We hope you enjoyed part 1 of our Back to Basics series where we introduced you to NoSQL and MongoDB.
In part II we will actually get to code and build a blogging application. We will start in the Mongo Shell to show you how to interact directly with MongoDB on the command line, then move to an IDE to show you how to build a complete web application with the MongoDB Python driver.
Once your application is built we will show you how to add indexes to improve the performance of your queries. Like most databases, indexes can dramatically improve query performance. MongoDB comes with a query analyser and a tool called Explain that can give detailed insight into query performance.
At the end of this webinar you will know how to:
- Run MongoDB
- Create a basic database and set of collections using the MongoDB Shell
- Create a basic database and collection using one of our language drivers
- Add an index to a collection to improve query performance
- Review the efficiency of your queries using the explain framework
Register now for this webinar and all the remaining webinars in the series.
MongoDB Connector for Apache Spark: Announcing Early Access Program & New Spark Course
**Update: August 4th 2016** Since this original post, the connector has been declared generally available, for production usage. Click through for a tutorial on using the new MongoDB Connector for Apache Spark . We live in a world of “big data”. But it isn’t only the data itself that is valuable – it’s the insight it can generate. How quickly an organization can unlock and act on that insight has become a major source of competitive advantage. Collecting data in operational systems and then relying on nightly batch ETL (Extract Transform Load) processes to update the Enterprise Data Warehouse (EDW) is no longer sufficient. Speed-to-insight is critical, and so analytics against live operational data to drive real-time action is fast becoming a necessity, enabled by a new generation of technologies like MongoDB and Apache Spark. The new native MongoDB Connector for Apache Spark provides higher performance, greater ease of use, and access to more advanced Spark functionality than any connector available today. The new MongoDB University course for Apache Spark provides a fast track introduction for developers and data scientists building new generations of operational applications incorporating sophisticated real-time analytics. The Rise of Apache Spark Apache Spark is one of the fastest-growing big data projects in the history of the Apache Software Foundation. With its memory-oriented architecture, flexible processing libraries, and ease-of-use, Spark has emerged as a leading distributed computing framework for real-time analytics. As a general-purpose framework, Spark is used for many types of data processing – it comes packaged with support for machine learning, interactive queries (SQL), statistical queries with R, graph processing, ETL, and streaming. Spark allows programmers to develop complex, multi-step data pipelines using a directed acyclic graph (DAG) pattern. It supports in-memory data sharing across DAGs, so that different jobs can work with the same data. Additionally, Spark supports a variety of popular programming languages including Scala, Java, and Python. Sign up for the new Spark course at MongoDB University. For loading and storing data, Spark integrates with a number of storage and messaging platforms including Amazon S3, Kafka, HDFS, machine logs, relational databases, NoSQL datastores, MongoDB, and more. MongoDB and Spark Today While MongoDB natively offers rich real-time analytics capabilities , there are use cases where integrating the Spark engine can extend the processing of operational data managed by MongoDB. This allows users to operationalize results generated from Spark within real-time business processes supported by MongoDB. Examples of users already using MongoDB and Spark to build modern-data driven applications include: A multinational banking group operating in 31 countries with 51 million clients has implemented a unified real-time monitoring application with Apache Spark and MongoDB . The platform enables the bank to improve customer experience by continuously monitoring client activity across its online channels to check service response times and identify potential issues. A global manufacturing company estimates warranty returns by analyzing material samples from production lines. The collected data enables them to build predictive failure models using Spark machine learning and MongoDB. A video sharing website is using Spark with MongoDB to place relevant advertisements in front of users as they browse, view, and share videos. A global airline has consolidated customer data scattered across more than 100 systems into a single view stored in MongoDB. Spark processes are run against the live operational data in MongoDB to update customer classifications and personalize offers in real time, as the customer is live on the web or speaking with the call center. Artificial intelligence personal assistant company x.ai uses MongoDB and Spark for distributed machine learning problems. There are a number of ways users integrate MongoDB with Spark. For example, the MongoDB Connector for