MongoDB 3.0 comes with a set of innovations regarding storage engine, operational facilities and improvements has well of security enhancements. This presentations describes these improvements and new features ready to be tested.
https://www.mongodb.com/lp/white-paper/mongodb-3.0
WiredTiger is MongoDB's first officially supported pluggable storage engine and exposes several new features and configuration options. This talk will highlight the major differences between the MMAPV1 and WiredTiger storage engines as well as unique characteristics of both.
MongoDB 3.0 introduces several important and exciting features to the MongoDB Ecosystem. These include a pluggable storage API, the WiredTiger storage engine, and improved concurrency controls. Learn how to take advantage of these new features and how they will improve your database performance in this webinar.
MongoDB Days Silicon Valley: A Technical Introduction to WiredTiger MongoDB
Presented by Osmar Olivo, Product Manager, MongoDB
Experience level: Introductory
WiredTiger is MongoDB's first officially supported pluggable storage engine as well as the new default engine in 3.2. It exposes several new features and configuration options. This talk will highlight the major differences between the MMAPV1 and WiredTiger storage engines including currency, compression, and caching.
Presented by Norberto Leite, Developer Advocate, MongoDB
MongoDB 3.0 introduces a pluggable storage architecture and a new storage engine called WiredTiger. The engineering team behind WiredTiger team has a long and distinguished career, having architected and built Berkeley DB, now the world's most widely used embedded database. In this session, we'll describe the original design goals for WiredTiger, including considerations we made for heavily threaded hardware, large on-chip caches, and SSD storage. We'll also look at some of the latch-free and non-blocking algorithms we've implemented, as well as other techniques that improve scaling, overall throughput and latency. Finally, we'll take a look at some of the features we hope to incorporate into WiredTiger and MongoDB in the future.
MongoDB 3.0 comes with a set of innovations regarding storage engine, operational facilities and improvements has well of security enhancements. This presentations describes these improvements and new features ready to be tested.
https://www.mongodb.com/lp/white-paper/mongodb-3.0
WiredTiger is MongoDB's first officially supported pluggable storage engine and exposes several new features and configuration options. This talk will highlight the major differences between the MMAPV1 and WiredTiger storage engines as well as unique characteristics of both.
MongoDB 3.0 introduces several important and exciting features to the MongoDB Ecosystem. These include a pluggable storage API, the WiredTiger storage engine, and improved concurrency controls. Learn how to take advantage of these new features and how they will improve your database performance in this webinar.
MongoDB Days Silicon Valley: A Technical Introduction to WiredTiger MongoDB
Presented by Osmar Olivo, Product Manager, MongoDB
Experience level: Introductory
WiredTiger is MongoDB's first officially supported pluggable storage engine as well as the new default engine in 3.2. It exposes several new features and configuration options. This talk will highlight the major differences between the MMAPV1 and WiredTiger storage engines including currency, compression, and caching.
Presented by Norberto Leite, Developer Advocate, MongoDB
MongoDB 3.0 introduces a pluggable storage architecture and a new storage engine called WiredTiger. The engineering team behind WiredTiger team has a long and distinguished career, having architected and built Berkeley DB, now the world's most widely used embedded database. In this session, we'll describe the original design goals for WiredTiger, including considerations we made for heavily threaded hardware, large on-chip caches, and SSD storage. We'll also look at some of the latch-free and non-blocking algorithms we've implemented, as well as other techniques that improve scaling, overall throughput and latency. Finally, we'll take a look at some of the features we hope to incorporate into WiredTiger and MongoDB in the future.
Presented by Ruben Terceno, Senior Solutions Architect, MongoDB
Getting ready to deploy? MongoDB is designed to be simple to administer and to manage. An understanding of best practices can ensure a successful implementation. This talk will introduce you to Cloud Manager, the easiest way to run MongoDB in the cloud. We'll walk through demos of provisioning, expanding and contracting clusters, managing users, and more. Cloud Manager makes operations effortless, reducing complicated tasks to a single click. You can now provision machines, configure replica sets and sharded clusters, and upgrade your MongoDB deployment all through the Cloud Manager interface. You'll walk from this session knowing that you can run MongoDB with confidence.
