Storage-viz is a suite of web-based visualizers and new experimental database commands that may help you understand how MongoDB utilizes storage and organizes btrees. Storage-viz is now available in the MongoDB Nightly builds.
When a MongoDB collection is created, an on-disk extent is allocated to store the documents. Each time a newly created or updated document cannot fit into the existing collection’s extents, a new extent is created. Each document occupies a contiguous storage area - a record - in one of the collection’s extents. Storage-viz’ experimental storageDetails command extracts information about how the disk storage is used and the web-based visualizer generates an easy-to-read graphical representation. Storage-viz also showcases which parts of the collection’s extents are currently in RAM [NOTE: the visualizer doesn’t display how much memory is available].
MongoDB Indexing is accomplished with Btrees. Storage-viz’ _indexStats_ command and its web-based visualizer collect and display statistics related to the tree layout.
Want to try it?
Download the MongoDB Nightly Build (or 2.3.1 as soon as it’s available) from here and head to the Github repository for more information on how to use Storage-viz, submit feature requests or bug reports.
Keep in mind that the new commands are resource intensive and should be considered highly experimental for the time being. We suggest running them on a non-production server on a snapshot of your datafiles.
Storage-viz was designed, coded and tested by Andrea Lattuada, one of 10gen’s Interns. It has been an invaluable and greatly rewarding experience to work closely with 10gen’s Server Engineers and write code that is now available for everyone in the MongoDB community to use.
Get Ready for MongoSV: MongoDB Ops Track
For the first time at MongoSV , 10gen will offer a full track dedicated to operations for those interested in learning about the maintenance strategies and best practices for your MongoDB clusters. This track will include introductory and advanced sessions covering topics such as performance tuning and deployment. Here are some highlights to expect in the ops track : MongoDB Sharding with Brandon Black : This session will review MongoDB's sharding support, including an architectural overview, design principles, and strategies for automating load distribution. You will also gain insight into how to choose a shard key, which is an important design decision for building successful system. Advanced Sharding Features with Bernie Hackett: If you would like to take an in-depth look at shard keys and look at multi-data center and tag aware sharding, this talk will give you the full details. Attendees should be well versed in basic sharding and familiar with concepts in the morning’s basic sharding talk. Capacity Planning with Scott Hernandez: Deploying MongoDB can be a challenge if you don’t understand how resources are used nor how to plan for the capacity of your systems. If you need to deploy, or grow, a MongoDB single instance, replica set, or tens of sharded clusters then you probably share the same challenges in trying to size that deployment. This talk will cover what resources MongoDB uses, and how to plan for their use in your deployment. Topics covered will include understanding how to model and plan capacity needs from the perspective of a new deployment, growing an existing one, and defining where the steps along scalability on your path to the top. The goal of this presentation will be to provide you with the tools needed to be successful in managing your MongoDB capacity planning tasks. Journaling and the Storage Engine with Antoine Girbal: In this session, you'll look under the hood and gain an understanding of MongoDB's storage architecture. Understanding these concepts should help you understand how you can ensure that your data is safe. Lessons from the Field: Performance and Operations with Scott Hernandez: The format of this talk is an interactive and fun adventure through some real-world cases and best practices of large deployments. This session will touch on backups, network availability, performance pitfalls, indexing/schema-design, log management, monitoring and alerting along with some good examples of diagnostic techniques with a goal of finding good solutions. MongoDB Security Features: In this talk, VP of Products and Services Ron Avnur will discuss security features for MongoDB 2.4 There will also be a number of community talks on operations with other technologies, such as Nathen Harvey's talk on MongoDB and Chef and Miles Ward’s talk on Optimizing Your MongoDB Database on AWS. For all other MongoDB Ops topics and questions, head to the MongoSV Community forum at the conference. At the forum, you can sit down with 10gen engineers at the ...Ask the Expertsâ€œ tables, and speak with 10gen Co-Founders and Dwight Merriman and Eliot Horowitz at the afternoon whiteboard session. Learn more about MongoSV and the Community Forum at MongoSV.com . Tagged with: mongodb, operations, Ops, security, features
How DataSwitch And MongoDB Atlas Can Help Modernize Your Legacy Workloads
Data modernization is here to stay, and DataSwitch and MongoDB are leading the way forward. Research strongly indicates that the future of the Database Management System (DBMS) market is in the cloud, and the ideal way to shift from an outdated, legacy DBMS to a modern, cloud-friendly data warehouse is through data modernization. There are a few key factors driving this shift. Increasingly, companies need to store and manage unstructured data in a cloud-enabled system, as opposed to a legacy DBMS which is only designed for structured data. Moreover, the amount of data generated by a business is increasing at a rate of 55% to 65% every year and the majority of it is unstructured. A modernized database that can improve data quality and availability provides tremendous benefits in performance, scalability, and cost optimization. It also provides a foundation for improving business value through informed decision-making. Additionally, cloud-enabled databases support greater agility so you can upgrade current applications and build new ones faster to meet customer demand. Gartner predicts that by 2022, 75% of all databases will be on the cloud – either by direct deployment or through data migration and modernization. But research shows that over 40% of migration projects fail. This is due to challenges such as: Inadequate knowledge of legacy applications and their data design Complexity of code and design from different legacy applications Lack of automation tools for transforming from legacy data processing to cloud-friendly data and processes It is essential to harness a strategic approach and choose the right partner for your data modernization journey. We’re here to help you do just that. Why MongoDB? MongoDB is built for modern application developers and for the cloud era. As a general