MongoDB 3.2.10-rc2 is out and is ready for testing. This is a release candidate containing only fixes since 3.2.9. The next stable release 3.2.10 will be a recommended upgrade for all 3.2 users.
Fixed in this release:
- SERVER-12048 Calling "service mongod start" with mongod running prevents "service mongod stop" from working
- SERVER-16801 Update considers a change in numerical type to be a noop
- SERVER-24885 The systemd MaxTasks feature can prevent mongod from accepting new connections
- SERVER-24971 Excessive memory held by sessions when application threads do evictions
- SERVER-25478 Use wtimeout in sh.setBalancerState
- SERVER-25951 Report additional metrics in getMore slowms logging
- SERVER-25039 Aggregation can attempt to re-plan after collection has been dropped
- SERVER-25478 Use wtimeout in sh.setBalancerState
- TOOLS-1429 mongostat panic when monitored server is restarted
- WT-2026 Maximum pages size at eviction too large
- WT-2924 Ensure we are doing eviction when threads are waiting for it
As always, please let us know of any issues.
-- The MongoDB Team
How Saavn Grew to India’s Largest Music Streaming Service with MongoDB
Building a push notification system on a sophisticated data analytics pipeline powered by Apache Kafka, Storm and MongoDB 2015 was an important year for the music industry. It was the first time digital became the primary revenue source for recorded music, overtaking sales of physical formats. Key to this milestone was the revenue generated by streaming services – growing over 45% in a single year. As with many consumer services, the music streaming market is fragmented across the globe. In India – the 2nd most populous country on the planet and second largest smartphone market – Saavn has grown to become the sub-continent’s largest music service. It has 80m subscribers, experiencing a 9x increase in Daily Active Users (DAU) in just 24 months, with 90% of its streams served to mobile users. There are many factors that collectively have driven Saavn’s growth – but at the heart of it is data. And for this, they rely on MongoDB. !(https://webassets.mongodb.com/_com_assets/cms/Saavn-Logo-Horizontal-White-500-eua0kyb1uk.png) Saavn started out using MongoDB as a persistent cache, replacing an existing memcached layer. They soon realised the versatility and flexibility of the database to serve as the system of record for its data on subscribers, devices, and user activity. It was MongoDB’s flexibility and scalability that proved instrumental to maintain pace with Saavn’s breakneck growth. Through its extensive collection of music, the company quickly attracted new users to its streaming service, but found engagement often dropped away. It identified that push notifications sent directly to client devices was key to reconnecting with users, and keeping them engaged by serving personalized playlists. At this year’s MongoDB World conference, CTO Sriranjan Manjunath, presented how Saavn has used MongoDB as part of a sophisticated analytics pipeline to drive a 3x increase in user engagement. As Sriranjan and his team observed, it wasn’t enough to simply broadcast generic notifications to its users. Instead Saavn needed to craft notifications that provided playlists personalized to each user. Saavn built a sophisticated data processing pipeline that uses a scheduler to extract device, activity and user data stored in MongoDB. From there, it computes relevant playlists by analyzing a user’s listening preferences, activity, device, location and more. It then sends the computed recommendations to a dispatcher process that delivers the playlist to each user’s device and inbox. To refine personalizations, all user activity is ingested back into a Kafka queue where it is processed by Apache Storm and written back to MongoDB. Saavn is also expanding its use of artificial intelligence to better predict users interests, and is using MongoDB to store the resultant machine learning models and serve them in real time to the recommender application. The system currently sends 30m notifications per day, but has been sized to support up to 1m per minute, providing plenty of headroom to support Saavn’s continued growth. In his presentation, Sriranjan discussed how Saavn migrated from MongoDB 2.6 to MongoDB 3.0, taking advantage of the WiredTiger storage engine’s document level concurrency control to deliver improved performance. He talks about his key learnings in modifying schema design to reflect the differences in how updates are handled by the underlying storage engine, and usage of TTL indexes to automatically expire data from MongoDB . Sriranjan also discusses shard key selection to optimize uniform data distribution across the cluster, and the benefits of using MongoDB Cloud Manager for system monitoring and continuous backups, including integration with Slack for automated alerting to the ops team. Click through to view Saavn’s presentation from MongoDB World To learn more about managing real time streaming data, download: The MongoDB and Kafka white paper About the author - Mat Keep Mat is a director within the MongoDB product marketing team, responsible for building the vision, positioning and content for MongoDB’s products and services, including the analysis of market trends and customer requirements. Prior to MongoDB, Mat was director of product management at Oracle Corp. with responsibility for the MySQL database in web, telecoms, cloud and big data workloads. This followed a series of sales, business development and analyst / programmer positions with both technology vendors and end-user companies.