Introducing MongoDB in a multi-site HA environmentSebastian Geib
This presentation was given by us at Mongo Munich on 10th of October 2011. It covers the introduction and mostly the durability and robustness testing of MongoDB at AutoScout24 before launching a new site.
With the latest release of MongoDB 3.0, we have announced several new exciting features, including our new pluggable storage API and the WiredTiger storage engine, which provides compression, improved concurrency control, and more. Learn how you will be able to take advantage of these new features and how they will improve your database performance with this upcoming webinar.
This is my talk from the July LVL.UP KL meeting (formerly WebCamp KL) held on August 6th at Mindvalley, Bangsar.
The talk covers a basic introduction to scalability, 5 things to consider/think about and 5 things you can do build at scale.
WebCampKL Group is here - https://www.facebook.com/groups/webcamp/
The video of this talk is available here: http://youtu.be/Djs-8lGpz_U (also added as the 19th slide).
My presentation from Wordconf 2011 about High Performance Wordpress. Covers tuning the whole LAMP stack, some stuff on Wordpress and Caching (both plugins and Varnish).
In this webinar, we'll discuss the different ways to back up and restore your MongoDB databases in case of a disaster scenario. We'll review manual approaches as well as premium solutions - using MongoDB Management Service (MMS) for managed backup to our cloud, or using Ops Manager at your own cloud/data centers.
Understanding how memory is managed with MongoDB is instrumental in maximizing database performance and hardware utilisation. This talk covers the workings of low level operating system components like the page cache and memory mapped files. We will examine the differences between RAM, SSD and hard disk drives to help you choose the right hardware configuration. Finally, we will learn how to monitor and analyze memory and disk usage using the MongoDB Management Service, linux administration commands and MongoDB commands.
Managing your own PostgreSQL servers is sometimes a burden your business does not want. In this talk we will provide an overview of some of the public cloud offerings available for hosted PostgreSQL and discuss a number of strategies for migrating your databases with a minimum of downtime.
WiredTiger is rethinking data management for modern hardware with a focus on multi-core scalability and maximizing the value of every byte of RAM.
CPUs are no longer getting faster, and the cost of additional CPUs is approaching zero. Disk transfer speeds are relatively slower, compared to memory speeds, than a decade ago. Finally, power is the single biggest cost of the data center. For these reasons, WiredTiger is focused on more efficient use of I/O bandwidth, multiple CPUs and large memory in a single server.
Scalability strategies for cloud based system architectureSangJin Kang
- Scalability & Availability for the Global Markets
- Global scaled Scalability, Availability and Security
- Architecture for 100, 1K, 100K, 500K, 1M and 10M global users
- Auto-Scaling
- Understand Cloud Services
- Cloud Demo(AWS, GCP, Azure and Cloudflare)
- Wrap-Up
Optimizing MongoDB: Lessons Learned at Localyticsandrew311
Tips, tricks, and gotchas learned at Localytics for optimizing MongoDB installs. Includes information about document design, indexes, fragmentation, migration, AWS EC2/EBS, and more.
The storage engine is responsible for managing how data is stored, both in memory and on disk. MongoDB supports multiple storage engines, as different engines perform better for specific workloads.
View this presentation to understand:
What a storage engine is
How to pick a storage engine
How to configure a storage engine and a replica set
Presented by Ruben Terceno, Senior Solutions Architect, MongoDB
Getting ready to deploy? MongoDB is designed to be simple to administer and to manage. An understanding of best practices can ensure a successful implementation. This talk will introduce you to Cloud Manager, the easiest way to run MongoDB in the cloud. We'll walk through demos of provisioning, expanding and contracting clusters, managing users, and more. Cloud Manager makes operations effortless, reducing complicated tasks to a single click. You can now provision machines, configure replica sets and sharded clusters, and upgrade your MongoDB deployment all through the Cloud Manager interface. You'll walk from this session knowing that you can run MongoDB with confidence.
