Monitoring Driven Development with NiFi 1.7+

I already discussed the Monitoring Driven Development (MDD) approach in this post and already covered some monitoring topics around NiFi in few posts (reading the posts would probably help you for reading this one – or at least refresh your memory on some concepts). In this post I want to present once again the MDD approach with some new features of NiFi 1.7 making things a bit easier and cleaner.

What do I mean when talking about MDD in NiFi? When I say MDD, I refer to the fact that, when a team is developing a “core workflow” for a use case, the team should also develop a “monitoring workflow” that will be in charge of monitoring and reporting the health of the “core workflow” based on their business requirements.

How does this work in NiFi? This approach completely relies on two things:

  • A proper naming convention for the components used in the workflows
  • The use of the Site-to-Site Reporting Tasks (see here)

Basically, the idea is to setup the S2S reporting tasks to send data to an input port dedicated to monitoring, then basic routing and filtering is performed assuming projects are following the naming convention so that all the data related to a given “core workflow” is automatically routed to the associated “monitoring workflow”. This way, it’s up to each project to implement the monitoring they want for their use case based on the requirements and SLA they have.

What’s new in NiFi that’s going to help me with MDD?

There are quite few interesting JIRAs but the two most important ones are:

  • [NIFI-4814] – Add distinctive attribute to S2S reporting tasks (NiFi 1.6.0)
  • [NIFI-5122] – Add record writer to S2S Reporting Tasks (NiFi 1.7.0)

Example

For my example, the naming convention is the following: any component that should be monitored should be named with a prefix ABC_ where ABC is the three letters abbreviation representing the use case/project.

I have two projects, one named PJA and one named PJB.

Let’s say the project A is using HandleHttpRequest/Response to receive XML data over HTTP and has two monitoring requirements:

  • Send an email to the team in case a bulletin is generated
  • Monitor in Grafana the amount of valid/invalid data being processed

Let’s say the project B is using ListenTCPRecord for a use case around logs ingestion and has one monitoring requirement:

  • Send an email to the team in case back pressure is enabled

The development of the workflows is outside the scope of this post. I’ll keep things simple as the objective is to demonstrate the MDD approach, not how to implement the workflows of the specific use cases I chose.

A *very basic* implementation of the “core workflow” of PJA could be:

Screen Shot 2018-08-29 at 3.55.19 PM.png

A *very basic* implementation of the “core workflow” of PJB could be:

Screen Shot 2018-08-29 at 4.36.29 PM

Now I’m going to configure the S2S Reporting Tasks – one for Controller Status data and one for Bulletins data. Note that there is a new feature allowing users to select a Record Writer for the generated data. I’m going to use the Avro one for the reporting tasks so that I don’t have to care about the schemas at all. For that I need to create a Controller Service (in Controller Settings menu since this controller service is exclusively used by reporting tasks).

Screen Shot 2018-08-29 at 5.02.26 PM.png

After enabling the CS, I can configure my reporting tasks.

The one for bulletins:

Screen Shot 2018-08-29 at 5.03.59 PM

And the one for controller status:

Screen Shot 2018-08-29 at 5.05.21 PM

Note that you have additional S2S Reporting Tasks if needed. The one about provenance events can be particularly useful for some monitoring requirements such as latency.

At the root level of my canvas, I’ve something looking like:

Screen Shot 2018-08-29 at 5.45.38 PM.png

I’m receiving all the monitoring data sent by the reporting tasks in the ‘monitoring’ input port. Then in my ‘Monitoring’ process group, I have something looking like:

Screen Shot 2018-08-29 at 5.46.48 PM.png

You can notice I’m sending all the data to a ‘Technical Monitoring’. That’s because, the team in charge of administrating/monitoring the platform probably wants to also have a look at the monitoring data to ensure the full system is performing OK (common examples: send the bulletins to an external tool like ES/Kibana, send email alerts in case back pressure is enabled on a connection, etc).

On the upper part of the workflow, that’s where I’m processing the monitoring data to split it and route the monitoring data by project. First I’m using a RouteOnAttribute to route the data based on the type (bulletins, controller status, etc):

Screen Shot 2018-08-29 at 5.50.48 PM

Then I’m using an UpdateRecord processor to add a ‘project’ field that I’m computing based on the component name.

For bulletins:

Screen Shot 2018-08-29 at 5.55.34 PM

For controller status:

Screen Shot 2018-08-29 at 5.52.43 PM.png

Since the data has been sent in Avro format by the reporting task, I’m using an Avro reader Controller Service with default settings. And, from now on, I want the data to be in JSON (as it’ll be easier in case I want to send the data via an email or via HTTP). I’m configuring few controller services: one Avro Schema Registry containing the schemas (that you can retrieve in the additional details of the reporting task documentation, see below), one JSON Reader and one JSON Writer.

For instance, to get the schema of the S2S Bulletin Reporting Task, go on the reporting task list:

Screen Shot 2018-08-29 at 5.59.40 PM.png

Click on the documentation icon on the left:

Screen Shot 2018-08-29 at 6.00.21 PM.png

And then click Additional Details:

Screen Shot 2018-08-29 at 6.00.58 PM.png

When adding the schemas in the Avro Schema Registry, use the type of the reporting task as the name of the schema and don’t forget to add a ‘project’ field in the schemas:

Screen Shot 2018-08-29 at 6.02.28 PM.png

Now you can easily reference the schemas to use in the configuration of the JSON Reader/Writer using the expression language because the flow files generated by the reporting tasks have an attribute with the type of the source reporting task.

Example of attributes on a flow file generated by a RT:

Screen Shot 2018-08-29 at 6.28.56 PM.png

JSON Reader:

Screen Shot 2018-08-29 at 6.03.44 PM.png

JSON Writer:

Screen Shot 2018-08-29 at 6.04.13 PM.png

Now I’ve a ‘project’ field in my data with the project name (if the naming convention has been correctly used), then you can use a QueryRecord processor to route the data for each project:

Screen Shot 2018-08-29 at 6.07.17 PM.png

And we can confirm that only the data for project A is routed to the ‘Monitoring Workflow’ for project A. If looking at the data in the relationship routing data for PJA:

Screen Shot 2018-08-29 at 6.08.53 PM.png

Now, when deploying a new project for the first time (ie importing the process group of the “core workflow”), you just need to update the QueryRecord processor to add a dynamic property for the new project as well as deploying the process group for the “monitoring workflow” of the project. In combination with the NiFi Registry it makes things really smooth when you need to update the workflows with a new version.

I’m not going to develop the monitoring workflows for PJA and PJB but it’d only be a matter of few record processors to filter the data (bulletin? back pressure enabled?), and then use a processor to send the data (ElasticSearch, PutEmail, InvokeHttp, etc):

  • “send an email when a bulletin is generated” – RouteOnAttribute to get the bulletins data and PutEmail to send an email
  • “monitor the number of valid/invalid XML files being processed” – filter the controller status statistics on the ‘Connection’ elements and keep only the statistics of the connections after the ValidateRecord processor, this can be easily done with a QueryRecord processor. The numbers can then be sent to an external system such as ElasticSearch for a dashboard in Kibana/Grafana
  • “send an email when back pressure is enabled” – use a QueryRecord processor on the controller status data by querying the field ‘isBackPressureEnabled’ and then use PutEmail processor.

