nosqlbench VS FlameGraph

Not directly related, but https://github.com/nosqlbench/nosqlbench is very flexible benchmark tool for Cassandra and other distributed systems

For performance testing, we also run common benchmarks: the popular YCSB (Yahoo! Cloud Serving Benchmark), NoSQLBench, LinkBench, SysBench, TPC-H, and TPC-C. We also run C Bench, our own Tarantool API benchmark. Its primitive operations are written in C, and scripts are described in Lua.

We used NoSQLBench to perform moderate load benchmarks. It comes with a convenient Docker image that we could use straight away to run stress jobs in our k8s cluster.

Jonathan Shook created NoSQLBench to be a cross-platform performance testing tool that is easier to use than cassandra-stress and (much) more powerful than YCSB; in fact, its scripting layer is powerful enough to support things that no other testing tool can enable, with particular emphasis on modeling complex workloads with fidelity, as well as simulating realistic scenarios such as load spikes. As its name suggests, NoSQLBench is not Cassandra-specific and encourages participation from all who want to contribute; today there are clients for Cassandra, CockroachDB, JDBC, and MongoDB, as well as non-database products Kafka and Pulsar. If you’re serious about performance testing in 2021, you should check out NoSQLBench. You can get started at GitHub. Other useful links: releases, discord, docs.

We'll use async-profiler and flame graphs for profiling. To simplify the process, we'll run the code using JBang.

And flame graphs excel and this kind of thing

https://www.brendangregg.com/flamegraphs.html

which can output in a format understood by Brendan Gregg's flame frames (https://www.brendangregg.com/flamegraphs.html)

But that's not quite the kind of tracing you're talking about. We also built a printf-style interface to our recording files, which seems closer:

Strategies included discontinuing or resizing underutilized services, transitioning to more cost-effective solutions, reducing the current resources to the amount of resources that we need for our application, and conducting detailed analyses of computing resource utilization through tools like flamegraphs. This detailed scrutiny helped identify and rectify significant cost-driving areas, such as garbage collection and application configurations.

Flame Graphs by Brendan Gregg

Historically brendangregg's since AIUI he basically invented flamegraphs

https://www.brendangregg.com/flamegraphs.html

So if you can make your tool eat whatever https://github.com/brendangregg/FlameGraph is fed with you're going to support a lot of existing tooling across OSes and languages.

“Flame graphs are a visualization of hierarchical data, created to visualize stack traces of profiled software so that the most frequent code-paths to be identified quickly and accurately.”

SVGs are amazing for interactive visualisation too. Like Flamegraphs: https://www.brendangregg.com/flamegraphs.html

This may be a good example of the application of a flame graph but it is not a good demonstration of flame graphs; the graph is nearly incidental. The source has an actual explanation.

A profiling flame graph from Graphile Crystal (a precursor to Grafast) using GraphQL.js' executor (each tick is 1ms, total: 29ms). As we removed more and more responsibilities from GraphQL.js, we ended up only using it for output. Replacing this final responsibility with a custom implementation in Graphile Crystal itself, we reduced execution time for this query down to 15.5ms (effectively removing the majority of the yellow portion of the flame graph).