From what I've read here and there, the flowfile repository serves as a Write Ahead Log for apache Nifi.
When walking the configuration files, I've seen that there is a state-management configuration section. When in a Standalone mode, a local-provider is used and writes the state (by default) to .state/local/.
It seems like both the flowfile repo and the state are used both, for example, to recover from a system failure.
Would someone please explain what's the difference between them? Do they work together ?
Also, it's a best practice to have the flowfile repo and the content repo on two separate disks. What about the local state ? Should we avoid using the "boot" disk and offload to another one ? Which one: a dedicated ? Co-locate with another one (I'm co-locating database and flowfile repos).
Thanks.
The flow file repository keeps track of all the flow files in the system, which content they point to, which attributes they have, and where they are in the flow.
State Management is an API provided to processors/services that can be used to store and retrieve key/value pairs, typically for remembering where something left off. For example, a source processor that pulls data since some timestamp would want to store the last timestamp it used so that if NiFi restarts it can retrieve this value and start from there again.
I'm very new to Apache NIFI so it's possible that this is already covered but most of the information I can find supports a slightly different use-case.
I've got a bunch of files that are posted to an FTP or whatever -- they're all associated with each other by filename:
ID_Part1.zip
ID_Attachments.zip
ID_Part2.zip
ID_Customizations.txt
ID.done
There are a variable number of files per logical processing group, some are mandatory, some are optional, and some may be unexpected. We know they're all associated based on their ID prefix and we'll know they're all delivered once a .done file exists.
What's an appropriate way, in NIFI parlance, to ensure that none of the files belonging to any given ID are processed until the .done file exists and that the processor that receives that group of file gets access to all of them?
Some of how the data splitting and segregating is done is still magical to me, but it'd be a catastrophic failure for my requirements if some processor happened to say see all of those files except ID_Customizations.txt and process them as a valid, but secretly incomplete, group.
What's an appropriate way, in NIFI parlance, to ensure that none of
the files belonging to any given ID are processed until the .done file
exists
In your GetFile or ListFile processor you can use the property "File Filter" to firstly retrieve your .done file.
processor that receives that group of file gets access to all of them
in you .done flowfile you can use fetchFile processor to fetch all file of a target directory.
More info about theses processors :
https://nifi.apache.org/docs/nifi-docs/components/org.apache.nifi/nifi-standard-nar/1.6.0/org.apache.nifi.processors.standard.ListFile/index.html
https://nifi.apache.org/docs/nifi-docs/components/org.apache.nifi/nifi-standard-nar/1.6.0/org.apache.nifi.processors.standard.FetchFile/
My Apache NiFi instance just hangs on the "Computing FlowFile lineage..." for a specific flow. Others work, but it won't show the lineage for this specific flow for any data files. The only error message in the log is related to an error in one of the processors, but I can't see how that would affect the lineage, or stop the page from loading.
This was related to two things...
1) I was using the older (but default) provenance repository, which didn't perform well, resulting in the lag in the UI. So I needed to change it...
#nifi.provenance.repository.implementation=org.apache.nifi.provenance.PersistentProvenanceRepository
nifi.provenance.repository.implementation=org.apache.nifi.provenance.WriteAheadProvenanceRepository
2) Fixing #1 exposed the second issue, which was that the EnforceOrder processor was generating hundreds of provenance events per file, because I was ordering on a timestamp, which had large gaps between the values. This is apparently not a proper use case for the EnforceOrder processor. So I'll have to remove it and find another way to do the ordering.
I have a processor that appears to be creating FlowFiles correctly (modified a standard processor), but when it goes to commit() the session, an exception is raised:
2016-10-11 12:23:45,700 ERROR [Timer-Driven Process Thread-6] c.s.c.processors.files.GetFileData [GetFileData[id=8f5e644d-591c-4df1-8c79-feea118bd8c0]] Failed to retrieve files due to {} org.apache.nifi.processor.exception.FlowFileHandlingException: StandardFlowFileRecord transfer relationship not specified
I'm assuming this is supposed to be indicating there's no connection available to commit the transfer; however, there is a "success" relationship registered during init() in same way as original processor did it, and the success relationship out is connected to another processor input as it should be.