The full workflow of this example is available as a gist here. Note that, since it’s a template, it does not contain the Reporting Tasks configuration. But you should be up and running quite quickly with the above screenshots.

As always, thanks for reading this post, feel free to comment and/or ask questions. In case you have suggestions to make things even better, just let me know and, maybe, I’ll have to publish a new version of this post to present the new features of a next release 😉

NiFi workflow monitoring – Wait/Notify pattern with split and merge

Thanks to NIFI-4262 and NIFI-5293, NiFi 1.7.0 contains a small improvement allowing users to extend the Wait/Notify pattern to merging situations.

If you’re not familiar with the Wait/Notify concept in NiFi, I strongly recommend you to read this great post from Koji about the Wait/Notify pattern (it’ll be much easier to understand this post).

When using the Merge* processors, you have one relationship to route the flow file containing the merged data and one relationship to route the original flow files used in the merging operation. With NIFI-4262 for the MergeContent processor (and NIFI-5293 for the MergeRecord processor), the original flow files used in the merge operation will now contain an attribute with the UUID of the flow file generated by the merge operation. This way, you now have a way to link things together and that’s really useful to leverage the Wait/Notify pattern.

Use case presentation

Let’s demonstrate the feature with a use case: I’m receiving ZIP files containing multiple CSV files that I want to merge together while converting the data into Avro and send it into a file system (for simplicity here, I’ll send the data to my local file system, but it could be HDFS for instance).

The idea is the following:

ListFile -> FetchFile -> UnpackContent -> MergeRecord -> PutFile

That’s easy and it works great… BUT… I want to be able to monitor and track exactly how each ZIP file is handled and when the data contained in a ZIP file is stored in the destination. I have a requirement to maintain in real-time the following tables:

Table ‘zip_files_processing’

  • zip_file_name – unique name of the received ZIP files
  • ff_uuid – flow file UUID (useful to replay events in case of errors)
  • ingestion_date – date when the zip file starts being processing in NiFi
  • storage_date – date when all the data of the ZIP file has been processed
  • number_files – number of CSV files contained in the ZIP file
  • number_files_OK – number of files successfully stored in the destination
  • number_files_KO – number of files unsuccessfully processed
  • status – current status of the process for the ZIP file (IN_PROGRESS, OK, KO)

Table ‘files_ingestion’

  • zip_file_name – unique name of the zip file containing the CSV file
  • file_name – name of the CSV file
  • ff_uuid – flow file UUID (useful to replay events in case of errors)
  • storage_date – date when the CSV file has been processed
  • storage_name – name of the destination file containing the data
  • status – current status of the process for the CSV file (IN_PROGRESS, OK, KO)

That’s where the Wait/Notify is going to be extremely useful.

High level description of the workflow

Here is the main idea behind the workflow:

Screen Shot 2018-06-13 at 4.14.17 PM.png

Let’s describe the workflow at a high level:

  • List the data available and insert rows in my zip_files_processing table for each listed file so that I can track ingested data
  • Fetch the data (in case of error, I update the zip_files_processing table to set the status to KO)
  • I unpack my ZIP files to get a flow file per CSV file, and I route the original flow file to a Wait processor. The flow file representing the ZIP file will be released only once all the associated CSV files will be fully processed
  • For each CSV file, I insert a row in my files_ingestion table to track the status of each CSV file being processed
  • I merge the data together while converting the data from CSV to Avro, and I route the original flow files to a Wait processor. The flow files will be released only once the merged flow file will be sent to the final destination
  • I send the merged flow file to the destination (local file system in this case) and then use the Notify processor to release all the original flow files used to create the merged flow file
  • Then I use the released flow files to notify the first Wait processor and also release the flow file representing the corresponding ZIP file
  • With all the released flow files, I can update my monitoring tables with the appropriate information

Workflow details

Here is the full configuration of the workflow and some explanations around the parameters I used. Also, here is the template of the workflow I used – it’ll be easier to understand if you have a look, give it a try and play around with it (even without the SQL processors) – note that you will need to add a Distributed Map Cache server controller service to have the Distributed Map Cache clients working with Wait/Notify processors:

Screen Shot 2018-06-26 at 1.50.35 PM.png

Let’s start describing the ingestion part:

Screen Shot 2018-06-26 at 1.52.57 PM.png

Nothing very strange here. Just note the UpdateAttribute processor I’m using to store the original UUID to a dedicated attribute so that I don’t loose it with the UnpackContent processor (that creates new flow files with new UUIDs).

Screen Shot 2018-06-26 at 1.56.13 PM

The first PutSQL is used to insert new lines in my ZIP monitoring table for each ZIP file I’m listing. I’m executing the below query:

Screen Shot 2018-06-26 at 1.55.31 PM

The second PutSQL is used in case of error when fetching the file from the local file system or when an error occurs while unpacking the archive. In such a case, I’m executing the below query:

Screen Shot 2018-06-26 at 1.57.24 PM.png

Let’s now focus on the second part of the workflow:

Screen Shot 2018-06-26 at 1.58.28 PM

The UnpackContent processor is used to extract the CSV files from the ZIP file. For one flow file representing the ZIP file, it will generate one flow file per CSV files contained in the ZIP file. The original flow file will then be routed in the original relationship while flow files for CSV data will be routed in the success relationship. The flow files containing the CSV data will have a ‘fragment.identifier’ attribute and that’s the common attribute between the original flow file and the generated ones that I’m going to use for Wait/Notify (this attribute is automatically generated by the processor). Besides the original flow file also has a ‘fragment.count’ attribute containing the number of generated flow files for the CSV data.

The Wait processor is configured as below:

Screen Shot 2018-06-26 at 2.03.46 PM

Basically I’m setting the identifier of the release signal to the common attribute shared between the original and the generated flow files, and I’m saying that I have to wait for ‘fragment.count’ signal before releasing the original flow file to the success relationship. Until then, the flow file is transferred to the ‘wait’ relationship which is looping back on the Wait processor. Also, I configured a 10 minutes timeout in case I didn’t received the expected signals, and in this case the flow file would be routed to ‘expired’ relationship.

After my Wait processor, in case of error, I’m using an UpdateAttribute to retain this error:

Screen Shot 2018-06-26 at 2.08.50 PM.png

And after that, I’m using the PutSQL processor to update the ZIP monitoring table by executing the below query:

Screen Shot 2018-06-26 at 2.09.46 PM.png

The ‘wait.*’ attributes are generated by the Wait processor when releasing the flow file and contain the number of signals sent by the Notify processor (I’ll come back to that when describing the corresponding Notify processor). In this case, it allows me to know how many files contained in the ZIP file have been successfully processed and how many have not been successfully processed.

After the UnpackContent and the success relationship, I’m using a PutSQL to insert lines in my CSV monitoring table by executing the below query:

Screen Shot 2018-06-26 at 2.19.41 PM

The ‘segment.original.filename’ is the name of the ZIP file from which the CSV files have been extracted.

Let’s move to the final part of the workflow (it’s a bit loaded because I wanted to have everything in my screenshot):

Screen Shot 2018-06-26 at 2.27.15 PM.png

The MergeRecord is used to merge the data together while converting from CSV to Avro:

Screen Shot 2018-06-26 at 2.28.11 PM

In a real world use case, you would configure the processor to merge much more data together… but for the below demonstration I’m merging few flow files together so that I can quickly see the results.

