PlanetScale’s Insights Anomalies aims to streamline the evaluation of a database’s well-being and the resolution of issues, as outlined in the company’s blog post. The main objective is to provide a concise snapshot of the database’s status and facilitate a straightforward troubleshooting process.

PlanetScale emphasizes the significance of not just identifying anomalies within a database but also comprehending their underlying origins. Insights furnishes pertinent metrics for each anomaly, encompassing high-level query metrics like the number of rows read and written per second, resource utilization metrics for database components (such as CPU and disk usage), and details regarding backups and deploy requests that could potentially affect shared resources.

Insights meticulously logs and preserves accurate query counts for each query pattern in a database. This meticulous recording facilitates a comparison between the execution rates of individual query patterns and the overall health metrics of the database, allowing for the detection of highly correlated queries.

PlanetScale Insights offers users an in-dashboard tool for a comprehensive examination of all active queries running on their database. This tool enables the identification of queries displaying issues such as excessive frequency, prolonged execution times, large data returns, or errors.

Users can navigate a performance graph to precisely determine when a query was affected and, when applicable, view the associated Deploy Request. Additionally, the tool provides a list of all queries executed in the last 24 hours, sortable by metrics such as rows read and time per query, facilitating thorough analysis.

This integrated tool empowers users to diagnose query issues effortlessly, streamlining the optimization of individual queries without the need for extensive investigation. The Anomalies tab further notifies users of any active issues, flagging queries that are running significantly slower than expected.

“PingCAP’s ongoing commitment to improving data management and accessibility for everyone has resulted in the development of our GitHub Data Explorer. Today’s organizations depend heavily on data and PingCAP continuously strives to make it more manageable and accessible. By providing our users with this product, we are empowering them to easily gain access and understand their GitHub data and make decisions that will advance their businesses,”

said Max Liu, CEO of PingCAP.

PingCAP’s GitHub Data Explorer provides a thorough dashboard for its users giving them real-time access to their GitHub activities. Developers may watch project metrics, discover patterns rapidly and acquire a deeper understanding of their open-source contributions with the help of configurable dashboards.

The GitHub Data Explorer is built using a variety of cutting-edge technologies, including:

– GH Archive and GitHub event API
– TiDB Cloud
– SQL generator: Chat2Query
– AI engine: OpenAI

With this exciting new tool for the GitHub community, PingCAP remains committed to providing developers the resources they require to create scalable, effective, accurate and dependable applications.

All workloads with time series and all performance
There are several enhancements and updates being made available to the global user base. Although InfluxDB has always excelled at processing metrics, the new engine can not only process them, but also handle events and track with dramatic improvements in performance and efficiency.

There are currently no other time series data stores available that are optimized to index, store and analyze metrics, event and tracking data together in one place.

InfluxDB Cloud, with its new high-performance time series engine, can ingest, store and analyze high-volume, high-speed data in real-time and extract metrics on the fly from events at high granularity. This expands the number and variety of use cases that InfluxDB can handle, such as observability and distributed tracking that rely on high-cardinality data.

Lower cost of ownership
InfluxDB Cloud now uses Apache Parquet as its persistence format. This not only contributes to dramatic improvements in compression, but also allows users to extend the value of their time series data by passing it to other systems and ecosystems that use Parquet.

Optimized for low latency queries
Ingesting and storing data in InfluxDB is only half the story. The ability to efficiently search and use all that data is just as important. We’ve optimized the new engine for performance thanks to several under-the-hood updates. While concepts like parallelism, pushdown, and in-memory caching are critical aspects determining the performance of low-latency queries, what really matters to users is that fast and reliable queries improve the user experience.

The new database engine is available in two AWS regions (Virginia (us-east-1) and Frankfurt (eu-central-1)), but new availability zones are planned as well as adding availability for Azure and Google Cloud.

Digital transformation in organizations has led to workloads moving to multi-cloud and hybrid cloud environments. Modern organizations are increasingly adopting cloud-native technologies including containers, microservices, serverless, etc.

Enterprises are in dire need of monitoring capabilities such as application performance management (APM), infrastructure monitoring, log management, error tracking, and more to monitor, optimize, and troubleshoot their applications and infrastructure.

Partnership with New Relic
New Relic, a leader in Gartner’s Magic Quadrant for 10 consecutive years in application performance management and observability, is one such Azure partner.

“Observability is essential in today’s modern, multi-cloud world. Whether our customers are running applications in data centers, spanning the public cloud, or operating at the edge, they need observability to be able to review all of these systems. Today’s news brings together more than a decade of innovation between New Relic and Microsoft to bring the power of full stack observability to Microsoft developers so they can accelerate enterprise cloud migration and multi-cloud initiatives.”

Said Bill Staples, CEO of New Relic

Azure Native New Relic Service simplifies the efforts of developers and IT administrators to monitor their cloud applications. With this new offering, they can:

Create New Relic accounts in the cloud with just a few clicks. Azure Native New Relic Service is a fully managed offering that enables easy onboarding and eliminates the need to create and manage monitoring for your infrastructure.