Any suggestions for troubleshooting?
What changes did you make to the standard processor? If you are calling methods on the ProcessSession object, ensure that you are saving the latest "version" of the FlowFile returned from those method calls, and transfer only the latest version to "success".
FlowFile references are immutable; often in code you will see an initial reference like "flowFile" pointing at the incoming flow file (from session.get() for example), then it gets updated as the flow file is mutated, such as flowFile = session.putAttribute(flowFile, "myAttribute", "myValue").
Also ensure that you have transferred or removed the latest version of each distinct flow file (not the various references to the same flow file) to some relationship (even Relationship.SELF if need be). If your processor creates a new flow file, ensure that new flow file is transferred. If the incoming flow file is no longer needed, be sure to call session.remove() on it.
There are some common patterns and additional guidance in the NiFi Developer's Guide, including test patterns; your unit test(s) for this processor should be able to flush out this error (by asserting how many flow files should have been transferred to which relationship(s) during the test).
How can I use a shell script check if an Amazon S3 file ( small .xml file) has been modified. I'm currently using curl to check every 10 seconds, but it's making many GET requests.
curl "s3.aws.amazon.com/bucket/file.xml"
if cmp "file.xml" "current.xml"
then
echo "no change"
else
echo "file changed"
cp "file.xml" "current.xml"
fi
sleep(10s)
Is there a better way to check every 10 seconds that reduces the number of GET requests? (This is built on top of a rails app so i could possibly build a handler in rails?)
Let me start by first telling you some facts about S3. You might know this, but in case you don't, you might see that your current code could have some "unexpected" behavior.
S3 and "Eventual Consistency"
S3 provides "eventual consistency" for overwritten objects. From the S3 FAQ, you have:
Q: What data consistency model does Amazon S3 employ?
Amazon S3 buckets in all Regions provide read-after-write consistency for PUTS of new objects and eventual consistency for overwrite PUTS and DELETES.
Eventual consistency for overwrites means that, whenever an object is updated (ie, whenever your small XML file is overwritten), clients retrieving the file MAY see the new version, or they MAY see the old version. For how long? For an unspecified amount of time. It typically achieves consistency in much less than 10 seconds, but you have to assume that it will, eventually, take more than 10 seconds to achieve consistency. More interestingly (sadly?), even after a successful retrieval of the new version, clients MAY still receive the older version later.
One thing that you can be assured of is: if a client starts download a version of the file, it will download that entire version (in other words, there's no chance that you would receive for example, the first half of the XML file as the old version and the second half as the new version).
With that in mind, notice that your script could fail to identify the change within your 10-second timeframe: you could make multiple requests, even after a change, until your script downloads a changed version. And even then, after you detect the change, it is (unfortunately) entirely possible the the next request would download the previous (!) version, and trigger yet another "change" in your code, then the next would give the current version, and trigger yet another "change" in your code!
If you are OK with the fact that S3 provides eventual consistency, there's a way you could possibly improve your system.
Idea 1: S3 event notifications + SNS
You mentioned that you thought about using SNS. That could definitely be an interesting approach: you could enable S3 event notifications and then get a notification through SNS whenever the file is updated.
How do you get the notification? You would need to create a subscription, and here you have a few options.
Idea 1.1: S3 event notifications + SNS + a "web app"
If you have a "web application", ie, anything running in a publicly accessible HTTP endpoint, you could create an HTTP subscriber, so SNS will call your server with the notification whenever it happens. This might or might not be possible or desirable in your scenario
Idea 2: S3 event notifications + SQS
You could create a message queue in SQS and have S3 deliver the notifications directly to the queue. This would also be possible as S3 event notifications + SNS + SQS, since you can add a queue as a subscriber to an SNS topic (the advantage being that, in case you need to add functionality later, you could add more queues and subscribe them to the same topic, therefore getting "multiple copies" of the notification).