Also I’m generating dummy data like:

Screen Shot 2018-06-26 at 2.29.41 PM.png

And the corresponding schema I’m using in the Avro Schema Registry controller service is:

Screen Shot 2018-06-26 at 2.30.55 PM.png

After the MergeRecord processor, there is the ‘original’ relationship where are routed the flow files used during the merge operation, and there is the ‘merged’ relationship where are routed the flow files containing the merged data. As explained above, the original flow files are containing a ‘merge.uuid’ attribute with the UUID of the merged flow file. That’s the attribute I’m using in the Wait processor configuration:

Screen Shot 2018-06-26 at 2.35.49 PM

Basically, I’m creating a signal identifier in the distributed cache with the ‘merge.uuid’ processor and as long as the signal counter is greater than 1, I’m releasing one flow file at a time. You’ll see in my Notify configuration (coming back to that in a bit) that, once my merged data is processed, I’m using the Notify processor to set the signal counter associated to the identifier with a value equal to the number of merged flow files. This way, once there is the notification, all the corresponding flow files will be released one by one.

I won’t describe the PutFile processor which is used to store my data (it could be a completely different processor), but just notice the UpdatAttribute I’m using in case of failure:

Screen Shot 2018-06-26 at 2.40.47 PM.png

And the UpdateAttribute I’m using in case of success:

Screen Shot 2018-06-26 at 2.41.24 PM

Note that I’m setting attributes with the same prefix ‘wn_ingestion.*’. Then, look at my Notify processor:

Screen Shot 2018-06-26 at 3.13.05 PM.png

The ‘uuid’ attribute is equal to the ‘merge.uuid’ I mentioned above for the original flow files used in the merge operation. Besides, I’m using the Attribute Cache Regew property to copy the corresponding attributes to all the flow files going to be released thanks to this signal. It means that when a merged flow file is sent to the Notify processor, all the original flow files with the corresponding in the ‘merge.uuid’ in the ‘wait’ relationship will be released and will get the ‘wn*’ attributes of the merged flow file. That’s how I’m “forwarding” the information about when the merged flow file has been sent to the destination, if the merged flow file has been successfully processed or not, and what is the filename used to save the file in the destination.

Once the original flow files are released from the Wait processor, they are sent to the Notify processor with the below configuration:

Screen Shot 2018-06-26 at 3.18.24 PM.png

In this case, I’m using the attribute inherited from the merged flow file sent to the destination to determine if the processing has been successful or not. If yes, then I’m setting the counter name to ‘OK’, otherwise it’s set to ‘KO’. That’s how I’m getting the information about how many CSV files from a given ZIP have been successfully processed or not. (remember the ‘wait.counter.OK’ and ‘wait.counter.KO’ attributes created when the flow file corresponding to the ZIP file is released?).

And now… I can execute my SQL query to update my CSV monitoring table with the latest information:

Screen Shot 2018-06-26 at 3.22.09 PM

Demonstration

Let’s see how the workflow is working and what’s the result in all kind of situations. I’m running a Postgres instance in a Docker container to store my monitoring data. Here are the statements I used to create the two tables.

CREATE TABLE IF NOT EXISTS zip_files_processing (
 zip_file_name varchar(255),
 ff_uuid varchar(255),
 ingestion_date timestamp DEFAULT current_timestamp,
 storage_date timestamp,
 number_files integer,
 number_files_OK integer,
 number_files_KO integer,
 status varchar(15)
);
CREATE TABLE IF NOT EXISTS files_ingestion (
 zip_file_name varchar(255),
 file_name varchar(255),
 ff_uuid varchar(255),
 storage_date timestamp,
 storage_name varchar(255),
 status varchar(15)
);

Case 1 – nominal situation

In this case, all the received data is OK and all is working flawlessly… Let’s say I’m receiving two ZIP files. Once the data is received, I can see something like:

Screen Shot 2018-06-15 at 11.53.58 PM

 

Then the ZIP files are unpacked, and I can see my CSV files in the other table:

Screen Shot 2018-06-15 at 11.54.42 PM

Then the data is merged and sent to the destination. At the end, my tables looks like:

Screen Shot 2018-06-15 at 11.56.28 PM.png

I can see that the data coming from the two ZIP files has been merged into the same final file and everything is OK. In the ZIP monitoring table, I have:

Screen Shot 2018-06-15 at 11.57.55 PM

Case 2 – cannot fetch ZIP file

In this case, the FetchFile processor is failing due to some permission issues. Here is what I get in my ZIP monitoring table:

Screen Shot 2018-06-16 at 12.04.35 AM

Note that I could a ‘comment’ column with the error cause.

Case 3 – cannot unpack ZIP file

Result will be identical to case 2 as I’m handling the situation in the same way.

Case 4 – cannot merge records because a CSV file is not schema valid

I’m processing 2 ZIP files but a CSV file contained in one of the ZIP file cannot be merged with the others because it does not respect the schema. Note that all the flow files used during a merge operation will be sent to the failure relationship if one of the flow file is not valid. That’s why multiple flow files will be routed to the failure relationship even though there is only one bad file. To avoid this situation, it would be possible to add an additional step by using the ValidateRecord processor.

For my ZIP files, I have:

Screen Shot 2018-06-22 at 8.42.00 PM

At the end, for this run, I have:

 

Screen Shot 2018-06-22 at 8.44.01 PM.png

Case 5 – cannot send a merged file to destination

In this case I’m simulating an error when sending a merged flow file to the destination. Here is the result at the end for the ZIP table:

Screen Shot 2018-06-22 at 9.57.09 PM

And the CSV files table:

Screen Shot 2018-06-22 at 9.57.44 PM

Conclusion

I’ve covered a non exhaustive list of situations and the workflow could be improved but it gives you a good idea on how the Wait/Notify pattern can be used in a NiFi workflow to help monitoring what’s going on when dealing with split / merge situations. In addition to that, it’d easy to add an automatic mechanism to try replaying failed flow files using the UUID and the provenance repository replay feature of NiFi.

Thanks for reading this post and, as always, feel free to comment and/or ask questions!

FoD Paris Jan 18′ – NiFi Registry and workflow monitoring with a use case

I got the chance to talk a bit about NiFi during the last Future of Data meetup in Paris, and I wanted to share in a blog what I explained during this event.

First of all, I want to remind you that Apache NiFi 1.5.0, MiNiFi 0.4.0 and NiFi Registry 0.1.0 are out and it represents a huge step forward in the community as it brings flow development life cycle (FDLC) to a completely new level. Besides, this first version of the registry is a solid foundation for what will come in the future.

Ok let’s get back to what I discussed at the meetup.

  • Introduction to NiFi Registry

What is the NiFi Registry? It’s a new component of the NiFi ecosystem, it’s a web-based application that is meant to be used as a central location for storage and management of shared resources across one or more instances of NiFi and/or MiNiFi.

In a typical multi-clusters environment, this could be represented as below:

Screen Shot 2018-02-02 at 2.03.45 PM.png

At the moment, the integration between NiFi and the NiFi Registry allows you to store, retrieve, and upgrade versioned flows. Next versions will allow users to store more different types of resources. And if you’re asking… yes the NiFi Registry can be secured and provides authorization support to control users and access policies to the versioned flows.