Seamlessly send logs and metrics to New Relic. Integrated right into the set up experience and also as part of the configuration, you can set up automatic discovery of resources within an Azure subscription to monitor logs and metrics. You no longer need to go through the tedious process of setting up event centers and writing custom code.

Batch deploy New Relic Agent to virtual machines (VMs) and application services with a single click. Then send logs and metrics from the host infrastructure and processes to New Relic.

Get unified billing for all resources you consume in Azure, including New Relic, from the Azure marketplace.

“Application performance monitoring and observability across the entire stack are critical to cloud transformation for enterprises of all sizes. With the Azure Native New Relic service, Microsoft and New Relic together have enabled a thoughtfully integrated, seamless and premium experience for these enterprises everywhere. With just a few clicks, developers and administrators can now seamlessly set up and monitor their cloud applications using the power of New Relic on Azure.”

Said Balan Subramanian, Director of Partner Product Management, Azure Developer Experiences.

Without providing an integrated managed service, New Relic offers AWS and Google integrations and options available in their respective marketplaces.

As part of the Azure marketplace, New Relic’s service billing is consumption-based and consolidates with Azure’s spend, contributing to Azure’s consumption commitment (MACC).

Ranker allows eBay to increase purchase metrics, clicks and ad revenue by 3.76%, 2.74% and 4.06% compared to the previous native app (Android and iOS) and web platform model.

There are different stages of the buyer’s journey when shopping, often referred to as the “funnel”. In the “lower funnel” stage of the journey, buyers have identified the product or type of item they are interested in within the eBay marketplace and clicked on a product listing. On the product listing page, there are several modules that recommend products to the buyer based on different topics. The top module “Similar Sponsored Items” recommends similar items in relation to the main item on the page, which is called the “home” item.

Machine learning ranking machine
Retrieves a subset of candidate PLS items (“recall set”) that are most relevant to the seed item.
Applies a trained machine learning ranker to rank the listings in the recall set according to the probability of purchase.
Reorders the listings by incorporating the seller’s advertising rate to balance the seller’s rate, enabled by promotion, with the relevance of the recommendations.

The ranking model in stage 2 of the engine is trained offline on historical data. The features or variables of the ranking model include data such as:

• Historical product recommendation data
• Similarity between recommended and initial item
• Context (country, product category)
• User customization features

MicroBERT Finely Tuned Siamese Vans
The well-known BERT model demonstrates robust performance on all language comprehension tasks. BERT is a pre-trained model that uses an extensive amount of unlabeled data – sentences from the Wikipedia and Toronto Books corpora – which allows it to recognize semantically similar words (i.e., synonyms) as well as generate contextual word embeddings. For example, the embedding of the word “server” would be different in the sentence “The server crashed” compared to the sentence “Can you ask the server for the bill?”. Pre-trained BERT models can be fine-tuned to solve different NLP problems by adding task-specific layers that map the contextualized embedded words into the desired output function.

This new ranking model achieves a 3.5% improvement in purchase rank (the average rank of the item sold), but its complexity makes it difficult to perform real-time recommendations. Therefore, title embeddings are generated from a daily batch job and stored in NuKV (eBay’s cloud key-value store) with item titles as key and ad value embeddings.

“Integrating Tableau’s technology into our billing solution allows us to provide up-to-the-minute dashboard-style metrics to our clients. Asset managers can easily access comprehensive, interactive views of their data so they can quickly and accurately answer all their business intelligence questions.”

He added:

“Tableau’s analytics platform is second to none; and we’re delighted to partner with them in our quest to provide extraordinary, data-driven business insight and strategic forecasting to our clients.”

Redi2 Revenue Manager v8 is a fee billing and revenue management solution, specifically designed for investment managers, according to a press release.

The dashboard shows real-time analytics, such as total revenue, AUM, payment, asset flows, top/bottom revenue clients, and period-over-period revenue change. Users can also drill down for more detailed information using function-specific dashboards.

 e.GO Connect is now available to iOS as well as Android users and can be downloaded from App Store and Google Play. The program has been commenced and vehicles will be connected to the e.GO platform gradually.

The first available features include real-time vehicle and battery status, trip planner, car finder, service contact as well as key metrics and statistics on driving behavior amongst others. The connectivity is the foundation to enable a range of future services such as predictive maintenance. Ali Vezvaei, Chairman of the Board, said:

“We aspire to enable our customers to buy their e.GO vehicle as convenient as they buy their smartphone, charge it as effortlessly and use it as seamlessly as they use their smartphone. This unique and connected digital user experience defines our innovation and digital strategy.”

The company is committed to contributing to zero-emission urban mobility as it further ramps up production in 2022 and beyond.

Tej Anand, a professor at The University of Texas at Austin’s McCombs School of Business, said that the U.S. has the most complicated health system in the world, with high administrative and patient costs, and that there’s a lack of incentive to keep people healthy.

Betty Jo Rocchio, senior vice president and system chief nursing officer at Mercy, agreed, adding that patients struggle to navigate the system.