Hadoop provides a plug-in for Spark. There are also multiple 3rd party connectors available. Today we are announcing the early access to a new native Spark connector for MongoDB. Introducing the MongoDB Connector for Apache Spark The new MongoDB Connector for Apache Spark provides higher performance, greater ease of use and, access to more advanced Spark functionality than the MongoDB Connector for Hadoop. The following table compares the capabilities of both connectors. MongoDB Connector for Spark MongoDB Connector for Hadoop with Spark Plug-In Written in Scala, Spark’s native language Yes No, Java Support for Scala, Java, Python & R APIs Yes Yes Support for the Spark interactive shell Yes Yes Support for native Spark RDDs Yes No Java RDDs. More verbose and complex to work with Support for Spark DataFrames and Datasets Yes DataFrames Only Schema must be manually inferred Automated MongoDB schema inference Yes No Support for Spark core Yes Yes Support for Spark SQL Yes Yes Support for Spark Streaming Yes Yes Support for Spark Machine Learning Yes Yes Support for Spark GraphX Yes No Data locality awareness Yes The Spark connector is aware which MongoDB partitions are storing data No Support for MongoDB secondary indexes to filter input data Yes Yes Support for MongoDB aggregation pipeline to filter input data Yes No Compatibility with MongoDB replica sets and sharded clusters Yes Yes Support for MongoDB 2.6 and higher Yes Yes Support for Spark 1.6 and above Yes Yes Supported for production usage Not Currently Available for early access evaluation Yes Written in Spark’s native language, the new connector provides a more natural development experience for Spark users as they are quickly able to apply their Scala expertise. The connector provides access to the Spark interactive shell for data exploration and rapid prototyping. The connector exposes all of Spark’s libraries, enabling MongoDB data to be materialized as DataFrames and Datasets for analysis with SQL (benefiting from automatic schema inference), streaming, machine learning, and graph APIs. The Spark connector can take advantage of MongoDB’s aggregation pipeline and rich secondary indexes to extract, filter, and process only the range of data it needs – for example, analyzing all customers located in a specific geography. This is very different from more simple NoSQL datastores that do not offer either secondary indexes or in-database aggregations. In these cases, Spark would need to extract all data based on a simple primary key, even if only a subset of that data is required for the Spark process. This means more processing overhead, more hardware, and longer time-to-insight for the analyst. To maximize performance across large, distributed data sets, the Spark connector is aware of data locality in a MongoDB cluster. RDDs are automatically co-located with the associated MongoDB shard to minimize data movement across the cluster. The nearest read preference can be used to route Spark queries to the closest physical node in a MongoDB replica set, thus reducing latency. Review the MongoDB Connector for Spark documentation to learn how to get started with the connector, and view code snippets for different APIs and libraries. Fast Track to Apache Spark: New MongoDB University Course To get the most out of any technology, you need more than documentation and code. Over 350,000 students have registered for developer and operations courses from MongoDB University. Now developers and budding data scientists can get a quick-start introduction to Apache Spark and the MongoDB connector with early access to our new online course. Getting Started with Spark and MongoDB provides an introduction to Spark and teaches students how to use the new connector to build data analytics applications. In this course, we provide an overview of the Spark Scala and Java APIs with plenty of sample code and demonstrations. Upon completing this course, students will be able to: Outline the roles of major components in the Spark framework Connect Spark to MongoDB Source data from MongoDB for processing in Spark Write data from Spark into MongoDB The course does not assume a prior knowledge of Spark, but does require an intermediate level of expertise with MongoDB. The course is free. Sign up at MongoDB University . Next Steps To wrap up, we are very excited about the possibilities Spark and MongoDB present together, and we hope with the new connector and course, you will be well on your way to building modern, data-driven applications. We would love to hear from you as you explore this new connector and put it through its paces - you can provide feedback and file bugs under the MongoDB Spark Jira project . Here’s a summary of how to get started: Read the MongoDB Connector for Spark documentation and download the connector If you have any questions, please send them to the MongoDB user mailing list Sign up for the new Spark course at MongoDB University
A Hub for Eco-Positivity
