Update to the README

Author: LucaCanali

README.md

@@ -2,22 +2,20 @@
 ![SparkPlugins CI](https://github.com/cerndb/SparkPlugins/workflows/SparkPlugins%20CI/badge.svg?branch=master&event=push)
 [![Maven Central](https://maven-badges.herokuapp.com/maven-central/ch.cern.sparkmeasure/spark-plugins_2.12/badge.svg)](https://maven-badges.herokuapp.com/maven-central/ch.cern.sparkmeasure/spark-plugins_2.12)
 
-Use Spark Plugins to extend Apache Spark with custom metrics and executors' startup actions.
-
+**Spark Plugins** are an Apache Spark feature for extending Spark with custom metrics and actions.
+This repository provides ready-to-use examples for deploying Spark plugins across various use cases.
 ### Key Features
 
-- **Spark Plugins** are a mechanism to extend Apache Spark with custom code for metrics and actions.
-- This repository provides examples of plugins that you can deploy to extend Spark with custom metrics and actions.
-  - **Extending Spark instrumentation** with custom metrics
-  - **Running custom actions** when the executors start up, typically useful for integrating with
-    external systems, such as monitoring systems.
-  - This repo provides code and examples of plugins applied to measuring Spark on cluster resources (YARN, K8S, Standalone), 
-    including measuring Spark I/O from cloud Filesystems, OS metrics, custom application metrics, and integrations with external systems like Pyroscope.
-- The code in this repo is for Spark 3.x. For Spark 2.x, see instead [Executor Plugins for Spark 2.4](https://github.com/cerndb/SparkExecutorPlugins2.4)
+* **Custom Metrics:** Extend Spark's instrumentation with user-defined metrics.
+* **Executor Actions:** Trigger custom actions upon executor startup, useful for integrations (e.g., monitoring systems).
+* **Resource Monitoring:** Measure Spark’s usage of cluster resources (YARN, K8S, Standalone).
+* **I/O Metrics:** Monitor I/O performance from cloud filesystems, OS metrics, and custom application metrics.
+* **External Integrations:** Connect with external systems like Pyroscope for performance insights.
 
 ### Contents
 - [Getting started](#getting-started---your-first-spark-plugins)
 - [Demo and basic plugins](#demo-and-basic-plugins)
+- [Implementation notes](#implementation-notes)
 - [Plugin for integrating Pyroscope with Spark](#plugin-for-integrating-with-pyroscope)
 - [Plugin for OS metrics instrumentation with Cgroups for Spark on Kubernetes](#os-metrics-instrumentation-with-cgroups-for-spark-on-kubernetes)
 - [Plugin to collect I/O storage statistics for HDFS and Hadoop-compatible filesystems](#plugins-to-collect-io-storage-statistics-for-hdfs-and-hadoop-compatible-filesystems)
@@ -33,19 +31,6 @@ Use Spark Plugins to extend Apache Spark with custom metrics and executors' star
 
 Author and contact: Luca.Canali@cern.ch 
 
-### Implementation Notes:
-- Spark plugins implement the `org.apache.spark.api.Plugin` interface, they can be written in Scala or Java
- and can be used to run custom code at the startup of Spark executors and driver.  
-- Plugins basic configuration: `--conf spark.plugins=<list of plugin classes>`
-- Plugin JARs need to be made available to Spark executors
-  - you can distribute the plugin code to the executors using `--jars` and `--packages`
-  - for K8S you can also consider making the jars available directly in the container image 
-- Most of the Plugins described in this repo are intended to extend the Spark Metrics System
-    - See the details on the Spark metrics system at  [Spark Monitoring documentation](https://spark.apache.org/docs/latest/monitoring.html#metrics).
-    - You can find the metrics generated by the plugins in the Spark metrics system stream under the
-      namespace `namespace=plugin.<Plugin Class Name>`
-- See also: [SPARK-29397](https://issues.apache.org/jira/browse/SPARK-29397), [SPARK-28091](https://issues.apache.org/jira/browse/SPARK-28091), [SPARK-32119](https://issues.apache.org/jira/browse/SPARK-32119).
-
 ---
 ## Getting Started - Your First Spark Plugins  
 - Deploy the code of the Spark plugins described here using from maven central
@@ -76,6 +61,20 @@ Author and contact: Luca.Canali@cern.ch
       ```
         - You can see if the plugin has run by checking that the file `/tmp/plugin.txt` has been
           created on the executor machines. 
+---
+### Implementation Notes:
+- Spark plugins implement the `org.apache.spark.api.Plugin` interface, they can be written in Scala or Java
+  and can be used to run custom code at the startup of Spark executors and driver.
+- Plugins basic configuration: `--conf spark.plugins=<list of plugin classes>`
+- Plugin JARs need to be made available to Spark executors
+    - you can distribute the plugin code to the executors using `--jars` and `--packages`
+    - for K8S you can also consider making the jars available directly in the container image
+- Most of the Plugins described in this repo are intended to extend the Spark Metrics System
+    - See the details on the Spark metrics system at  [Spark Monitoring documentation](https://spark.apache.org/docs/latest/monitoring.html#metrics).
+    - You can find the metrics generated by the plugins in the Spark metrics system stream under the
+      namespace `namespace=plugin.<Plugin Class Name>`
+- See also: [SPARK-29397](https://issues.apache.org/jira/browse/SPARK-29397), [SPARK-28091](https://issues.apache.org/jira/browse/SPARK-28091), [SPARK-32119](https://issues.apache.org/jira/browse/SPARK-32119).
+
 ---
 ## Plugins in this Repository