purpose, document-based, distributed database, it facilitates high productivity and can handle huge volumes of data. The document database stores data in JSON-like documents and is built on a scale-out architecture that is optimal for any kind of developer who builds scalable applications through agile methodologies. Ultimately, MongoDB fosters business agility, scalability and innovation. Key MongoDB advantages include: Rich JSON Documents Powerful query language Multi-cloud data distribution Security of sensitive data Quick storage and retrieval of data Capacity for huge volumes of data and traffic Design supports greater developer productivity Extremely reliable for mission-critical workloads Architected for optimal performance and efficiency Key advantages of MongoDB Atlas , MongoDB’s hosted database as a service, include: Multi-cloud data distribution Secure for sensitive data Designed for developer productivity Reliable for mission critical workloads Built for optimal performance Managed for operational efficiency To be clear, JSON documents are the most productive way to work with data as they support nested objects and arrays as values. They also support schemas that are flexible and dynamic. MongoDB’s powerful query language enables sorting and filtering of any field, regardless of how nested it is in a document. Moreover, it provides support for aggregations as well as modern use cases including graph search, geo-based search and text search. Queries are in JSON and are easy to compose. MongoDB provides support for joins in queries. MongoDB supports two types of relationships with the ability to reference and embed. It has all the power of a relational database and much, much more. Companies of all sizes can use MongoDB as it successfully operates on a large and mature platform ecosystem. Developers enjoy a great user experience with the ability to provision MongoDB Atlas clusters and commence coding instantly. A global community of developers and consultants makes it easy to get the help you need, if and when you need it. In addition, MongoDB supports all major languages and provides enterprise-grade support. Why DataSwitch as a partner for MongoDB? Automated schema re-design, data migration & code conversion DataSwitch is a trusted partner for cost-effective, accelerated solutions for digital data transformation, migration and modernization through a modern database platform. Our no-code and low-code solutions along with cloud data expertise and unique, automated schema generation accelerates time to market. We provide end-to-end data, schema and process migration with automated replatforming and refactoring, thereby delivering: 50% faster time to market 60% reduction in total cost of delivery Assured quality with built-in best practices, guidelines and accuracy Data modernization: How “DataSwitch Migrate” helps you migrate from RDBMS to MongoDB DataSwitch Migrate (“DS Migrate”) is a no-code and low-code toolkit that leverages advanced automation to provide intuitive, predictive and self-serviceable schema redesign from a traditional RDBMS model to MongoDB’s Document Model with built-in best practices. Based on data volume, performance, and criticality, DS Migrate automatically recommends the appropriate ETTL (Extract, Transfer, Transform & Load) data migration process. DataSwitch delivers data engineering solutions and transformations in half the timeframe of the existing typical data modernization solutions. Consider these key areas: Schema redesign – construct a new framework for data management. DS Migrate provides automated data migration and transformation based on your redesigned schema, as well as no-touch code conversion from legacy data scripts to MongoDB Atlas APIs. Users can simply drag and drop the schema for redesign and the platform converts it to a document-based JSON structure by applying MongoDB modeling best practices. The platform then automatically migrates data to the new, re-designed JSON structure. It also converts the legacy database script for MongoDB. This automated, user-friendly data migration is faster than anything you’ve ever seen. Here’s a look at how the schema designer works. Refactoring – change the data structure to match the new schema. DS Migrate handles this through auto code generation for migrating the data. This is far beyond a mere lift and shift. DataSwitch takes care of refactoring and replatforming (moving from the legacy platform to MongoDB) automatically. It is a game-changing unique capability to perform all these tasks within a single platform. Security – mask and tokenize data while moving the data from on-premise to the cloud. As the data is moving to a potentially public cloud, you must keep it secure. DataSwitch’s tool has the capability to configure and apply security measures automatically while migrating the data. Data Quality – ensure that data is clean, complete, trustworthy, consistent. DataSwitch allows you to configure your own quality rules and automatically apply them during data migration. In summary: first, the DataSwitch tool automatically extracts the data from an existing database, like Oracle. It then exports the data and stores it locally before zipping and transferring it to the cloud. Next, DataSwitch transforms the data by altering the data structure to match the re-designed schema, and applying data security measures during the transform step. Lastly, DS Migrate loads the data and processes it into MongoDB in its entirety. Process Conversion Process conversion, where scripts and process logic are migrated from legacy DBMS to a modern DBMS, is made easier thanks to a high degree of automation. Minimal coding and manual intervention are required and the journey is accelerated. It involves: DML – Data Manipulation Language CRUD – typical application functionality (Create, Read, Update & Delete) Converting to the equivalent of MongoDB Atlas API Degree of automation DataSwitch provides during Migration Schema Migration Activities DS Automation Capabilities Application Data Usage Analysis 70% 3NF to NoSQL Schema Recommendation 60% Schema Re-Design Self Services 50% Predictive Data Mapping 60% Process Migration Activities DS Automation Capabilities CRUD based SQL conversion (Oracle, MySQL, SQLServer, Teradata, DB2) to MongoDB API 70% Data Migration Activities DS Automation Capabilities Migration Script Creation 90% Historical Data Migration 90% 2 Catch Load 90% DataSwitch Legacy Modernization as a Service (LMaas): Our consulting expertise combined with the DS Migrate tool allows us to harness the power of the cloud for data transformation of RDBMS legacy data systems to MongoDB. Our solution delivers legacy transformation in half the time frame through pay-per-usage. Key strengths include: ● Data Architecture Consulting ● Data Modernization Assessment and Migration Strategy ● Specialized Modernization Services DS Migrate Architecture Diagram Contact us to learn more.