Introducing MongoDB in a multi-site HA environmentSebastian Geib
This presentation was given by us at Mongo Munich on 10th of October 2011. It covers the introduction and mostly the durability and robustness testing of MongoDB at AutoScout24 before launching a new site.
With the latest release of MongoDB 3.0, we have announced several new exciting features, including our new pluggable storage API and the WiredTiger storage engine, which provides compression, improved concurrency control, and more. Learn how you will be able to take advantage of these new features and how they will improve your database performance with this upcoming webinar.
This is my talk from the July LVL.UP KL meeting (formerly WebCamp KL) held on August 6th at Mindvalley, Bangsar.
The talk covers a basic introduction to scalability, 5 things to consider/think about and 5 things you can do build at scale.
WebCampKL Group is here - https://www.facebook.com/groups/webcamp/
The video of this talk is available here: http://youtu.be/Djs-8lGpz_U (also added as the 19th slide).
My presentation from Wordconf 2011 about High Performance Wordpress. Covers tuning the whole LAMP stack, some stuff on Wordpress and Caching (both plugins and Varnish).
In this webinar, we'll discuss the different ways to back up and restore your MongoDB databases in case of a disaster scenario. We'll review manual approaches as well as premium solutions - using MongoDB Management Service (MMS) for managed backup to our cloud, or using Ops Manager at your own cloud/data centers.
Understanding how memory is managed with MongoDB is instrumental in maximizing database performance and hardware utilisation. This talk covers the workings of low level operating system components like the page cache and memory mapped files. We will examine the differences between RAM, SSD and hard disk drives to help you choose the right hardware configuration. Finally, we will learn how to monitor and analyze memory and disk usage using the MongoDB Management Service, linux administration commands and MongoDB commands.
Managing your own PostgreSQL servers is sometimes a burden your business does not want. In this talk we will provide an overview of some of the public cloud offerings available for hosted PostgreSQL and discuss a number of strategies for migrating your databases with a minimum of downtime.
WiredTiger is rethinking data management for modern hardware with a focus on multi-core scalability and maximizing the value of every byte of RAM.
CPUs are no longer getting faster, and the cost of additional CPUs is approaching zero. Disk transfer speeds are relatively slower, compared to memory speeds, than a decade ago. Finally, power is the single biggest cost of the data center. For these reasons, WiredTiger is focused on more efficient use of I/O bandwidth, multiple CPUs and large memory in a single server.
Scalability strategies for cloud based system architectureSangJin Kang
- Scalability & Availability for the Global Markets
- Global scaled Scalability, Availability and Security
- Architecture for 100, 1K, 100K, 500K, 1M and 10M global users
- Auto-Scaling
- Understand Cloud Services
- Cloud Demo(AWS, GCP, Azure and Cloudflare)
- Wrap-Up
Optimizing MongoDB: Lessons Learned at Localyticsandrew311
Tips, tricks, and gotchas learned at Localytics for optimizing MongoDB installs. Includes information about document design, indexes, fragmentation, migration, AWS EC2/EBS, and more.
The storage engine is responsible for managing how data is stored, both in memory and on disk. MongoDB supports multiple storage engines, as different engines perform better for specific workloads.
