Thank you. Yeah. In other words, their spend on our platform is directly aligned with the usage of their underlying application, therefore, the value they derive from it. So first, the strong execution, I think, that Dev was talking about really ties more to the new business environment, which remember is very valuable for the medium to long term, but the near term is much more governed by the performance of existing applications. Yes. And so, that's something that people also value, so we still see a lot of demand. weekdays, there are large gaps in the dataset where there is no data. Perfect. And one of the things I wanted to highlight, on that EA strength in Q1, I believe we were expecting EA to actually decline sequentially. And again, as I mentioned earlier, it's all about us acquiring high-quality workloads. We talked about the China Mobile example where it was a very, very large workload servicing a very, very large user population. As Dev mentioned, consumption growth in Q1 was above our expectations. So like these are pretty good signs that customers are still prioritizing innovation and they're doing so leveraging modern platforms like MongoDB. But why did we see that sequential decline in deferred revenue that we haven't typically seen? With that, I'd like to turn the call over to Dev. Yes, what I would say is, I think, in the short term, the consumption trends are clearly tied to our customers' underlying business. Overall, we delivered a strong Q1. We have this very close value linkage, and so it maps quite tightly to the underlying application usage for our customers and their end users. Grafana automatically connects the last non-null value to the nearest other Let's say we want to calculate the average closing stock price per month for each stock in the collection. Finally, we expect to see a significant sequential uptick in expenses since we have some of our largest sales and marketing events in Q2, most notably MongoDB.local in New York. The telecom leader is using MongoDB to support one of its largest and most critical push services, which sends out billing details to more than 1 billion users every month. That's the first part. Experience the magic of switching from metrics to logs with preserved label filters and time ranges. Obviously there's the China Mobile case study or vignette that Dev walked through, and you can always find those in every quarter. Operator? We can see that this data set does not contain any movies after 2015, so that explains it. Additionally, it could monitor air pollution to produce alerts or analysis before a crisis occurs. Organizations, including Anywhere Real Estate, GE Healthcare and Intuit are leveraging the power of our developer data platform. I would say, in terms of the usage trends, it's again tied to our customers' underlying business. This concludes today's conference call. I wanted to sanity check. MongoDB will optimize this data stored by time to reduce size footprint and optimize time series access patterns and write throughput. Or it could be that they're -- they can't add new features fast enough on a brittle legacy platform so they need to migrate to a new modern platform where they continue to service their own business well. Yes, thanks for the question, Brad, yes. no data. Thanks. Using the following query, we can look at movie production over the last two decades. If you query for all transactions Give Threshold a value This will be a compelling reason for customers to migrate from legacy technologies to MongoDB. So wanted to get your sort of latest perspective on whether you see cloud spend and optimization headwinds fading anytime soon? Wouldnt it be great to monitor your MongoDB so you can quickly see, at a glance, everything that is happening under the covers? 7 Powerful Time-Series Database for Monitoring Solution - Geekflare In terms of what's happening in terms of the macro environment, I definitely agree with you that it's tough out there, but what we see is innovation is still a priority. I go back to a couple of thoughts. How many users visited a website page each day in the past week. And Q1 tends to be a seasonally slower quarter for new EA business. Thanks for the question, Howard. In your query, replace time_bucket with time_bucket_gapfill. With the Grafana MongoDB plugin, weve shown that its possible to quickly visualize and observe not only MongoDB data, but also diagnostic metrics. For example, its much easier to understand the impact of an increase in request latency if I can visualize my e-commerce platforms transaction volume and duration alongside it, the software release that preceded it, and the support tickets that were opened as a result. Thank you. So -- and so it's a technology that's worked well for a long period of time, but it really doesn't suit the needs of modern applications. And so the fact that it had a slight sequential gain, It was great to see and speaks to all the points Dev is underscoring. panel, you need to provide multiple value columns. Again, for generating content that's accurate in a performant way, you do need to use vector embeddings which are stored in a database. Also, no How it can be resolved? Thinking about a long-term opportunity, I feel exceptionally confident about our core underlying growth driver, the need for companies to use software as a competitive advantage. To ensure this doesnt happen in the future, please enable Javascript and cookies in your browser. MongoDB's New Time Series Collections MongoDB is a document-oriented database which means it works on principles of dealing with "documents"; it allows you to express data in its natural form, the way it's meant to be. Grafana is a well known open-source dashboarding product with versatile functionality, a broad community, and a vast ecosystem. That being said, our revenue is driven by usage, so when usage goes up, our revenue goes up. Prometheus , (time series database). If they're not doing well, then obviously they're not going to drive a lot of consumption. They use software to deliver