Month: June 2023

With the Fed not quite committing yet to lowering or even pausing interest rate hikes, many investors are wondering if the sharp rally that we’ve seen in the markets since the start of May can be sustained. The gains have been especially notable in tech stocks, which shook off a very weak second half of 2022 to rebound.

MongoDB (NASDAQ:MDB), in particular, has nearly clawed its way back to 2022 levels. Up more than 100% year to date, the gains started accelerating in June as MongoDB reported excellent Q1 results. The question for investors now is: can the rally keep going?

I made a fortunate bullish call on MongoDB at the start of this year that has, needless to say, paid off handsomely. It’s tempting to look at the strength of MongoDB’s price chart over the past two months and say the stock has reached a cliff, but now is not the right time to be greedy – especially with so much uncertainty over interest rates. Owing primarily to valuation, I’m pivoting to neutral on MongoDB and locking in the gains on my trade.

It would be remiss not to acknowledge the fact that MongoDB is still a fantastic company, fundamentally speaking. I still see a number of tailwinds driving the bull case for MongoDB:

• Growth at scale. Very few companies that have reached a >$1 billion annual run rate are still growing revenue north of >50% y/y, and MongoDB is one of those few. That’s a testament to the all-encompassing, horizontal nature of MongoDB’s product. Almost all companies now have a use for managing unstructured data, and its technology is broadly applicable across a wide variety of use cases.

• Secular tailwinds. More and more these days, companies and brand marketers want to capture consumer data coming from “unstructured” sources – Tweets, social media posts, and the like. Traditional databases which store data in a columnar format are not equipped to handle this. MongoDB’s generous growth rates are a reflection of the largesse of the company’s space.

At the same time, however, at higher prices I’m more cognizant of several risks:

• GAAP losses are still large. Though MongoDB has notched positive pro forma operating and net income levels, the company is still burning through large GAAP losses because of its reliance on stock-based compensation. In boom times investors may look the other way, but in this more cautious market environment MongoDB’s losses may stand out.
• Competition. MongoDB may have called itself an “Oracle killer” at the time of its IPO, but Oracle (ORCL) is also making headway in autonomous and non-relational databases. Given Oracle’s much broader software platform and ease of cross-selling, this may eventually cut into MongoDB’s momentum.

The biggest thing we have to watch out for, however, is MongoDB’s enormous valuation. At current share prices near $390, MongoDB trades at a market cap of $27.33 billion. After we net off the $1.90 billion of cash and $1.14 billion of convertible debt on MongoDB’s most recent balance sheet, the company’s resulting enterprise value is $26.57 billion.

Meanwhile, for the current fiscal year MongoDB has guided to $1.522-$1.542 billion in revenue, representing 19-20% y/y growth:

Taking even the high end of this guidance range at face value (as MongoDB has had an unbroken tendency to “beat and raise”), the stock’s valuation stands at 17.2x EV/FY24 revenue. Let’s not even mention that from a P/E basis (though as EPS growth is still in its nascent stages, this isn’t an entirely fair valuation either) MongoDB is trading at a >200x P/E ratio.

The bottom line here: I view MongoDB as one of the few tech stocks to have returned very close to pandemic-era mania levels. While I can’t argue that the company’s fundamentals and market opportunity is very strong in a vacuum, it’s difficult to see any further upside at MongoDB’s high-teens revenue multiple.

Q1 download

Let’s now go through MongoDB’s latest quarterly results in greater detail. The Q1 earnings summary is shown below:

MongoDB’s revenue grew 29% y/y to $368.3 million, beating Wall Street’s expectations of $347.1 million (+22% y/y) by a seven-point margin, but also decelerating seven points from Q4’s growth rate of 36% y/y. Subscription revenue also grew at 29% y/y, while the smaller slice of professional services revenue grew 25% y/y.

Where MongoDB also exceeded expectations was on net customer adds, adding 2.3k net-new customers in the quarter to end with 43.1k total customers. As the company has focused more on its direct-sales business, it has been able to bring in a wider net of smaller self-service customers. That being said, MongoDB also added 110 net-new customers with greater than $100k in ARR, up 28% y/y.

Management noted that customer consumption trends are above expectations, driven by higher-than-expected underlying application usage. That being said, consumption growth levels are still lower than where they were when MongoDB entered into the macro-driven slowdown in Q2 of last year. Net ARR expansion rates were 120%, indicating that a typical installed base customer spends 20% more in the following year.

Additional investor enthusiasm poured into MongoDB as the company highlighted that customers are choosing the MongoDB platform to build and deploy AI-driven applications. Per CEO Dev Ittycheria’s remarks on the Q1 earnings call:

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. We are observing an emerging trend where customers are increasingly choosing Atlas as a platform to build and run new AI applications. For example, in Q1, more than 200 of the new Atlas customers were AI or ML companies. Well finance startups like Hugging Face, Tekion, One AI are examples of companies using MongoDB to help deliver the next wave of AI-powered applications to their customers. We also believe that many existing applications will be re-platformed to be AI-enabled. This will be a compelling reason for customers to migrate from legacy technologies to MongoDB.