Note that, even if you have a single environment, using the NiFi Registry will also make a lot of sense. Firstly, it’s a convenient way to save your work and to version it (and to be honest, this argument alone should convince you), but it’s also a practical way to share common reusable parts of flows between multiple workflows.

For example, let’s say you have a process group designed to perform a very common task like transforming XML data to JSON and adding attributes to the flow files. If you have to update this process groups with few changes and if this process group is used in multiple places, you don’t need to copy/paste your changes everywhere. You just have to version it and as soon as you update it, you’ll be able to update all the other instances of this process group to the latest version.

  • NiFi monitoring NiFi

I already posted some blogs around NiFi monitoring but that’s a recurrent subject and we have quite a lot of options to answer monitoring requirements someone might have.

What do we mean by “monitoring”? In my opinion, we have two main subjects:

  1. Dashboarding: expose various metrics data to measure the application performance and stability.
  2. Alerting: send notifications when something is wrong.

In the context of NiFi, we can also have two types of monitoring:

  1. “Technical” monitoring: is the NiFi service working as expected? are the nodes up and alive?
  2. “Business” monitoring: is a particular workflow for a given project behaving as expected?

For both types of monitoring, my recommendation is to use the reporting tasks available in NiFi. Reporting tasks provide an efficient way to extract and process the data generated by the framework for technical related metrics as well as workflow related metrics.

  • Use case study

I’m going to articulate this blog around a very simple use case that will demonstrate the use of the NiFi Registry to perform FDLC tasks. And I’ll also use it as a baseline to show various ways of monitoring both the NiFi service and a particular workflow.

The use case is to expose an HTTP REST end point using NiFi so that an application can send data through HTTP calls to NiFi. The idea is to route the received data based on the URI used in the HTTP call. For each type of data, I’ll perform XML validation against a schema, XML to JSON conversion, perform simple data transformations and send the data into a Kafka topic.

  • NiFi monitoring: survival kit

Before going into the details of the use case, I’ll give my personal opinion of what should be the minimum setup for efficient NiFi monitoring. Here are the 6 reporting tasks that should be configured:

Screen Shot 2018-02-02 at 3.15.03 PM.png

# The Ambari reporting task is used to send metrics to the Ambari Metrics Service (obviously, this reporting task only makes sense if you have Ambari and AMS available, and, if not, there are similar reporting tasks for different systems). When using the reporting task, the following metrics will be available in Ambari on the NiFi service page:

Screen Shot 2018-02-02 at 3.51.48 PM.png

Things to monitor carefully are:

  1. JVM heap usage to avoid OutOfMemory errors. Even though NiFi is not requiring a lot of RAM, some processors/use cases might need more RAM. It also depends how much flow files NiFi is going to handle at one time.
  2. Active threads is the mean number of active threads per node in the NiFi cluster. By default, each NiFi node will initialize a pool of 10 threads for timer driven components. If you see that this metric is always equal to the size of the thread pool, then you might need to increase the thread pool size. But this number needs to be changed with care in coordination with CPU/load metrics of your NiFi nodes. To change the thread pool size, go into Controller Settings / General (change will be immediately effective, no restart required).
  3. Flow files queued is the number of flow files currently being processed by NiFi. If this graph is continually increasing, it can be a symptom of something going wrong. It can totally be expected to have peaks at some specific hours of the day based on the use cases, and NiFi will protect itself using the backpressure mechanism but, in any case, always good to keep an eye on this metric.
  4. Total task duration can also be something to look at: in case this graph is constantly going higher, it means that it takes more and more time to process the data. It can be expected if volumetry is changing or if workflows are updated but it can also be the symptom that a processor is not performing well.

# The MonitorDiskUsage reporting tasks are useful to be sure that bulletins will be emitted before having a “no space left on device” error message. I recommend setting one per repository (at least 3, but it can be more if you have multiple content/provenance repositories). In my screenshot, I defined one for provenance repository, one for content repository and one for flow file repository.

# The Site to Site bulletin reporting task is used to reingest all the generated bulletins into NiFi. Generally speaking Site to Site reporting tasks are a really powerful tool as it allows you to reingest data generated by NiFi’s framework into NiFi itself so you can use all the processors you want to process this data. With this specific reporting task, you’re able to deal with every generated bulletins into a dedicated workflow and do whatever suits you: email notification, dashboarding, automatic issue creation in JIRA, etc…

# The Site to Site status reporting task is used to frequently take a screenshot of what is going on in all the workflows existing on the canvas. It will generate an JSON array of JSON documents representing the status of every components (processors, ports, relationships, etc) including the last 5-minutes statistics data.

  • NiFi monitoring NiFi

Using strict naming conventions, you can leverage generic workflows dedicated to monitoring and perform quite powerful things. My approach is really to use NiFi as a way to monitor itself. You might ask: what is going to monitor the workflows dedicated to monitoring? To be honest, if there is something wrong going on with the monitoring workflows, you will detect it very quickly through the technical monitoring / Ambari reporting task. Or the NiFi service will just be red in Ambari…

Consequently, my personal recommendation is to ask each project team to respect a strict naming convention and to deliver a workflow designed to fulfill the monitoring requirements for the project workflows. Each workflow will certainly require different notification mechanisms, and different metrics in the dashboards.

At the root level of the canvas, I’ll have one process group per project in order to ensure proper authorization policies. I’ll also have a process group dedicated to monitoring, and two input ports to receive the data sent by reporting tasks (with NIFI-4814, it’ll be possible to only use one):

Screen Shot 2018-02-07 at 2.34.36 PM

In this use case study, I assume that the naming convention is the following: <acronym of the project>_<meaningful component name>

Besides, if the metrics of a component should be captured for dashboarding, the name should be suffixed by _GRAFANA.

With the above assumptions, here is what I have in my monitoring process group:

Screen Shot 2018-02-07 at 2.39.16 PM

The UpdateAttribute is used to add a “type” attribute with the value “bulletins” or “status”. I’ll use this attribute to perform routing at a later stage.

The UpdateRecord is used to populate a “project” field by extracting the acronym from the component name. Here is the schema I use for bulletins, and here is the schema I use for status.

For bulletins:

Screen Shot 2018-02-07 at 2.46.21 PM.png

For status data:

Screen Shot 2018-02-07 at 2.46.41 PM

At this point, I have bulletins and status data and I’m able to link this data to every project running in NiFi. Now I send this data in two process groups: one that is dedicated to technical monitoring (used and managed by the team in charge of NiFi service), and one that is dedicated to business (per-project) monitoring:

Screen Shot 2018-02-07 at 2.49.20 PM

Technical monitoring

In the technical monitoring, it’s really up to you regarding what you want… What I usually do is to send the bulletin data in a tool like AMS, Solr or Elasticsearch to have dedicated dashboards. I also set email notifications when backpressure is enabled in a connection.

For bulletins dashboarding, I slightly change the schema of the data and send it into an Elasticsearch instance:

Screen Shot 2018-02-07 at 2.53.40 PM.png

I then create a dashboard in Grafana:

Screen Shot 2018-02-07 at 3.00.03 PM

If necessary I can use the notification mechanism available in Grafana to get email messages when some thresholds are reached.