Wilson To, the head of worldwide healthcare, life sciences and genomic at Amazon Web Services, raised the issue of unstructured data. He said:

“We see a lot of unstructured data that’s out there. To what degree are they looking for new solutions to put some structure into that, to make it more usable or find ways of removing these data silos that exist within these organizations in order to make it more interoperable, for example?”

Payers have the most data that goes unshared across the spectrum, Rocchio said, and stakeholders aren’t working together on a standardized data set. She said:

“It’s not helping us across the continuum of care, but it’s siloing us on purpose. We’re getting exactly what we designed.”

The fragmented data ecosystems within healthcare have produced what Anand called “the three Rs of efficiency.”

Why Interoperability Is Important to Healthcare Delivery

“Data is so important to the workflow of medicine,” said Dr. Mujeeb Basit, associate chief medical informatics officer and assistant professor at the UT Southwestern Medical Center in Dallas. “We’ve spent so much time creating complex solutions to solve downstream data problems, when in reality, we should be spending our time fixing the upstream data access.”

He highlighted the difficulties of creating quality metrics for every department in his organization and said improvements in institutionwide foundational systems are needed.

Healthcare organizations must design strategies to increase data liquidity and elevate meaningful information sharing among stakeholders seamlessly inside and outside of hospital walls, Basit added.

During a separate spotlight keynote session, Cerner President Donald Trigg spoke on the strategy behind excellent care delivery inside and outside the hospital.

Dynamic forces of change, accelerated by the COVID-19 pandemic, are rapidly reorganizing the healthcare industry and models of care delivery, Trigg said, so that leaders are focused on not only solving today’s challenges those of the next five years and beyond.

Moving Toward Equity in Healthcare

Blue Cross Blue Shield Association President and CEO Kim Keck outlined the importance of data sharing to addressing health inequities. She said:

“We can create lasting change. As an industry, we should commit to sharing insights that provide transparency to the prevalence and magnitude of health disparities.”

Data collection for information on race, ethnicity and language needs to be improved and standardized across the board, she added. Building trust among patients and committing to patient data privacy will also be an ongoing effort.

The service is $100 per year and enables developers to keep track of the performance from mainstream API providers such as Netflix, Slack, and Microsoft, to more specialist and smaller vendors.
Developers can benefit from thematic collections of APIs to determine what ones have provided the best historical performance. David O’Neill, CEO of APImetrics, said:

“People are buying, integrating, and using services without a standard way to agree if they are working effectively, or even if they are getting what they pay for. If you are building your own product experience or business around a third party API, your success relies upon the performance of that API. Better be confident it works well.”

APImetrics believes that its service can remove the uncertainty around API performance in a way that’s unique and at a scale that would be difficult for any individual or business to replicate.

Developers who upgrade to the new premium version gain access to more detailed metrics, including historical data, details on individual API endpoints, and daily and weekly performance alerts.

Shay Bannon, the CEOs of Elastic and Grafana, explicitly referred to AWS and Amazon Elasticsearch Service as the reason for the license change, while the other CEO, Raj Dutt, described the move as balancing value creation and revenue creation while maintaining an open-source philosophy.

Elastic had previously filed a lawsuit against Amazon for trademark violation, and Amazon quickly responded to the license change by forking Elasticsearch. Grafana, on the other hand, recently entered a partnership with Amazon — a first for Amazon. Nevertheless, Dutt also referred to the need to protect Grafana.

The people working on the CHAOSS project, standing for Community Health Analytics Open Source Software, under the auspices of the Linux Foundation want to change that. In 2013, a company called Bitergia was founded to provide services around the software, as the interest in understanding how open source communities work was growing. In 2017, the CHAOSS project was adopted by the Linux Foundation, and Link joined as a co-founder.

A virtuous cycle of community health metrics evolution

The process of defining and managing those metrics is a virtuous cycle of evolution. There are Common Metrics, metrics for Diversity and Inclusion, Evolution, Risk, and Value. Some metrics are raw numbers, counting things such as number of commits, number of issues, or number of people. Relying on metrics alone can be deceiving — context is always needed to interpret metrics.

Measuring community-related value

The CHAOSS project also includes aggregate, synthesized metrics that pull different threads together and take them to the next level. A good part of those metrics apply to open source projects just the same as they would apply to an internal software development team. It all begins with asking the right questions, which should help ensure the right metric is used to answer those questions.

Photo Credits: CHAOSS

Metrics and side effects

Besides metric gamification, there are more things to be careful about. For example, when we talk about diversity, equity and inclusion, we start dealing with personally identifiable information that can be sensitive.

Toward more fine-grained open-source economics?

In 2020, ZDNet wrote about an analysis based on Github commits for various commercial open source projects. What that analysis showed was that vendors backing the development of those projects were the ones contributing the majority of code.

Commercial open source vendors have a legitimate interest to be concerned about capturing the value they generate. Third parties like Amazon may also have a legitimate argument in claiming they contribute in ways that go beyond code.

License engineering is one-way commercial open source vendors have to deal with the situation. However, it seems like a rather blunt instrument, with considerable side effects. Link noted that in terms of seeing how license changes affect the community, community metrics may help.