[SPARK-39944][BUILD] Upgrade dropwizard metrics to 4.2.10

dev/deps/spark-deps-hadoop-2-hive-2.3

@@ -193,11 +193,11 @@ log4j-slf4j-impl/2.18.0//log4j-slf4j-impl-2.18.0.jar
 logging-interceptor/3.12.12//logging-interceptor-3.12.12.jar
 lz4-java/1.8.0//lz4-java-1.8.0.jar
 mesos/1.4.3/shaded-protobuf/mesos-1.4.3-shaded-protobuf.jar
-metrics-core/4.2.7//metrics-core-4.2.7.jar
-metrics-graphite/4.2.7//metrics-graphite-4.2.7.jar
-metrics-jmx/4.2.7//metrics-jmx-4.2.7.jar
-metrics-json/4.2.7//metrics-json-4.2.7.jar
-metrics-jvm/4.2.7//metrics-jvm-4.2.7.jar
+metrics-core/4.2.10//metrics-core-4.2.10.jar
+metrics-graphite/4.2.10//metrics-graphite-4.2.10.jar
+metrics-jmx/4.2.10//metrics-jmx-4.2.10.jar
+metrics-json/4.2.10//metrics-json-4.2.10.jar
+metrics-jvm/4.2.10//metrics-jvm-4.2.10.jar
 minlog/1.3.0//minlog-1.3.0.jar
 netty-all/4.1.77.Final//netty-all-4.1.77.Final.jar
 netty-buffer/4.1.77.Final//netty-buffer-4.1.77.Final.jar

dev/deps/spark-deps-hadoop-3-hive-2.3

@@ -179,11 +179,11 @@ log4j-slf4j-impl/2.18.0//log4j-slf4j-impl-2.18.0.jar
 logging-interceptor/3.12.12//logging-interceptor-3.12.12.jar
 lz4-java/1.8.0//lz4-java-1.8.0.jar
 mesos/1.4.3/shaded-protobuf/mesos-1.4.3-shaded-protobuf.jar
-metrics-core/4.2.7//metrics-core-4.2.7.jar
-metrics-graphite/4.2.7//metrics-graphite-4.2.7.jar
-metrics-jmx/4.2.7//metrics-jmx-4.2.7.jar
-metrics-json/4.2.7//metrics-json-4.2.7.jar
-metrics-jvm/4.2.7//metrics-jvm-4.2.7.jar
+metrics-core/4.2.10//metrics-core-4.2.10.jar
+metrics-graphite/4.2.10//metrics-graphite-4.2.10.jar
+metrics-jmx/4.2.10//metrics-jmx-4.2.10.jar
+metrics-json/4.2.10//metrics-json-4.2.10.jar
+metrics-jvm/4.2.10//metrics-jvm-4.2.10.jar
 minlog/1.3.0//minlog-1.3.0.jar
 netty-all/4.1.77.Final//netty-all-4.1.77.Final.jar
 netty-buffer/4.1.77.Final//netty-buffer-4.1.77.Final.jar

docs/monitoring.md

@@ -796,7 +796,7 @@ The JSON end point is exposed at: `/applications/[app-id]/executors`, and the Pr
 The Prometheus endpoint is conditional to a configuration parameter: `spark.ui.prometheus.enabled=true` (the default is `false`).
 In addition, aggregated per-stage peak values of the executor memory metrics are written to the event log if
 `spark.eventLog.logStageExecutorMetrics` is true.  
-Executor memory metrics are also exposed via the Spark metrics system based on the [Dropwizard metrics library](http://metrics.dropwizard.io/4.2.0).
+Executor memory metrics are also exposed via the Spark metrics system based on the [Dropwizard metrics library](https://metrics.dropwizard.io/4.2.0).
 A list of the available metrics, with a short description:
 
 <table class="table">
@@ -1000,7 +1000,7 @@ keep the paths consistent in both modes.
 # Metrics
 
 Spark has a configurable metrics system based on the
-[Dropwizard Metrics Library](http://metrics.dropwizard.io/4.2.0).
+[Dropwizard Metrics Library](https://metrics.dropwizard.io/4.2.0).
 This allows users to report Spark metrics to a variety of sinks including HTTP, JMX, and CSV
 files. The metrics are generated by sources embedded in the Spark code base. They
 provide instrumentation for specific activities and Spark components.

pom.xml

@@ -142,7 +142,7 @@
     If you changes codahale.metrics.version, you also need to change
     the link to metrics.dropwizard.io in docs/monitoring.md.
     -->
-    <codahale.metrics.version>4.2.7</codahale.metrics.version>
+    <codahale.metrics.version>4.2.10</codahale.metrics.version>
     <!-- Should be consistent with SparkBuild.scala and docs -->
     <avro.version>1.11.0</avro.version>
     <aws.kinesis.client.version>1.12.0</aws.kinesis.client.version>