June 17, 2014 This release is packed with fun and new UI items that should make MongoDB Monitoring and Backup customers happy. Such as:
- Brand new cluster page charts
- Upgraded Java driver to 2.6 version
- All agent logs can now be downloaded
- Add button to “Delete all Deactivated Hosts”
- Allow users to specify an extension when providing 2FA phone number
- And a new alert for repl lag on secondaries getting close to the oplog window on the primary.
Our MongoDB Backup Service also received some major tweaks this release. Backup restores can now read from secondaries, and there were further optimizations to improve backup processing and restore time, such as automatic clean up of orphaned heads and streaming oplog processing. The backup agent also received another update - MongoDB Backup Agent Version 22.214.171.124-1 contains support for new API which allows oplogs to be ingested before the entire payload has reached the MMS servers.
Our automation beta is also continuing with select customers. We’ve gotten a lot of feedback thus far, and are really looking forward to the official debut at MongoDB World on June 23rd. You can read more about that (and sign up to receive early access!) here.
Have an issue or a bug or a feature request? File a ticket in our feature request queue!
6 Rules of Thumb for MongoDB Schema Design: Part 3
By William Zola, Lead Technical Support Engineer at MongoDB This is our final stop in this tour of modeling One-to-N relationships in MongoDB. In the first post , I covered the three basic ways to model a One-to-N relationship. Last time , I covered some extensions to those basics: two-way referencing and denormalization. Denormalization allows you to avoid some application-level joins, at the expense of having more complex and expensive updates. Denormalizing one or more fields makes sense if those fields are read much more often than they are updated. Read part one and part two if you’ve missed them. Whoa! Look at All These Choices! So, to recap: You can embed, reference from the “one” side, or reference from the “N” side, or combine a pair of these techniques You can denormalize as many fields as you like into the “one” side or the “N” side Denormalization, in particular, gives you a lot of choices: if there are 8 candidates for denormalization in a relationship, there are 2 8 (1024) different ways to denormalize (including not denormalizing at all). Multiply that by the three different ways to do referencing, and you have over 3,000 different ways to model the relationship. Guess what? You now are stuck in the “paradox of choice” – because you have so many potential ways to model a “one-to-N” relationship, your choice on how to model it just got harder. Lots harder. Rules of Thumb: Your Guide Through the Rainbow Here are some “rules of thumb” to guide you through these indenumberable (but not infinite) choices One: favor embedding unless there is a compelling reason not to Two: needing to access an object on its own is a compelling reason not to embed it Three: Arrays should not grow without bound. If there are more than a couple of hundred documents on the “many” side, don’t embed them; if there are more than a few thousand documents on the “many” side, don’t use an array of ObjectID references. High-cardinality arrays are a compelling reason not to embed. Four: Don’t be afraid of application-level joins: if you index correctly and use the projection specifier (as shown in part 2) then application-level joins are barely more expensive than server-side joins in a relational database. Five: Consider the write/read ratio when denormalizing. A field that will mostly be read and only seldom updated is a good candidate for denormalization: if you denormalize a field that is updated frequently then the extra work of finding and updating all the instances is likely to overwhelm the savings that you get from denormalizing. Six: As always with MongoDB, how you model your data depends – entirely – on your particular application’s data access patterns. You want to structure your data to match the ways that your application queries and updates it. Your Guide To The Rainbow When modeling “One-to-N” relationships in MongoDB, you have a variety of choices, so you have to carefully think through the structure of your data. The main criteria you need to consider are: What is the cardinality of the relationship: is it “one-to-few”, “one-to-many”, or “one-to-squillions”? Do you need to access the object on the “N” side separately, or only in the context of the parent object? What is the ratio of updates to reads for a particular field? Your main choices for structuring the data are: For “one-to-few”, you can use an array of embedded documents For “one-to-many”, or on occasions when the “N” side must stand alone, you should use an array of references. You can also use a “parent-reference” on the “N” side if it optimizes your data access pattern. For “one-to-squillions”, you should use a “parent-reference” in the document storing the “N” side. Once you’ve decided on the overall structure of the data, then you can, if you choose, denormalize data across multiple documents, by either denormalizing data from the “One” side into the “N” side, or from the “N” side into the “One” side. You’d do this only for fields that are frequently read, get read much more often than they get updated, and where you don’t require strong consistency, since updating a denormalized value is slower, more expensive, and is not atomic. Productivity and Flexibility The upshot of all of this is that MongoDB gives you the ability to design your database schema to match the needs of your application. You can structure your data in MongoDB so that it adapts easily to change, and supports the queries and updates that you need to get the most out of your application.
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.