To summarize, AI is just the latest example of the technology that promises to accelerate the production of more applications and greater demand for operational data stores, especially the ones best suited for modern data requirements such as MongoDB.

From a profitability standpoint, MongoDB’s pro forma gross margins lifted one point y/y to 76%. Pro forma operating margins clocked in at 12%, six points richer than 6% in the year-ago Q1.

Key takeaways

In my view, the ~2x gain in MongoDB since the start of the year has been a generous run. Yes, a good chunk of that is justified by fundamentals as MongoDB continues on a path of ~30% y/y growth alongside substantial margin expansion, fueled by potential further tailwinds from AI workloads migrating onto the platform – but it’s difficult to justify a ~17x forward revenue multiple. Retreat to the sidelines here.

The Microsoft security team recently released AzDetectSuite, a collection of KQL queries and detection alerts against security threads on Azure and AzureAD. The open-source project provides basic detection capabilities at a low cost, targeting small environments within the Microsoft cloud platform.

AzDetectSuite is an open-source library designed to help developers detect and understand tactics, techniques, and procedures used in cyber-attacks on Azure networks.

Written to match the Azure Threat Research Matrix (ATRM), a knowledge base built to document known TTPs within Azure and Azure AD, the detections are grouped according to the different tactics involved: reconnaissance, initial access, execution, privilege escalation, persistence, credential access, and exfiltration. Ryan Hausknecht, senior security researcher at Microsoft, explains:

AzDetectSuite is a project created to allow Azure users to establish a basic defense within Azure by giving pre-built KQL queries for each technique within ATRM that are deployable Alerts to Azure Monitor. In ATRM, most (85%+) techniques will have a KQL query and a button that will deploy the query to their Azure subscription.

For example, AzDetectSuite supports detections for attacks like Azure Key Vault dumping, account creation or manipulation, or password spraying. The detections are written using the Kusto Query Language (KQL), a language designed to explore data and discover patterns, identify anomalies and outliers, and create statistical modeling.

The new library relies on Azure Monitor, the centralized service that ingests data from different log sources, including general Azure Log (AzureActivity) and more detailed logs, such as Service Principal Sign-Ins (AADServicePrincipalSignInLogs).

AzDetectSuite is not the main option available for TTPs on Azure and AzureAD. Hausknecht warns:

AzDetectSuite (ADS) is not meant to compete with Microsoft Defender for Cloud (MDC). MDC provides advanced detections based on your subscription plan and will give more granular control based on the telemetry in a tenant. ADS is meant to be an open-source suite of basic detections for techniques found within ATRM.

The announcement explains how to build alerts for anomalous behaviors and how to handle baselining in KQL. On Twitter, Hausknecht adds:

The goal of this is to continue releasing OSS tooling that will benefit Azure users. It definitely goes against some of the mentality I’ve come across internally, but I’m firm in my belief that people should be able to have a security baseline for free.

The project’s GitHub repository contains the KQL queries and the PowerShell script Invoke-AzDetectSuite.ps1 to import detections for all or specific tactics. The detections are available for free but customers might still be charged for alert fees.

On June 26, 2023, the options scanner of a leading financial news outlet detected 15 unusual options trades for MongoDB. The traders who made these trades were divided in their sentiment, with 26% being bullish and 73% bearish. The options uncovered included 6 puts, worth $303,072 in total, and 9 calls, worth $605,197 in total. These trades suggest that large investors, or “whales,” have been targeting a price range of $70.0 to $650.0 for MongoDB over the last three months.

In other news, Capital One recently initiated coverage of MongoDB with an Equal-Weight recommendation on June 26, 2023. The average one-year price target for the company, as of June 2, 2023, is $266.84, with forecasts ranging from $181.80 to $383.25. This represents a decrease of 31.58% from the latest reported closing price of $389.99.

Meanwhile, Victory Capital Management Inc. has reduced its stake in MongoDB by 50.9% during the fourth quarter. As of June 21, 2023, the stock is trading at $371.25, a drop of $8.53 or 2.25% from the previous closing.

MDB Stock Analysis: Significant Increase in Earnings and Revenue Growth in 2023

On June 26, 2023, MDB stock opened at $388.00, up from the previous day’s close of $379.85. Throughout the day, the stock’s range fluctuated between $375.31 and $393.60, with a volume of 78,699 shares traded.

MDB has a market capitalization of $27.5 billion, and its earnings growth over the past year has been -5.89%. However, the company has seen a significant increase in earnings growth this year, with a growth rate of +92.12%. Over the next five years, the company is expected to see a steady growth rate of +8.00%.

In terms of revenue growth, MDB has seen a significant increase of +46.95% over the past year. The company’s P/E ratio is not available, but its price/sales ratio is 11.45, and its price/book ratio is 36.88.

MDB’s next reporting date is August 31, 2023, with an EPS forecast of $0.46 for this quarter.

Overall, MDB has seen a significant increase in earnings growth this year and revenue growth over the past year. However, its net profit margin remains negative, and the company’s stock price experienced significant fluctuations throughout the day. Investors should continue to monitor the company’s financial performance and market trends to make informed investment decisions.

