How do I tell what percentage of the data in a Kinesis stream a reader has already processed? I know each reader has a per-shard checkpoint sequence number, and I can also get the StartingSequenceNumber of each shard from describe-stream, however, I don't know how far along in my data the reader currently is (I don't know the latest sequence number of the shard).
I was thinking of getting a LATEST iterator for each shard and getting the last record's sequence number, however that doesn't seem to work if there's no new data since I got the LATEST iterator.
Any ideas or tools for doing this out there?
Thanks!
I suggest you implement a custom metric or metrics in your applications to track this.
For example, you could append a message send time within your Kinesis message, and on processing the message, record the time difference as an AWS CloudWatch custom metric. This would indicate how close your consumer is to the front of the stream.
You could also record the number of messages pushed (at the pushing application) and messages received at the Kinesis consumer. If you compare these in a chart on CloudWatch, you could see that the curves roughly follow each other indicating that the consumer is doing a good job at keeping up with the workload.
You could also try monitoring your Kinesis consumer, to see how often it idly waits for records (i.e, no results are returned by Kinesis, suggesting it is at the front of the stream and all records are processed)
Also note there is not a way to track a "percent" processed in the stream, since Kinesis messages expire after 24 hours (so the total number of messages is constantly rolling). There is also not a direct (API) function to count the number of messages inside your stream (unless you have recorded this as above).
If you use KCL you can do that by comparing IncomingRecords from the cloudwatch built-in metrics of Kinesis with RecordsProcessed which is a custom metric published by the KCL.
Then you select a time range and interval of say 1 day.
You would then get the following type of graphs:
As you can see there were much more records added than processed. By looking at the values in each point you will know exactly if your processor is behind or not.
Related
I have a DynamoDB Table and it is linked with one Stream, and that stream is linked with one lambda function which processed it.
Question - With above set up if an event comes to the stream and is ingested in Lambda, does that event still resides in that stream or it gets POPPED out as soon as it got ingested in Lambda just like a Queue?
Question 2 Can someone kindly tell me about the inner working of DDB Stream and how it passes the data to Lambda? Like are there any states for the stream events?
P.S: AWS Documentation says that events stay in stream for 24 hour window.
There are two concepts to understand here
Streams
Triggers
Whenever there is a change in the table like an addition, update or deletion, the Kinesis Stream feature of AWS stores that change for a period of 24 hrs. It does this through 4 methods:-
Keys only:- only the keys are stored after the change
New image:- The entire item on which the change is performed is stored
Old image:- When a change is performed on an item, the old item is stored instead of the new one
New and old:- self-explanatory
To associate a lambda function with your streams, a feature called Triggers are used. The changes invoke the Trigger which in-turn performs the lambda function associated with the change.
Part 1 of your question:-
By default, Lambda invokes your function as soon as records are available in the stream. If the batch it reads from the stream only has one record in it, Lambda only sends one record to the function. To avoid invoking the function with a small number of records, you can tell the event source to buffer records for up to 5 minutes by configuring a batch window. Before invoking the function, Lambda continues to read records from the stream until it has gathered a full batch, or until the batch window expires. If the Lambda fails it will try and process that message indefinitely (or until it expires), keeping other messages from being processed as a result. To avoid stalled shards(I'll talk about this later), you can configure the event source mapping to retry with smaller batch size, limit the number of retries, or discard records that are too old(you can set the age of the record that lambda can read).
Part 2 of your question:-
The streams which we are talking about are Kinesis Streams It is a feature to be used by multiple producers and consumers. Here the producer is DynamoDb and the consumer is lambda. Consumers have dedicated read throughput so they don't have to compete with other consumers of the same data. With consumers, Kinesis pushes records to Lambda over an HTTP/2 connection, which can also reduce latency between adding a record and function invocation.
The capacity of the streams is determined by the number of shards it contains. Shards are small units of capacity in the Stream. Hence higher the shard value, higher the capacity.
I guess I have explained the working in the part1 of this answer. Feel free to ask follow up questions.
I know there is a lot materials online for this question, however I have not found any that can explain this question quite clearly to a rookie like me... Appreciate it if some one can help me understand the key differences between these two services and use cases with real life examples. Thank you!
