In this post, we discuss the enhanced Observability support in Spring Boot 3.2 and Java 21. By leveraging the advanced features of Java 21 and Spring Boot 3.2, developers can enhance the observability of their systems and effectively monitor various metrics and logs. Java 21 also introduces Virtual threads and Structured concurrency, which in turn make Observability easier and clearer.
Observability is the ability to monitor a system just by looking at its external features/outputs. On software systems, Observability is used to determine how a running system fulfils requests, responds to events, how its interfaces/modules interact with each other, its responsiveness when handling a huge number of user requests, throughput at different loads etc. Observability traces your code's performance, responsiveness stability etc.; in all the locations or running modules at the same time, thereby enabling a bird's eye view of how your code is performing.
A core actor in Observability is a "trace"; as a request/event propagates in the software system, the actions it triggers OR the response it generates from the system is tracked via a trace. So a "trace" provides visibility into how requests behave as they move throughout the entire software system.
Observability clearly shows the performance patterns that are emerging, the time taken by processes, busy or idle endpoints, and parallelly running queries/clients etc. All this is reported in clear and concise numbers or figures, e.g.
All this performance data is aggregated and presented visually, for discerning patterns and trends easily. This information lets you know about any possible bottlenecks, possibilities for improvements, and if the memory allocation is optimal.
It's necessary to benchmark your current Java setup to know how well it's performing as the sum of its parts, how individual modules and processes are performing, and the overall behaviour of your system when juxtaposed with the demands and expectations that it should deliver.
Software these days is run in complex environments; like SAAS offerings, Mobile apps and their servers, code copies running on many Virtual machines and software running on an application stack while coordinating with other software. As applications go through variable demands on their scalability, throughput, responsiveness and resilience; their monitoring and observation process is becoming more and more complex and needs to be fine-grained too.
How about an automated tool for observability that adds visual reports too?
Micrometer is a metrics instrumentation library that records/follows traces as the system responds to requests/events and further processes them. Then Micrometer can feed those traces to tracing systems that aggregate and process them, to provide useful metrics and visualisations. Micrometer traditionally supports many tracing systems like Jaeger, Zipkin, Prometheus and Datadog etc.
Further it allows you to trace your Threads via the <span class="pink">JvmThreadMetrics</span> binder, loaded and unloaded classes via the <span class="pink">ClassLoaderMetrics</span> binder, CPU's average and total load via <span class="pink">ProcessorMetrics</span>, garbage collector via <span class="pink">JvmGcMetrics</span> etc. ; each via a specialised binder.
Micrometer is a very popular tool that has a minimal code footprint and it tries to avoid java-reflection, thus it can run alongside your applications using minimal system resources.
Before Spring Boot 3, programmers needed to use Micrometer (external dependency ) for metrics , <span class="pink">spring-cloud sleuth</span> for distributed tracing (the external library that depended on Micrometer), where micrometer itself was a dependency on the spring-framework.
Other observability libraries were also available that worked well with Spring Boot, but all of them were external to the framework and needed significant expertise and configuration to be useful. This made using Micrometer a bit tedious and error-prone.
For any observation to happen, one needed to register ObservationHandler objects through an ObservationRegistry.
In the example above, we use the function call <span class="teal">highCardinalityKeyValue("nnnn", "xx")</span> for illustration only, calling it should be avoided unless you have a specific reason to do so. Low cardinality key-value pairs can be attached with each trace for serving many purposes, like identifying the host/region/instance that generates these traces OR detecting anomalies and classifying/quantifying constituents of your application stack.
In general, the property management.observations.key-values.* can be used to automatically apply low-cardinality key-values to all observations.
With Spring Boot 3 onwards, there is no need to configure and use external libraries for observability.
With Spring Boot3.2, all this becomes simpler, and you just need to use an annotation. You just need to add the Spring Boot AOP library in the classpath (spring-boot-starter-aop), and use
Micrometer's existing annotations like <span class="pink">@Observed, @Counted, @Timed, @TimedSet, @NewSpan and @ContinueSpan</span> etc. The aspects for these annotations are now auto-
configured if you have AspectJ on the classpath. e.g.
So no need to define custom observations programmatically using the Observation and <span class="pink">ObservationRegistery</span> classes, you can just focus on your application's business logic,
Spring Boot 3.2 does the Observation for you inherently. Spring 3.2 has a new configuration property named <span class="pink">spring.reactor.context-propagation</span>, if you set this to auto, it automatically propagates observations, trace IDs, and span IDs in your reactive pipelines.
Also, Spring Boot 3.2 now has inbuilt support for Reactive Relational Database Connectivity or R2DBC, To enable it, add the <span class="pink">io.r2dbc:r2dbc-proxy</span> dependency to your project. If you're using Kafka, you can set the <span class="pink">spring.kafka.template.observation-enabled</span> property to support Micrometer observations.