MongoDB Incs Stock Shows Bullish Sentiment with Strong Financial Performance and Growth Prospects

On June 26, 2023, MongoDB Inc (MDB) was trading at a price of 379.85. The 22 analysts offering 12-month price forecasts for the company had a median target of 420.50, with a high estimate of 445.00 and a low estimate of 210.00. The current consensus among 27 polled investment analysts was to buy stock in MongoDB Inc. The company had reported earnings per share of $0.46 in the current quarter, with sales of $393.9M. The bullish sentiment was driven by the company’s strong financial performance and growth prospects.

The developer-led data platform isn’t new at MongoDB Inc., but what is new is the advent of artificial intelligence — something that led the company to spring to action.

But in some ways, the company’s developer data platform was “prescient,” according to Mindy Lieberman (pictured, right), chief information officer of MongoDB. 

“It’s all about data, and applications on top of data,” she said. “I look at it, for IT, as a portfolio. There’s some that is built; there is some that is bought. When you can’t go to the market and find things that are fit for purpose, you have to build. And to have a developer platform available, and I get good pricing, that can’t be beat.”

Lieberman and Tara Hernandez (left), vice president of developer productivity at MongoDB, spoke with theCUBE industry analyst John Furrier at the MongoDB .local NYC event, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed how to approach security, the role of AI and how developers are driving things forward. (* Disclosure below.)

Data in everything

These days, data is ubiquitous across entire enterprises, and companies need to be careful about how they manage it, according to Lieberman.

“You have to figure out ways to gather [data], to disseminate it, to cleanse it. So it’s all about the data,” she said.

On the developer side of things, whether it comes to testing or analysis, it’s critical to be mindful of how customer data is managed and how to build guardrails so developers aren’t looking at anything they aren’t supposed to, according to Hernandez. That also goes for ensuring that data is saved in a safe way, without getting in the way.

“That’s what DevSecOps is. That idea of a shift left is interesting, not because it’s making the develops more acutely aware of security — I mean, that’s a little part of it — but it’s that we ideally have created a platform where they don’t have to worry about it,” Hernandez said. “But they get the benefit of the protections that were built into how they do their development.”

Open-source has proven that developers are driving the bus. So, what are the operations good enough to enable developers and not foreclose them from being curious, experimental and playful with code? Aside from providing developer tooling to experiment with and figure out what works versus what is hype, MongoDB’s goal is focused on collaboration and a “very light touch” on coordination, according to Lieberman.

“Basically, we are saying yes to safe cases. We are trying to shorten the process to make sure that people can get their hands on things early and often. We test, and learn, and then expand,” she said.

MongoDB is based on open-source technology at its heart, which enables people to grab the community build or go to GitHub to grab the source, according to Hernandez. That applies to technology like ChatGPT too.

“One of the first things we said is, we took a quick pass and like, ‘You want to play with ChatGPT?’” Hernandez said. “Point it at the public repos, because the guardrail is already public.”

The internal stuff can be worked on as fast as possible, but there’s no reason that the company couldn’t have a reasonable set of policies to get started, Hernandez added.

Here’s the complete video interview with Mindy Lieberman and Tara Hernandez, part of SiliconANGLE’s and theCUBE’s coverage of the MongoDB .local NYC event:

(* Disclosure: TheCUBE is a paid media partner for the MongoDB .local NYC event. Neither MongoDB Inc., the sponsor of theCUBE’s event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Photo: SiliconANGLE

Enterprise Database Management Suite Market is growing at a +6% CAGR during the forecast period 2023-2030. The increasing interest of the individuals in this industry is that the major reason for the expansion of this market

An enterprise database management suite is a suite of software tools used to manage a large collection of databases in an enterprise. It typically includes database management systems, data integration and transformation tools, query and reporting tools, database security and access control tools, and data warehouse and analytics tools. Enterprise database management suites provide a single unified platform for managing complex and large data stores and may also include application development, database maintenance, and data analytics and visualization features.

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Enterprise Database Management Suite market research is an intelligence report with meticulous efforts undertaken to study the right and valuable information. The data which has been looked upon is done considering both, the existing top players and the upcoming competitors. Business strategies of the key players and the new entering market industries are studied in detail. Well explained SWOT analysis, revenue share and contact information are shared in this report analysis.

Top Companies of this Market includes: Oracle, Microsoft, IBM, SAP, AWS, MongoDB, Google, Broadcom, MarkLogic, MariaDB, InterSystems, Cloudera, Teradata, Vertica, Alibaba Cloud, Knack

This report provides a detailed and analytical look at the various companies that are working to achieve a high market share in the global Enterprise Database Management Suite market. Data is provided for the top and fastest growing segments. This report implements a balanced mix of primary and secondary research methodologies for analysis. Markets are categorized according to key criteria. To this end, the report includes a section dedicated to the company profile. This report will help you identify your needs, discover problem areas, discover better opportunities, and help all your organization’s primary leadership processes. You can ensure the performance of your public relations efforts and monitor customer objections to stay one step ahead and limit losses.

The report provides insights on the following pointers:

Market Penetration: Comprehensive information on the product portfolios of the top players in the Enterprise Database Management Suite market.

Product Development/Innovation: Detailed insights on the upcoming technologies, R&D activities, and product launches in the market.

