for ActiveMQ like transactional messaging, market data distribution, clustering, and integrating messaging with REST APIs.
HP PolyServe Software for Microsoft SQL Serverwebhostingguy
The HP PolyServe Software for Microsoft SQL Server enables consolidation of multiple SQL Server instances onto fewer servers and centralized storage. It provides high availability, virtualization-like flexibility, scalable capacity allocation, and instance mobility. Key features include an adaptive SQL platform, consolidated data management, and high availability for mission-critical applications. It goes beyond consolidation and failovers to meet Microsoft's SQL Server Always On requirements, making it ideal for database consolidation.
1) VMware Virtual SAN 6.0 combined with Diablo Memory Channel Storage allows hosting large SQL Server databases in a single virtual machine without performance degradation.
2) Testing showed the combination could support over 100,000 I/O operations per second with latency under 1 millisecond, outperforming all-flash storage arrays which bottlenecked around 40,000 IOPS.
3) The combination provides unprecedented database performance, density, and cost savings by running databases alongside other workloads on a common converged infrastructure platform.
Compaction and Splitting in Apache AccumuloHortonworks
The document discusses compaction and splitting in Apache Accumulo distributed key-value stores. It explains that Accumulo tables are divided into non-overlapping ranges called tablets, and that compaction merges sorted files within a tablet into a single file to improve read performance. Splitting divides large tablets into two in order to balance workload. The document provides details on Accumulo's and HBase's compaction algorithms and how they determine when to compact and split tablets.
Megastore is a scalable data storage system developed by Google to meet the requirements of modern interactive online services. It blends the scalability of NoSQL databases with the convenience of SQL, providing ACID transactions across entity groups. Megastore uses Bigtable for data storage and an improved Paxos algorithm to synchronously replicate transaction logs across data centers, achieving high availability even in the case of data center failures.
With MySQL being the most popular open source DBMS in the world and with an estimated growth of 16 percent anually until 2020,we can assume that sooner or later an Oracle DBA will be handling a MySQL database in their shop. This beginner/intermediate-level session will take you through my journey of an Oracle DBA and my first 100 days of starting to administer a MySQL database, show several demos and all the roadblocks and the success I had along this path.
The document provides an overview of the key components and functionality of Exchange Server mailbox databases and mailboxes. It discusses:
- The mailbox server role which hosts mailbox databases and provides transport and unified messaging services.
- Managing mailbox databases including adding databases, moving databases, enabling circular logging, setting quotas and retention policies.
- The structure and caching of mailbox databases using transaction log files, checkpoint files and the information store process.
- Managing mailboxes such as creating new user mailboxes, assigning existing users mailboxes, and different mailbox types.
Memcached is an in-memory key-value store used to improve performance by caching data and database queries. It uses a simple get/set interface and stores data in memory for low latency access. Memcached is commonly used to cache data from dynamic database-driven websites to reduce the load on databases and improve response time. It provides basic caching functions but has no persistence or security features. Improving areas include networking, parallel access, data structures and memory management.
This document provides an overview of NoSQL databases and key-value stores. It discusses why NoSQL databases were created, examples of different NoSQL categories like key-value stores and document stores. It then focuses on key-value stores like Memcached and MemcacheDB. Memcached is an in-memory key-value store while MemcacheDB provides persistence. Both use the BerkeleyDB for storage with MemcacheDB.
Configuration and Deployment Guide For Memcached on Intel® ArchitectureOdinot Stanislas
This Configuration and Deployment Guide explores designing and building a Memcached infrastructure that is scalable, reliable, manageable and secure. The guide uses experience with real-world deployments as well as data from benchmark tests. Configuration guidelines on clusters of Intel® Xeon®- and Atom™-based servers take into account differing business scenarios and inform the various tradeoffs to accommodate different Service Level Agreement (SLA) requirements and Total Cost of Ownership (TCO) objectives.
This document provides an overview and technical discussion of Membase. It begins with introducing Membase and how it allows both applications and databases to scale horizontally. The rest of the document discusses Membase architecture, deployment options, use cases, and a demo. It also briefly explores developing with Membase and the future direction of NodeCode, which will allow extending Membase through custom modules.
Membase is an open source, distributed key-value database management system optimized for storing data behind interactive web applications. It is designed to be simple, fast, and elastic. Membase allows data to scale out linearly by just adding more nodes, maintaining consistency when accessing data. It also includes a built-in Memcached caching layer.
This document provides an overview of Membase, including:
- Membase is a distributed database that allows applications and data to scale independently. It uses the Memcached protocol and architecture.
- Membase can be deployed in various ways, including using the built-in Memcached caching layer or standalone proxies. It also supports secure multitenant buckets.
- The document demonstrates Membase's use cases with examples from large companies and discusses its architecture, including clustering, data access protocols, and a future NodeCode capability.
Mache is a NoSQL near cache with eventing; built from a 'mash-up' of Open Source technologies with multiple pluggable NoSQL support and multiple pluggable messaging platforms. We have used all the well known Open-Source technologies. The heart of the system is akin to a HashMap - using Google's Guava cache and Spring Data.
How to boost performance of your rails app using dynamo db and memcachedAndolasoft Inc
DynamoDB and Memcached is a powerful combination for your Rails app. If you're looking to improve the performance of your Rails application, this may be the solution for you.
