My use case is like this. I have some X tables to be pulled from MySQL. I am splitting them using SplitText to put each table in a individual flow file and pull using GenerateTableFetch and ExecuteSQL.
And I want to be notified or put some other action when import is done for all the tables. At SplitText text processor I have routed original relationship to Wait on ${filename} with target count ${fragment.count}. This will track how many tables are done.
But now I am not able to figure out how to know when a particular table is done. GenerateTableFetch forks flow file into multiple based on Partition Size. But it does not write attributes like fragment.count which I can use to wait on for each table.
Is there a way I can achieve this? Or maybe is there a way to know at the end of the entire flow if all flow files in the flow have been processed and nothing is in queue or being processed?
If you have a standalone instance of NiFi (or are not distributing the flow files among a cluster to ExecuteSQL nodes), then you could use QueryDatabaseTable instead, it (by default) will only issue all flow files when the entire result set is processed. If you have all the rows go into a single flow file, then the fact that the flow file has been transferred downstream is an indication that the fetch is complete.
I have written NIFI-5601 to cover the improvement of adding fragment.* attributes to flow files generated by GTF.
Till NiFi add's support for this, I managed to make it work using MergeContent. Use table_name as Correlation attribute name and then use merged relation to Wait processor using ${merge.count} as target. Refer screenshots if someone is looking to do the same.
Related
I have a requirement where we have a template which uses SQL as source and SQL as destination and data would be more than 100GB for each table so here template will be instantiated multiple times based on tables to be migrated and also each table is partitioned into multiple flowfiles. How do we know when the process is completed? As here there will be multiple flowfiles we are unable to conclude as it hits end processor.
I have tried using SitetoSiteStatusReportingTask to check queue count, but it provides count based on connection and its difficult to fetch connectionid for each connection then concatenate as we have large number of templates. Here we have another problem in reporting task as it provides data on all process groups which are available on NIFI canvas which will be huge data if all templates are running and may impact in performance even though I used avro schema to fetch only queue count and connection id.
Can you please suggest some ideas and help me to achieve this?
you have multiple solution :
1 - you can use the wait/notify duo processor.
if you dont want multiple flowfile running parallely :
2 - set backpressure on Queue
3 - specify group level flow file concurrency (recommended but Nifi 1.12 only )
We have a flow where GenerateTableFetch takes inpute from splitJson which gives TableName, ColumnName as argument. At once multiple tables are passed as input to GenerateTableFetch and next ExecuteSql executes the query.
Now i want to trigger a new process when all the files for a table has been processed by the below processor (At the end there is PutFile).
How to find that all the files created for a Table has been processed?
You may need NIFI-5601 to accomplish this, there is a patch currently under review at the time of this writing, I hope to get it into NiFi 1.9.0.
EDIT: Adding potential workarounds in the meantime
If you can use ListDatabaseTables instead of getting your table names from a JSON file, then you can set Include Count to true. Then you will get attributes for the table name and the count of its rows. Then you can divide the count by the value of the Partition Size in GTF and that will give you the number of fetches (let's call it X). Then add an attribute via UpdateAttribute called "parent" or something, and set it to ${UUID()}. Keep these attributes in the flow files going into GTF and ExecuteScript, then you can use Wait/Notify to wait until X flow files are received (setting Target Signal Count to ${X}) and using ${parent} as the Release Signal Identifier.
If you can't use ListDatabaseTables, then you may be able to have ExecuteSQLRecord after your SplitJSON, you can execute something like SELECT COUNT(*) FROM ${table.name}. If using ExecuteSQL, you may need a ConvertAvroToJSON, if using ExecuteSQLRecord use a JSONRecordSetWriter. Then you can extract the count from the flow file contents using EvaluateJsonPath.
Once you have the table name and the row count in attributes, you can continue with the flow I outlined above (i.e. determine the number of flow files that GTF will generate, etc.).
