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
How to Prepare for Your Engineering Interview at MongoDB
MongoDB’s Engineering team is full of creative individuals who play an impactful role in building our industry-leading technology. Our interview process is designed to ensure that you and MongoDB are a great match, and, no matter how many interviews you have done in the past, being prepared is the key to being successful. At MongoDB, we do our best to make sure you have a great interview experience and an opportunity to learn about our company, culture, and the people you will be working with. To help you prepare for your technical interviews, we want to share some tips. Research is key Candidates who do research and come prepared for interviews at MongoDB are able to make the most of their interview process. People sometimes think they do not need to do research because they are already familiar with our products, but that will set you up for unexpected surprises. Before beginning your interviews, you should have high-level knowledge of our company’s mission, values, and goals . The in-depth technical information you can learn about MongoDB and the role and team you are interviewing for may also help set you apart from other candidates. MongoDB has a variety of products and Engineering teams, and this information will give you a chance to learn more about what we are working on, technical stacks we use, and what you’d be contributing to if you joined. Take a look at some of the resources below, and use them to your advantage. MongoDB Blog : Our blog is updated regularly with new posts about life at MongoDB, news, products, and events. MongoDB University : This platform was created to empower developers through education. We offer completely free online courses led by Curriculum Engineers for any learner, whether you’re just getting started or already familiar with MongoDB. MongoDB Documentation : The documentation page has detailed information about our products and tools that will give you an idea of what you will be working on as an engineer. MongoDB Developer Hub : The developer hub provides articles on and resources for how to get started with MongoDB. Learn from our Developer Advocates and the MongoDB community! Types of interviews After doing some initial research, it is important to prepare for the actual interviews. Our interview process usually includes one or two virtual interviews and then an onsite interview, which we are currently conducting via Zoom. This may change in accordance with company and COVID-19 guidelines. These interviews and what they cover will vary by team, so it is important to speak with your recruiter and ask for any additional tips or insight into what to expect. Our recruiting process is primarily team-based, which means you’ll interview for a role on a specific team, and many of your interviewers will be team members, as well as your manager. In general, you can expect to receive questions about your background, interest in MongoDB, and why you are interviewing to work with that team. You’ll also have the opportunity to ask your interviewers questions about all things MongoDB. Technical Interviews Technical interviews have a variety of areas that may be covered, including concurrency, distributed systems, algorithms, system design, and language-specific coding. An important part of the technical interview that often goes under the radar is the need for effective communication when talking through your thought process or discussing the problems that are presented. Below are some of the things our engineers look for in a good technical performance. Writing code: strong understanding of the language being used, code is concurrency-safe, works in edge cases, good object-oriented design Software engineering: understanding of data structures and algorithms, considering trade-offs (e.g., run time vs. memory), testing your code Collaboration: clear and concise code that is readable and organized, responding well to suggestions or hints, effective communication about difficulties faced Systems design: design a solution to scale to high levels of concurrency, throughput, and reliability. Does it avoid common bottlenecks, how do we prove its correctness, and what are the trade-offs or alternative solutions? Behavioral Interviews Behavioral interviews focus on how you may add to the culture we continue to build at MongoDB. Reviewing our code of conduct and core values will show you how we operate as a company and what we expect from our employees. Other topics of discussion you should expect in these interviews are successes and failures, what you have learned from these experiences, and what you are looking for in your next role. We will also ask you about your experience with mentoring and learning from other engineers and leaders, your goals and aspirations for the future, and your experience with owning or leading projects. What we offer There are a few things we can promise if you decide to interview for an Engineering role at MongoDB. First, you’ll have a speedy and transparent process with a single, dedicated recruiter. We tailor each of our interview processes to fit the role’s responsibilities and seniority level, and you won’t be asked any riddle questions that aren’t related to the work you’d be doing. Our interview questions are typically sourced from real problems we have had to solve. You’ll also have the opportunity to interact with your future manager and some future teammates, and we hope you find that your interviewers are genuinely interested in you as a person and seeing you succeed at MongoDB. We believe different experiences, identities, and perspectives build a unique culture that helps us create and innovate the next generation of MongoDB. In short, following this guide will help prepare you for a successful interview at MongoDB. Ensure you have gained some knowledge about our company, mission, and goals; the role you’re interviewing for and the team you’d be working on; and the types of interview questions you may be asked. And be prepared with questions for us! We’re so glad you’re interested in joining our team, and we look forward to seeing you in the interview process. Interested in pursuing a career in engineering at MongoDB? We have several open roles on our teams across the globe and would love for you to transform your career with us!