their core value proposition, provide customers with great experiences and drive operational efficiency. It could be for cost reasons. MongoDB added native support for time series data in version 5.0, making it even easier, faster, and cheaper to work with time series data. Thank you squeezing me in off the hour mark. In our case, we will create a secondary index that allows for efficient searching of both symbol and company. And so the applications of building on MongoDB are clearly being used. Thank you very much. Time series data are simply measurements or events that are tracked, monitored, downsampled, and aggregated over time. Since most companies understand that they and their competition are all differentiating themselves through software, the speed of software development becomes existential. Good evening. I would just go back to your comment around sort of our long-term target model was 70-plus. We believe this is a testament to how well our value proposition is aligned to our customer success. As Dev mentioned, we continue to see a healthy new business environment, both in terms of acquiring new customers, as well as acquiring new workloads within existing customers. It is spread over the duration. It's a much more graceful migration than having to replatform on to another technology when they want to move that workload to the cloud. I apologize. Tyler, we're already seeing high customer engagement of customers already talking to us about new AI use cases that they want to build and run on MongoDB, so that's obviously a very positive trend. One for Dev and one for Michael. Below is a list of six best practices for working with time series data in MongoDB: Time series data is everywhere, but storing and querying it can be challenging. For example, you can consume time series data to perform calculations using aggregation pipelines and plot graphs on the application side, via MongoDB Charts. I have Grafana v8.3.2. Your question please, Howard. The result of running the above aggregation is a set of documents. Then, we can use $group to first, group the documents by month and symbol and, second, calculate the average for each group. We have really focused our teams to acquire workloads either through the acquisition of new customers or the acquisition of workloads in existing customers. Save 50% on registration to MongoDB.local NYC with code BANNER50! Step 3 Building a MongoDB Dashboard in Grafana. It MongoDB, Inc. (NASDAQ:MDB) Q1 2024 Earnings Call Transcript June 1, 2023 5:00 PM ET, Dev Ittycheria - President & Chief Executive Officer, Michael Gordon - Chief Operating Officer & Chief Financial Officer, Thank you for standing by, and welcome to MongoDB's First Quarter Fiscal Year 2024 Earnings Conference Call. You can read more. Good afternoon, and thank you for joining us today to review MongoDB's first quarter fiscal 2024 financial results, which we announced in our press release issued after the close of market today. The plugin supports template variables, which allow that feature. So that's really what's happening in terms of what's driving our revenue. I'll see you guys in New York in June. And how can it be analyzed? timeField indicates the name of the field that includes the date in each document. Time series data is any data that is collected over time and is uniquely identified by one or more unchanging parameters. Obviously all that's factored into the full year guide, and you can see the significant upgrade in the bottom line outlook. I just want to again just close by saying that we had another strong quarter of new business performance, while Atlas consumption rebounded from last quarter. We added approximately 2,300 customers during the quarter, the highest number in over two years, including over 300 new direct sales customers with notable strength in our Enterprise channel. SELECT DISTINCT(symbol) FROM company ORDER BY symbol ASC; GROUP BY time_bucket_gapfill('$bucket_interval', time), symbol, Create a time-series graph with pre-aggregated data using time_bucket(), this tutorial on adding variables to Grafana. So how does this make you think about the business model ahead? Let's examine some of the new operators and a stage that were added in version 5.0 to make working with dates and times easier. And we had a record number of new workloads added this quarter from existing customers. MongoDB is an essential platform in this drive for innovation, making us the critical investment priority. Second, we continue to expect that Atlas consumption growth will be impacted by the difficult macro environment throughout fiscal 2024. Grafana returns a graph similar to this one: This graph allows you to better visualize the stock price during the week. So obviously, to the extent that there is outperformance above this further increased level, like, that could impact things. In today's digital economy, most companies express their business strategies through software. Net income for the first quarter was $45.3 million or $0.56 per share based on 81.5 million diluted weighted average shares outstanding. They're the same length as eachother, and the FRAME array simply ascends from 0 (eg. And I'm just curious, from your perspective, how you see this playing out. change the $symbol variable to a multi-value answer and make a slight I think you're seeing that, I mean, people forget that the relational database has been around for almost 45 years, right? Thank you. Great. time series - Ways to connect mongodb to grafana - Stack Overflow Yes. All right. More information can be found at https://grafana.com/ Device Monitor That's focus on the input metrics that drive the outputs that you see. We are pleased with our results this quarter, especially given the difficult macro environment. Separating hot data retained in the operational Atlas cluster database and cold archived data maintained in the Online Archive storage, Optimizing costs while retaining a significantly large dataset for various purposes, e.g., compliance and history purposes, Maximum storage and query