The latest release of Hazelcast’s Jet event stream processing engine for AI and ML deployments of mission-critical applications adds new app development features designed to simplify the integration of an event-driven architecture into brownfield deployments to gain new functionality around real-time and in-memory processing.

“Stream processing” is about processing data in motion–users take action on data at the time it is created. It typically involves multiple tasks performed on an incoming series of data, and it can be performed serially, in parallel, or both. The stream processing pipeline starts with the generation of the data, followed by the processing of the data, and finally the delivery of the data to its destination.

With the release of Hazelcast 4.2, the in-memory computing platform maker
is making it possible to add Jet’s extensibility to existing applications through real-time caching and stateful microservices.

The list of updates and enhancements in this release includes new support for streaming integration with MySQL and PostgreSQL databases using a unified high-level API. Traditional RDBMS-based applications require hundreds or thousands of lines of code to add functionality, as well as significant testing. With the new API in Jet 4.1, the integration becomes more of a declarative task to reduce the custom error-prone code. 

Additionally, Hazelcast Jet makes the database available as a stream. It deals with connectivity, object mapping and unifies the event handling across database vendors. The series of database updates form an event stream on which developers can more easily add microservices without impacting existing applications. This simplification lets developers focus on adding new business logic in high-performance applications rather than managing complex and error-prone integrations. 

The transactional data from MySQL or PostgreSQL can be augmented and enriched with other datasets from Hadoop, Amazon S3, Google Cloud Storage, Azure Data Lake, and others, the company says, and served through thousands of concurrent low-latency queries and fine-grained, key-based access.

“With these enhancements, enterprises can take advantage of in-memory speeds to accelerate analytical queries to scale your architecture by offloading certain workloads from your transactional database into an in-memory store,” the company said in a statement.

Hazelcast provided support in Jet earlier this year for change data capture (CDC) via the open-source Debezium project. In version 4.2 the CDC integration has been optimized to reduce the manual coding required to utilize this capability, the company says.

Also, over the last year, the library of connectors for Hazelcast Jet has been expanded to include Apache Beam, Confluent, MongoDB, JDBC, Apache Cassandra, and others. Version 4.2 includes connectors for Elasticsearch and Apache Pulsar. By connecting Jet to Elasticsearch, the company says, enterprises can rapidly enrich large data sets, including those from relational databases, and transform them into formats suitable for indexing and search-based analysis by Elasticsearch.

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In today’s modern software world, applications and infrastructure are melding together in different ways. Nowhere is that more apparent than with microservices, delivered in containers that also hold infrastructure configuration code.

That, combined with more complex application architectures (APIs, multiple data sources, multicloud distributions and more), and the ephemeral nature of software as temporary and constantly changing, is also changing the world of monitoring and creating a need for observability solutions.

First-generation application monitoring solutions struggle to provide the same level of visibility into today’s more virtual applications – i.e., containerized and/or orchestrated environments running Docker and Kubernetes. Massively distributed microservices-based applications create different visibility issues for legacy tools. Of course, application monitoring is still important, which has driven the need to add observability into the applications running in those environments. 

While legacy application monitoring tools have deep visibility into Java and .NET code, new tools are emerging that are focused on modern application and infrastructure stacks. According to Chris Farrell, technical director and APM strategist at monitoring solution provider Instana, one of the important things about a microservice monitoring tool is that it has to recognize and support all the different microservices. “I think of it like a giant T where the vertical bar represents visibility depth and the horizontal bar represents visibility breadth,” he explained. “Legacy APM tools do great on the vertical line with deep visibility for code they support;  meanwhile, microservices tools do well on the horizontal line, supporting a broad range of different technologies. Here’s the thing – being good on one axis doesn’t necessarily translate to value along the other because their data model is built a certain way. When I hear microservices APM, I think, ‘That’s what we do.’ [Instana has] both the depth of code-level visibility and the breadth of microservices support because that’s what we set out to do, solve the problem of ephemeral, dynamic, complex systems built around microservices.”

When talking about observability and application monitoring, it’s important to think about the different kinds of IT operations individuals and teams you have to deal with. According to Farrell “whether you’re talking about SREs, DevOps engineers or traditional IT operators, each has their own specific goals and data needs. Ultimately, it’s why a monitoring solution has to be flexible in what data it gathers and how it presents that data. 

Even though it’s important for modern monitoring solutions to recognize and understand complexity, it’s not enough. They must also do so programmatically, Farrell said, because today’s systems are simply too complex for a person to understand. “You add in the ephemeral or dynamic aspect, and by the time a person could actually create a map or understand how things are related, something will change, and your knowledge will be obsolete,” he said.

