partition record nifi example partition record nifi example

NiFi Registry and GitHub will be used for source code control. Node 3 will then be assigned partitions 6 and 7. The first will contain an attribute with the name state and a value of NY. Output Strategy 'Write Value Only' (the default) emits flowfile records containing only the Kafka The name of the attribute is the same as the name of this property. "GrokReader" should be highlighted in the list. The hostname that is used can be the fully qualified hostname, the "simple" hostname, or the IP address. 565), Improving the copy in the close modal and post notices - 2023 edition, New blog post from our CEO Prashanth: Community is the future of AI. In order to provide a static mapping of node to Kafka partition(s), one or more user-defined properties must be added using the naming scheme Routing Strategy First, let's take a look at the "Routing Strategy". ssl.client.auth property. The Record Reader and Record Writer are the only two required properties. However, The number of records in an outgoing FlowFile, The MIME Type that the configured Record Writer indicates is appropriate, All partitioned FlowFiles produced from the same parent FlowFile will have the same randomly generated UUID added for this attribute, A one-up number that indicates the ordering of the partitioned FlowFiles that were created from a single parent FlowFile, The number of partitioned FlowFiles generated from the parent FlowFile. However, there are cases NiFi cluster has 3 nodes. The third would contain orders that were less than $1,000 but occurred before noon, while the last would contain only orders that were less than $1,000 and happened after noon. Consumes messages from Apache Kafka specifically built against the Kafka 1.0 Consumer API. This tutorial was tested using the following environment and components: Import the template: In the above example, there are three different values for the work location. A RecordPath that points to a field in the Record. 04:15 AM. Pretty much every record/order would get its own FlowFile because these values are rather unique. See Additional Details on the Usage page for more information and examples. where Kafka processors using the PlainLoginModule will cause HDFS processors with Keberos to no longer work. Start the PartitionRecord processor. The second FlowFile will consist of a single record for Janet Doe and will contain an attribute named state that has a value of CA. from Kafka, the message will be deserialized using the configured Record Reader, and then Sample input flowfile: MESSAGE_HEADER | A | B | C LINE|1 | ABCD | 1234 LINE|2 | DEFG | 5678 LINE|3 | HIJK | 9012 . For example, what if we partitioned based on the timestamp field or the orderTotal field? Note that no attribute will be added if the value returned for the RecordPath is null or is not a scalar value (i.e., the value is an Array, Map, or Record). If that attribute exists and has a value of true then the FlowFile will be routed to the largeOrder relationship. All using the well-known ANSI SQL query language. For each dynamic property that is added, an attribute may be added to the FlowFile. The customerId field is a top-level field, so we can refer to it simply by using /customerId. attempting to compile the RecordPath. More details about these controller services can be found below. The result determines which group, or partition, the Record gets assigned to. To reference a particular field with RecordPath, we always start with a / to represent the root element. Note: The record-oriented processors and controller services were introduced in NiFi 1.2.0. The data will remain queued in Kafka until Node 3 is restarted. In the list below, the names of required properties appear in bold. Sample NiFi Data demonstration for below Due dates 20-02-2017,23-03-2017 My Input No1 inside csv,,,,,, Animals,Today-20.02.2017,Yesterday-19-02.2017 Fox,21,32 Lion,20,12 My Input No2 inside csv,,,, Name,ID,City Mahi,12,UK And,21,US Prabh,32,LI I need to split above whole csv (Input.csv) into two parts like InputNo1.csv and InputNo2.csv. Each record is then grouped with other like records and a FlowFile is created for each group of like records. What it means for two records to be like records is determined by user-defined properties. For a simple case, let's partition all of the records based on the state that they live in. The result will be that we will have two outbound FlowFiles. Specifically, we can use the ifElse expression: We can use this Expression directly in our PublishKafkaRecord processor as the topic name: By doing this, we eliminate one of our PublishKafkaRecord Processors and the RouteOnAttribute Processor. No, the complete stack trace is the following one: What version of Apache NiFi?Currently running on Apache NiFi open source 1.19.1What version of Java?Currently running on openjdk version "11.0.17" 2022-10-18 LTSHave you tried using ConsumeKafkaRecord processor instead of ConsumeKafka --> MergeContent?No I did not, but for a good reason. In any case, we are going to use the original relationship from PartitionRecord to send to a separate all-purchases topic. PartitionRecord processor with GrokReader/JSONWriter controller services to parse the NiFi app log in Grok format, convert to JSON and then group the output by log level (INFO, WARN, ERROR). What does 'They're at four. Making statements based on opinion; back them up with references or personal experience. This FlowFile will consist of 3 records: John Doe, Jane Doe, and Jacob Doe. Instead of Using ExtractGrok processor, use Partition Record processor in NiFi to partition as this processor Evaluates one or more RecordPaths against the each record in the incoming FlowFile. But two of them are the most important. Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. The addition of these attributes makes it very easy to perform tasks such as routing, or referencing the value in another Processor that can be used for configuring where to send the data, etc. Say we want to partition data based on whether or not the purchase time was before noon. the username and password unencrypted. When the value of the RecordPath is determined for a Record, an attribute is added to the outgoing FlowFile. Find answers, ask questions, and share your expertise, [NiFi][PartitionRecord] When using Partition Record it fails with IllegalArgumentException: newLimit > capacity (90>82). By default, this processor will subscribe to one or more Kafka topics in such a way that the topics to consume from are randomly It does so using a very simple-to-use RecordPath language. It also supports powerful and scalable means of data routing and transformation, which can be run on a single server or in a clustered mode across many servers. The first FlowFile will contain records for John Doe and Jane Doe. *'), ${largeOrder:equals('true'):ifElse('large-purchases', 'smaller-purchases')}. Looking at the properties: 'parse.failure' relationship.). For example, we might decide that we want to route all of our incoming data to a particular Kafka topic, depending on whether or not its a large purchase. We can add a property named state with a value of /locations/home/state. are handled. FlowFiles that are successfully partitioned will be routed to this relationship, If a FlowFile cannot be partitioned from the configured input format to the configured output format, the unchanged FlowFile will be routed to this relationship. The second FlowFile will contain the two records for Jacob Doe and Janet Doe, because the RecordPath will evaluate to null for both of them. The name of the attribute is the same as the name of this property. An example of the JAAS config file would Did the drapes in old theatres actually say "ASBESTOS" on them? There is currently a known issue In this case, the SSL Context Service selected may specify only Due to NiFi's isolated classloading capability, NiFi is able to support multiple versions of the Kafka client in a single NiFi instance. Thanks for contributing an answer to Stack Overflow! This FlowFile will have an attribute named "favorite.food" with a value of "chocolate." Value Only'. See Additional Details on the Usage page for more information and examples. Connect and share knowledge within a single location that is structured and easy to search. ('Key Format') is activated. We will rectify this as soon as possible! The table also indicates any default values. These properties are available only when the FlowFile Output Strategy is set to 'Write If the broker specifies ssl.client.auth=required then the client will be required to present a certificate. I will give it a try with ConsumeKafkaRecord using CSVReader and CSVRecordSetWriter, to see if I still encounter the same issue.Do you have issue only when using the ParquetRecordSetWriter?Unfortunately I can only test with parquet as this file format is somehow mandatory for the current project. In order to make the Processor valid, at least one user-defined property must be added to the Processor. The value of the attribute is the same as the value of the field in the Record that the RecordPath points to. This FlowFile will have no state attribute (unless such an attribute existed on the incoming FlowFile, The PartitionRecord processor allows you to group together like data. We define what it means for two Records to be like data using RecordPath. The value of the property is a RecordPath expression that NiFi will evaluate against each Record. Once a FlowFile has been written, we know that all of the Records within that FlowFile have the same value for the fields that are value of the /geo/country/name field. When the value of the RecordPath is determined for a Record, an attribute is added to the outgoing FlowFile. This option uses SASL with an SSL/TLS transport layer to authenticate to the broker. This limits you to use only one user credential across the cluster. The result will be that we will have two outbound FlowFiles. The most . 