I am getting files from remote server using Nifi: my files are as follow:
timestamp (ms), nodeID,value
12345,x,12.4
12346,x,12.7
12348,x,13.4
12356,x,13,6
12355,y,12.0
I am now just get and fetch and split lines and send them to Kafka, but before hand, I need to apply a checksum approach on my records and aggregate them based on time stamp, what I need to do to add an additional column to my content and count the records based on aggregated time stamps, for example aggregation based on each 10 milliseconds and nodeID..
timestamp (ms), nodeID,value, counts
12345,x,12.4,3
12346,x,12.7,3
12348,x,13.4,3
12356,x,13,6,1
12355,y,12.0,1
How to do above process in NiFi. I am totally new to Nifi but need to add above functinality to my Nifi process. I am currently using below nifi process
This may not answer your question directly, but you should consider refactoring your flow to use the "record" processors. It would greatly simplify things and would probably get you closer to being able to do the aggregation.
The idea is to not split up the records, and instead process them in place. Given your current flow, the 4 processors after FetchSFTP would like change to a single ConvertRecord processor that converts CSV to JSON. You would first need to defined a simple Avro schema for your data.
Once you have the record processing setup, you might be able to use PartitionRecord to partition the records by the node id, and then from there the missing piece would be how to count by the timestamps.
Some additional resources...
https://blogs.apache.org/nifi/entry/record-oriented-data-with-nifi
https://bryanbende.com/development/2017/06/20/apache-nifi-records-and-schema-registries
https://www.slideshare.net/BryanBende/apache-nifi-record-processing
I've created a simple NiFi pipeline that reads a stream of data from a Kafka topic (using ConsumeKafka) and writes it to the HDFS (using PutHDFS). Currently, I'm seeing lots of small files being created on the HDFS. A new file is created about once a second, some with only one or two records.
I want fewer, larger files to be written to the HDFS.
I have the following settings in ConsumeKafka:
Message Demarcator = <new line>
Max Poll Records = 10000
Max Uncommitted Time = 20s
In the past I've used Flume instead of Nifi, and it has batchSize and batchDurationMillis, which allow me to tweak how big HDFS files are. It seems like ConsumeKafka in Nifi is missing a batchDurationMillis equivalent.
What's the solution in NiFi?
Using the Message Demarcator and Max Poll Records is the correct approach to get multiple messages per flow file. You may want to slow down the ConsumeKafka processor by adjusting the Run Schedule (on the scheduling tab) from 0 sec which means run as fast as possible, to something like 1 second or whatever makes sense for you to grab more data.
Even with the above, you would likely still want to stick a MergeContent processor before PutHDFS, and merge together flow files based on size so that you can wait til you have the appropriate amount of data before writing to HDFS.
How to use MergeContent will depend on the type of data you are merging... If you have Avro, there is a specific merge strategy for Avro. If you have JSON you can merge them one after another, or you can wrap them with a header, footer, and demarcator to make a valid JSON array.
I created a job batch to extract data from csv file to a jdbc using filejdbc module, it worked properly, but when I scheduled the batch to run every 5 minutes, it did not work with the incremental load concept, it loaded all the data again, Is there any feature to schedule the batch with incremental load?
Is the solution to run the batch once, and to create a stream to do the incremental load? Will the stream load all the data again, or it will just continue from a certain point.
Please explain how can I achieve the incremental load concept using spring XD?
Thanks,
Moha.
I suppose what is missing is the concept of 'state'...the filejdbc module does not seem to know where the last import stopped. I do something similar but I use a custom batch job and I use a meta store to keep track of where the last load stopped - that is where the next incremental will pick up from, etc.
Since you're using a module that came with spring-xd itself, you may not have this flexibility but you may have to options:
a- your destination table can define unique fields that will prevent duplicates. That way, even if its trying to load ALL the data again, only new rows will get inserted. This assumes that the module is using 'insert ignore' (or something similar and not just basic insert (which will throw an error/exception). This, I must say, will end up being non-optimal pretty quickly, pretty soon.
b- If its an option, write a module that can delete the file after its uploaded into the db. You can construct a complex stream that will first do your data load and then file delete.