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Four Key Takeaways for Application Performance and Monitoring | @DevOpsSummit #APM #DevOps

The latest Guide to Performance & Monitoring covers the verifiable & unknowable sides of building & maintaining performant apps

Designing for performance is absolutely essential; but runtime is so crazy a variable that we can reasonably blame too-early optimization for a non-negligible chunk of lousy UX and unmaintainable code.

The latest Guide to Performance and Monitoring covers both the static and dynamic, the verifiable and the unknowable sides of building and maintaining performant applications.

As Tony Hoare notoriously observed, "Premature optimization is the root of all evil:" that is, the benefits of absolutely maximal optimization are usually much lower than the increased cost of maintenance and debugging that results from the brittleness caused by that optimization. On the other hand, the natural tendency of OOP to prioritize form over performance can generate a codebase that is highly readable but partitioned such that performance-oriented refactoring may prove extremely difficult. To help you steer between the Scylla of overeager optimization and the Charybdis of runtime-indifferent code structure, we've split this publication between ways to design performant systems and ways to monitor performance in the real world. To shed light on how developers are approaching application performance, and what performance problems they encounter (and where, and at what frequency), we present the following points in summary of the four most important takeaways of our research.

1) Application code is most likely to cause performance problems frequently; database performance problems are most challenging to fix:

DATA: Frequent performance issues appear most commonly in application code (43% of respondents) and in databases second most commonly (27%). Challenging performance issues are most likely to appear in the database (51%) and second in application code (47%).

IMPLICATIONS: Enterprise application performance is most likely to suffer from higher-level, relatively shallow suboptimalities. Deep understanding of system architecture, network topology, and even pure algorithm design is not required to address most performance issues.

RECOMMENDATIONS: Optimize application code first and databases second (all other things being equal). On first optimization pass, assume that performance problems can be addressed without investing in superior infrastructure.

2) Parallelization is regularly built into program design by a large minority (but still a minority) of enterprise developers:

DATA: 43% of developers regularly design programs for parallel execution. Java 8 Parallel Streams are often used (18%), slightly more frequently than ForkJoin (16%). ExecutorService was most popular by far, with 47% using it often. Race conditions and thread locks are encountered monthly by roughly one fifth of developers (21% and 19% respectively). Of major parallel programming models, only multithreading is often used by more than 30% of developers (81%).

IMPLICATIONS: Enterprise developers do not manage parallelization aggressively. Simple thread pool management (ExecutorService) is much more commonly used for concurrency than upfront work splitting (ForkJoin), which suggests that optimization for multicore processors can be improved.

RECOMMENDATIONS: More deliberately model task and data parallelization, and consider hardware threading more explicitly (and without relying excessively on synchronization wrappers) when designing for concurrency.

3) Performance is still a second-stage design consideration, but not by much:

DATA: 56% of developers build application functionality first, then worry about performance.

IMPLICATIONS: Extremely premature optimization is generally recognized as poor design, but performance considerations are serious enough that almost half of developers do think about performance while building functionality.

RECOMMENDATIONS: Distinguish architectural from code-level performance optimizations. Set clear performance targets (preferably cascading from UX tolerance levels) and meet them. Optimize for user value, not for the sake of optimization.

4) Manual firefighting, lack of actionable insights, and heterogeneous IT environments are the top three monitoring challenges:

DATA: 58% of respondents count firefighting and manual processes among the top three performance management challenges. 49% count lack of actionable insights to proactively solve issues. 47% count rising cost and complexity of managing heterogeneous IT environment.

IMPLICATIONS: Performance management is far from a solved problem. Monitoring tools and response methods are not providing insights and solutions effectively, whether because they are not used adequately or need feature refinement.

RECOMMENDATIONS: Measure problem location, frequency, and cost, and compare with the cost (both monetary and performance overhead) of an additional management layer. Consider tuning existing monitoring systems or adopting new systems (e.g. something more proactive than logs).

More Stories By John Esposito

John Esposito is Editor-in-Chief at DZone, having recently finished a doctoral program in Classics from the University of North Carolina. In a previous life he was a VBA and Force.com developer, DBA, and network administrator. John enjoys playing piano and looking at diagrams, and raises two cats with his wife, Sarah.

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