Modern solutions also have to be able to spot problems and deliver data in context. Context is why it’s practically impossible for even a very good and knowledgeable operations team to understand exactly everything that’s going on inside their application themselves. This is where solutions that support both proprietary automatic visibility and manually injected instrumentation can be valuable. Even if you have the ability to instrument an application with an automated solution, there still is room for an observability piece to add some context. “Maybe it’s a parameter that was passed in; maybe it’s something to do with the specific code that the developer needs to understand the performance of their particular piece of code,” Farrell said of the need for contextual understanding.

“That’s why a good modern monitoring tool will have its own metrics and have the ability to bring in metrics from observability solutions like OpenTracing, for example,” Farrell added. “Tracing is where a lot of this nice context comes out.  Like Instana, it’s important to have the ability to do both. That way, you provide the best of both worlds.”

To make the ongoing decisions and take the right actions to deliver proper service performance, modern IT operations teams really require that deep context. It’s valuable for ongoing monitoring, deployment or rollback verification, troubleshooting and reporting. While observability on its own can provide information to an individual or a few individuals. It is the monitoring tool that provides understanding into how things work together; that can shift between a user-centric or an application-centric view, and that can give you a framework to move from monitoring to decision-making to troubleshooting and then, when necessary, moving into reporting or even log analysis.

Farrell pointed out that “the APM piece is the part that ties it all together to provide that full contextual visibility that starts with individual component visibility and ultimately ties it all together for application-level performance and service-level performance.”

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The automatic operation and maintenance of optical network is important for ensuring information communication and network operation. The growing variety of services has forced operation and maintenance personnel to face tremendous operational pressure. A recent study has constructed a control architecture called intent defined optical networks (IDON) to cope with the issue.

The research related paper “Intent Defined Optical Network with Artificial Intelligence-based Automated Operation and Maintenance” is recently published in a special focus on artificial intelligence for optical communications in Science China Information Sciences. The paper is lead by Hui Yang as the corresponding author and associate professor of Beijing University of Posts and Telecommunications. Researchers have adopted a self-adapted generation and optimization policy (SAGO) to perform control research on intent defined optical network architectures, revealing two key closed-loop operations, namely closed-loop strategies generation and closed-loop intent guarantee.

Traditionally, the operation and maintenance of optical networks rely on the experience of engineers to configure network parameters, including command line interfaces, middleware scripts, and troubleshooting. With the rapid development of Internet of Things and high bitrate applications beyond 5G and 6G, a large number of applications are presented on the network in the form of intentions, such as the Internet of Vehicles which closely interact with the environment. As an important support of the policy of “network power,” the optical network faces the challenges of accurate match of the intent application and the complex control of highly dynamic applications, resulting in unsatisfactory levels of manual operation and maintenance. In such cases, operators urgently need to consider the upgrade of their optical network architecture to achieve automatic intelligent operation and maintenance.

Research on intent defined optical networks (IDON) introduces an adaptive generation and optimization (SAGO) strategy in a self-optimizing way. The IDON architecture can achieve intent-oriented configuration conversion, realize adaptive generation and optimization strategies, and perform closed-loop intent-guaranteed operations. IDON specifically addresses communication intent and uses natural language processing to construct semantic graphs to understand, interact, and operate the required network configuration. Then, deep reinforcement learning (DRL) is utilized to dynamically integrate fine-grained strategies to find a synthesis strategy which meets the requirements of the intent. Finally, a deep neural evolution network (DNEN) was introduced to respond to failures to achieve intent guarantee at the millisecond level. The researchers have validated feasibility and efficiency on an enhanced SDN test platform.

These results have enriched the research on intelligent operation and automation of optical networks based on artificial intelligence. IDON with SAGO is not only of great significance to the research of operation automation of optical networks, but also has very important scientific significance and reference value for the application of zero-touch operation and artificial intelligence in networks.

“To the best of our knowledge,” the researchers wrote, “We have investigated and presented the functional entities of the architecture and interworking procedure in optical network automatic operation. The performances are demonstrated on the testbed for intent-based control. Our experiments verify that IDON with SAGO can effectively perform intent translation and zero-touch configuration.”

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Application performance management (APM) solutions need to adapt now that the age of monolithic applications has evolved into microservice-based architectures, which are innately distributed and complex and therefore harder to monitor. 

Collecting vast troves of data on how apps are performing is no longer enough, and APM providers have been adding new ways to analyze that data that will drive meaningful and hyper-fast solutions to expose any bottlenecks or code dependencies. Whether that’s by adding AI, ML, new plugins or methods of monitoring, reliability and speed is on everyone’s mind. 

“It’s not just enough to monitor specific isolated metrics because it’s not enough to just detect that something’s wrong. You need to act fast because the environment is fast. The end user reaction to degradation is catastrophic,” said Daniella Pontes, the senior product marketing manager at InfluxData. “If you are in a big event day, you are talking about hundreds of thousands of dollars per minute or billions per day. So you can’t afford a degradation that can not be quickly identified and most importantly, fixed.”