In this guest blog post, Natalia Goncharova, founder and web developer for EcoHub — an online platform where people can search for and connect with more than 13,000 companies, NGOs, and governmental agencies across 200-plus countries — describes how the company uses MongoDB to generate momentum around global environmental change. There is no denying that sustainability has become a global concern. In fact, the topic has gone mainstream. A 2021 report by the Economist Intelligence Unit (EIU) shows a 71% rise in the popularity of searches for sustainable goods over the past five years. The report “measures engagement, awareness and action for nature in 27 languages, across 54 countries, covering 80% of the world’s population.” The EIU report states that the sustainability trend is accelerating in developing and emerging countries including Ecuador and Indonesia. For me, it’s not a lack of positive sentiment that is holding back change; it is our ability to turn ideas and goodwill into action. We need a way of harnessing this collective sentiment. In 2020, the decision to found EcoHub and devote so much time to it was a difficult one to make. I had just been promoted to team leader at work, and things were going well. Leaving my job with the goal of helping to protect our environment sounded ridiculous at times. Many questions raced through my mind, the most insistent one being: Will I be able to actually make a difference? However, as you’ll see in this post, my decision was ultimately quite clear. What is EcoHub? When I created EcoHub, my principal aim was to connect ecological NGOs and businesses. Now, EcoHub enables users to search a database of more than 10,000 organizations in more than 200 countries. You can search via a map or keyword. By making it easier to connect, EcoHub lets users quickly build networks of sustainably minded organizations. We believe networks are key to spreading good ideas, stripping out duplication, and building expertise. Building the platform has been a monumental task. I have developed it myself over the past few months, acting as product manager, project manager, and full-stack developer. (It wouldn’t be possible without my research, design, and media teams as well.) During the development of the EcoHub platform on MongoDB, the flexible schema helped us edit and add new fields in a document because the process doesn’t require defining data types. We had a situation in which it was necessary to change the schema and implement changes for all documents in the database. In this case, modifying the entire collection with MongoDB didn’t take long for an experienced developer. Additionally, MongoDB’s document-oriented data model works well with the way developers think. The model reflects how we see the objects in the codebase and makes the process easier. In my experience, the best resource to find answers when I ran into a question or issue was MongoDB documentation . It provides a good explanation of almost anything you want to do in your database. Search is everything In technical terms, my choices were ReactJS, NodeJS, and MongoDB. It is the latter that is so important to the effectiveness of the EcoHub platform. Search is everything. The easier we can make it for individuals or organizations to find like minds, the better. I knew from the start that I’d need a cloud-based database with strong querying abilities. As an experienced developer, I had previous experience with MongoDB and knew the company to be reliable, with excellent documentation and a really strong community of developers. It was a clear choice from the start. Choosing our partners carefully is also important. If EcoHub is to build awareness of environmental issues and foster collaboration, then we must ensure we make intelligent choices in terms of the companies we work with. I have been impressed with MongoDB’s sustainability commitments , particularly around diversity and inclusion, carbon reduction, and its appetite for exploring the way the business has an impact globally and locally. EcoHub search is built on the community version of MongoDB , which enables us to work quickly, implement easily and deliver the right performance. Importantly, as EcoHub grows and develops, MongoDB also allows us to make changes on the fly. As environmental concerns continue to grow, our database will expand. MongoDB enables our users to search, discover, and connect with environmental organizations all over the world. I believe these connections are key to sharing knowledge and expertise and helping local citizens coordinate their sustainability efforts. Commitment to sustainability When it came down to it, the decision to build EcoHub wasn’t as difficult as I initially thought. My commitment to sustainability actually started when I was young: I can remember myself at 8 years old, glued to the window, waiting for the monthly Greenpeace magazine to arrive. Later, that commitment grew as I went to university and graduated with a degree in Environmental Protection and Engineering. Soon after, I founded my first ecology organization and rallied our cityagainst businesses wanting to cut down our beautiful city parks. Starting EcoHub was a natural and exciting next step, despite the risks and unknown factors. I hope we can all join hands to create a sustainable future for ourselves, our children, and our animals and plants, and keep our planet beautiful and healthy. MongoDB Atlas makes operating MongoDB a snap at any scale. Determine the costs and benefits with our cost calculator .