View this presentation to understand:
What a storage engine is
How to pick a storage engine
How to configure a storage engine and a replica set
MongoDB Days Silicon Valley: Best Practices for Upgrading to MongoDBMongoDB
Presented by Achille Brighton, Principal Consulting Engineer, MongoDB
Experience level: Deep dive
MongoDB 3.2 brings major enhancements. New pluggable storage engines optimized for in-memory computing and the most security-sensitive applications. Simplified data governance with document validation, coupled with GUI-based schema discovery and visualization. Improved operational efficiency with enhanced management platforms, continuous uptime across distributed, multi-region deployments, and zero-downtime upgrades. To take advantage of these features, your team needs an upgrade plan. In this session, we’ll walk you through how to build an upgrade plan. We’ll show you how to validate your existing deployment, build a test environment with a representative workload, and detail how to carry out the upgrade. You’ll walk away confident that you're prepared to upgrade.
Follow on from Back to Basics: An Introduction to NoSQL and MongoDB
•Covers more advanced topics:
Storage Engines
• What storage engines are and how to pick them
Aggregation Framework
• How to deploy advanced analytics processing right inside the database
The BI Connector
• How to create visualizations and dashboards from your MongoDB data
Authentication and Authorisation
• How to secure MongoDB, both on-premise and in the cloud
In this webinar, we will be covering general best practices for running MongoDB on AWS.
Topics will range from instance selection to storage selection and service distribution to ensure service availability. We will also look at any specific best practices related to using WiredTiger. We will then shift gears and explore recommended strategies for managing your MongoDB instance on AWS.
This session also includes a live Q&A portion during which you are encouraged to ask questions of our team.
MongoDB World 2015 - A Technical Introduction to WiredTigerWiredTiger
MongoDB 3.0 introduces a new pluggable storage engine API and a new storage engine called WiredTiger. The engineering team behind WiredTiger team has a long and distinguished career, having architected and built Berkeley DB, now the world's most widely used embedded database. In this talk we will describe our original design goals for WiredTiger, including considerations we made for heavily threaded hardware, large on-chip caches, and SSD storage. We'll also look at some of the latch-free and non-blocking algorithms we've implemented, as well as other techniques that improve scaling, overall throughput and latency. Finally, we'll take a look at some of the features we hope to incorporate into WiredTiger and MongoDB in the future.
MongoDB 3.0 introduces a new pluggable storage engine API and a new storage engine called WiredTiger. The engineering team behind WiredTiger team has a long and distinguished career, having architected and built Berkeley DB, now the world's most widely used embedded database. In this talk we will describe our original design goals for WiredTiger, including considerations we made for heavily threaded hardware, large on-chip caches, and SSD storage. We'll also look at some of the latch-free and non-blocking algorithms we've implemented, as well as other techniques that improve scaling, overall throughput and latency. Finally, we'll take a look at some of the features we hope to incorporate into WiredTiger and MongoDB in the future.
Benchmarking, Load Testing, and Preventing Terrible DisastersMongoDB
"Have you ever crossed your fingers before performing an upgrade or switching storage engines, because you weren't quite sure what would happen? Have you ever been bitten by a slight change in behavior that turned out to be unexpectedly significant for your workload? At Parse we have developed a workflow that lets us repeatedly capture and replay real production workloads offline. This has allowed us to confidently perform upgrades across a large fleet with a minimum amount of canarying, and has helped us load test a variety of storage engines with real workloads so we can compare and understand the performance tradeoffs.
In this talk we will cover best practices for upgrades and migrations, and we will walk through how to use our open-sourced tooling to demonstrate how you can do the same. We will also share some fun war stories about various disasters found and averted *before* putting them into production thanks to offline benchmarking."
Webinar slides: Our Guide to MySQL & MariaDB Performance TuningSeveralnines
If you’re asking yourself the following questions when it comes to optimally running your MySQL or MariaDB databases:
- How do I tune them to make best use of the hardware?
- How do I optimize the Operating System?
- How do I best configure MySQL or MariaDB for a specific database workload?
Then this replay is for you!
We discuss some of the settings that are most often tweaked and which can bring you significant improvement in the performance of your MySQL or MariaDB database. We also cover some of the variables which are frequently modified even though they should not.