Amazon SQS is a queue. The basic process is:
Messages are sent to the queue. They stay there for up to 14 days.
Worker programs can request a message (or up to 10 messages) from the queue.
When a message is retrieved from the queue:
It stays in the queue but is marked as invisible
When the worker has finished processing the message, it tells SQS to delete the message from the queue
If the worker does not delete the message within the queue's invisibility timeout period, then the message reappears on the queue for another worker to process
The worker can, if desired, periodically tell SQS to keep a message invisible because it is still being processed
Thus, once a message is processed, it is deleted.
In Amazon Kinesis, a message is sent to a stream. The stream is divided into shards (think of them as mini-streams). When a message is received, Kinesis stores the message in sequential order. Then, workers can request a message from the start of the stream, or from a specific spot in the stream. For example, if it has already processed 5 messages, it can ask for the 6th message. The messages are retained in the stream for a period of time (eg 24 hours).
I like to think of it like a film strip — each frame in a film is kept in order. You can play a film from the start, or you can fast-forward to the middle and start playing from there. In addition, you can rewind to an earlier part and watch it. The same is true for a Kinesis stream, and multiple consumers can read from various parts of the stream simultaneously.
So, which to choose?
If a message is used once and then discarded, a queue is probably the better choice.
If retaining message order is important and/or messages will be used more than once, then a stream is probably better.
This article sums it up pretty nicely, imo:
https://sookocheff.com/post/aws/comparing-kinesis-and-sqs/
but basically, if you don't know which one you need, start with SQS until it can't do what you want. SQS is dead-simple to setup and use, and requires almost no experise to use it well.
Kinesis takes a lot more time and expertise to setup to use, so unless you need it, don't bother - even though it could be used for many of the same things as SQS.
One big difference, with SQS if you have multiple consumers reading from the queue, than each consumer will only ever see thge messages they consume - because other consumers will be blocked from seeing them; with Kinesis, many consumers can access the stream at the same time, and each consumer sees the entire streem - so SQS is good for taking a large number of tasks and doling out pieces to lots of consumers to work on in parallel (among other things), where as with Kinesis multiple consumers could read and see the entire streem and do something with ALL of the data in the stream.
The linked article explains it better than me.
I try to give a simple answer based on my practical experience:
Consider SQS as temporary storage service. Use cases:
manage data with different queue priorities
store data for a limited period of time
Lambda DLQ
reduce costs with long polling
create a FIFO
Consider Kinesis as a collector of large stream of real-time data. Use cases:
very very large stream of data from different sources
backup of data just enabling Firehose (you get a data lake for free)
get statistics at once during the collecting phase integrating Kinesis Analytics
have checkpoints to keep track in DynamoDB of records processed/failed
Note: consider that both services can be integrated with Lambda Functions very easily, so there are a plenty of use cases that can be solved both with SQS and Kinesis. Anyway, I tried to list some use cases where I found that one of the two performed peculiarly better than the other. Hope it can be helpful :)
We currently have an application that receives a large amount of sensor data. Each sensor has its own unique sensor id (eg '5834f7718273f92cc326f620') and emits its status at different intervals. The processing order of the messages that come in is not important, for example a newer message of one sensor can be processed before an older message of another sensor. What does matter though, is that each message for a given sensor must be processed sequentially; in the order that that they arrived in the stream.
I have taken a look at the Kinesis Client Library and understand that KCL pushes messages to a single processor per shard. Does this mean that if a stream has only one shard it will have only one processor and couldn't this create a bottleneck? Or does KCL have more than one processor, and somehow, perhaps using the partition key ensures messages with the same partition key are never processed concurrently?
Note: We have taken a look at sqs fifo, but ruled it out as the 300 messages per second limit would soon become an issue.
Yes, each shard can only have one processor at a given moment (per application).
But, you can use the sensor id as the partition key for your kinesis put record request. (see here)
This will make sure that all of this sensor events will get into the same shard and processor.
If you will do that you'll be able to scale your processes and shards and still get each sensor events processed in a single processor
How are checkpoints and trimming related in AWS KCL library?
