MongoDB 3.4.0-rc3 is out and is ready for testing. This is the culmination of the 3.3.x development series.
Fixed in this release candidate:
- SERVER Configurable connection pools size for mongos
- SERVER Limit total memory utilization for bulk index builds
- SERVER Additional tests for views on sharded collections
- SERVER Over 25% regression on mongodb using YCSB workload
- SERVER Minor speed regression (13%) and 'choppy' performance in 3.4 vs 3.2
- TOOLS A single invocation of mongoreplay replays the ops twice
- TOOLS Connections never closed during replay
As always, please let us know of any issues.
-- The MongoDB Team
Leaf in the Wild: How Loopd uses MongoDB to Power its Advanced Location-Tracking Platform for Conferences
Conferences can be incredibly hectic experiences for everyone involved. You have attendees wanting to meet and exchange information, sponsors and exhibitors looking to maximize foot traffic to their booths, and the conference hosts trying to get a sense of how they can optimize their event and if it was all worth it in the end. While sponsors usually do get a lead list immediately after an event for their troubles, attendees often struggle to remember who they actually spoke to and event hosts are often left in the dark about what they can do to maximize the returns on their investments. Enter Loopd, an advanced events engagement platform. I sat down with their CEO, Brian Friedman, to understand how they’re using MongoDB to help conference attendees and event hosts separate the signal from the noise. Tell us about Loopd. Loopd provides physical intelligence for corporate events. We help corporate marketers learn how people interact with each other, with their company, and with their company's products. The Loopd event engagement system is the industry's only bi-directional CRM solution that enables the exchange of content and contact information passively and automatically. We equip conference attendees with Loopd wearable badges, which can be used to easily exchange contact information or gain entry into sessions. Through our enterprise IoT analytics and sensors, we then gather and interpret rich data so that marketers have a more sophisticated understanding of business relationships and interactions at conferences, exhibits and product activation events. Some of our clients include Intel, Box, Twilio, and MongoDB. Bluetooth LE Loopd Badges How are you using MongoDB? We use MongoDB to store millions of datapoints from connected advertising and Bluetooth LE Loopd Badges on the conference floor. All of the attendee movement data captured by the Loopd Badge at an event can be thought of as time series data associated with location information. We track each Loopd Badge’s location and movement path in real time during the event. As a result, we handle heavy write operations during an event to make sure any and all calculations are consistent, timely, and accurate. We also use the database for real-time analysis. For example, we calculate the number of attendee visits & returns, and average time durations in near real time. We use the aggregation framework in MongoDB to make this happen. What did you use before MongoDB? Before MongoDB, we used PostgreSQL as our main data store. We used Redis as a temporary data buffer queue for storing new movement data. The data was dumped, inserted, and updated into rows in the SQL database once per second. The raw location data was read and parsed from the SQL database into a user-readable format. We needed a temporary buffer because the high volume of insert and update requests drained available resources. What challenges did you face with PostgreSQL? With PostgreSQL, we needed a separate Redis caching server to buffer write and update operations before storing them in the database, which added architectural and operational complexity. It also wasn’t easy to scale as it’s not designed to be deployed across multiple instances. How did MongoDB help you resolve those challenges? When we switched to MongoDB from PostgreSQL, our write throughput significantly increased, removing the need for a separate caching server in between the client and the database. We were able to halve our VM resource consumption (CPU power and memory), which translated to significant cost savings. As a bonus, our simplified underlying architecture is now much easier to manage. Finally, one of the great things about MongoDB is its data model flexibility, which allows us to rapidly adapt our schema to support new application demands, without the need to incur downtime or manage complex schema migrations. Please describe your MongoDB deployment. We typically run one replica set per event. The database size depends on the event — for MongoDB World 2016, we generated about 2 million documents over the course of a couple of days. We don’t shard our MongoDB deployments yet but having that ability in our back pocket will be very important for us going forward. At the moment, all of our read queries are executed on the secondaries in the replica set, which means write throughput isn’t impacted by read operations. The smallest analytics window in our application is a minute, which means we can tolerate any eventual consistency from secondary reads. Our MongoDB deployments are hosted in Google Cloud VM instances. We’re exploring using containers but they’re currently not in use for any production environments. We’re also evaluating Spark and Hadoop for doing some more interesting things with the data we have in MongoDB. What version of MongoDB are you running? We use MongoDB 3.2. We find the added document validation feature very valuable for checking data types. While we will still perform application-level error validation, we appreciate this added level of security. What advice do you have for other companies looking to start with MongoDB? MongoDB is flexible, scalable, and quite developer and DBA friendly, even if you’re used to RDBMS. We would recommend familiarizing yourself with the basic concepts of MongoDB first, heavily leaning on the community during learning. I’d also recommend reading the production notes to optimize system configuration operational parameters. Brian, thanks for taking the time to share your story with the MongoDB community. Thinking of migrating to MongoDB from a relational database? Learn more from our guide: Download RDBMS Migration Guide
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.