Competitive Assessment: In-depth assessment of the market strategies, geographic and business segments of the leading players in the market.

Market Development: Comprehensive information about emerging markets. This report analyzes the market for various segments across geographies.

Market Diversification: Exhaustive information about new products, untapped geographies, recent developments, and investments in the Enterprise Database Management Suite market.

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The cost analysis of the Global Enterprise Database Management Suite Market has been performed while keeping in view manufacturing expenses, labor cost, and raw materials and their market concentration rate, suppliers, and price trend. Other factors such as Supply chain, downstream buyers, and sourcing strategy have been assessed to provide a complete and in-depth view of the market. Buyers of the report will also be exposed to a study on market positioning with factors such as target client, brand strategy, and price strategy taken into consideration.

Global Enterprise Database Management Suite Market Segmentation:

Market Segmentation by Type:

• Relational Database
• Nonrelational Database

Market Segmentation by Application:

• SMEs
• Large Enterprise

Reasons for buying this report:

• It offers an analysis of changing competitive scenario.
• For making informed decisions in the businesses, it offers analytical data with strategic planning methodologies.
• It offers seven-year assessment of Enterprise Database Management Suite
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• It offers regional analysis of Enterprise Database Management Suite Market along with business profiles of several stakeholders.
• It offers massive data about trending factors that will influence the progress of the Enterprise Database Management Suite

Table of Contents

Global Enterprise Database Management Suite Market Research Report 2023

Chapter 1 Enterprise Database Management Suite Market Overview

Chapter 2 Global Economic Impact on Industry

Chapter 3 Global Market Competition by Manufacturers

Chapter 4 Global Production, Revenue (Value) by Region

Chapter 5 Global Supply (Production), Consumption, Export, Import by Regions

Chapter 6 Global Production, Revenue (Value), Price Trend by Type

Chapter 7 Global Market Analysis by Application

Chapter 8 Manufacturing Cost Analysis

Chapter 9 Industrial Chain, Sourcing Strategy and Downstream Buyers

Chapter 10 Marketing Strategy Analysis, Distributors/Traders

Chapter 11 Market Effect Factors Analysis

Chapter 12 Global Enterprise Database Management Suite Market Forecast

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After trying to broaden its user base to include traditional database professionals last year, MongoDB is switching gears, adding features to turn its NoSQL Atlas database-as-a-service (DBaaS) into a more complete data platform for developers, including capabilities that support building generative AI applications.

In addition to introducing vector search for Atlas and integrating Google Cloud’s Vertex AI foundation models, the company announced a variety of new capabilities for the DBaaS at its MongoDB.local conference in New York Thursday, including new Atlas Search, data streaming, and querying capabilities.

“Everything that MongoDB has announced can be seen as a move to make Atlas a more comprehensive and complete data platform for developers,” said Doug Henschen, principal analyst at Constellation Research. “The more that MongoDB can provide to enable developers with all the tools that they need, the stickier the platform becomes for those developers and the enterprises they work for.”

Henschen’s perspective seem reasonable, given that the company has been competing with cloud data platform suppliers such as Snowflake, which offers a Native Application Framework, and Databricks, which recently launched Lakehouse Apps.  

Vector search helps build generative AI apps

In an effort to help enterprise build applications based on generative AI from data stored in MongoDB, the company has introduced a vector search capability inside Atlas, dubbed Atlas Vector Search.

This new search capability, according to the company, will help support a new range of workloads, including semantic search with text, image search, and highly personalised product recommendations.

The search runs on vectors — multidimensional mathematical representations of features or attributes of raw data that could include text, images, audio or video, said Matt Aslett, research director at Ventana Research.

“Vector search utilises vectors to perform similarity searches by enabling rapid identification and retrieval of similar or related data,” Aslett said, adding that vector search can also be used to complement large language models (LLMs) to reduce concerns about accuracy and trust through the incorporation of approved enterprise content and data.

MongoDB Atlas’ Vector Search will also allow enterprises to augment the capabilities of pretrained models such as GPT-4 with their own data via the use of open source frameworks such as LangChain and LlamaIndex, the company said.

These frameworks can be used to access LLMs from MongoDB partners and model providers, such as AWS, Databricks, Google Cloud, Microsoft Azure, MindsDB, Anthropic, Hugging Face and OpenAI, to generate vector embeddings and build AI-powered applications on Atlas, it added.

MongoDB partners with Google Cloud

MongoDB’s partnership with Google Cloud to integrate Vertex AI capabilities is meant to accelerate the development of generative AI-based applications.  Vertex AI, according to the company, will provide the text embedding API required to generate embeddings from enterprise data stored in MongoDB Atlas.

These embeddings can be later combined with the PaLM text models to create advanced functionality like semantic search, classification, outlier detection, AI-powered chatbots, and text summarisation.

The partnership will also allow enterprises to get hands-on assistance from MongoDB and Google Cloud service teams on data schema and indexing design, query structuring, and fine-tuning AI models.

Databases from Dremio, DataStax and Kinetica are also adding generative AI capabilities.