With the status data, I decided to design a workflow sending email notifications as soon as backpressure is enabled in a connection:

Screen Shot 2018-02-07 at 9.28.30 PM

To do that, I use the QueryRecord processor to only select records representing connections with backpressure enabled:

Screen Shot 2018-02-07 at 9.30.32 PM.png

Again… with the site to site reporting tasks you are ingesting into NiFi its own data and you can use all the processors you want to process this data and fulfill your requirements.

Business (per-project) monitoring

If we go back to the first level of my monitoring process group, you noticed that I’m also sending both bulletin and status data to another process group for “per-project” monitoring:

Screen Shot 2018-02-07 at 2.49.20 PM

In this process group, here is what I’m doing:

Screen Shot 2018-02-07 at 9.35.11 PM

I’m using a PartitionRecord processor to partition all the data based on the field /project that we computed earlier. This way, each flow file will only contain data with a unique value for the /project field and this value is also extracted as an attribute of the flow files. Then I just have to use a RouteOnAttribute processor to route the data based on the project that generated this data (if a project didn’t respect the naming convention, the project value will not match any project acronym and the data will be dropped, unless you want to process it somehow – to detect teams not respecting the naming convention!). After the RouteOnAttribute, you have one process group per project and that’s this specific piece that should be developed by every project deployed in NiFi. This way, each project receives both bulletins and status related to the project and each project can implement its own workflow to deal with bulletin and status data.

Before presenting what monitoring I’m doing for the use case of this post, I’ll step back a little and present the use case itself and how I leverage the NiFi Registry to deploy my workflow from a development environment to a production environment.

  • Use case presentation

The use case itself is pretty simple and I won’t go too much in the details since it’s not the purpose of this blog. I’m using a HandleHttpRequest processor to listen for HTTP calls on a given port. Then based on what URI has been called by the client I’m routing the flow files to different process group. I’m expecting to receive HTTP POST requests on specific endpoint, each one representing a type of data. The received data is expected to be XML and I’m checking that the data is valid according to an XSD schema. At this point, three options:

  1. if the client requests an endpoint that is not expected, I route the flow file to the HandleHttpResponse processor and return a 404 HTTP code.
  2. if the client posts data on the expected endpoint but the data is not valid, I route the flow file to the HandleHttpResponse processor and return a 400 HTTP code.
  3. if the received data is valid, then I return a 200 HTTP code.

Screen Shot 2018-02-07 at 9.56.24 PM

Screen Shot 2018-02-07 at 9.57.00 PM.png

What I’m doing with valid data in the process group does not really matter and I won’t describe that (this post is already way too long and I’m surprised you’re still reading…).

  • Deployment with NiFi Registry

Let’s try to keep things simple. The NiFi Registry is a web application living outside of NiFi. However, NiFi integrates with the NiFi Registry. This is done by going into the Controller Settings menu where there is a new tab to define the registries to connect with:

Screen Shot 2018-02-07 at 10.01.54 PM.png

With this integration between NiFi and NiFi Registry, you have new icons in the UI you need to get familiar with:

Screen Shot 2018-02-07 at 10.04.10 PM.png

From left to right:

  1. Number of versioned Process Groups that are up to date with the Registry
  2. Number of versioned Process Groups that are locally modified (you can commit your local changes to create a new version of your process group in the Registry)
  3. Number of versioned Process Groups that are stale (there is a more recent version of the Process Group in the Registry and you can update your local process group to this version)
  4. Number of versioned Process Groups that are stale and also with local changes
  5. Number of versioned Process Groups with a synchronization failure (the Registry is not available / unreachable, etc)

The interactions with the Registry through the NiFi UI can be done by right clicking on a process group or on the canvas inside a process group: this will show a Version menu with available commands.

Screen Shot 2018-02-07 at 10.09.36 PM.png

On the NiFi Registry side, here is what is looks like:

Screen Shot 2018-02-07 at 10.12.33 PM.png

In the Registry, it’s possible to create buckets (you’ll usually have one bucket per project). Then, from the NiFi UI, you’ll be able to start versioning a workflow in a bucket. To do that: right click on the process group, Version menu, start version control.

Screen Shot 2018-02-07 at 10.14.44 PM.png

Then you’ll be able to choose the bucket and to give some information about your workflow:

Screen Shot 2018-02-07 at 10.15.18 PM

You now have your workflow versioned in the registry !

Screen Shot 2018-02-07 at 10.16.39 PM

Now… how do I deploy my workflow on the production cluster?

I just have to drag and drop a process group component on the canvas, and I’ll see:

Screen Shot 2018-02-07 at 10.27.14 PM.png

And I can just click on Import to select the bucket, workflow and version I want:

Screen Shot 2018-02-07 at 10.28.26 PM

I now have my workflow imported on the production cluster. Two things to do:

  1. Update the variables of the process group that are used to externalize environment-specific properties of the workflow components. Note that changes to the variables value are not considered as local changes from Registry point of view because two instances of the same workflow version will have different values for different environments.
  2. Start the controller services and processors. When importing a new workflow for the first time, everything is stopped to let users change the variables. When updating an existing workflow to a new version, only the new components will need to be started (no service interruption).

To access variables, you can right click on the process group and click on the Variables menu:

Screen Shot 2018-02-07 at 10.46.10 PM.png

And then you can update the variables with values corresponding to your environment:

Screen Shot 2018-02-07 at 10.46.20 PM.png

If you have multi-levels process groups and if you defined variables at different levels, you will also need to update variables in sub-process groups.

  • Business monitoring for my use case

If you paid attention, in my screenshot of the NiFi Registry, you probably noticed two versioned workflows in the bucket dedicated to my use case. The other workflow is the one dedicated to the monitoring that you also noticed in a previous screenshot:

Screen Shot 2018-02-07 at 9.35.11 PM

Let’s quickly discuss what I suggest for project-specific monitoring. The first thing you might want to do is to receive email notifications when bulletins are generated by components of your workflow.

Then, what I usually suggest for per-project dashboarding is to export the statistics of specific points of the workflow to an external tool and create dashboards. As I said before, in my example, I’m exporting to the Ambari Metrics Service the last 5-minutes count and bytes sum of the flow files going through connections with a name suffixed with _GRAFANA. In my example, I renamed connections to automatically export statistics to get the number of HTTP code 200, 400 and 404:

Screen Shot 2018-02-07 at 10.40.04 PM.png

And here is my monitoring workflow for this specific use case:

Screen Shot 2018-02-07 at 10.42.43 PM.png

I route the monitoring data to have a specific processing for bulletins (email notifications in this case) and a specific processing for status. First, I use a PartitionRecord processor and a RouteOnAttribute to only extract status data for connections (I ignore data about processors, process groups, ports, etc).

Screen Shot 2018-02-07 at 10.45.39 PM.png

Then I split my JSON arrays and extract the field containing the name of the connection to only keep the connections suffixed by _GRAFANA. Then I transform the content to match the format expected by AMS and send the request to AMS (more details here).

And… that’s it… I’m now able to create Grafana dashboards for my specific use case:

Screen Shot 2018-02-07 at 10.53.06 PM.png

Another thing you might want to do is to define a dedicated Ambari Reporting Task with the UUID of the process group dedicated to the project. This way you have additional statistics about this specific process group: number of active threads, number of flow files queued, volume of data received, etc.