01:31 PM. Please note that, at this time, the Processor assumes that all records that are retrieved from a given partition have the same schema. It's not them. Now, we could instead send the largeOrder data to some database or whatever wed like. But TLDR: it dramatically increases the overhead on the NiFi framework and destroys performance.). This example performs the same as the template above, and it includes extra fields added to provenance events as well as an updated ScriptedRecordSetWriter to generate valid XML. If I were to use ConsumeKafkaRecord, I would have to define a CSV Reader and the Parquet(or CSV)RecordSetWriter and the result will be very bad, as the data is not formatted as per the required schema. Additionally, if partitions that are assigned Description: Sends the contents of a FlowFile as individual records to Apache Kafka using the Kafka 2.6 Producer API. By The name of the attribute is the same as the name of this property. Consider a scenario where a single Kafka topic has 8 partitions and the consuming NiFi cluster has 3 nodes. Now let's say that we want to partition records based on multiple different fields. We do so . But what if we want to partition the data into groups based on whether or not it was a large order? Run the RouteOnAttributeProcessor to see this in action: Here are some links to check out if you are interested in more information on the record-oriented processors and controller services in NiFi: Find and share helpful community-sourced technical articles. where this is undesirable. NiFi's bootstrap.conf. If we use a RecordPath of /locations/work/state with a property name of state, then we will end up with two different FlowFiles. We can add a property named state with a value of /locations/home/state . For most use cases, this is desirable. PartitionRecord provides a very powerful capability to group records together based on the contents of the data. The second FlowFile will consist of a single record: Jacob Doe. Co-creator, Apache NiFi; Principal Software Engineer/Tech Lead, Cloudera The contents of the FlowFile are expected to be record-oriented data that can be read by the configured Record Reader. The PartitionRecord offers a handful of properties that can be used to configure it. In the list below, the names of required properties appear in bold. I have CSV File which having below contents, NOTE: The Kerberos Service Name is not required for SASL mechanism of PLAIN. ConsumeKafka ----> MergeContent (as I have plenty of small files I prefer to merge them in bigger files for further processing) ----> ReplaceText (I have some empty spaces and I want them removed) ---> PartitionRecord. Once all records in an incoming FlowFile have been partitioned, the original FlowFile is routed to this relationship. In the list below, the names of required properties appear in bold. To do this, we add one or more user-defined properties. Consumer Partition Assignment. Each record is then grouped with other "like records" and a FlowFile is created for each group of "like records." Lets assume that the data is JSON and looks like this: Consider a case in which we want to partition the data based on the customerId. The number of records in an outgoing FlowFile, The MIME Type that the configured Record Writer indicates is appropriate, All partitioned FlowFiles produced from the same parent FlowFile will have the same randomly generated UUID added for this attribute, A one-up number that indicates the ordering of the partitioned FlowFiles that were created from a single parent FlowFile, The number of partitioned FlowFiles generated from the parent FlowFile. This is achieved by pairing the PartitionRecord Processor with a RouteOnAttribute Processor. Once all records in an incoming FlowFile have been partitioned, the original FlowFile is routed to this relationship. This makes it easy to route the data with RouteOnAttribute. Tags: This means that for most cases, heap usage is not a concern. How to split this csv file into multiple contents? added partitions. The problems comes here, in PartitionRecord. So, if we have data representing a series of purchase order line items, we might want to group together data based on the customerId field. However, it can validate that no The AvroSchemaRegistry contains a "nifi-logs" schema which defines information about each record (field names, field ids, field types). Consider a scenario where a single Kafka topic has 8 partitions and the consuming If we use a RecordPath of /locations/work/state it has already pulled from Kafka to the destination system. and the same value for the home address. As a result, this means that we can promote those values to FlowFile Attributes. The possible values for 'Key Format' are as follows: If the Key Format property is set to 'Record', an additional processor configuration property name 'Key Record Reader' is The name of the attribute is the same as the name of this property. All large purchases should