MongoDB’s move to add vector search to Atlas is not unique but it will enhance the company’s competitiveness, Aslett said. “There is a growing list of specialist vector database providers, while multiple vendors of existing databases are working to add support to bring vector search to data already stored in their data platforms,” Aslett said.

Managing real-time streaming data in a single interface

In order to help enterprises manage real-time streaming data from multiple sources in a single interface, MongoDB has added a stream processing interface to Atlas.

Dubbed Atlas Stream Processing, the new interface, which can process any kind of data and has a flexible data model, will allow enterprises to analyse data in real-time and adjust application behavior to suit end customer needs, the company said.

Atlas Stream Processing bypasses the need for developers to use multiple specialised programming languages, libraries, application programming interfaces (APIs), and drivers, while avoiding the complexity of using these multiple tools, MongoDB claimed.

The new interface, according to Aslett, helps developers to work with both streaming and historical data using the document model.

“Processing data as it is ingested enables data to be queried continuously as new data is added, providing a constantly updated, real-time view that is triggered by the ingestion of new data,” Aslett said.

A report from Ventana Research claims that more than seven in 10 enterprises’ standard information architectures will include streaming data and event processing by 2025, so that they can provide better customer experiences.

Atlas Stream Processing, according to SanjMo’s principal analyst Sanjeev Mohan, can also be used by developers to perform functions like aggregations, as well as filter and do anomaly detection on data that is in Kafka topics, Amazon Kinesis or even MongoDB change data capture.

The flexible data model inside Atlas Stream Processing can also be modified over time to suit needs, the company said.

The addition of the new interface to Atlas as a move to play catchup with rival data cloud providers such as Snowflake and Databricks, which have already introduced features for processing real-time data, noted Constellation’s Henschen.

New Atlas search features

In order to help enterprises to maintain database and search performance on Atlas, the company has introduced a new feature, dubbed Atlas Search Nodes, that isolates search workloads from database workloads.

Targeted at enterprises that have already scaled their search workloads on MongoDB, Atlas Search Nodes provides dedicated resources and optimises resource utilisation to support performance of these specific workloads, including vector search, the company said.

“Enterprises may find that dedicating nodes in a cluster, specifically to search, can support operational efficiency by avoiding performance degradation on other workloads,” Aslett said, adding that this is a capability that was being adopted by multiple providers of distributed databases.  

MongoDB’s updates to Atlas also include a new time-series data editing feature that the company claims is usually not allowed in most time-series databases.

The company’s Time Series Collections features will now allow enterprises to modify time-series data resulting in better storage efficiency, accurate results, and better query performance, the company said.  

The feature to modify time-series data will help most enterprises, according to Mohan.

Other updates to MongoDB Atlas include the ability to tier and query databases on Microsoft Azure using the Atlas Online Archive and Atlas Data Federation features, the company said, adding that Atlas already supported tiering and querying on AWS.

MongoDB Atlas for financial services and other industries

As part of the updates announced at its MongoDB local conference, the company said that it will be launching a new industry-specific Atlas database program for financial services, followed by other industry sectors such as retail, healthcare, insurance, manufacturing and automotive.

These industry-specific programs will see the company offer expert-led architectural design reviews, technology partnerships via workshops and other instruments for enterprises to build vertical-specific solutions. The company will also offer tailored MongoDB University courses and learning materials to enable developers for their enterprise projects.

While the company did not immediately provide information on the availability and pricing of the new features, it said that it was making its Relational Migrator tool generally available.

The tool is designed to help enterprises move their legacy databases to modern document-based databases.

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Microsoft has recently unveiled several new features for Azure Cosmos DB to enhance cost efficiency, boost performance, and increase elasticity. These features are burst capacity, hierarchical partition keys, serverless container storage of 1 TB, and priority-based execution.

Azure Cosmos DB is Microsoft’s globally distributed, multi-model database service that offers low-latency, scalable storage and querying of diverse data types. The burst capacity feature for the service was announced at the recent annual Build conference and is generally available (GA) to allow users to take advantage of your database or container’s idle throughput capacity to handle traffic spikes.  In addition, the company also announced the GA of hierarchical partition keys, also known as sub-partitioning for Azure Cosmos DB for NoSQL, and the expansion of the storage capacity of serverless containers to 1 TB (previously, the limit was 50 GB).

Beyond Build, the company also introduced the public preview of priority-based execution, a capability that allows users to specify the priority for the request sent to Azure Cosmos DB. When the number of requests exceeds the configured Request Units per second (RU/s) in Azure Cosmos DB, low-priority requests are throttled to prioritize the execution of high-priority requests, as determined by the user-defined priority.

Richa Gaur, a Product Manager at Microsoft, explains in a blog post:

This capability allows a user to perform more important tasks while delaying less important tasks when there are a higher number of requests than what a container with configured RU/s can handle at a given time. Less important tasks will be continuously retried by any client using an SDK based on the retry policy configured.

