JVM Profiling in Action

This page summarizes the projects mentioned and recommended in the original post on dev.to

InfluxDB - Power Real-Time Data Analytics at Scale
Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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  • async-profiler

    Sampling CPU and HEAP profiler for Java featuring AsyncGetCallTrace + perf_events

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

  • FlameGraph

    Stack trace visualizer

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

  • InfluxDB

    Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

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  • Gson

    A Java serialization/deserialization library to convert Java Objects into JSON and back

  • Most of the time is spent in JSON parsing, specifically in AccessLogAggregator.parseLineJson. Within parsing, the majority of time is spent in org/json/JSONObject.. Since there are faster JSON libraries available, we'll switch to Gson and repeat the tests. To do this, we'll add the following method to our code:

  • itu

    An extremely fast parser and formatter of standardized date and date-times supporting RFC-3339 (ISO-8601 profile) and more.

  • Now that the JSON parsing time has decreased, we see that timestamp parsing, specifically java/time/OffsetDateTime.parse, is taking a significant amount of the total parsing time. Java's built-in timestamp parsing is notoriously slow. Luckily, a faster alternative exists: https://github.com/ethlo/itu.

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a more popular project.

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