go to the large-purchase Kafka topic. configuration when using GSSAPI can be provided by specifying the Kerberos Principal and Kerberos Keytab 03-28-2023 Two records are considered alike if they have the same value for all configured RecordPaths. This means that for most cases, heap usage is not a concern. An example server layout: NiFi Flows Real-time free stock data is. Looking at the contents of a flowfile, confirm that it only contains logs of one log level. The records themselves are written If any of the Kafka messages are pulled . In this scenario, Node 1 may be assigned partitions 0, 1, and 2. After 15 minutes, Node 3 rejoins the cluster and then continues to deliver its 1,000 messages that If will contain an attribute named favorite.food with a value of spaghetti. However, because the second RecordPath pointed to a Record field, no home attribute will be added. Please try again. Each record is then grouped with other "like records". Node 2 may be assigned partitions 3, 4, and 5. Once one or more RecordPath's have been added, those RecordPath's are evaluated against each Record in an incoming FlowFile. In order to use a static mapping of Kafka partitions, the "Topic Name Format" must be set to "names" rather than "pattern." Its contents will contain: The second FlowFile will have an attribute named customerId with a value of 333333333333 and the contents: Now, it can be super helpful to be able to partition data based purely on some value in the data. However, if the RecordPath points to a large Record field that is different for each record in a FlowFile, then heap usage may be an important consideration. ConvertRecord, SplitRecord, UpdateRecord, QueryRecord, Specifies the Controller Service to use for reading incoming data, Specifies the Controller Service to use for writing out the records. But we must also tell the Processor how to actually partition the data, using RecordPath. The complementary NiFi processor for sending messages is PublishKafkaRecord_1_0. The records themselves are written immediately to the FlowFile content. 'Record' converts the Kafka Record Key bytes into a deserialized NiFi record, using the associated This will result in three different FlowFiles being created. The value of the attribute is the same as the value of the field in the Record that the RecordPath points to. Like QueryRecord, PartitionRecord is a record-oriented Processor. In order to use this The answers to your questions is as follows: Is that complete stack trace from the nifi-app.log? We now add two properties to the PartitionRecord processor. You can choose to fill any random string, such as "null". Rather than using RouteOnAttribute to route to the appropriate PublishKafkaRecord Processor, we can instead eliminate the RouteOnAttribute and send everything to a single PublishKafkaRecord Processor. But because we are sending both the largeOrder and unmatched relationships to Kafka, but different topics, we can actually simplify this. makes use of NiFi's RecordPath DSL. What it means for two records to be "like records" is determined by user-defined properties. assigned to the nodes in the NiFi cluster. Now lets say that we want to partition records based on multiple different fields. Example Input (CSV): starSystem, stellarType Wolf 359, M Epsilon Eridani, K Tau Ceti, G Groombridge 1618, K Gliese 1, M Expression Language is supported and will be evaluated before Example 1 - Partition By Simple Field For a simple case, let's partition all of the records based on the state that they live in. The second FlowFile will consist of a single record: Jacob Doe. We do so by looking at the name of the property to which each RecordPath belongs. NiFi cannot readily validate that all Partitions have been assigned before the Processor is scheduled to run. Here is an example of FlowFile content that is emitted by JsonRecordSetWriter when strategy "Use Wrapper" is active: These new processor properties may be used to extend the capabilities of ConsumeKafkaRecord_2_6, by Now, those records have been delivered out of order. 03-30-2023 This FlowFile will have an attribute named state with a value of NY. Now that weve examined how we can use RecordPath to group our data together, lets look at an example of why we might want to do that. PartitionRecord Description: Receives Record-oriented data (i.e., data that can be read by the configured Record Reader) and evaluates one or more RecordPaths against the each record in the incoming FlowFile. 03-31-2023 In order to use this (If you dont understand why its so important, I recommend checking out this YouTube video in the NiFi Anti-Pattern series. In this scenario, if Node 3 somehow fails or stops pulling data from Kafka, partitions 6 and 7 may then be reassigned to the other two nodes. Similarly, Jacob Doe has the same home address but a different value for the favorite food. be the following: NOTE: The Kerberos Service Name is not required for SASL mechanism of SCRAM-SHA-256 or SCRAM-SHA-512.