Analysis At the recent MongoDB conference in New York, the company demonstrated its ambition by supporting other database workloads.

The company has made significant inroads into the database market with a developer-friendly distributed document database to help developers build modern, web-based transactional systems.

Time series and search have become targets, with the promise of secondary index support in the former, and search facets to help developers build search experiences faster in the latter.

But it was the continued push towards analysis that impressed commentators, who were also keen to point out the limits of what could be achieved in a documentary database.

Available later this year, Column Store Indexing will help developers create and maintain a purpose-built index that dramatically speeds up many common analytical queries without requiring changes to document structure or having to move data to a another system, the company said.

Talk to The registerSahir Azam, product manager of MongoDB, said developers often have to aggregate data in a third-party system and then feed it back into their database to somehow operationalize complex analytical queries within their application. .

“We have added a multitude of features in the database and [DBaaS] Atlas to make it easier to enable app experiences in the app,” he said.

“We’re seeing much richer or smarter app experiences where what would typically be a human making a decision and off of a Tableau dashboard is now something that a development team automates in the software.

“But these queries are often very different from what you think of a traditional transaction type. They look much more like an analytic-style column query than a transactional query. So we worked on improving performance of our query engine, a new type of indexing called column store indexing, which aims to improve the performance of complex analytical queries so that they can be integrated into the application experience,” he said. he declares.

Tests with synthetic data and real customer workloads showed that MongoDB improved its performance on complex analytical queries by 5x to 200x, he said. Applications could include fraud analysis in financial services, the next best deal in e-commerce or supply chain management, he said.

“They try to ensure that database performance is not negatively affected by the scan”

Kimberly Wilkins, technical lead for MongoDB with database consultancy Percona, said that by running heavy analytics in MongoDB – even running it in a separate node – developers have previously seen a negative impact on performance.

She said there have been significant improvements in its synchronization capabilities, and it also now allows for higher numbers of crawls in replica sets than other replica set numbers used for the writing and for irregular readings. “That’s a huge thing they’ve been able to do. They’re trying to make sure that the database performance isn’t negatively impacted by the scan, so you can use run your heavy scans against MongoDB” , she said.

Even so, developers and data architects who have started building a cloud data warehouse such as Snowflake or AWS Redshift for analytics alongside MongoDB are unlikely to change their minds due to MongoDB improvements. However, that could affect future decision-making, Wilkins said.

“If people are starting to think they need a little bit of parsing, but they have a document database and it’s going to kill their write and read performance, it won’t be the case, if they do it right with MongoDB. It’s actually very, very impressive,” she said.

Tony Baer, ​​principal of analyst firm dbInsight, described MongoDB’s move to analytics as “a small step” – enabling lightweight queries without impacting operational performance with significant limits on its use.

“The first principle in operational databases is that the last thing you want to do is slow it down. To do all this complex modeling that you would do in Databricks or complex analytics that you would do in Snowflake, you don’t really don’t want to burden the operational database with this, and that’s not what it’s intended for, although you can partition the load into [MongoDB DBaaS] Atlas and have separate nodes. What it’s for is that you can make an intelligent decision on the spot,” he said.

Speaking on SiliconAngle’s The Cube, he said similar ideas were behind Oracle’s move with MySQL Heatwave and Google’s AlloyDB.

Matt Aslett, vice president and research director at Ventana Research, said cluster-to-cluster synchronization to synchronize data across clouds and on-premises clusters and data federation to query data across multiple clusters were among the news from last week’s event, adding to the momentum that MongoDB has been building with modern application developers.

“The company has done well in engaging with developers building new web applications using the document model and JSON format, especially for web applications.

“While much of the company’s initial success was driven by internet and app startups, it is increasingly gaining traction with established companies in industries such as financial services, insurance, healthcare and government, especially being embraced for workloads that were historically the domain of relational databases,” he said The register.

However, the company managed to muddy the waters on what it was commercially supporting in the current version. “Some of the announcements were for generally available features, and some were previews of upcoming features. regarding whether individual features are commercially supported or not,” Aslett said.

MongoDB has ambitious expansion plans and held a glitzy event in New York last week, though it remains loss-making. Still, investor confidence is bolstered by the company’s rapid growth, Aslett said.

“Not only does MongoDB’s revenue continue to grow, but quarterly year-over-year revenue growth accelerated in its fiscal year 2022. I expect it to maintain investor confidence. as long as it continues to meet or exceed expectations,” he said.

If that remains the case, it could be among the few NoSQL startups to see a vision for bringing analytical and operational workloads closer together. ®