The documentation page Handling Startup, Shutdown, and Throttling says:
By default, the KCL begins reading records from the tip of the
stream;, which is the most recently added record. In this
configuration, if a data-producing application adds records to the
stream before any receiving record processors are running, the records
are not read by the record processors after they start up.
To change the behavior of the record processors so that it always
reads data from the beginning of the stream, set the following value
in the properties file for your Amazon Kinesis Streams application:
initialPositionInStream = TRIM_HORIZON
The documentation page Developing an Amazon Kinesis Client Library Consumer in Java says:
Streams requires the record processor to keep track of the records
that have already been processed in a shard. The KCL takes care of
this tracking for you by passing a checkpointer
(IRecordProcessorCheckpointer) to processRecords. The record processor
calls the checkpoint method on this interface to inform the KCL of how
far it has progressed in processing the records in the shard. In the
event that the worker fails, the KCL uses this information to restart
the processing of the shard at the last known processed record.
The first page seems to say that the KCL resumes at the tip of the stream, the second page at the last known processed record (that was marked as processed by the RecordProcessor using the checkpointer). In my case, I definitely need to restart at the last known processed record. Do I need to set the initialPositionInStream to TRIM_HORIZON?
With kinesis stream you have two options, you can read the newest records, or start from the oldest (TRIM_HORIZON).
But, once you started your application it just reads from the position it stopped using its checkpoints.
You can see those checkpoints in dynamodb (Usually the table name is as the app name).
So if you restart your app it will usually continue from where it stopped.
The answer is no, you don't need to set the initialPositionInStream to TRIM_HORIZON.
When you are reading events from a kinesis stream, you have 4 options:
TRIM_HORIZON - the oldest events that are still in the stream shards before they are automatically trimmed (default 1 day, but can be extended up to 7 days). You will use this option if you want to start a new application that will process all the records that are available in the stream, but it will take a while until it is able to catch up and start processing the events in real-time.
LATEST - the newest events in the stream, and ignore all the past events. You will use this option if you start a new application that you want to process in teal time immediately.
AT/AFTER_SEQUENCE_NUMBER - the sequence number is usually the checkpoint that you are keeping while you are processing the events. These checkpoints are allowing you to reliably process the events, even in cases of reader failure or when you want to update its version and continue processing all the events and not lose any of them. The difference between AT/AFTER is based on the time of your checkpoint, before or after you processed the events successfully.
Please note that this is the only shard specific option, as all the other options are global to the stream. When you are using the KCL it is managing a DynamoDB table for that application with a record for each shard with the "current" sequence number for that shard.
AT_TIMESTAMP - the estimate time of the event put into the stream. You will use this option if you want to find specific events to process based on their timestamp. For example, when you know that you had a real life event in your service at a specific time, you can develop an application that will process these specific events, even if you don't have the sequence number.
See more details in Kinesis documentation here: https://docs.aws.amazon.com/kinesis/latest/APIReference/API_GetShardIterator.html
You should use the "TRIM_HORIZON". It will only have effect on the first time your application starts to read records from the stream.
After that, it will continue from last known position.
I'm very new to Amazon Kinesis so maybe this is just a problem in my understanding but in the AWS Lambda FAQ it says:
The Amazon Kinesis and DynamoDB Streams records sent to your AWS Lambda function are strictly serialized, per shard. This means that if you put two records in the same shard, Lambda guarantees that your Lambda function will be successfully invoked with the first record before it is invoked with the second record. If the invocation for one record times out, is throttled, or encounters any other error, Lambda will retry until it succeeds (or the record reaches its 24-hour expiration) before moving on to the next record. The ordering of records across different shards is not guaranteed, and processing of each shard happens in parallel.
My question is, what happens if for some reason some malformed data gets put onto a shard by a producer and when the Lambda function picks it up it errors out and then just keeps retrying constantly? This then means that the processing of that particular shard would be blocked for 24 hours by the error.
Is the best practice to handle application errors like that by wrapping the problem in a custom error and sending this error downstream along with all the successfully processed records and let the consumer handle it? Of course, this still wouldn't help in the case of an unrecoverable error that crashed the program like a null pointer: again we'd be back to the blocking retry loop for the next 24 hours.
