MMS Release Notes, January 06 2015

MongoDB
January 12, 2015
#Cloud

This iteration was primarily spent wrapping up the final touches on Ops Manager 1.6. Ops Manager 1.6 will include automation, a Windows build (backup and monitoring only), as well as an Automation API, and will be released in February alongside MongoDB 2.8!

Nevertheless, quite a few fixes were completed this iteration - the cluster view was not displaying zero value points on charts (fixed!), we now show better information about servers that cannot be reached, and we no longer show the last ping tab for arbiters. Additionally, we fixed an issue where cron jobs were getting stuck in “running” status.

Backup: We continue to evolve our support for WiredTiger as 2.8 comes together. We’re actively working on queryable restores for 2.8 (mmapv1) and making theft jobs interruptable. We also fixed some edge cases that were preventing termination of backup jobs and fixed sync failures when indexes were created during inital sync. We now validate SCP credentials before starting data transfer of the restore files (i.e., fail fast).

Automation: We added support for new command line options, including popular setParameter variations and verbosity. We added some additional validations in the UI which will prevent users from adding arbiters with votes = 0, added support for another AWS region (Frankfurt, eu-central-1), and alphabetized the security group pulldown list elements. Users also can now provision servers with exceptionally long names, and we added more explanation in the UI for servers that cannot be terminated.

There were also updates to the monitoring and backup agents:

  • Monitoring agent (version 2.9.0.164) has improved error handling on Windows
  • Backup agent (version 3.0.0.246) has further enhancements to support backups of MongoDB 2.8
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Building your business is hard. Scaling your business data should not be.

Building your business is hard. Scaling your business data should not be. That's the message Sailthru CTO and co-founder Ian White relayed recently in New York. Over the course of a half-hour, White explained how Sailthru first did application-level sharding of its data out of necessity, but later moved to MongoDB's auto-sharding to massively simplify development. Success in the Billions Sailthru makes it easy for ecommerce and media brands to personalize content across a variety of channels, including email, onsite, mobile, social and more. As the company's customer base has swelled to over a billion users, 125 million content documents (e.g., URLs and products relevant to particular users) and 5 billion messages per month, Sailthru has come to store over 40 terabytes of data in MongoDB across 120 nodes on mostly physical infrastructure. As White suggests, "You can’t store this volume of data on just one node. We had to shard." Application-level Sharding at Sailthru When Sailthru first started, it didn't need sharding. But within two years Sailthru's customer count and data volumes were high enough that the company needed to partion its data. The question was: How? While some applications are either read heavy (online media site) or write heavy (logging and clickstream), Sailthru is both. As White explains, "We have to be able to read data and write personalized recommendations in real-time. MongoDB is a great database for this." Sailthru adopted MongoDB in the early days -- over four years ago. Prior to MongoDB 1.6, Sailthru partitioned much of its infrastructure using in-app sharding logic, as MongoDB didn't yet support auto-sharding. Sailthru partitioned data by client. Their application would examine each query, and dispatch to the appropriate replica set and collections based on a mapping configuration. This approach worked fine for a time at Sailthru. However, as Sailthru’s data grew, application-level sharding introduced significant code complexity and administration overhead. Application-level sharding also contributed to uneven load distribution, something Sailthru was able to Band-Aid by scaling up with more expensive servers. But the database team still had to manually rebalance and reallocate resources – every time Sailthru onboarded a sizable client that required a new shard, the database team would have to go in and add another line to the config file and redeploy. It was painful and demanding. Enter Auto-Sharding With the introduction of automatic sharding in 2010’s 1.6 release, the database itself manages the effort of distributing and balancing data across shards automatically. Sharding is transparent to applications – for 1 or 100 shards, the application code is the same. Setting up a sharded cluster involves making a critical decision - choosing a shard key. The shard key is the value the databse uses to determine placement of the document within shards. The Sailthru team considered several options, including sharding on client ID, MongoDB ID, or email. MongoDB supports multiple sharding strategies, and each is appropriate for different use cases. Ultimately, they opted to use hash-based sharding and MongoDB’s ObjectId as the shard key. With this approach, MongoDB does the work of ensuring a uniform distribution of reads and writes by randomizing the placement of documents across shards. To make the actual migration from application-level sharding to auto-sharding, the team used an open source tool created by MongoDB called MongoConnector. In the process of the migration, Sailthru forked the project, making significant contributions specific to their use case. With this change, it’s now possible for Sailthru to add shards without making any change to the code base. This meant that during a critical ramp-up time of tight resources and tight cash, Sailthru was able to focus their engineering efforts on improving their service and building new features, ensuring their phenomenal success. Build the Next Big Thing on MongoDB Thousands of organizations use MongoDB to build high-performance systems at scale . If you're interested in reading up on your own, download our Operations Best Practices white paper for additional information on operating and deploying a MongoDB system: Ops Best Practices About Kelly Stirman Kelly Stirman is Director of Products at MongoDB. Kelly works closely with customers, partners and the open-source community to articulate how MongoDB is quickly becoming the world's most popular database. For over 15 years he has worked at the forefront of database technologies. Prior to MongoDB, Kelly served in executive and leadership roles at Hadapt, MarkLogic, Oracle, GE, and PricewaterhouseCoopers.