Performance tuning is not easy, especially if you’re not an experienced DBA, but you can go a surprisingly long way with a few basic guidelines.
This webinar builds upon blog posts by Krzysztof from the ‘Become a MySQL DBA’ series.
AGENDA
- What to tune and why?
- Tuning process
- Operating system tuning
- Memory
- I/O performance
- MySQL configuration tuning
- Memory
- I/O performance
- Useful tools
- Do’s and do not’s of MySQL tuning
- Changes in MySQL 8.0
SPEAKER
Krzysztof Książek, Senior Support Engineer at Severalnines, is a MySQL DBA with experience managing complex database environments for companies like Zendesk, Chegg, Pinterest and Flipboard.
MongoDB 3.2 introduces a host of new features and benefits, including encryption at rest, document validation, MongoDB Compass, numerous improvements to queries and the aggregation framework, and more. To take advantage of these features, your team needs an upgrade plan.
In this session, we’ll walk you through how to build an upgrade plan. We’ll show you how to validate your existing deployment, build a test environment with a representative workload, and detail how to carry out the upgrade. By the end, you should be prepared to start developing an upgrade plan for your deployment.
Back to Basics Webinar 6: Production DeploymentMongoDB
This is the final webinar of a Back to Basics series that will introduce you to the MongoDB database. This webinar will guide you through production deployment.
MongoDB SoCal 2020: Migrate Anything* to MongoDB AtlasMongoDB
During this talk we'll navigate through a customer's journey as they migrate an existing MongoDB deployment to MongoDB Atlas. While the migration itself can be as simple as a few clicks, the prep/post effort requires due diligence to ensure a smooth transfer. We'll cover these steps in detail and provide best practices. In addition, we’ll provide an overview of what to consider when migrating other cloud data stores, traditional databases and MongoDB imitations to MongoDB Atlas.
MongoDB SoCal 2020: Go on a Data Safari with MongoDB Charts!MongoDB
These days, everyone is expected to be a data analyst. But with so much data available, how can you make sense of it and be sure you're making the best decisions? One great approach is to use data visualizations. In this session, we take a complex dataset and show how the breadth of capabilities in MongoDB Charts can help you turn bits and bytes into insights.
MongoDB SoCal 2020: Using MongoDB Services in Kubernetes: Any Platform, Devel...MongoDB
MongoDB Kubernetes operator and MongoDB Open Service Broker are ready for production operations. Learn about how MongoDB can be used with the most popular container orchestration platform, Kubernetes, and bring self-service, persistent storage to your containerized applications. A demo will show you how easy it is to enable MongoDB clusters as an External Service using the Open Service Broker API for MongoDB
MongoDB SoCal 2020: A Complete Methodology of Data Modeling for MongoDBMongoDB
Are you new to schema design for MongoDB, or are you looking for a more complete or agile process than what you are following currently? In this talk, we will guide you through the phases of a flexible methodology that you can apply to projects ranging from small to large with very demanding requirements.
MongoDB SoCal 2020: From Pharmacist to Analyst: Leveraging MongoDB for Real-T...MongoDB
Humana, like many companies, is tackling the challenge of creating real-time insights from data that is diverse and rapidly changing. This is our journey of how we used MongoDB to combined traditional batch approaches with streaming technologies to provide continues alerting capabilities from real-time data streams.
MongoDB SoCal 2020: Best Practices for Working with IoT and Time-series DataMongoDB
Time series data is increasingly at the heart of modern applications - think IoT, stock trading, clickstreams, social media, and more. With the move from batch to real time systems, the efficient capture and analysis of time series data can enable organizations to better detect and respond to events ahead of their competitors or to improve operational efficiency to reduce cost and risk. Working with time series data is often different from regular application data, and there are best practices you should observe.
This talk covers:
Common components of an IoT solution
The challenges involved with managing time-series data in IoT applications
Different schema designs, and how these affect memory and disk utilization – two critical factors in application performance.
