title: inconsistent cumulative count in kafka streams
body: <p>I'm using kafka streams for counting cumulative users count for each chat room.</p>
  <ol>
  <li>Topology receives data from the 'connection' topic.</li>
  <li>Each data in the 'connection' topic contains roomNo, userNo, userId, sessionId, and so on.</li>
  <li>Once the kafka streams topology received the connection data, in the first processor it repartitions data with <strong>roomNo and userNo</strong> as a <strong>key</strong> and <strong>0</strong> as a <strong>dummy value</strong> and store the repartitioned data by using a KTable.</li>
  <li>Next, the processor in this topology uses the groupBy method in KTable and groups the repartitioned data, by roomNo as a new key, and produces KGroupedTable.</li>
  <li>Finally, the last processor part executes the count, and send the count result to the output topic named 'stat'.</li>
  </ol>
  <p>This works perfectly on local and staging (or development) environments. But in the production environment, where there is a massive amount of data is transferred, the count result in the output topic 'stat' is intermittently less than the previous one.</p>
  <p>This topology is a cumulative counter, it never decrements the count. How could this ever happen? What could possibly cause this kind of problem?</p>