performance for archived time series data through MongoDBs Atlas Online * Archive and Atlas Data Lake. Another example could be how a connected weather measurement device might obtain telemetry such as humidity levels and temperature change to forecast weather. time-series graphs in Grafana: A very common use case of the time-series graph is displaying stock data. I would just add, we were expecting enterprise events to be down. Please refer to the tables in our earnings release on the Investor Relations portion of our website for a reconciliation of these measures to their most directly comparable GAAP financial measures. And I think you're going to see a lot of things happening over the course of the next few months and quarters and years, but we feel we're in a very good position to take advantage of this new trend. The input datasource is Elastic. Select Time series as your visualization type. Yes. So what would maybe explain it? Turning to the balance sheet and cash flow. But how does Mongo kind of fit into this new world? This could be server metrics, application performance monitoring, network data, sensor data, events, clicks, trades in a market, and . And so recently, we heard from another data platform [indiscernible] seeing some of the customers move data out of the platform to maybe economize on costs. Your question please, Karl. Because the time-series graph is the most common graph in Grafana, it's also Moreover, the shift to AI will favor modern platforms that offer a rich and sophisticated set of capabilities, delivered in a performance and scalable way. Note: By signing up, you agree to be emailed related product-level information. Consider a stock day trader constantly looking at feeds of stock prices over time and running algorithms to analyze trends to identify opportunities. We are observing an emerging trend where customers are increasingly choosing Atlas as a platform to build and run new AI applications. We do believe AI will increase the velocity of software development and, in turn, the number and sophistication of new applications developed. And that's what's included in our guidance for the balance of the year. So what has changed besides maybe slightly higher revenue to kind of come up with these kind of much higher numbers? It's common that time series data might not be relevant after a certain time period. Thanks, Dev. That's helpful. You get the same revenue, but there's less upfront. The MongoDB shell will return one document: MongoDB optimizes the data, as it stores data ordered by time as opposed to the natural order in regular collections. MongoDB Atlas, a cloud provider of MongoDB (you can try this out for free). It is especially well-suited for analyzing time-series data, such as the data generated by Step. For the time-series visualization, the Our next question comes from the line of Sanjit Singh of Morgan Stanley. In version 5.0, MongoDB added these aggregation pipeline operators: These new operators make working with time series data even easier. It's not going to happen overnight. We're not assuming things get materially worse, and we don't have any data that would suggest either of those directions. It now appears that you have a cadence where you -- despite challenging consumption trends on a per-customer basis, you've been able to add new customers at record pace, so results have been actually quite resilient. Otherwise, you get an error. For the second quarter, we expect revenue to be in the range of $388 million to $392 million. Thanks very much for taking the question. And so content is assigned vectors and the vectors are stored in a database. Change the $symbol variable to the Query type. Implementing Time Series in MongoDB - DZone Heres a look at some of the things you can do with the MongoDB data source: Lets take a look at some sample data, from a database called sample_mflix, provided by And you guys delivered some slight outperformance there. two separate panels with 2 separate symbol variables. As of MongoDB 5.0, MongoDB natively supports time series data. That's part of the reason why we talk about and go to great pains to explain the EA compares and some of those other things. And then further on the 2Q guide, the three extra days relative to Q1, does that loosely offer kind of an added three point sequential boost? MongoDB Monitoring with Grafana & Prometheus - Junos Notes A McKinsey report found that companies that score in the top quartile of developer velocity generate revenue growth that is four times to five times faster than companies in the bottom quartile. Thank you. Add the $bucket_interval variable of type Interval to the Grafana dashboard. So that is a very attractive part of the MongoDB value proposition. 7 Powerful Time-Series Database for Monitoring Solution | Last updated: October 29, 2022 7 Powerful Time-Series Database for Monitoring Solution Invicti Web Application Security Scanner - the only solution that delivers automatic verification of vulnerabilities with Proof-Based Scanning. There's got to be some compelling event for a customer to do so. The unchanging parameters that identify your time series data is generally your data source's metadata. So I'll try and tackle it a couple of different ways, Fred. Thank you. And we had another strong quarter of customer growth, ending the quarter with over 43,100 customers. So I want -- maybe explain it, but I'm not sure. Thank you. Or do you -- when do you think that this starts to accelerate the pace in which companies modernize their apps? We believe AI will be the next frontier of development productivity -- developer productivity and will likely lead to a step-function increase in software development velocity. Thank you. Well finance startups like Hugging Face, Tekion, One AI and [Nuro] (ph) are examples of companies using MongoDB to help deliver the next wave of AI-powered applications to their customers. I appreciate the perspective, Dev. InfluxDB is 5x Faster vs. MongoDB for Time Series Workloads
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