Using Mircosoft.Azure.Cosmos.PartitionKey; 
Using Mircosoft.Azure.Cosmos.PriorityLevel; 

//update products catalog  
RequestOptions catalogRequestOptions = new ItemRequestOptions{PriorityLevel = PriorityLevel.Low}; 

PartitionKey pk = new PartitionKey(“productId1”); 
ItemResponse catalogResponse = await this.container.CreateItemAsync(product1, pk, requestOptions); 

//Display product information to user 
RequestOptions getProductRequestOptions = new ItemRequestOptions{PriorityLevel = PriorityLevel.High}; 

string id = “productId2”; 
PartitionKey pk = new PartitionKey(id); 

ItemResponse productResponse = await this.container.ReadItemAsync(id, pk, getProductRequestOptions); 

Similarly, users can maintain performance for short, temporary bursts, as requests that otherwise would have been rate-limited (429) can now be served by burst capacity when available. In a deep dive Cosmos DB blog post, the authors explain:

With burst capacity, each physical partition can accumulate up to 5 minutes of idle capacity. This capacity can be consumed at a rate of up to 3000 RU/s. Burst capacity applies to databases and containers that use manual or autoscale throughput and have less than 3000 RU/s provisioned per physical partition.

Source: https://devblogs.microsoft.com/cosmosdb/deep-dive-new-elasticity-features/

Next to optimizing performance, the hierarchical partition keys help in elasticity for Cosmos DB. The feature can help in scenarios where users leverage synthetic partition keys or logical partition keys that can exceed 20 GB of data. They can use up to three keys with hierarchical partition keys to further sub-partition their data, enabling more optimal data distribution and a larger scale. Behind the scenes, Azure Cosmos DB will automatically distribute their data among physical partitions such that a logical partition prefix can exceed the limit of 20GB of storage.

Leonard Lobel, a Microsoft Data Platform MVP, outlines the benefit of hierarchical keys in an IoT scenario in a blog post:

You could define a hierarchical partition key based on device ID and month. Then, devices that accumulate less than 20 GB of data can store all their telemetry inside a single 20 GB partition, but devices that accumulate more data can exceed 20 GB and span multiple physical partitions, while keeping each month’s worth of data “sub-partitioned” inside individual 20 GB logical partitions. Then, querying on any device ID will always result in either a single-partition query (devices with less telemetry) or a sub-partition query (devices with more telemetry).

Lastly, with the expansion of the storage capacity of serverless containers to 1 TB, users can benefit from the maximum throughput for serverless containers starting from 5000 RU/s, and they can go beyond 20,000 RU/s depending on the number of partitions available in the container. In addition, it also offers increased burstability.

Microsoft has recently unveiled several new features for Azure Cosmos DB to enhance cost efficiency, boost performance, and increase elasticity. These features are burst capacity, hierarchical partition keys, serverless container storage of 1 TB, and priority-based execution.

Azure Cosmos DB is Microsoft’s globally distributed, multi-model database service that offers low-latency, scalable storage and querying of diverse data types. The burst capacity feature for the service was announced at the recent annual Build conference and is generally available (GA) to allow users to take advantage of your database or container’s idle throughput capacity to handle traffic spikes.  In addition, the company also announced the GA of hierarchical partition keys, also known as sub-partitioning for Azure Cosmos DB for NoSQL, and the expansion of the storage capacity of serverless containers to 1 TB (previously, the limit was 50 GB).

Beyond Build, the company also introduced the public preview of priority-based execution, a capability that allows users to specify the priority for the request sent to Azure Cosmos DB. When the number of requests exceeds the configured Request Units per second (RU/s) in Azure Cosmos DB, low-priority requests are throttled to prioritize the execution of high-priority requests, as determined by the user-defined priority.

Richa Gaur, a Product Manager at Microsoft, explains in a blog post:

This capability allows a user to perform more important tasks while delaying less important tasks when there are a higher number of requests than what a container with configured RU/s can handle at a given time. Less important tasks will be continuously retried by any client using an SDK based on the retry policy configured.

Using Mircosoft.Azure.Cosmos.PartitionKey; 
Using Mircosoft.Azure.Cosmos.PriorityLevel; 

//update products catalog  
RequestOptions catalogRequestOptions = new ItemRequestOptions{PriorityLevel = PriorityLevel.Low}; 

PartitionKey pk = new PartitionKey(“productId1”); 
ItemResponse catalogResponse = await this.container.CreateItemAsync(product1, pk, requestOptions); 

//Display product information to user 
RequestOptions getProductRequestOptions = new ItemRequestOptions{PriorityLevel = PriorityLevel.High}; 

string id = “productId2”; 
PartitionKey pk = new PartitionKey(id); 

ItemResponse productResponse = await this.container.ReadItemAsync(id, pk, getProductRequestOptions); 

Similarly, users can maintain performance for short, temporary bursts, as requests that otherwise would have been rate-limited (429) can now be served by burst capacity when available. In a deep dive Cosmos DB blog post, the authors explain:

With burst capacity, each physical partition can accumulate up to 5 minutes of idle capacity. This capacity can be consumed at a rate of up to 3000 RU/s. Burst capacity applies to databases and containers that use manual or autoscale throughput and have less than 3000 RU/s provisioned per physical partition.