  • What about workflow update?

Ok… so I deployed my workflows in production and have great ways to monitor things. What if I’ve a new endpoint to handle in my workflow. Really simple! So far I only process “purchase” data, let’s add another process group to deal with “customer” data:

Screen Shot 2018-02-07 at 11.03.15 PM

I updated my RouteOnAttribute to accept a new endpoint and added a process group to process this data. I added my HTTP 200 & 400 relationships for this new type of data and I carefully renamed the output connections to automatically get this new data into my dashboards.

I can now see that my process group does have local changes:

Screen Shot 2018-02-07 at 11.06.34 PM.png

And I can commit the changes as a new version of my workflow:

Screen Shot 2018-02-07 at 11.07.19 PM.png

Screen Shot 2018-02-07 at 11.07.40 PM

I can now check that, on production’s side, there is a new version available and I can update to this new version:

Screen Shot 2018-02-07 at 11.10.20 PM.png

I can right click on the process group and update to the latest version:

Screen Shot 2018-02-07 at 11.11.23 PM.png

I select the version I want:

Screen Shot 2018-02-07 at 11.11.33 PM

And that’s it! Note that the update from a version A to a version B will stop and restart existing components if and only if modifications have been done on the components. In my case, the HandleHttpRequest has not been modified… consequently, the update of the workflow will not cause any service interruption. That’s pretty cool!

Also note that the update will not cause any data loss in case the update includes the deletion of connections that are currently containing flow files.

And finally, I do have to update, if necessary, the variables of my newly added process group (for customer data) and to start it.

  • Conclusion

I’ve already talked about way too much things, but I hope it’ll get you excited about the NiFi Registry! Please install it and use it, that’s really going to ease your life. As always I invite you to join the Apache NiFi community by subscribing to mailing lists, submitting JIRAs for NiFi, MiNiFi Java/C++, NiFi Registry, and contribute code on NiFi, MiNiFi Java/C++ and NiFi Registry.

Feel free to comment and ask your questions on this post! Thanks for reading me!

Monitoring NiFi – Scripted Reporting Task

Note – This article is part of a series discussing subjects around NiFi monitoring.

In the new release of Apache NiFi (1.2.0), you can now develop Scripted Reporting Task thanks to NIFI-1458. It is the same approach as with the ExecuteScript processor for which you have tons of great examples here.

You might also want to read the following posts:

With the ScriptedReportingTask you can define your own implementation of the onTrigger() method and get access to:

  • ReportingContext context (which gives you access to various information such as events, provenance, bulletins, controller services, process groups, etc)
  • VirtualMachineMetrics vmMetrics (to access the metrics of the JVM)
  • ComponentLog log (if you want to log messages)

Let’s start with a very easy example: I want to log the number of threads inside my JVM every minute. Here is my code:

log.info("Thread count = " + vmMetrics.daemonThreadCount())

Screen Shot 2017-05-12 at 5.43.45 PM.png

And I can check in my nifi-app.log file that I do have:

2017-05-12 17:43:27,639 INFO [Timer-Driven Process Thread-5] o.a.n.r.script.ScriptedReportingTask ScriptedReportingTask[id=fd1668eb-015b-1000-1974-5ef96e1f9a8b] Thread count = 29

OK… now I won’t go into the details of all the information you can access using the “context” variable but let’s try another example…

I want to send a POST request over HTTP containing a JSON representation of the summary of my root process group.

I’m not really used to Groovy so please excuse my coding style ;-). But here is a working code (available here as well):

def json = Class.forName("javax.json.Json")
def httpClients = Class.forName("org.apache.http.impl.client.HttpClients")
def contentType = Class.forName("org.apache.http.entity.ContentType")

def status = context.getEventAccess().getControllerStatus();
def factory = json.createBuilderFactory(Collections.emptyMap());
def builder = factory.createObjectBuilder();

builder.add("componentId", status.getId());
builder.add("bytesRead", status.getBytesRead());
builder.add("bytesWritten", status.getBytesWritten());
builder.add("bytesReceived", status.getBytesReceived());
builder.add("bytesSent", status.getBytesSent());
builder.add("bytesTransferred", status.getBytesTransferred());
builder.add("flowFilesReceived", status.getFlowFilesReceived());
builder.add("flowFilesSent", status.getFlowFilesSent());
builder.add("flowFilesTransferred", status.getFlowFilesTransferred());
builder.add("inputContentSize", status.getInputContentSize());
builder.add("inputCount", status.getInputCount());
builder.add("outputContentSize", status.getOutputContentSize());
builder.add("outputCount", status.getOutputCount());
builder.add("queuedContentSize", status.getQueuedContentSize());
builder.add("activeThreadCount", status.getActiveThreadCount());
builder.add("queuedCount", status.getQueuedCount());

def requestEntity = new org.apache.http.entity.StringEntity(builder.build().toString(), contentType.APPLICATION_JSON);
def httpclient = httpClients.createDefault();
def postMethod = new org.apache.http.client.methods.HttpPost("http://localhost:9999/rootStatus");
postMethod.setEntity(requestEntity);
httpclient.execute(postMethod);
httpclient.close();

Note – this should be improved to, for instance, properly handle potential exceptions.

To get this code working, I also have to specify the required dependencies (JSON, Apache HTTP, etc). For that, in the module directory property of the reporting task, I gave the following paths (because I’m lazy, I am pointing to much dependencies than required):

  • /var/lib/nifi/work/nar/extensions/nifi-standard-nar-1.2.0.nar-unpacked/META-INF/bundled-dependencies/
  • /var/lib/nifi/work/nar/extensions/nifi-site-to-site-reporting-nar-1.2.0.nar-unpacked/META-INF/bundled-dependencies/

In my example I’m sending my JSON payload with a POST HTTP request to localhost on port 9999 with the path rootStatus. To receive the request, I started a ListenHttp processor with the following configuration:

Screen Shot 2017-05-12 at 8.00.31 PM.png

Once my reporting task is started, I start receiving the information as flow files:

Screen Shot 2017-05-12 at 8.08.55 PM.png

This scripted reporting task allows you to quickly develop proof of concept to send information to your internal systems using the interfaces you want. However, according to your needs, it might be more interesting to develop your own reporting task in Java and to build the corresponding NAR. It will give you more flexibility/options (you’ll be able to implement more interfaces) and better performances.

As usual feel free to ask questions and comment this post.

Monitoring NiFi – Ambari & Grafana

Note – This article is part of a series discussing subjects around NiFi monitoring.

When using Apache NiFi (note that version 1.2.0 is now released!) as part of HDF, a lot of of things are simplified using Apache Ambari to deploy NiFi and manage its configuration. Also, using Ambari Metrics service and Grafana, you have a way to easily and visually monitor NiFi performances. And you can also use Apache Ranger to centralize the authorizations management for multiple components (NiFi, Kafka, etc) in one single place.

This article will discuss how you can use Ambari Metrics and Grafana to improve your NiFi monitoring. Let’s start with a quick discussion around AMS (Ambari Metrics System). By default this service is running a Metrics Collector with an embedded HBase instance (and a Zookeeper instance) to store all the metrics, and Ambari will also deploy Metrics Monitor instances on all the nodes of the cluster. The monitors will collect the metrics at system level and send the metrics to the collector. However, the collector also exposes a REST API and that’s what NiFi is going to use with the AmbariReportingTask.