In 2017, The Economist reported that the world’s most valuable resource is no longer oil, but data.

But data in application monitoring isn’t effective if it can’t be analyzed, which makes it all the more crucial to have easy-to-use and intuitive monitoring to transform that data into outcomes, Pontes added. 

Most commonly, teams use APM tools when they find out that their app is running slow, according to Denny LeCompte, the general manager of application management at SolarWinds. 

“You’re then trying to find out as rapidly as you can, is it the code? Is it the infrastructure? Is it the network? Is it the database? You’re trying to figure out where in the stack it is. If you can provide an application team a way to reduce the meantime to resolution or meantime to innocence, that’s it,” LeCompte explained.

APM solutions leverage data that is collected through API gateways, service mesh, business transaction tracking, log analytics and container APIs to determine both the performance experienced by end users of an application and to measure the computational resources to see whether there is an adequate capacity to support a load and to find potential bottlenecks. 

Service mesh is a relatively new method that aids APM in microservices. 

“Instead of using an API gateway which can be challenging, service meshes are a very new modern way that we can concentrate, be a proxy, and provide a point that all microservices can report to,” said Charley Rich, a senior director analyst at Gartner. “And then a monitoring tool can inquire to the service mesh to capture the collection of data. So it can act as a collection point and you can help in terms of ease of deployment and potentially performance.”

Another trend is the use of OpenTracing. OpenTracing is a CNCF project that includes a set of vendor-neutral APIs and instrumentation that is used for distributed tracing. 

“OpenTracing, census telemetry, service mesh and others need to be explored and utilized,” Rich said. “We’re moving from an era of the monitoring solutions go out and collect the data they need to an area where the infrastructure and applications are reporting back that information.”

Another major change in who uses the APMs in an organization has shifted more towards the developers, according to LeCompte. 

“Ten years ago the app dev guys would not have cared. That was not their problem. Whereas now, they’re definitely more involved and when there is a problem, they are more likely to go into the tool and expect the monitor tool to help them understand,” LeCompte said. “It’s getting to the point where any sort of application team would feel naked without a tool to provide them with visibility.”

Meanwhile, Pontes said APM solutions have evolved to a point where all parts of a team are using it. The developers are using APM to understand how fragmented code performs before moving forward with it in the production environment. The CI/CD teams are using it to understand what kind of impact that change can do and IT teams are using it to make sure everything stays as it should.

What used to be one slowly changing monolith is now all of a sudden dozens of quickly changing microservices that get changed on a weekly or even daily cadence, according to Ivo Mägi, the CEO of Plumbr. 

“Every change is risky by nature so you need to keep a closer eye on your microservices-based architecture because errors are just more likely to happen in situations where you have really agile release cadences,” Mägi said.

He added that APM helps users with availability metrics so that whenever those metrics drop below tolerable levels, the teams are aware of the issues emerging. Another important aspect is the distributed tracing throughout all the microservices in the back end that allows one to zoom in to the exact service failing and, better yet, into the single line of source code in a particular service failing. These functionalities cut down the time to resolution for every incident. 

“Technical monitoring solutions like APMs are similar to sport watches in the sense that through some sensors they gather data and turn it  into information. It would be like monitoring the heart rate or steps done during the day. Now if I just see that I did 3000 steps during the day, I don’t know whether I just broke the world record or am I the laziest guy in the world.. I actually haven’t changed my habits nor really gained anything It’s just a distraction after a while,” Mägi explained. “But if I know that 10,000 steps a day keeps the doctor away and that coupling this with an actual action and doing the remaining 7,000 steps, I have gained quality in my life. And to me this is really similar to what APMs are able to do. If you understand how and why performance and availability can impact your business and know when to respond then you can actually have a significant impact on your business.”

However, despite all of its benefits, creating an effective APM solution comes with a set of challenges. According to Rich, the biggest challenge when monitoring microservices is its ephemerality, and APM vendors have to adapt to work with it. 

“Usually agents for most cases are specific, so that’s problematic for a lot of vendors. To package agents in the containers, I need to know in advance what’s going to go into a container image. That’s a lot of work. And it also makes me more static when I’m trying to be agile,” Rich said. “They’re just there for moments, then gone and somewhere else, which makes monitoring challenging. That’s different from the traditional approaches to monitoring within an enterprise in a cloud,”

Another challenge, according to a Gartner report, is that many organizations don’t provide production visibility for the application development and DevOps teams that build microservice-based applications, resulting in an isolation from the IT teams that are responsible for operational deployment.

To fix these problems, Gartner recommends companies adopt a coordinated monitoring strategy between operations, developers and DevOps teams, enabling service discovery by using the API gateway layer, leveraging service mesh and maintaining up-to-date service metrics. 