Source: https://devblogs.microsoft.com/cosmosdb/deep-dive-new-elasticity-features/

Next to optimizing performance, the hierarchical partition keys help in elasticity for Cosmos DB. The feature can help in scenarios where users leverage synthetic partition keys or logical partition keys that can exceed 20 GB of data. They can use up to three keys with hierarchical partition keys to further sub-partition their data, enabling more optimal data distribution and a larger scale. Behind the scenes, Azure Cosmos DB will automatically distribute their data among physical partitions such that a logical partition prefix can exceed the limit of 20GB of storage.

Leonard Lobel, a Microsoft Data Platform MVP, outlines the benefit of hierarchical keys in an IoT scenario in a blog post:

You could define a hierarchical partition key based on device ID and month. Then, devices that accumulate less than 20 GB of data can store all their telemetry inside a single 20 GB partition, but devices that accumulate more data can exceed 20 GB and span multiple physical partitions, while keeping each month’s worth of data “sub-partitioned” inside individual 20 GB logical partitions. Then, querying on any device ID will always result in either a single-partition query (devices with less telemetry) or a sub-partition query (devices with more telemetry).

Lastly, with the expansion of the storage capacity of serverless containers to 1 TB, users can benefit from the maximum throughput for serverless containers starting from 5000 RU/s, and they can go beyond 20,000 RU/s depending on the number of partitions available in the container. In addition, it also offers increased burstability.

This week’s Java roundup for June 19th, 2023 features news from JDK 22, JDK 21, updates to: Spring Boot; Spring Security; Spring Vault; Spring for GraphQL; Spring Authorization Server and Spring Modulith; Liberica NIK 23.0, Semeru 20.0.1, Micronaut 4.0-RC2 and 3.9.4, JNoSQL 1.0, Vert.x 4.4.4, updates to: Apache Tomcat, Camel, Log4j and JMeter; JHipster Lite 0.35, KCDC 2023 and JCON Europe 2023.

JDK 21

Build 28 of the JDK 21 early-access builds was also made available this past week featuring updates from Build 27 that include fixes to various issues. Further details on this build may be found in the release notes.

JDK 22

Build 3 of the JDK 22 early-access builds was also made available this past week featuring updates from Build 2 that include fixes to various issues. More details on this build may be found in the release notes.

For JDK 22 and JDK 21, developers are encouraged to report bugs via the Java Bug Database.

Spring Framework

Versions 3.1.1, 3.0.8 and 2.7.13 of Spring Boot 3.1.1 deliver improvements in documentation, dependency upgrades and notable bug fixes such as: difficulty using the from() method defined in the SpringApplication class in Kotlin applications; SSL configuration overwrites other customizations from the WebClient interface; and support for JDK 20, but no defined value for it in the JavaVersion enum. Further details on these versions may be found in the release notes for version 3.1.1, version 3.0.8 and version 2.7.13.

Versions 6.1.1, 6.0.4, 5.8.4, 5.7.9 and 5.6.11 of Spring Security have been released featuring bug fixes, dependency upgrades and new features such as: align the OAuth 2.0 Resource Server documentation with Spring Boot capabilities; a new section in the reference manual to include information related to support and limitations when working with native images; and a migration to Asciidoctor Tabs. More details on these versions may be found in the release notes for version 6.1.1, version 6.0.4, version 5.8.4, version 5.7.9 and version 5.6.11.

The release of Spring Vault 3.0.3 delivers bug fixes, improvements in documentation, dependency upgrades and new features such as: a refinement in logging to log the token accessor upon token revocation failure; AWS Identity and Access Management (IAM) authentication added to the EnvironmentVaultConfiguration class; and the inclusion of a key_version attribute to the encrypt() method in the VaultTransitOperations interface. Further details on this release may be found in the release notes.

Versions 1.2.1 and 1.1.5 of Spring for GraphQL have been released featuring bug fixes, dependency upgrades and new features such as: an enhanced GraphQL request body check to prevent a 500 Internal Server Error when a 400 Bad Request is expected; elimination of the IllegalArgumentException due to no defined ConnectionAdapter interface when using existing Java Connection types. More details on these versions may be found in the release notes for version 1.2.1 and version 1.1.5.

Versions 1.1.1, 1.0.3 and 0.4.3 of Spring Authorization Server have been released featuring bug fixes and dependency upgrades. Version 1.1.1 ships with a new feature in which there was a performance enhancement by simply replacing the replaceFirst() method with the substring() method from the String class while using the OAuth2AuthorizationConsent class. Further details on these versions may be found in the release notes for version 1.1.1, version 1.0.3 and version 0.4.3.

The first milestone release of Spring Modulith 1.0.0 ships with bug fixes, dependency upgrades and a new feature that propagates instances of the ExecutorService interface defined in an application into instances of the Scenario class by default. This project has been promoted from its experimental status yielding these breaking changes: a rename of the actuator endpoint from applicationmodules to application-modules; a rename of the group identifier from org.springframework.experimental to org.springframework.modulith; and the removal of the previously deprecated configuration properties, spring.modulith.events.jdbc-*, in the JDBC-based event registry. More details on this release may be found in the release notes.

BellSoft

BellSoft has released version 23.0 of their Liberica Native Image Kit (NIK) featuring: the integration of the ParallelGC garbage collector as an experimental feature; implementation of the JFR ThreadCPULoad event; a removal of type checks from JNI-to-Java call stubs that can break compatibility; and implementation of the user CPU time thread with the getThreadCpuTime() method in the LinuxThreadCpuTimeSupport class.

