docs/nifi-docs/components/org.apache.nifi/nifi-standard-nar/1.19.1/org.apache.nifi.processors.standard.SampleRecord/additionalDetails.html - nifi-site - Git at Google

 <!DOCTYPE html>
 <html lang="en">
 <!--
   Licensed to the Apache Software Foundation (ASF) under one or more
   contributor license agreements.  See the NOTICE file distributed with
   this work for additional information regarding copyright ownership.
   The ASF licenses this file to You under the Apache License, Version 2.0
   (the "License"); you may not use this file except in compliance with
   the License.  You may obtain a copy of the License at
       http://www.apache.org/licenses/LICENSE-2.0
   Unless required by applicable law or agreed to in writing, software
   distributed under the License is distributed on an "AS IS" BASIS,
   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   See the License for the specific language governing permissions and
   limitations under the License.
 -->
 <head>
     <meta charset="utf-8" />
     <title>SampleRecord</title>

     <link rel="stylesheet" href="../../../../../css/component-usage.css" type="text/css" />
 </head>
 <body>
     <p>This processor takes in a record set and samples records from the set according to the specified sampling
     strategy. The available sampling strategies are:</p>
     <ul>
         <li><b>Interval Sampling</b>
             <p>Select every <i>N</i>th record based on the value of the Sampling Interval property. For example, if there are
                 100 records in the set and the Sampling Interval is set to 4, there will be 25 records in the output,
                 namely every 4th record. This performs uniform sampling of the record set so is best suited for record
                 sets that are uniformly distributed. For example a record set representing user information that is
                 uniformly distributed will result in the output records also being uniformly distributed. The outgoing
                 record count is deterministic and is exactly the total number of records divided by the Sampling Interval value.
             </p>
         </li>
         <li><b>Probabilistic Sampling</b>
             <p>Select each record with probability <i>P</i>, an integer percentage specified by the Sampling Probability value.
                 For example, an incoming record set of 100 records with a Sampling Probability value of 20 should have
                 roughly 20 records in the output. Use this when you want to output record sets of roughly the same size
                 (but not exactly) and when you want each record to have the same "chance" to be selected for the output
                 set. As another example, if you send the same flow file into the processor twice, a sampling strategy of
                 Interval Sampling will always produce the same output, where Probabilistic Sampling may output different
                 records (and a different total number of records).
             </p>
         </li>
         <li><b>Reservoir Sampling</b>
             <p>Select <i>K</i> records from a record set having <i>N</i> total values, where <i>K</i> is the value of
                 the Reservoir Size property and each record has an equal probability of being selected (exactly K / N).
                 For example, an incoming record set of 100 records with a Reservoir Size value of 20 should have
                 exactly 20 records in the output, randomly chosen from the input record set. Use this when you want to
                 control the exact number of output records and have each input record have the same probability of being
                 selected. As another example, if you send the same flow file into the processor twice, a sampling strategy of
                 Interval Sampling will always produce the same output (same records and number of records), where
                 Probabilistic Sampling may output different records (and a different total number of records), and
                 Reservoir Sampling may output different records but the same total number of records. Note that the
                 reservoir is kept in-memory, so if the size of the reservoir is very large, it may cause memory issues.
             </p>
         </li>
     </ul>
 <p>
     The "Random Seed" property applies to strategies/algorithms that use a pseudorandom random number generator, such as
     Probabilistic Sampling and Reservoir Sampling. The property is optional but if set will guarantee the same records
     in a flow file will be selected by the algorithm each time. This is useful for testing flows using non-deterministic
     algorithms such as Probabilistic Sampling and Reservoir Sampling.
 </p>
 </body>
 </html>
	<!DOCTYPE html>
	<html lang="en">
	<!--
	Licensed to the Apache Software Foundation (ASF) under one or more
	contributor license agreements. See the NOTICE file distributed with
	this work for additional information regarding copyright ownership.
	The ASF licenses this file to You under the Apache License, Version 2.0
	(the "License"); you may not use this file except in compliance with
	the License. You may obtain a copy of the License at
	http://www.apache.org/licenses/LICENSE-2.0
	Unless required by applicable law or agreed to in writing, software
	distributed under the License is distributed on an "AS IS" BASIS,
	WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	See the License for the specific language governing permissions and
	limitations under the License.
	-->
	<head>
	<meta charset="utf-8" />
	<title>SampleRecord</title>

	<link rel="stylesheet" href="../../../../../css/component-usage.css" type="text/css" />
	</head>
	<body>
	<p>This processor takes in a record set and samples records from the set according to the specified sampling
	strategy. The available sampling strategies are:</p>
	<ul>
	<li><b>Interval Sampling</b>
	<p>Select every <i>N</i>th record based on the value of the Sampling Interval property. For example, if there are
	100 records in the set and the Sampling Interval is set to 4, there will be 25 records in the output,
	namely every 4th record. This performs uniform sampling of the record set so is best suited for record
	sets that are uniformly distributed. For example a record set representing user information that is
	uniformly distributed will result in the output records also being uniformly distributed. The outgoing
	record count is deterministic and is exactly the total number of records divided by the Sampling Interval value.
	</p>
	</li>
	<li><b>Probabilistic Sampling</b>
	<p>Select each record with probability <i>P</i>, an integer percentage specified by the Sampling Probability value.
	For example, an incoming record set of 100 records with a Sampling Probability value of 20 should have
	roughly 20 records in the output. Use this when you want to output record sets of roughly the same size
	(but not exactly) and when you want each record to have the same "chance" to be selected for the output
	set. As another example, if you send the same flow file into the processor twice, a sampling strategy of
	Interval Sampling will always produce the same output, where Probabilistic Sampling may output different
	records (and a different total number of records).
	</p>
	</li>
	<li><b>Reservoir Sampling</b>
	<p>Select <i>K</i> records from a record set having <i>N</i> total values, where <i>K</i> is the value of
	the Reservoir Size property and each record has an equal probability of being selected (exactly K / N).
	For example, an incoming record set of 100 records with a Reservoir Size value of 20 should have
	exactly 20 records in the output, randomly chosen from the input record set. Use this when you want to
	control the exact number of output records and have each input record have the same probability of being
	selected. As another example, if you send the same flow file into the processor twice, a sampling strategy of
	Interval Sampling will always produce the same output (same records and number of records), where
	Probabilistic Sampling may output different records (and a different total number of records), and
	Reservoir Sampling may output different records but the same total number of records. Note that the
	reservoir is kept in-memory, so if the size of the reservoir is very large, it may cause memory issues.
	</p>
	</li>
	</ul>
	<p>
	The "Random Seed" property applies to strategies/algorithms that use a pseudorandom random number generator, such as
	Probabilistic Sampling and Reservoir Sampling. The property is optional but if set will guarantee the same records
	in a flow file will be selected by the algorithm each time. This is useful for testing flows using non-deterministic
	algorithms such as Probabilistic Sampling and Reservoir Sampling.
	</p>
	</body>
	</html>