January 12, 2015
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Ahamove Rides Vietnam’s E-commerce Boom with AI on MongoDB

The energy in Vietnam’s cities is frenetic as millions of people navigate the busy streets with determination and purpose. Much of this traffic is driven by e-commerce, with food and parcel deliveries perched on the back of the country’s countless motorcycles or in cars and trucks. In the first quarter of 2024, online spending in Vietnam grew a staggering 79% over the previous year. Explosive growth like this is expected to continue, raising the industry’s value to $32 billion by 2025 , with 70% of the country’s 100 million population making e-commerce transactions . With massive numbers like this, in logistics, efficiency is king. The high customer expectations for rapid deliveries drive companies like Ahamove to innovate their way to seamless operations with cloud technology. Ahamove is Vietnam’s largest on-demand delivery company, handling more than 200,000 e-commerce, food, and warehouse deliveries daily, with 100,000 drivers and riders plying the streets nationwide. The logistics leader serves a network of more than 300,000 merchants, including regional e-commerce giants like Lazada and Shopee, as well as nationwide supermarket chains and small restaurants. The stakes are high for all involved, so maximizing efficiency is of utmost importance. Innovating to make scale count Online shoppers’ behavior is rarely predictable, and to cope with sudden spikes in daily delivery demand, Ahamove needed to efficiently scale up its operations to enhance customer and end-user satisfaction. Moving to MongoDB Atlas on Amazon Web Services (AWS) in 2019, Ahamove fundamentally changed its ability to meet the rising demand for deliveries and new services that please e-commerce providers, online shoppers, and diners. The scalability of MongoDB is crucial for Ahamove, especially during peak times, like Christmas or Lunar New Year, when the volume of orders surges to more than 200,000 a day. “MongoDB's ability to scale ensures that the database can handle increased loads, including data requests, without compromising performance and leading to quicker order processing and improved user experience,” said Tien Ta, Strategic Planning Manager at Ahamove. One of the powerful services that improves e-commerce across Vietnam is geospatial queries enabled by MongoDB. Using this geospatial data associated with specific locations on Earth's surface, Ahamove can easily locate drivers, map drivers to restaurants to accelerate deliveries, and track orders without relying on third-party services to provide information, which slows deliveries. Meanwhile, the versatility of MongoDB’s developer data platform empowers Ahamove to store its operational data, metadata, and vector embeddings on MongoDB Atlas and seamlessly use Atlas Vector Search to index, retrieve, and build performant generative artificial intelligence (AI) applications. AI evolution Powered by MongoDB Atlas , Ahamove is transforming Vietnam’s e-commerce industry with innovations like instant order matching, real-time GPS vehicle tracking, generative AI chatbots, and services like driver rating and variable delivery times, all available 24 hours a day, seven days a week. In addition to traffic, Vietnam is also famous for its excellent street food. Recognizing the importance of the country’s rapidly growing food and beverage (F&B) industry, which is projected to be worth more than US$27.3 billion in 2024 , Ahamove decided to help Vietnam’s small food vendors benefit from the e-commerce boom gripping the country. Using the latest models, including ChatGPT-4o-mini and Llama 3.1, Ahamove’s fully automated generative AI chatbot on MongoDB integrates with restaurants’ Facebook pages. This makes it easier for hungry consumers to handle the entire order process with the restaurant in natural language, from seeking recommendations to placing orders, making payments, and tracking deliveries to their doorsteps. How AhaFood AI chatbot automates the food order journey “Vietnam’s e-commerce industry is growing rapidly as more people turn to their mobile devices to purchase goods and services,” added Ta. “With MongoDB, we meet this customer need for new purchase experiences with innovative services like generative AI chatbots and faster delivery times.” Anticipated to achieve 10% of food deliveries at Da Nang market and take the solution nationwide in the first half of 2025, AhaFood.AI - Ahamove’s latest initiative, also provides personalized dish recommendations based on consumer demographics, budgets, or historical preferences, helping people find and order their favorite food faster. Moreover, merchants receive timely notifications of incoming orders via the AhaMerchant web portal, allowing them to start preparing dishes earlier. AhaFood.AI also collects and securely stores users’ delivery addresses and phone numbers, ensuring better driver assignment and fulfilling food orders in less than 15 minutes. “Adopting MongoDB Atlas was one of the best decisions we’ve ever made for Ahamove, allowing us to build an effective infrastructure that can scale with growing demand and deliver a better experience for our drivers and customers,” said Ngon Pham, CEO, Ahamove. “Generative AI will significantly disrupt the e-commerce and food industry, and with MongoDB Vector Search we can rapidly build new solutions using the latest database and AI technology.” The vibrant atmosphere of Vietnam's bustling cities is part of the country's charm. Rather than seeking to bring calm to this energy, Vietnam thrives on it. Focusing on improving efficiency and supporting street food vendors in lively urban areas with cloud technology will benefit all. Learn how to build AI applications with MongoDB Atlas . Head over to our quick-start guide to get started with Atlas Vector Search today.

September 19, 2024