How to query, analyze and present IoT time-series data using MongoDB Compass and MongoDB Charts
At the end of the session, you will have a better understanding of key best practices in managing IoT time-series data with MongoDB.
Join this talk and test session with a MongoDB Developer Advocate where you'll go over the setup, configuration, and deployment of an Atlas environment. Create a service that you can take back in a production-ready state and prepare to unleash your inner genius.
MongoDB .local San Francisco 2020: Powering the new age data demands [Infosys]MongoDB
Our clients have unique use cases and data patterns that mandate the choice of a particular strategy. To implement these strategies, it is mandatory that we unlearn a lot of relational concepts while designing and rapidly developing efficient applications on NoSQL. In this session, we will talk about some of our client use cases, the strategies we have adopted, and the features of MongoDB that assisted in implementing these strategies.
MongoDB .local San Francisco 2020: Using Client Side Encryption in MongoDB 4.2MongoDB
Encryption is not a new concept to MongoDB. Encryption may occur in-transit (with TLS) and at-rest (with the encrypted storage engine). But MongoDB 4.2 introduces support for Client Side Encryption, ensuring the most sensitive data is encrypted before ever leaving the client application. Even full access to your MongoDB servers is not enough to decrypt this data. And better yet, Client Side Encryption can be enabled at the "flick of a switch".
This session covers using Client Side Encryption in your applications. This includes the necessary setup, how to encrypt data without sacrificing queryability, and what trade-offs to expect.
MongoDB .local San Francisco 2020: Using MongoDB Services in Kubernetes: any ...MongoDB
MongoDB Kubernetes operator is ready for prime-time. Learn about how MongoDB can be used with most popular orchestration platform, Kubernetes, and bring self-service, persistent storage to your containerized applications.
MongoDB .local San Francisco 2020: Go on a Data Safari with MongoDB Charts!MongoDB
These days, everyone is expected to be a data analyst. But with so much data available, how can you make sense of it and be sure you're making the best decisions? One great approach is to use data visualizations. In this session, we take a complex dataset and show how the breadth of capabilities in MongoDB Charts can help you turn bits and bytes into insights.
MongoDB .local San Francisco 2020: From SQL to NoSQL -- Changing Your MindsetMongoDB
When you need to model data, is your first instinct to start breaking it down into rows and columns? Mine used to be too. When you want to develop apps in a modern, agile way, NoSQL databases can be the best option. Come to this talk to learn how to take advantage of all that NoSQL databases have to offer and discover the benefits of changing your mindset from the legacy, tabular way of modeling data. We’ll compare and contrast the terms and concepts in SQL databases and MongoDB, explain the benefits of using MongoDB compared to SQL databases, and walk through data modeling basics so you feel confident as you begin using MongoDB.
MongoDB .local San Francisco 2020: MongoDB Atlas JumpstartMongoDB
Join this talk and test session with a MongoDB Developer Advocate where you'll go over the setup, configuration, and deployment of an Atlas environment. Create a service that you can take back in a production-ready state and prepare to unleash your inner genius.
MongoDB .local San Francisco 2020: Tips and Tricks++ for Querying and Indexin...MongoDB
Query performance should be the unsung hero of an application, but without proper configuration, can become a constant headache. When used properly, MongoDB provides extremely powerful querying capabilities. In this session, we'll discuss concepts like equality, sort, range, managing query predicates versus sequential predicates, and best practices to building multikey indexes.
MongoDB .local San Francisco 2020: Aggregation Pipeline Power++MongoDB
Aggregation pipeline has been able to power your analysis of data since version 2.2. In 4.2 we added more power and now you can use it for more powerful queries, updates, and outputting your data to existing collections. Come hear how you can do everything with the pipeline, including single-view, ETL, data roll-ups and materialized views.