This document discusses considerations for planning Oracle VM 3 server pool deployments for scalability, availability, and reliability. It describes key concepts of Oracle VM 3 including Oracle VM Manager, Oracle VM Server, and server pools. Server pools group multiple physical servers with shared storage so virtual machines can run on any server and live migrate between servers. The document provides best practices for configuring server pools for high availability, including enabling high availability options, sizing the server pool file system, using live migration, ensuring excess pool capacity, and planning multiple pools for large infrastructures.
Mesos is an open source cluster management framework that provides efficient resource isolation and sharing across distributed applications or frameworks. It divides resources into CPU, memory, storage, and other compute resources and shares those resources dynamically and efficiently across applications. Mesos abstracts the underlying infrastructure to provide a unified API to applications while employing operating system-level virtualization through interfaces like Docker to maximize resource utilization. It works by having a Mesos master that negotiates resources among Mesos slaves to run applications or frameworks, which are made up of a scheduler to negotiate for resources and executors to run tasks. Common frameworks that run on Mesos include Spark, Hadoop and Docker containers.
Silicon India Java Conference: Building Scalable Solutions For Commerce Silic...Kalaiselvan (Selvan)
The document discusses various techniques for building scalable e-commerce solutions, including:
- Separating different application components (e.g. web server, app server, database) across physical servers.
- Scaling hardware resources vertically by upgrading components, and horizontally by adding more servers.
- Employing caching extensively using technologies like Memcached to improve performance.
- Implementing load balancing, connection pooling, asynchronous processing to optimize resource usage.
- Employing database sharding/partitioning and NoSQL databases to distribute data across servers.
This document discusses approaches to scaling in-memory databases on multicore hardware. There are two main approaches: employing a symmetric database engine where a single process uses all cores to access shared memory, and employing a partitioned database engine where the database is divided into partitions each managed by a dedicated core. A challenge is that cache coherency limits scalability as it does not scale to thousands of cores. The document recommends a software-hardware co-design approach, avoiding centralized critical sections, leveraging hardware message passing, and using techniques like optimistic concurrency control to improve scalability on high core count systems.
This document discusses approaches to scaling in-memory databases on multicore hardware. There are two main approaches: employing a symmetric database engine where a single process uses multiple threads across cores to access shared memory, and employing a partitioned database engine where the database is divided into partitions managed by dedicated cores. A challenge is that cache coherency limits scalability as it does not scale to thousands of cores. The document recommends a software-hardware co-design approach, avoiding centralized critical sections, leveraging hardware message passing, and using techniques like optimistic concurrency control to improve scalability on high core count systems.
Scalable Web Architecture and Distributed Systemshyun soomyung
Scalable web architectures distribute resources across multiple servers to improve availability, performance, reliability, and scalability. Key principles for designing scalable systems include availability, performance, reliability, scalability, manageability, and cost. These principles sometimes conflict and require tradeoffs. To improve scalability, services can be split and data distributed across partitions or shards. Caches, proxies, indexes, load balancers, and queues help optimize data access and manage asynchronous operations in distributed systems.
Migrating Very Large Site Collections (SPSDC)kiwiboris
This document discusses migrating a large 8 TB SharePoint site collection to a new farm within a 96 hour maintenance window. Key points:
- The site collection is too large to migrate as-is, so it will be split by promoting some subsites to new site collections.
- Metalogix Content Matrix will be used to script the migration in parallel batches to complete it on time.
- Challenges include maintaining performance over the large data set and validating a 99% accurate migration within the narrow window. Careful scripting and testing is required to successfully migrate such a large amount of content.
Improving Website Performance with Memecached Webinar | Achieve InternetAchieve Internet
Improving the performance and scalability of your Drupal website with a Memcached implementation.
In this webinar, you will learn about:
• The components of a Memcached system
• Installing a simple Memcached installation
• Complex distributed installations and when to use them
• Verifying the installation
Improving Website Performance with Memecached Webinar | Achieve InternetAchieve Internet
Improving the performance and scalability of your Drupal website with a Memcached implementation.
In this webinar, you will learn about:
• The components of a Memcached system
• Installing a simple Memcached installation
• Complex distributed installations and when to use them
• Verifying the installation
This document summarizes strategies for scaling a Ruby on Rails application. It discusses starting with shared hosting and moving to dedicated servers, scaling the database horizontally using replication or clustering, scaling the web servers by adding more application servers behind a load balancer, implementing user clusters to shard user data, adding caching at various levels using solutions like Squid, Memcached, and fragment caching, and using elastic cloud architectures on services like Amazon EC2. The key steps are horizontal scaling of databases, vertical and horizontal scaling of application servers, implementing user sharding and caching to optimize performance, and using elastic cloud services for on-demand scaling.
The document discusses scalable storage systems and key-value stores as an alternative to traditional databases. It provides an overview of vertical and horizontal scalability. Traditional databases are not well-suited for scalable systems due to their complexity, wasted features, and multi-step query processing. Key-value stores offer simpler data models and interfaces that are designed from the start for scaling across hundreds of machines. Performance comparisons show key-value stores significantly outperforming traditional databases. The document also outlines how key-value storage systems work at the aggregation and storage layers.