Perhaps try following this guidance in the docs??? Consumer Partition Assignment
By default, this processor will subscribe to one or more Kafka topics in such a way that the topics to consume from are randomly assigned to the nodes in the NiFi cluster. Consider a scenario where a single Kafka topic has 8 partitions and the consuming NiFi cluster has 3 nodes. In this scenario, Node 1 may be assigned partitions 0, 1, and 2. Node 2 may be assigned partitions 3, 4, and 5. Node 3 will then be assigned partitions 6 and 7. 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. For most use cases, this is desirable. It provides fault tolerance and allows the remaining nodes to pick up the slack. However, there are cases where this is undesirable. One such case is when using NiFi to consume Change Data Capture (CDC) data from Kafka. Consider again the above scenario. Consider that Node 3 has pulled 1,000 messages from Kafka but has not yet delivered them to their final destination. NiFi is then stopped and restarted, and that takes 15 minutes to complete. In the meantime, Partitions 6 and 7 have been reassigned to the other nodes. Those nodes then proceeded to pull data from Kafka and deliver it to the desired destination. After 15 minutes, Node 3 rejoins the cluster and then continues to deliver its 1,000 messages that it has already pulled from Kafka to the destination system. Now, those records have been delivered out of order. The solution for this, then, is to assign partitions statically instead of dynamically. In this way, we can assign Partitions 6 and 7 to Node 3 specifically. Then, if Node 3 is restarted, the other nodes will not pull data from Partitions 6 and 7. The data will remain queued in Kafka until Node 3 is restarted. By using this approach, we can ensure that the data that already was pulled can be processed (assuming First In First Out Prioritizers are used) before newer messages are handled. 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 partitions.<hostname> with the value being a comma-separated list of Kafka partitions to use. For example, partitions.nifi-01=0, 3, 6, 9, partitions.nifi-02=1, 4, 7, 10, and partitions.nifi-03=2, 5, 8, 11. The hostname that is used can be the fully qualified hostname, the "simple" hostname, or the IP address. There must be an entry for each node in the cluster, or the Processor will become invalid. If it is desirable for a node to not have any partitions assigned to it, a Property may be added for the hostname with an empty string as the value. NiFi cannot readily validate that all Partitions have been assigned before the Processor is scheduled to run. However, it can validate that no partitions have been skipped. As such, if partitions 0, 1, and 3 are assigned but not partition 2, the Processor will not be valid. However, if partitions 0, 1, and 2 are assigned, the Processor will become valid, even if there are 4 partitions on the Topic. When the Processor is started, the Processor will immediately start to fail, logging errors, and avoid pulling any data until the Processor is updated to account for all partitions. Once running, if the number of partitions is changed, the Processor will continue to run but not pull data from the newly added partitions. Once stopped, it will begin to error until all partitions have been assigned. Additionally, if partitions that are assigned do not exist (e.g., partitions 0, 1, 2, 3, 4, 5, 6, and 7 are assigned, but the Topic has only 4 partitions), then the Processor will begin to log errors on startup and will not pull data. In order to use a static mapping of Kafka partitions, the "Topic Name Format" must be set to "names" rather than "pattern." Additionally, all Topics that are to be consumed must have the same number of partitions. If multiple Topics are to be consumed and have a different number of partitions, multiple Processors must be used so that each Processor consumes only from Topics with the same number of partitions. On Dec 13, 2023 at 8:59 AM -0500, edi mari <edim2...@gmail.com>, wrote: Hi Pierre, Yes, We tried to use FIFO prioritize in the queue, but it didn't help. Some records in the target topic are ordered differently from the source topic(which is critical in cleanup policy: compact) . Edi On Wed, Dec 13, 2023 at 3:46 PM Pierre Villard <pierre.villard...@gmail.com> wrote: > Hi Edi, > > Did you try setting the FIFO prioritizer on the connection between the > processors? > > Thanks, > Pierre > > Le mer. 13 déc. 2023 à 14:19, edi mari <edim2...@gmail.com> a écrit : > >> >> Hello , >> I'm using NIFI v1.20.0 to replicate 250 million messages between Kafka >> topics. >> The problem is that NIFI replicates messages in a non-sequential order, >> resulting in the destination topic storing messages differently than the >> source topic. >> >> for example >> *source topic - partition 0* >> offset:5 key:a value:v1 >> offset:6 key:a value:v2 >> offset:7 key:a value:v3 >> >> *destination topic - partition 0* >> offset:5 key:a value:v2 >> offset:6 key:a value:v1 >> offset:7 key:a value:v3 >> >> The topics are configured with a cleanup policy: compact. >> >> I'm using ConsumeKafka and PublishKafka processors to replicate topics. >> >> <image.png> >> <image.png> >> <image.png> >> <image.png> >> <image.png> >> Thanks >> Edi >> >