MongoDB .local San Francisco 2020: A Complete Methodology of Data Modeling fo...MongoDB
Are you new to schema design for MongoDB, or are you looking for a more complete or agile process than what you are following currently? In this talk, we will guide you through the phases of a flexible methodology that you can apply to projects ranging from small to large with very demanding requirements.
MongoDB .local San Francisco 2020: MongoDB Atlas Data Lake Technical Deep DiveMongoDB
MongoDB Atlas Data Lake is a new service offered by MongoDB Atlas. Many organizations store long term, archival data in cost-effective storage like S3, GCP, and Azure Blobs. However, many of them do not have robust systems or tools to effectively utilize large amounts of data to inform decision making. MongoDB Atlas Data Lake is a service allowing organizations to analyze their long-term data to discover a wealth of information about their business.
This session will take a deep dive into the features that are currently available in MongoDB Atlas Data Lake and how they are implemented. In addition, we'll discuss future plans and opportunities and offer ample Q&A time with the engineers on the project.
MongoDB .local San Francisco 2020: Developing Alexa Skills with MongoDB & GolangMongoDB
Virtual assistants are becoming the new norm when it comes to daily life, with Amazon’s Alexa being the leader in the space. As a developer, not only do you need to make web and mobile compliant applications, but you need to be able to support virtual assistants like Alexa. However, the process isn’t quite the same between the platforms.
How do you handle requests? Where do you store your data and work with it to create meaningful responses with little delay? How much of your code needs to change between platforms?
In this session we’ll see how to design and develop applications known as Skills for Amazon Alexa powered devices using the Go programming language and MongoDB.
MongoDB .local Paris 2020: Realm : l'ingrédient secret pour de meilleures app...MongoDB
aux Core Data, appréciée par des centaines de milliers de développeurs. Apprenez ce qui rend Realm spécial et comment il peut être utilisé pour créer de meilleures applications plus rapidement.
MongoDB .local Paris 2020: Upply @MongoDB : Upply : Quand le Machine Learning...MongoDB
Il n’a jamais été aussi facile de commander en ligne et de se faire livrer en moins de 48h très souvent gratuitement. Cette simplicité d’usage cache un marché complexe de plus de 8000 milliards de $.
La data est bien connu du monde de la Supply Chain (itinéraires, informations sur les marchandises, douanes,…), mais la valeur de ces données opérationnelles reste peu exploitée. En alliant expertise métier et Data Science, Upply redéfinit les fondamentaux de la Supply Chain en proposant à chacun des acteurs de surmonter la volatilité et l’inefficacité du marché.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
StarCompliance is a leading firm specializing in the recovery of stolen cryptocurrency. Our comprehensive services are designed to assist individuals and organizations in navigating the complex process of fraud reporting, investigation, and fund recovery. We combine cutting-edge technology with expert legal support to provide a robust solution for victims of crypto theft.
Our Services Include:
Reporting to Tracking Authorities:
We immediately notify all relevant centralized exchanges (CEX), decentralized exchanges (DEX), and wallet providers about the stolen cryptocurrency. This ensures that the stolen assets are flagged as scam transactions, making it impossible for the thief to use them.
Assistance with Filing Police Reports:
We guide you through the process of filing a valid police report. Our support team provides detailed instructions on which police department to contact and helps you complete the necessary paperwork within the critical 72-hour window.
Launching the Refund Process:
Our team of experienced lawyers can initiate lawsuits on your behalf and represent you in various jurisdictions around the world. They work diligently to recover your stolen funds and ensure that justice is served.
At StarCompliance, we understand the urgency and stress involved in dealing with cryptocurrency theft. Our dedicated team works quickly and efficiently to provide you with the support and expertise needed to recover your assets. Trust us to be your partner in navigating the complexities of the crypto world and safeguarding your investments.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
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2.