Don't overthink it, the Kinesis is just a queue. You have to consume a record (ie. pop from the queue) successfully in order to proceed to the next one. Just like a FIFO stack.
The appropriate approach should be:
Get a record from stream.
Process it in a try-catch-finally block.
If the record is processed successfully, no problem. <- TRY
But if it fails, note it down to another place to investigate the
reason why it failed. <- CATCH
And at the end of your logic blocks, always persist the position to
DynamoDB. <- FINALLY
If an internal occurs in your system (memory error, hardware error
etc) that is another story; as it may affect processing all of the
records, not just one.
By the way, if processing of a record takes more than 1 minute, it is obvious you are doing something wrong. Because Kinesis is designed to handle thousands of records per second, you should not have the luxury of processing such long jobs for each of them.
The question you are asking is a general problem of queue systems, sometimes called "poisonous message". You have to handle them in your business logic to be safe.
http://www.cogin.com/articles/SurvivingPoisonMessages.php#PoisonMessages
This is a common question on processing events in Kinesis and I'll try to give you some points to build your Lambda function to handle such issues with "corrupted" data. Since it is best practice to have separated parts of your system writing to the Kinesis stream and other parts reading from the Kinesis stream, it is common that you will have such problems.
First, why do you have such problematic events?
Using Kinesis to process your events is a good way to break up a complex system that is doing both front-end processing (serving end users), and at the same time/code back-end processing (analyzing events), into two independent parts of your system. The front-end people can focus on their business, while the back-end people don't need to push code changes to the front-end, if they want to add functionality to serve their analytic use cases. Kinesis is a buffer of events that both breaks the need for synchronization as well simplifies the business logic code.
Therefore, we would like events written to the stream to be flexible in their "schema", and if the front-end teams wish to change the event format, add fields, delete fields, change the protocol or the encryption keys, they should be able to do that as often as they want.
Now it is up to the teams that are reading from the stream to be able to process such flexible events in an efficient way, and not break their processing every time such change is happening. Therefore, it should be common that your Lambda function will see events that it can't process, and "poison-pill" is not that rare event as you might expect.
Second, how do you handle such problematic events?
Your Lambda function will get a batch of events to process. Please note that you shouldn't get the events one by one, but in large batches of events. If your batches are too small, you will quickly get large lags on the stream.
For each batch you will iterate over the events, process them and then check-point in DynamoDB the last sequence-id of the batch. Lambda is doing most of these steps automatically with (see more here: http://docs.aws.amazon.com/lambda/latest/dg/walkthrough-kinesis-events-adminuser-create-test-function.html):
console.log('Loading function');
exports.handler = function(event, context) {
console.log(JSON.stringify(event, null, 2));
event.Records.forEach(function(record) {
// Kinesis data is base64 encoded so decode here
payload = new Buffer(record.kinesis.data, 'base64').toString('ascii');
console.log('Decoded payload:', payload);
});
context.succeed();
};
This is what is happening in the "happy path", if all the events are processed without any problem. But if you encounter any problem in the batch and you don't "commit" the events with the success notification, the batch will fail and you will get all the events in the batch again.
Now you need to decide what is the reason of the failure in the processing.
Temporary problem (throttling, network issue...) - it is OK to wait a second and try again for a couple of times. In many cases the issue will resolve itself.
Occasional problem (out of memory...) - it is best to increase the memory allocation of the Lambda function or decrease the batch size. In many cases such modification will resolve the issue.
Constant failure - it means that you have to either ignore the problematic event (put it in a DLQ - dead-letter-queue) or modify your code to handle it.
The problem is to identify the type of failure in your code and handle it differently. You need to write your Lambda code in a way to identify it (type of exception, for example) and react differently.
You can use the integration with CloudWatch to write such failures to the console and create the relevant alarms. You can use the CloudWatch Logs also as a way to log your "dead-letter-queue" and see what is the source of problem.
In your lambda you can either throw an error and thus returning back the whole batch, or you can not throw an error and instead push it to an SQS queue to handle those messages differently. SQS has a retention period of 14 days. You could also have checkpoints with each record to know if the record was processed in the previous run.
If you have a lot of incoming data and you don't want to introduce any latency you could just ignore the error and just move on while adding those events to an SQQ queue.