Revolutionizing Data Storage and Analytics with MongoDB Atlas on Google Cloud and HCL
Every organization requires data they can trust—and access—regardless of its format, size, or location. The rapid pace of change in technology and the shift towards cloud computing is revolutionizing how companies handle, govern and manage their data by freeing them from the heavy operational burden of on-premise deployments. Enterprises are looking for a centralized, cost-effective solution that allows them to scale their storage and analytics so they can ingest data and perform artificial intelligence (AI) and machine learning (ML) operations, ultimately expanding their marketing horizon. This blog post explores why companies should partner with MongoDB Atlas on Google Cloud to begin their data revolution journey, and how HCL Technologies can support customers looking to migrate. MongoDB Atlas as the distributed data platform MongoDB Atlas is the leading database-as-a-service on the market for three main reasons: Unparalleled developer experience - allows organizations to bring new features to market at a high velocity Horizontal scalability - supports hundreds of terabytes of data with sub-second queries Flexibility - stores data to meet various regulatory, operational, and high availability requirements. The versatility offered by MongoDB’s document model makes it ideal for modern data-driven use cases that require support for structured, semi-structured, and unstructured content all within a single platform. Its flexible schema allows changes to support new application features without costly schema migrations typically required with relational databases. MongoDB Atlas extends the core database by offering services like Atlas Search and MongoDB Realm that are a necessity for modern applications. Atlas Search provides a powerful Apache Lucene-based full text search engine that automatically indexes data in your MongoDB database without the need for a separate dedicated search engine or error-prone replication processes. Realm provides edge-to-cloud sync and backend services to accelerate and simplify mobile and web development. Atlas’ distributed architecture supports horizontal scaling for data volume, query latency, and query throughput which offers the scalability benefits of distributed data storage alongside the rich functionality of a fully-featured general purpose database. MongoDB Atlas is unique in its ability to provide the most wanted database as a managed service and is relied on by the world’s largest companies for their mission-critical production applications. Innovation powered by collaboration with HCL Technologies MongoDB’s versatility as a general-purpose database, in addition to its massive scalability, makes it a perfect foundation for analytics, visualization, and AI/ML applications on Google Cloud. As an MSP partner for Google Cloud, HCL Technologies helps enterprises accelerate and risk-mitigate their digital agenda, powered by Google Cloud. We’ve successfully implemented applications leveraging MongoDB Atlas on Google Cloud, building upon MongoDB’s flexible JSON-like data model, rich querying and indexing, and elastic scalability in conjunction with Google Cloud’s class-leading cloud infrastructure, data analytics, and machine learning capabilities. HCL is working with some of the world’s largest enterprises in building secure, performant, and cost-effective solutions with MongoDB and Google. Possessing technical expertise in Google Cloud, MongoDB, machine learning, and data science, our dedicated team developed a reference architecture that ensures high performance and scalability. This is simplified by MongoDB Atlas’ support for Google Cloud services which allows it to essentially operate as a cloud-native solution. Highlighted features include: Integration with Google Cloud Key Management Service Use of Google Cloud’s native storage snapshot for fast backup and restore Ability to create read-only MongoDB nodes in Google Cloud to reduce latency with Google Cloud-native services regardless of where the primary node is located (even other public cloud providers!) Integrated billing with Google Cloud Ability to span a single MongoDB cluster across Google Cloud regions worldwide, and more As represented in Figure 1 below, MongoDB Atlas on Google Cloud can be used as a single database solution for transactional, operational, and analytical workloads across a variety of use cases. Figure 1: MongoDB's core characteristics and features The following architecture in Figure 2 demonstrates the ease of reading and writing data to MongoDB from Google Cloud services. Dataflow, Cloud Data Fusion, and Dataproc can be leveraged to build data pipelines to migrate data from heterogeneous databases to MongoDB and to feed data to create interactive dashboards using Looker. These data pipelines support both batch and real-time ingestion workloads and can be automated and orchestrated using Google Cloud - native services.. Figure 2: MongoDB Atlas' integration with core Google Cloud services A data platform built using MongoDB Atlas and Google Cloud offers an integrated suite of services for storage, analysis, and visualization. Address your business challenges with HCL: Industry use cases Data-driven solutions built with MongoDB Atlas on Google Cloud have multiple applications across industries such as financial services, media and entertainment, healthcare, oil and gas, energy, manufacturing, retail, and the public sector. Every industry can benefit from this highly integrated storage and analytical solution. Use Cases and Benefits Data lake modernization with low cost and high availability for media and entertainment customers: Maintaining high availability and a low-cost data lake is an obstacle for any online entertainment platform that builds mobile or web ticketing applications. However, building on Google App Engine with MongoDB Atlas Clusters in the backend allows for a high-availability, low-cost data platform that seamlessly feeds data to downstream analytics platforms in real time. Unified data platform for retail customers: The retail business frequently requests an agile environment in order to encourage innovation among its engineers. With its agility in scaling and resource management, seamless multi-region clusters, and premium monitoring, running MongoDB Atlas on Google Cloud is a fantastic choice for building a single data platform. This simplifies the management of different data platforms and allows developers to focus on new ideas. High-speed real-time data platform of supply chain system for manufacturing units: By having real-time visibility and distributed data services, supply chain data can become a competitive advantage. MongoDB Atlas on Google Cloud provides a solid foundation for creating distributed data services with a unified, easy-to-maintain architecture. The unrivaled speed of MongoDB Atlas simplifies supply chain operations with real-time data analytics. The way forward Even in just the past decade, organizations have been forced to adapt to the extremely fast pace of innovation in the data analytics landscape: moving from batch to real-time, on-premise to cloud, gigabytes to petabytes, and the increased accessibility of advanced AI/ML models thanks to providers like Google Cloud. With our track record of success in this domain, HCL Technologies is uniquely positioned to help organizations realize the joint benefits of building data analytics applications with best-of-breed solutions from Google Cloud and MongoDB. Visit us to learn more about the HCL Google Ecosystem Business Unit and how we can help you harness the power of MongoDB Atlas and Google Cloud Platform to change the way you store and analyze your data through these solutions.