![]() “We need a database capable of handling this load without any issues or latency, even during extremely high-volume periods such as New Year’s Eve. “We get hundreds of thousands of requests per minute,” he explains. With thousands of riders on the streets of India continuously publishing their locations at very high rates, flexible and responsive persistent storage is vital. System simplifies the onboarding process by collecting and storing drivers’ names, addresses, driving licenses, vehicle type and more. A key marketing tool, it creates bespoke campaigns and offers, and suggests local restaurants to users. System is what most people see when using Zomato app. System displays items and quantities for each order to ensure smooth fulfillment.įeature combines key information such as a rider’s location, their distance from the partner restaurant and customer, and the type of transport used. ![]() System is one of the most critical components, and one of the most complex. ![]() System covers live order statuses as well as live locations of delivery partners. Using features such as index suggestions, geospatial queries and analytics nodes, MongoDB is now the driving force behind many of Zomato’s key operational systems: As Zomato and its portfolios kept on expanding, so did our use cases for MongoDB.” “We realized that it would be prudent to migrate all our self-hosted clusters to a managed platform, which is when we integrated with MongoDB Atlas. “Zomato was expanding at a remarkable pace,” says Abhishek Jain, senior software engineer, Zomato at MongoDB.local New Delhi earlier this year. However, in 2017 Zomato became increasingly aware of the pain points of database management. With over 17.5 million customers, 220,000 restaurant partners and 350,000 delivery partners, Zomato deals with data in huge quantities. The restaurant aggregator and food delivery operator provides restaurant information, menus and user reviews, and food delivery options from partner restaurants in over 1,000 Indian cities and towns. Zomato is one of India’s largest consumer technology companies. Zomato manages high-volume data and delivers high-speed success In Compass, exporting data is simple: just select a collection, optionally filter the data with a query and use the export functionality to save it as JSON or CSV. Sometimes, however, you may want to export your data (or a subset of it) to use it in other tools. ![]() When your data is stored in MongoDB, you can query and aggregate it in many different ways to extract insights, and you can visualize your insights with MongoDB Charts. ![]() How can you export data to MongoDB using Compass? Additionally, for each field you can specify a data type when something other than “String” is selected, the values will be converted automatically during the import operation without requiring additional batch operations after the import is completed. You are now able to configure the separator that is used in the CSV file you are trying to import and you can also choose what fields should be imported. When it comes to importing CSV files, we’ve given you more control over what is being imported. When you import JSON files, in addition to JSON lines files with Extended JSON, we now support importing from files containing JSON arrays, which is closer to how developers think, is a standard format for REST API responses, and is consistent with the functionality of mongoimport. With this new release, we’ve made it even easier and more powerful. In Compass, it has always been quite easy to import data – from JSON and CSV files – into a collection. How can you import data to MongoDB using Compass? ![]()
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