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Given a sorted list of integers <code>ints</code> with no duplicates, write an efficient function <code>nearest_entries</code> that takes in integers <code>N</code> and <code>k</code> and does the following:

Finds the element of the list that is closest to <code>N</code> and returns that element along with the <code>k</code>-next and <code>k</code>-previous elements of the list.

Notes: 
1. If <code>k</code> is large enough to go out of the bounds of <code>ints</code>, return all entries up to that bound but do not throw an error.
2. If <code>N</code> is not an entry in <code>ints</code> and there are two values equally close to <code>N</code>, use the lower value as your starting point.

Examples:

Input:

<pre tabindex="0" class="chroma"><code>#Example 1:
ints = [1,2,3,4,5,6,7,8,9]
N = 4
k = 2
def nearest_entries(ints, N, k) -&gt; [2,3,4,5,6]

#Example 2:
ints = [1,3,4,6,8]
N = 5
k = 1
def nearest_entries(ints, N, k) -&gt; [3,4,6]

#Example 3:
ints = [1,3,4,7,9,11,15,22,77]
N = 22
k = 4
def nearest_entries(ints, N,k) -&gt; [7, 9, 11, 15, 22, 77]
</code></pre>

Finds the element of a list that is closest to some integer, along with the k-next and k-previous elements

K Nearest Entries

Let’s say that your company is running a standard control and variant AB test on a feature to increase conversion rates on the landing page. The PM checks the results and finds a .04 p-value.

How would you assess the validity of the result?

Experiment Validity

Let’s say we have a table with an <code>id</code> and <code>name</code> fields. The table holds over 100 million rows and we want to sample a random row in the table without throttling the database.

Write a query to randomly sample a row from this table.

Input:

<code>big_table</code> table

<table>
<thead>
<tr>
<th>Columns</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>name</code></td>
<td>VARCHAR</td>
</tr>
</tbody>
</table>

Write a query to randomly sample a row from a big table.

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