Rich said companies that are undergoing digital transformation are the primary candidates for using APM solutions. Mode 2 applications that emphasize agility and speed need to be monitored the most because these are the ones that change frequently. Sometimes changes occur several times a day; therefore, protecting the money-making applications is most critical.

“Anything that’s built now really does need some sort of APM. I don’t really think there’s an application in modern times that doesn’t do better with some level of  monitoring,” SolarWind’s LeCompte said. “Lots of customers only monitor the most mission-critical things, but if you built it and it’s running part of your business, then if you’re not monitoring it, you’re just going to be surprised.”

LeCompte said this includes things many people would not immediately regard as an application, such as websites. Yet, web dev and web operations teams are constantly monitoring how different users are perceiving it. 

He added that users expect an APM solution to work out-of-the-box and to automate agent deployment. 

“Customers don’t want to have to spend weeks rolling this thing out. We do not think that a modern product should require some third party to go spend a bunch of time and money to make it work. It should all be a sort of automatic out of the box,” LeCompte said. 

Increasing automation to keep up with continuous deployment
In order to keep up with the rapid pace of monitoring, many APM solutions are adding AI and ML capabilities. Manual APMs are no longer equipped to deal with the dynamism and the scale that microservices require, said Pontes.

“You need to feed the data into artificial intelligence and machine learning frameworks to start automating certain aspects of the workflow. Because the human factor is actually the bottleneck,” Pontes said.

These machine learning additions do things like correlation and analysis to reduce the volume of alerts, preventing a storm, reducing false alarms, detecting anomalies and finding unusual values to then correlate them and then predicting the potential impact, Rich added.

“Machine learning has been embedded in many APM solutions, not necessarily to do anything new but to do what they did before much better,” Rich said. 

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Microservices architectures (MSA) break down domain-level problems into independent modular capacities so they become easier to manage and deploy, which is great for many situations. When that system is organized around events — an event-driven microservices architecture (EDM) — you are streamlining microservices into event-defined clusters, which then function faster and with improved productivity than they did in a monolithic formation. This event-driven microservices architecture offers benefits that you won’t find in other MSA patterns.

There are challenges, too, however. Scaling an MSA, for example, is more complex than scaling a monolith because the microservice design has so many more moving parts. While you may want to simply duplicate your entire function in a new setting, you may also only need to scale elements of it. Both cases require a careful analysis of how the scaled elements will impact the existing system so you can design around their particular concerns.

 Why Event-Driven Integration Architecture (EDA)?

“Event emitters,” “consumers” and “channels” deliver the event-driven integration functionality that can’t be found in traditional point-to-point software architecture. Their flexibility and scalability improve performance within high-performance computing (HPC) and high throughput computing (HTC) clusters and are especially useful in cases where:

• Multiple subsystems must process the same events.

• Real-time processing is required with minimum time lag.

• Complex event processing is needed, such as those projects that involve aggregation over time-windows or pattern matching.

• There is a need for swift processing of high volumes and velocities of incoming data.

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AI and machine learning are powered by lots of data, so much so that one futurist thinks today’s cloud architectures aren’t enough.

Businesses must continually evolve their data storage strategies to keep pace with emerging data usage requirements. Most of today’s enterprises now have a hybrid cloud architecture. However, as they move more data and workloads to the cloud, they may be in for a surprise when it comes to production-level artificial intelligence and machine learning. Specifically, storing the requisite data may become too expensive to be practical.

In fact, futurist Tom Koulopoulos thinks AI and machine learning will drive the next wave of data storage innovations out of necessity. That’s the premise for his latest book, The Bottomless Cloud.

“One of the Achille’s heels of AI and machine learning is their voracious appetite for data,” said Koulopoulos.”The problem is that learning has a certain cost associated with it, and in case of AI and machine learning it’s how much it costs to capture, transfer, and store data.”

For example, file systems and storage software provider Tuxera estimates that one autonomous car generates between 11 terabytes and 192 TB of data per day.

“According to Google, a Waymo generates 2 TB of information in a single day. I imagine it will be more like 20 TB per day when they’re fully autonomous,” said Koulopoulos. “Even at 2 TB per day, over a single year, you’d end up with about $3 million-plus dollars of storage costs in Amazon, Google or Microsoft cloud. $3 million dollars [worth of storage costs] for a $30,000 Tesla doesn’t make a lot of sense.”

Who will sow the seeds of economic change

Koulopoulos expects startups to lead the next wave of storage innovations rather than the incumbents. For example, cloud startup Wasabi, one of his clients, promises an 80% reduction in storage costs, up to 6 times the speed of Amazon S3, free egress and 11 9s of data durability.

Although the AWS and Microsoft Azure teams were invited to share their views on the future of storage for this story, both declined to participate.

Naturally, Amazon, Google and Microsoft are well aware of the startup activity in the space.

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