From f6ed5e1aa3d672dafdf463870e3b141d669dd44d Mon Sep 17 00:00:00 2001
From: Rishabh Maurya <rishabhmaurya05@gmail.com>
Date: Tue, 7 May 2024 12:47:21 -0700
Subject: [PATCH] Use Randomness#get()

Signed-off-by: Rishabh Maurya <rishabhmaurya05@gmail.com>
---
 .../org/opensearch/index/mapper/FieldTypeInference.java  | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/server/src/main/java/org/opensearch/index/mapper/FieldTypeInference.java b/server/src/main/java/org/opensearch/index/mapper/FieldTypeInference.java
index f649e3c1ca251..933b53fc8e34e 100644
--- a/server/src/main/java/org/opensearch/index/mapper/FieldTypeInference.java
+++ b/server/src/main/java/org/opensearch/index/mapper/FieldTypeInference.java
@@ -10,6 +10,7 @@
 
 import org.apache.lucene.index.IndexReader;
 import org.apache.lucene.index.LeafReaderContext;
+import org.opensearch.common.Randomness;
 import org.opensearch.common.xcontent.XContentFactory;
 import org.opensearch.common.xcontent.json.JsonXContent;
 import org.opensearch.core.common.bytes.BytesReference;
@@ -32,12 +33,12 @@
  * <p>The sample size should be chosen carefully to ensure a high probability of selecting at least one document where the field is present.
  * However, it's essential to strike a balance because a large sample size can lead to performance issues since each sample document's _source field is loaded and examined until the field is found.
  *
- * <p>Determining the sample size (<var>S</var>) is akin to deciding how many balls to draw from a bin, ensuring a high probability ((<var>&gt;=P</var>)) of drawing at least one green ball (documents with the field) from a mixture of <var>R</var> red balls (documents without the field) and <var>G</var> green balls:
+ * <p>Determining the sample size ({@code S}) is akin to deciding how many balls to draw from a bin, ensuring a high probability ({@code >=P}) of drawing at least one green ball (documents with the field) from a mixture of {@code R } red balls (documents without the field) and {@code G } green balls:
  * <pre>{@code
  * P >= 1 - C(R, S) / C(R + G, S)
  * }</pre>
- * Here, <var>C()</var> represents the binomial coefficient.
- * For a high confidence level, we aim for <var>P &gt;= 0.95</var>. For example, with 10^7 documents where the field is present in 2% of them, the sample size <var>S</var> should be around 149 to achieve a probability of 0.95.
+ * Here, {@code C()} represents the binomial coefficient.
+ * For a high confidence level, we aim for {@code P >= 0.95 }. For example, with {@code 10^7 } documents where the field is present in {@code 2% } of them, the sample size {@code S } should be around 149 to achieve a probability of {@code 0.95}.
  */
 public class FieldTypeInference {
     private final IndexReader indexReader;
@@ -156,7 +157,7 @@ private void setNextLeaf() {
 
         private static int[] getSortedRandomNum(int sampleSize, int upperBound, int attempts) {
             Set<Integer> generatedNumbers = new HashSet<>();
-            Random random = new Random();
+            Random random = Randomness.get();
             int itr = 0;
             while (generatedNumbers.size() < sampleSize && itr++ < attempts) {
                 int randomNumber = random.nextInt(upperBound);