The second part in a series of articles looking at the microbenchmarking of big data solutions running on the JVM. In this part the performance model is further refined over a number of configuration steps, each step building on the previous steps with the purpose of deriving a smaller, simpler and more relevant model of the microbenchmark to facilitate more targeted code inspection as well as a better understanding of the nature of execution flow across method, class and package boundaries.
The challenge in measuring not just the microbenchmark code but the underlying method invocations within the software under test (performance analysis) is ensuring that the required instrumentation, measurement and collection does not perturb the system to such an extent that the system is an entirely different system (measurement is not relevant) and the data collected is neither accurate or actionable. This challenge can be overcome with adaptive profiling of the codebase, both online and offline. Here this advanced application performance monitoring approach is applied to Apache Spark.
There are a few interpretations of “static tracing” but at this point I can only assume that the above tweet is referring to the more common case of traces (probes) being explicitly coded or compiled into software at build time. The reason for the different possible interpretations of static, dynamic and adaptive is that in the …
This week Simz 2.3 broke all previous benchmark records in simulating 270 million metered network streamed calls a second on a Google Cloud n1-highcpu-32 machine type instance. That is 540 million call events a second – 32 billion events a minute. The software execution calls originated in 28 client JVMs also running on a n1-highcpu-32 machine type instance. On average each client was able to invoke 9.6 million instrumented method calls a second from a single thread per process with an average client call latency of 100 nanoseconds. The CPU utilization on the Simz machine was pegged at just over 90% with the incoming network data transmission at 985MB a second.
I’ve long fascinated over how best to perceive the behavior of software machines that for the most part appear as black boxes; consuming input we feed them and producing output we consume, directly or indirectly. I cannot help feeling there is a lost beauty in the motion of action that needs to be rediscovered in order to acquire a far greater understanding of what it is that software and hardware machines do and how in the course of action(s) and state changes (encompassing the environment) such a system changes behavior in ways not observed, or accurately predicted nor fully understood.
A proposal for a different approach to application performance monitoring that is far more efficient, effective, extensible and eventual than traditional legacy approaches based on metrics and event logging. Instead of seeing logging and metrics as primary datasources for monitoring solutions we should instead see them as a form of human inquiry over some software execution behavior that is happening or has happened. With this is mind it becomes clear that logging and metrics do not serve as a complete, contextual and comprehensive representation of software execution behavior.
The following is a graphic I’ve used in the past to frame various software performance optimization techniques. It is not a comprehensive inventory of all software performance optimization techniques (or concerns) but I’ve found it serves a purpose in managing the amount of effort that, in general, should be spent on each technique outside of extreme cases such as trading platforms (or profilers). The left side is your typical localized bottom up approach to speeding up code execution steps.
The Good Regulator Theorem states “every good regulator of a system must be a model of that system”. But what exactly would such a model look like? What elements should the model contain and how might they be related and reasoned about? The theorem itself does not address this so in this article I present my own research findings covering dramatism, observational learning, experiential learning, activity theory, simulation theory and mirror neurons as well as software activity metering and software performance measurement.
“They [autoletics] are more autonomous and independent because they cannot be as easily manipulated with threats or rewards from the outside. At the same time, they are more involved with everything around them because they are fully immersed in the current of life.”
FLOW: THE PSYCHOLOGY OF HAPPINESS
To fight current levels of complexity in IT systems we must look to imbue software with the ability to sense, perceive, reason and act locally with immediacy. Software must adapt not simply react. Feedback signals need to flow freely across machine boundaries as well as man-and-machine interfaces.
MIRRORED SOFTWARE SIMULATION
In projecting software execution behavior and contextual state across space and time software engineers have the capability to develop new and augmented systems that bridge the past, present and future, allowing software machines to transcend structures formed in the early stages of design and over the course of extemporaneous reactive change.
SOFTWARE MACHINE MEMORIES
Your software has memory but your software has no memories. What if software could recall past memories for the purpose of learning? What if we could observe machine memories to more effectively reason about complex software execution behavior?
SOFTWARE SYSTEM ENGINEERING
INTELLIGENT ADAPTIVE MONITORING
Using self-adaptive instrumentation and measurement tooling, performance and scalability problem identification is all but guaranteed. Within a matter of minutes, measuring a representative workload, various potential bottlenecks and optimization calls sites will be accurately identified.
SOFTWARE PERFORMANCE VISUALIZED
Efficient data collection coupled with unique software execution visualizations ensures that all parties involved in a performance investigation will gain an unprecedented insight into the execution nature and resource consumption patterns of applications and more importantly, a high degree of confidence in report findings.
POST EXECUTION ANALYSIS
Through distributed software recording and simulated playback the time spent in performance measuring an application under observation and analysis is greatly reduced. This allows much of the investigative work to be moved outside of business critical operating windows.
SOFTWARE EXECUTION MODEL
The software execution model is focused on the algorithmic and resource consumption behavior of a particular processing pattern such as a transaction, service request or workflow.
Imagine you need to drive across town from location A to B. You use a navigation system to plan the steps and to estimate the expected time of arrival.
Different navigation systems (algorithms) and options (context) will likely result in a different route plan.
SYSTEM EXECUTION MODEL
The navigation system creates a basic route plan and calculates the time for each leg based on the distance and allowed speed. But it naively assumes that no other driver is on the road and sharing the same time and space. It also is unaware of possible roadworks and accidents that will result in a delay or detour.
The system execution model looks at the impact of sharing resources across concurrent and competing processing call flows. What are the utilization levels? How is resource consumption policed? What additional costs and penalties are incurred in the co-ordination of sharing? Is the policy fair? What variations in performance is introduced due to contention? Is prolonged resource starvation possible?
SOFTWARE ADAPTATION MODEL
Naturally when we do drive and encounter obstacles or delays we change course. More importantly this information is retained and used to train future behaviors and predictions.
The software adaptation model looks at what degree of self and situational awareness does software hold, create and manage. Can changes in the environment and variation in the performance be sensed? Can the software reason about what is sensed and alter its behavior accordingly? Can the software reason about the effectiveness of its reactions and reinforce good behavioral patterns and adaptations?
SYSTEM DYNAMICS MODEL
Traffic and transportation management is crucially important for a city, especially as many suffer from traffic congestion. In our drive through the city we encounter many methods to control traffic flow in order to globally minimize delay or prioritize particular traffic types. These include signaled junctions, roundabouts, bus and car poll lanes, metering ramps, traffic flow signage, etc.
The system dynamics model looks at the nature of processing (flows) and how adaptive policies around resources (stocks) can be used to influence the processing behavior in order to optimize throughput or response, increase resilience (to surges) as well as to improve stability in performance.