Finally, you can also disable Micrometer by setting the following property: <span class="pink">management.metrics.export.enabled=false</span>
If you don't want to use Micrometer for some reason OR want fine-grained control over your observations, Spring Boot’s actuator module includes basic support for OpenTelemetry. You will have to configure OpenTelemetry on your own, as no auto-configuration is provided, but that's not difficult. Spring Boot provides two major beans, one of type <span class="pink">OpenTelemetry</span> and another of type <span class="pink">Resource</span>.
The OpenTelemetry bean will automatically register any bean of type <span class="pink">SdkLoggerProvider</span> or <span class="pink">SdkMeterProvider</span>, if found in the context.
The attributes of the Resource can be configured via the <span class="pink">management.opentelemetry.resource-attributes</span> configuration property.
You need to add the following dependency in your POM.xml
OR gradle build
Now, as soon as Spring Boot detects the <span class="pink">MeterProvider</span> bean, it will register it on the <span class="pink">BatchSpanProcessor</span>.
To trace your JDBC connections, you can add the OpenTelemetry driver class and your JDBC URL(starting with JDBC:otel) to your application.properties.
and add the following dependencies:-
OR, if using Gradle
OS threads as the name suggests are operating system threads that the OS generates. A fraction of the available threads is given to the JVM, and the JVM distributes them among the Java processes and multi-threaded programs. So traditionally, a Java thread used to be assigned an OS thread to complete its task. Since the OS threads are limited, and some Java threads perform long-term computations that need many thread cycles whereas some other Java threads just need a minute fraction of the CPU cycle(thread) to complete their task, Java 21’s JVM creates its lightweight threads called virtual threads to manage the thread allocation optimally.
The Java 21 runtime can give the illusion of plentiful threads by mapping a large number of virtual threads to a small number of OS threads and managing them effectively. The virtual thread consumes an OS thread only while it performs calculations on the CPU and frees it when its immediate task is done, instead of holding on to it for the lifetime of the request it is handling. Virtual threads now always support thread-local variables, and need(should) not be pooled as they are short-lived by design and have a shallow call stack. Virtual threads are not faster than OS threads, rather they enable the higher concurrency needed for higher throughput.
The above properties of Virtual threads necessitate the need to observe them closely for better application design and post-production performance.
Micrometer’s low-overhead profiling and monitoring mechanism can associate events from application code (such as a web request or I/O operations) with the correct virtual thread assigned for the operation. It can step through virtual threads, show call stacks, and inspect variables in stack frames.
Java 21 introduces Virtual threads, Scoped values, and Concurrency APIs for better concurrent programming. Imagine how the ability to create child threads from parent threads, share variables without the danger of concurrent updates or race conditions, and manage all the thread framework via Structured Concurrency APIs, can do wonders for your multi-threaded code. Structured concurrency treats groups of related tasks running in different threads as a single unit of work, thereby streamlining error handling and cancellation, improving reliability, and enhancing observability. Now you can easily visualize your threads in the form of hierarchical trees with shared variables, along with thread dumps.
Thus, error handling is improved as the API makes it very easy to do the right thing and hard to accidentally do the wrong thing: it's hard to fail to notice errors from child threads, fail to stop or wait for child threads, and fail to shut down fast in error scenarios.
Observability is improved because the parent-child thread relationship is formalized and recognized by the tooling.
E.g., consider the following code snippet:-
The above program:-
This new feature of Java 21, paired with a powerful tool like Open Telemetry or Micrometer helps improve concurrent programming with continuous feedback.
In addition to the above, Java 21 provides advanced logging capabilities including structured logging and log correlation. This enables better traceability and analysis of application logs, making it easier to identify and troubleshoot issues.
Another useful tool is the improved Flight Recorder which keeps collecting diagnostic and profiling data about a running Java application, without any performance overhead. The profiling information in the Flight Recorder includes thread execution details, memory usage, and garbage collector metrics.
Combining these with other observability tools eases performance evaluation, anomaly detection, and fault correction.
1. Avoid tracking metrics that might have high cardinality, e.g. unique <span class="teal">user_id</span> of every logged-in user, as this could, at times, result in millions of values being tracked. This in turn could lead to a resource shortage in your computing system, causing a possible crash in either the running application or the observability tool. Moreover, high cardinality data could overshadow/clog other important observability metrics.
2. Always disable/prevent observations that are not important to you, from being reported.
To prevent certain observations from being reported, you can use the <span class="pink">management.observations.enable</span> properties.
To prevent/disable a group of observations, e.g. all observations with a name starting with denied prefix , just set the <span class="pink">management.observations.enable.denied.prefix=false</span>
If you want to prevent a specific observation, e.g. Spring Boot Security from reporting observations, set the property <span class="pink">management.observations.enable.spring.security=false</span>.
If you want to decide at runtime , you can register beans of type <span class="pink">ObservationPredicate</span>; then an Observation will only be reported if all the <span class="pink">ObservationPredicate</span> beans return true for that observation. e.g. to prevent all observations whose name contains "denied", you can do this:-
3. Choose metrics as per the scalability and responsiveness demands of your application, not all metrics are to be tracked.
The combination of Java 21 with Spring Boot 3.2 provides effective tools and libraries to ease Observability, and how to make the best use of it.