To retrieve the notification you'd make a call to SQS. You'd still have to poll - ie, have a loop and call GET on SQS (which cost about the same, or maybe a tiny bit more depending on the region, than S3 GETs). The slight difference is that you could reduce a bit the number of total requests -- SQS supports long-polling requests of up to 20 seconds: you make the GET call on SQS and, if there are no messages, SQS holds the request for up to 20 seconds, returning immediately if a message arrives, or returning an empty response if no messages are available within those 20 seconds. So, you would send only 1 GET every 20 seconds, to get faster notifications than you currently have. You could potentially halve the number of GETs you make (once every 10s to S3 vs once every 20s to SQS).
Also - you could chose to use one single SQS queue to aggregate all changes to all XML files, or multiple SQS queues, one per XML file. With a single queue, you would greatly reduce the overall number of GET requests. With one queue per XML file, that's when you could potentially "halve" the number of GET request as compared to what you have now.
Idea 3: S3 event notifications + AWS Lambda
You can also use a Lambda function for this. This could require some more changes in your environment - you wouldn't use a Shell Script to poll, but S3 can be configured to call a Lambda Function for you as a response to an event, such as an update on your XML file. You could write your code in Java, Javascript or Python (some people devised some "hacks" to use other languages as well, including Bash).
The beauty of this is that there's no more polling, and you don't have to maintain a web server (as in "idea 1.1"). Your code "simply runs", whenever there's a change.
Notice that, no matter which one of these ideas you use, you still have to deal with eventual consistency. In other words, you'd know that a PUT/POST has happened, but once your code sends a GET, you could still receive the older version...
Idea 4: Use DynamoDB instead
If you have the ability to make a more structural change on the system, you could consider using DynamoDB for this task.
The reason I suggest this is because DynamoDB supports strong consistency, even for updates. Notice that it's not the default - by default, DynamoDB operates in eventual consistency mode, but the "retrieval" operations (GetItem, for example), support fully consistent reads.
Also, DynamoDB has what we call "DynamoDB Streams", which is a mechanism that allows you to get a stream of changes made to any (or all) items on your table. These notifications can be polled, or they can even be used in conjunction with a Lambda function, that would be called automatically whenever a change happens! This, plus the fact that DynamoDB can be used with strong consistency, could possibly help you solve your problem.
In DynamoDB, it's usually a good practice to keep the records small. You mentioned in your comments that your XML files are about 2kB - I'd say that could be considered "small enough" so that it would be a good fit for DynamoDB! (the reasoning: DynamoDB reads are typically calculated as multiples of 4kB; so to fully read 1 of your XML files, you'd consume just 1 read; also, depending on how you do it, for example using a Query operation instead of a GetItem operation, you could possibly be able to read 2 XML files from DynamoDB consuming just 1 read operation).
Some references:
http://docs.aws.amazon.com/AmazonS3/latest/dev/NotificationHowTo.html
http://docs.aws.amazon.com/lambda/latest/dg/with-ddb.html
http://docs.aws.amazon.com/AWSSimpleQueueService/latest/APIReference/API_ReceiveMessage.html
I can think of another way by using S3 Versioning; this would require the least amount of changes to your code.
Versioning is a means of keeping multiple variants of an object in the same bucket.
This would mean that every time a new file.xml is uploaded, S3 will create a new version.
In your script, instead of getting the object and comparing it, get the HEAD of the object which contains the VersionId field. Match this version with the previous version to find out if the file has changed.
If the file has indeed changed, get the new file, and also get the new version of that file and save it locally so that next time you can use this version to check if a newer-newer version has been uploaded.
Note 1: You will still be making lots of calls to S3, but instead of fetching the entire file every time, you are only fetching the metadata of the file which is much faster and smaller in size.
Note 2: However, if your aim was to reduce the number of calls, the easiest solution I can think of is using lambdas. You can trigger a lambda function every time a file is uploaded that then calls the REST endpoint of your service to notify you of the file change.
You can use --exact-timestamps
see AWS discussion
https://docs.aws.amazon.com/cli/latest/reference/s3/sync.html
Instead of using versioning, you can simply compare the E-Tag of the file, which is available in the header, and is similar to the MD-5 hash of the file (and is exactly the MD-5 hash if the file is small, i.e. less than 4 MB, or sometimes even larger. Otherwise, it is the MD-5 hash of a list of binary hashes of blocks.)
With that said, I would suggest you look at your application again and ask if there is a way you can avoid this critical path.