GrafanaBlogOverview

Source and documentation is on the Hortonworks website here.

When using HDF, the Ambari Reporting task should be already up and running for you. If not, you can add it and configure it with a frequency of one minute (it does matter) and use the following parameters:

Screen Shot 2017-05-11 at 9.30.46 AM

Note that “ambari.metrics.collector.url” is an environment variable already set for you when Ambari is starting NiFi. You could also directly give the address, in my case:

http://pvillard-hdf-1:6188/ws/v1/timeline/metrics

Once this reporting task is up and running, you should be able to see the metrics on the NiFi service page in Ambari:

Screen Shot 2017-05-11 at 9.38.57 AM.png

Also, you can go into Grafana to display dashboards with the metrics of your components. You have pre-configured dashboards and here is the one for NiFi:

Screen Shot 2017-05-11 at 9.46.03 AM.png

Now, all the metrics we have here are at cluster level. We are not able to display metrics for specific workflows. With the latest release of Apache NiFi (1.2.0), there is now an optional parameter in the AmbariReportingTask to specify a process group ID. This way, by creating a second reporting task (keep the one providing cluster-level metrics) and by specifying the ID of a specific process group, you can actually create your Grafana dashboards at workflow level.

Let’s say I’ve the following workflow:

Screen Shot 2017-05-11 at 9.52.49 AM

And inside my process group, I have:

Screen Shot 2017-05-11 at 9.52.59 AM.png

Now, my process group having the ID “75973b6e-2d38-1cf3-ffff-fffffdea8cbc”, I can define the following Ambari reporting task:

Screen Shot 2017-05-11 at 9.54.50 AM.png

Note – you must keep “nifi” as the Application ID as it has to match the configuration of the Ambari Metrics System.

Once your reporting task is running, in Grafana, you can create your own dashboard for this workflow and display the metrics you want:

Screen Shot 2017-05-11 at 10.08.59 AM.png

For my Kafka example, here is the dashboard I defined:

Screen Shot 2017-05-11 at 10.39.47 AM.png

In this example, I can see that my workflow is running fine but the free disk space on one of my node is decreasing very quickly. It turns out that when my disk is completely filled, back pressure will be enabled in my workflow and there is no more data sent to Kafka. Instead data is queued in NiFi.

This simple example gives me a lot of information:

  • Everything is default configuration in Ambari and I chose my three NiFi nodes to also host Kafka brokers. By default, for Kafka, the replication factor is set to 1, the number of partitions is set to 1 and the automatic creation of topic is allowed (that’s why I didn’t need to create the topic before starting my workflow). Because of the default parameters, all of the data is sent to only one Kafka broker (pvillard-hdf-2) and that’s why the disk space is quickly decreasing on this node since my three NiFi nodes are sending data to this broker.
  • Also, we clearly see that’s not a good idea to collocate NiFi and Kafka on the same nodes since they are both IO intensive. In this case, they are using the same disk… and we can see that the task duration (for NiFi) is clearly higher on the Kafka node that is receiving the data (pvillard-hdf-2). Long story short: keep NiFi and Kafka on separated nodes (or at the very least with different disks).

With HDF and the Ambari Metrics System, it gives you the ability to create custom relevant dashboards for specific use cases. It also allows you to mix information from Kafka, from NiFi and from the hosts to have all the needed information in one single place.

Also, by using the REST API of the Metrics Collector (you may be interested by this article), you could also send your own data (not only the data gathered at the process group level) to add more information into your dashboards. An example that comes in mind would be to send the lineage duration (see Monitoring of Workflow SLA) at the end of the workflow using an InvokeHTTP processor and sending a JSON payload using a POST request to the API endpoint.

Let’s say I want to monitor how long it takes between my GenerateFlowFile and the end of my workflow to check if some particular events are taking longer. Then I could have something like:

Screen Shot 2017-05-11 at 5.58.02 PM.png

What am I doing here? I want to send to AMS the information about the lineage duration of the flow files I sent into my Kafka topic. However I don’t want to send the duration of every single event (that’s not really useful and it’s going to generate a lot of requests/data). Instead I want to make an API call only once per minute. The idea is to compute the mean and max of the lineage duration with a rolling window of one minute and to only send this value to AMS.

I could use the new AttributeRollingWindow processor but it is not as fast as the PublishKafka and I don’t want to generate back pressure in my relationships. So I use the InvokeScriptedProcessor to build my own rolling processor (it’s faster because I am not using any state information):

  • this processor takes a frequency duration as a parameter (that I’ll set to 1 minute in this example)
  • for every flow file coming in, it will extract the lineage start date to compute max and mean lineage duration over the rolling window. If the last flow file sent in the success relationship was less than one minute ago, I’ll route the flow file to drop relationship (that I set to auto-terminated). If it was more than one minute ago, I update the attributes of the current flow file with the mean and max of all the flow files since the last “success” flow file and route this flow file in the success relationship

Since I’ve flow files coming in my processor at a high rate, I know that my processor will release one flow file every minute with the mean and max of the linage duration for the flow files of the last minute.

Then I use a ReplaceText processor to construct the JSON payload that I’ll send to the Metrics Collector using the InvokeHttp processor.

Here is the configuration of the InvokeScriptedProcessor:

Screen Shot 2017-05-11 at 7.54.00 PM

The Groovy script used can be found here.

Then I create the JSON payload with the ReplaceText processor:

Screen Shot 2017-05-11 at 7.56.33 PM.png

Note that I use the ID of the processor as the “instanceid” attribute in the JSON.

Then, I use the InvokeHttp processor (with a scheduling/frequency of 1 minute):

Screen Shot 2017-05-11 at 7.57.45 PM.png

Now, I can use this information to build the corresponding graph in my Grafana dashboard:

Screen Shot 2017-05-11 at 7.59.27 PM

I can see that, in average, it takes about 150 milliseconds to generate my flow file, publish it in my Kafka topic and get it into my scripted processor. I could also generate one metric per host of my cluster to check if a node is performing badly compared to the others.

Now you can easily send your custom data into AMS and create dashboards for your specific use cases and workflows.

As usual feel free to ask questions and comment this post.

Monitoring NiFi – Workflow SLA

Note – This article is part of a series discussing subjects around NiFi monitoring.

Depending of the workflows and use cases, you may want to retrieve some metrics per use-case/workflow instead of high level metrics. One of the things you could be looking after is Workflow SLA.

First of all, we need to agree on what is “Workflow SLA”. In this article, by SLA (Service-Level Agreement) monitoring I mean that I want to ensure that a workflow is processing every single event quickly enough. In other words, if an event took a long time to be processed, I want to be aware of that so I can understand what is going on. A workflow could be perfectly running in NiFi (no bulletin generated, no error) but the processing time could get bigger because of external causes (resources exhaustion, network bandwidth with external systems, abnormal event size, etc).

Luckily for us, every single flow file within NiFi contains two core attributes that are perfectly designed for our needs. From the documentation:

  • entryDate: The date and time at which the FlowFile entered the system (i.e., was created). The value of this attribute is a number that represents the number of milliseconds since midnight, Jan. 1, 1970 (UTC).
  • lineageStartDate: Any time that a FlowFile is cloned, merged, or split, this results in a “child” FlowFile being created. As those children are then cloned, merged, or split, a chain of ancestors is built. This value represents the date and time at which the oldest ancestor entered the system. Another way to think about this is that this attribute represents the latency of the FlowFile through the system. The value is a number that represents the number of milliseconds since midnight, Jan. 1, 1970 (UTC).