IBM Semeru Open Edition

IBM has released version 20.0.1 their Semeru Runtime, Open Edition 20.0.1 built on OpenJDK 20.0.1 and Eclipse OpenJ9 0.39.0. Further details on this release may be found in the release notes.

Micronaut

The second release candidate of Micronaut 4.0.0 was also released providing bug fixes, dependency upgrades and these improvements: use of unsafe setters for Jackson; a new UnsafeBeanInstantiationIntrospection interface, a variation of the BeanIntrospection interface that includes an instantiateUnsafe() method for allowing to skip instantiation validation; and support for the All-open compiler plugin for the Kotlin Symbol Processing API.

The Micronaut Foundation has released Micronaut Framework 3.9.4 featuring bug fixes and updates to modules: Micronaut Security and Micronaut Servlet. There was also a dependency upgrade to Netty 4.1.94. More details on this release may be found in the release notes.

Eclipse Foundation

More than six years after its inception in March 2017, version 1.0.0 of JNoSQL, the compatible implementation of the Jakarta NoSQL specification, has been released. New features include: a migration to the jakarta.* namespace, support for the Jakarta Data specification; an implementation of new methods that explore fluent-API for the Graph, Document, Key-Value and Document NoSQL database types; and new methods, count() and exists(), as default on the DocumentManager and ColumnManager interfaces. Before it became a compatible implementation in November 2019, JNoSQL was a project for developers to more easily create NoSQL database applications using Java.

Two months after MicroStream had announced that their Java-native persistence layer had become an Eclipse Project, the first release of Eclipse Store, formerly known as MicroStream Persistence, has been made available to the Java community. Current non-Eclipse integrations in the MicroStream code base, such as Spring Boot, Quarkus and Helidon, will remain open source and the code will be hosted in a new MicroStream repository after they have been refactored to make use of the Eclipse Store and Eclipse Serializer projects.

Eclipse Vert.x 4.4.4 has been released featuring an upgrade to Netty 4.1.94.Final to address CVE-2023-34462, a vulnerability in which an attacker can manipulate the SniHandler class, with no configured idle timeout handler, to buffer the maximum 16MB of data per connection that can quickly lead to an OutOfMemoryError error and potential for a distributed denial of service. Further details on this release may be found in the release notes.

Apache Software Foundation

The Apache Tomcat team has disclosed that versions 11.0.0-M5, 10.1.8, 9.0.74 and 8.5.88 are affected by CVE-2023-34981, a vulnerability in which a regression in the fix for Bug 66512 could lead to an information leak if a response did not include any HTTP headers, then no Apache JServ Protocol (AJP) SEND_HEADERS message would be sent for the response. This was fixed in Bug 66591 and developers are encouraged to migrate to minimal versions 11.0.0-M6, 10.1.9, 9.0.75 or 8.5.89.

The release of Apache Camel 3.20.6 provides bug fixes and improvements such as: ensure that the REQUEST_CONTEXT and RESPONSE_CONTEXT headers are mapped when populating a Camel CXF message from Camel Message; and enhancements to the Camel JBang module to support OpenAPI. More details on this release may be found in the release notes.

Similarly, the release of Apache Camel 3.14.9 ships with these bug fixes: use the createTempFile() method in the Files class within the FileConverter class instead of directly creating a file; and a potential NullPointerException when using XML Tokenize on an Woodstox XML namespace. Further details on this release may be found in the release notes.

The first alpha release of Apache Log4j 3.0.0 delivers notable changes such as: allow plugins to be created through more flexible dependency injection patterns; split support for Kafka, ZeroMQ, CSV, JMS, JDBC and Jackson to their own modules; and removal of support for the Serializable interface in several classes and interfaces that include Message, Layout, LogEvent, Logger, and ReadOnlyStringMap.

Apache JMeter 5.6.0 has been released featuring bug fixes and new features such as: use Caffeine for caching HTTP headers instead of the Apache Commons Collections LRUMap class; use the Java ServiceLoader class for loading plugins instead of classpath scanning for improved startup; and improved computation when many threads actively produce samplers by using the Java LongAdder and similar concurrency classes to avoid synchronization in the Calculator class. More details on this release may be found in the release notes.

JHipster

The JHipster team has released version 0.35.0 of JHipster Lite with bug fixes, improvements in documentation, dependency upgrades and an improved Sonar analysis that provides more error details and an option to wait. Further details on this release may be found in the release notes.

Kansas City Developer Conference

The 2023 Kansas City Developer Conference (KCDC) was held at the Kansas City Convention Center in Kansas City, Missouri this past week featuring speakers from the Java community who presented workshops and sessions on topics such as: Java, architecture, cloud, data science, JavaScript, project management and security. The conference also featured puppies available for adoption from the Great Plains SPCA.

JCON Europe

Also this past week, JCON Europe 2023 was held at the Cinedom in Kön, Germany featuring speakers from the Java community who presented sessions on topics such as: Java, developer productivity engineering, security, web components, microservices and cloud native.