3. Let the Tiger Roar!
MongoDB 3.0
Bryan Reinero
US Developer Advocate
bryan@mongodb.com
@blimpyacht
Github.com/breinero
8. How does MongoDB persist data?
• <= MongoDB 2.6
– One unique mechanism using Memory Mapped Files
– "mmapv1" Storage Engine
• MongoDB 3.0 has a few more options
– mmapv1 – default
– wiredTiger
– (in_memory – experimental only)
9. Pluggable Storage Engine API
http://www.livingincebuforums.com/ipb/uploads/monthly_10_2011/post-198-0-67871200-1318223706.jpg
10. Storage Engine API
• Allows to "plug-in" different storage engines
– Different work sets require different performance
characteristics
– mmapv1 is not ideal for all workloads
– More flexibility
• Can mix storage engines on same replica
set/sharded cluster
• Opportunity to integrate further ( HDFS, native
encrypted, hardware optimized …)
13. MMAPv1
• Improved concurrency control
• Great performance on read-heavy workloads
• Data & Indexes memory mapped into virtual
address space
• Data access is paged into RAM
• OS evicts using LRU
• More frequently used pages stay in RAM
15. What is WiredTiger?
• Storage engine company founded by BerkeleyDB alums
• Recently acquired by MongoDB
• Available as a storage engine option in MongoDB 3.0
16. Why is WiredTiger Awesome
• Document-level concurrency
• Disk Compression
• Consistency without journaling
• Better performance on certain workloads
– write heavy
17. Improving Concurrency
• 2.2 – Global Lock
• 2.4 – Database-level Locking
• 3.0 MMAPv1 – Collection-level Locking
• 3.0 WT – Document-level
– Writes no longer block all other writes
– Higher level of concurrency leads to more
CPU usage
18. Compression
• WT uses snappy compression by default
• Data is compressed on disk
• 2 supported compression algorithms
– snappy: default. Good compression, relatively low
overhead
– zlib: Better
• Indexes are compressed using prefix
compression
– Allows compression in memory
19. Consistency without Journaling
• MMAPv1 uses write-ahead log (journal) to
guarantee consistency
• WT doesn't have this need: no in-place updates
– Write-ahead log committed at checkpoints
• 2GB or 60sec by default – configurable!
– No journal commit interval: writes are written to
journal as they come in
– Better for insert-heavy workloads
• Replication guarantees the durability
25. Playing nice together
• Can not
– Can't copy database files
– Can't just restart w/ same dbpath
• Yes we can!
– Initial sync from replica set works perfectly!
– mongodump/restore
• Rolling upgrade of replica set to WT:
– Shutdown secondary
– Delete dbpath
– Relaunch w/ --storageEngine=wiredTiger
– Rollover
26. Other WT configuration options
• Compression: --wiredTigerCollectionBlockCompressor
• YAML format for configuration
27. Gotcha's!!!
• No 32-bit Support
– WT is 64bit only
• system.indexes & system.namespaces
deprecated
– Explicit commands: db.getIndexes() db.getCollectionNames()
31. Wider Range of Use Cases
How: Flexible Storage Architecture
• Fundamental rearchitecture, with new pluggable storage engine API
• Same data model, same query language, same ops
• But under the hood, many storage engines optimized for many use
cases
Single View Content Management
Real-Time Analytics Catalog
Internet of Things (IoT)Messaging
Log Data Tick Data
32. Up to 95% Lower Operational
Overhead
How: MongoDB Ops Manager
• The best way to run MongoDB
• Automates core management
tasks
• Single-click provisioning, scaling,
upgrades, administration
• Monitoring, with charts,
dashboards & alerts on 100+
metrics
• Backup and restore, with point-in-
time recovery
33. 7x-10x Performance, 50%-80% Less
Storage
How: WiredTiger Storage Engine
• Same data model, same query
language, same ops
• Write performance gains driven
by document-level concurrency
control
• Storage savings driven by native
compression
• 100% backwards compatible
• Non-disruptive upgrade
MongoDB 3.0MongoDB 2.6
Performance
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