In our case we are really interested by the “lineageStartDate” attribute but based on your specific use cases and needs you could also be interested by “entryDate”.

At the end of my workflow I want to monitor, I could use a RouteOnAttribute processor to route the event if and only if the duration since the lineage start date is over a given threshold (let’s say one minute for the below example). And if an event took longer than expected, I’m routing the flow file to a PutEmail processor to send an email and receive a notification.

Screen Shot 2017-05-10 at 3.33.34 PM.png

My RouteOnAttribute configuration:

Screen Shot 2017-05-10 at 3.34.02 PM.png

This way, at a workflow level, you are able to check if the processing time of an event is meeting your requirements. You could obviously follow the same approach to check the processing time at multiple critical points of the workflow.

Note that, instead of sending an email notification, you could perfectly use any other kind of processor to integrate this alert/event with any system you are using on your side.

One last comment, I recommend you to check out the article about the Ambari Reporting Task and Grafana where I’m also discussing Workflow SLA and how to send the metrics to Ambari Metrics System and display the information into a Grafana dashboard.

As usual feel free to ask questions and comment this post.

Monitoring NiFi – Site2Site reporting tasks

Note – This article is part of a series discussing subjects around NiFi monitoring.

If we look at the development documentation about reporting tasks:

So far, we have mentioned little about how to convey to the outside world how NiFi and its components are performing. Is the system able to keep up with the incoming data rate? How much more can the system handle? How much data is processed at the peak time of day versus the least busy time of day?

In order to answer these questions, and many more, NiFi provides a capability for reporting status, statistics, metrics, and monitoring information to external services by means of the ReportingTask interface. ReportingTasks are given access to a host of information to determine how the system is performing.

Out of the box, you have quite a lot of available reporting tasks and, in this article, we are going to focus on few of them (have a look at the other articles to find more about the other reporting tasks).

Before going into the details, I am going to discuss Site To Site related reporting tasks and we need to understand what is Site To Site (S2S):

When sending data from one instance of NiFi to another, there are many different protocols that can be used. The preferred protocol, though, is the NiFi Site-to-Site Protocol. Site-to-Site makes it easy to securely and efficiently transfer data to/from nodes in one NiFi instance or data producing application to nodes in another NiFi instance or other consuming application.

In this case, we are going to use S2S reporting tasks, it means that the reporting tasks are continually running to collect data and to send this data to a remote NiFi instance, but you can use S2S to send data to the instance running the reporting task as well. This way, by using an input port on the canvas, you can actually receive the data generated by the reporting task and use it in a NiFi workflow. That’s really powerful because, now, you can use all the NiFi capabilities to process this data and do whatever you want with it.

Let’s go over some examples!

  • Monitoring bulletins

With the new version of Apache NiFi (1.2.0), you can transform every bulletin into a flow file sent to any NiFi instance using Site-To-Site. This way, as soon as a processor / controller service / reporting task is generating a bulletin this can be converted into a flow file that you can use to feed any system you want.

Let’s configure this reporting task to send the bulletins (as flow files) to an input port called “bulletinMonitoring” and use the flow files to send emails.

First, since my NiFi cluster is secured, I create a StandardSSLContextService in the Controller Services tab of the Controller Settings menu (this way, it can be used by reporting tasks).

Screen Shot 2017-05-10 at 12.28.54 PM.png

Screen Shot 2017-05-10 at 12.28.46 PM.png

Then, I can define my reporting task by adding a new SiteToSiteBulletinReportingTask:

Screen Shot 2017-05-10 at 12.30.55 PM.png

Screen Shot 2017-05-10 at 12.31.29 PM.png

Before starting the task, on my canvas, I have the following:

Screen Shot 2017-05-10 at 12.32.55 PM.png

Note – in a secured environment, you need to set the correct permissions on the components. You need to allow NiFi nodes to receive data via site-to-site on the input port and you also need to grant the correct permissions on the root process group so the nodes are able to see the component, view and modify the data.

I configured my PutEmail processor to send emails using the Gmail SMTP server:

Screen Shot 2017-05-10 at 12.45.13 PM.png

Now, as soon as bulletins are generated I’ll receive a notification by email containing my message with the attributes of the flow file, and there will the bulletins as attachment of my email.

Obviously, instead of sending emails, you could, for example, use some other processors to automatically open tickets in your ticketing system (like JIRA using REST API).

  • Monitoring disk space

Now, using the task we previously set, we can take advantage of the task monitoring disk space. This reporting task will generate warn logs (in the NiFi log file) and bulletins when the disk partition to monitor is used over a custom threshold. In case I want to monitor the Content Repository, I could configure my reporting task as below:

Screen Shot 2017-05-10 at 1.28.24 PM.png

Screen Shot 2017-05-10 at 1.28.34 PM.png

Using the combination of this Reporting Task and the SiteToSiteBulletinReportingTask, I’m able to generate flow files when the disk utilization is reaching a critical point and to receive notifications using all the processors I want.

  • Monitoring memory use

The same approach can be used to monitor the memory utilization within the NiFi JVM using the MonitorMemory reporting task. Have a look at the documentation of this reporting task here.

  • Monitoring back pressure on connections

There is also the SiteToSiteStatusReportingTask that will send details about the NiFi Controller status over Site-to-Site. This can be particularly useful to be notified when some processors are stopped, queues are full (and back pressure is enabled), or to build reports regarding NiFi performances. This reporting task will slightly be improved regarding back pressure (with NIFI-3791). In the meantime, if you want to receive notifications when back pressure is enabled on any connection, here is what you can do (assuming you know the back pressure thresholds):

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Note that I configured the task to only send data about the connections but you can receive information for any kind of component.

And I use the following workflow: my input port to receive the flow files with the controller status (containing an array of JSON elements for all of my connections), then I split my array using SplitJson processor, then I use EvaluateJsonPath to extract as attributes the values queuedCount and queuedBytes and then I use a RouteOnAttribute processor to check if one of the two attributes I have is greater or equal than my thresholds, and if that’s the case I send the notification by email.

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My RouteOnAttribute configuration:

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Site to site reporting tasks are really useful and there are many ways to use the data they can send for monitoring purpose.

  • SiteToSiteProvenanceReportingTask

Note that you have also a Site2Site reporting task to send all the provenance events over S2S. This can be really useful if you want to send this information to external system.

While monitoring a dataflow, users often need a way to determine what happened to a particular data object (FlowFile). NiFi’s Data Provenance page provides that information. Because NiFi records and indexes data provenance details as objects flow through the system, users may perform searches, conduct troubleshooting and evaluate things like dataflow compliance and optimization in real time. By default, NiFi updates this information every five minutes, but that is configurable.

Besides, with NIFI-3859, this could also be used in a monitoring approach to only look for specific events according to custom parameters. It could be used, for instance, to check how long it takes for an event to go through NiFi and raise alerts in case an event took an unusual duration to be processed (have a look to the next article to see how this can be done differently).

As usual feel free to ask questions and comment this post.