I am working on an application that will read and analyze the logs of payment transactions. I know I will use Kinesis Analytics as per my requirements, which takes the input from the Data Streams and Firehose. But I am having trouble deciding which input method should I use for my system. My requirements are:
It can tolerate latency, but Data shouldn't lose data.
Must record all the errors in DynamoDB or S3 buckets.
Which input stream is suitable for my use case?
Data Streams vs Firehose
Streams:
Kinesis data streams is highly customizable and best suited for developers building custom applications or streaming data for specialized needs.
Going to write custom code
Real time (200ms latency for classic, 70ms latency for enhanced fan-out)
You must manage scaling (shard splitting/merging)
Data storage for 1 to 7 days, replay capability, multi consumers
Use with Lambda to insert data in real-time to ElasticSearch
Firehose:
Firehose handles loading data streams directly into AWS products for processing.
Fully managed, send to S3, Splunk, Redshift, ElasticSearch
Serverless data transformations with Lambda
Near real time (lowest buffer time is 1 minute)
Automated Scaling
No data storage
Kinesis Data Streams allows consumers to READ streaming data. And it gives you a plenty of options to do so. It is best suitable for use cases that require custom processing, choice of stream processing frameworks, and sub-second processing latency.
Data is reliably stored in streams up to 7 days and distributed across 3 Availability Zones.
Kinesis Firehose is used to LOAD streaming data to a target destination (S3, Elasticsearch, Splunk, etc). You can also transform streaming data (by using Lambda) before loading it to destination.
Data from failed attempts will be saved to S3.
So, if your goal is to only load data to Kinesis Data Analytics service with minimal or no pre-processing then try Kinesis Firehose first.
Please note, that you also would need to consider such aspects as cost, development efforts, scaling options, volume of the data when choosing a proper service.
Please take a look at the following AWS Solutions Implementation for reference:
https://aws.amazon.com/solutions/implementations/real-time-web-analytics-with-kinesis/
https://aws.amazon.com/solutions/implementations/real-time-iot-device-monitoring-with-kinesis/
There are some key differences between Kinesis Stream (KS) and Firehose (FH):
KS is real time, while FH is near-real time.
KS requires manual scaling and setup of its provisioning (shards) , while FH is basically serverless.
KS records are immutable (they persist in stream for its retention period - default 24h), while records in FH are gone from FH the moment they are delivered to destination.
From what you wrote, I think FH should be considered first, as you are not concerned about non-real-time nature of FH, it is much easier to manage and setup, and you can specify S3 as a backup for failed or all messages:
Kinesis Data Firehose uses Amazon S3 to backup all or failed only data that it attempts to deliver to your chosen destination.
The S3 backup ensures you are not loosing records, if delivery or lambda processing fail. Subsequently, in my view, Firehose addresses your two points well.
You can use firehose to feed into analytics, but question is how firehose gets data? You can write your own code to feed data or use kinesis data steams. Firehose mainly is delivery system for stream data that can be written in to various destinations such as S3, Redshift or others with optional capability to perform data transformation.
Check this link https://www.slideshare.net/AmazonWebServices/abd217from-batch-to-streaming?from_action=save and see how your use case can benefit from the information.
More info: https://docs.aws.amazon.com/kinesisanalytics/latest/dev/how-it-works.html
https://docs.aws.amazon.com/firehose/latest/dev/what-is-this-service.html
If you are creating s3 files from the kinesis stream but you dont require cleaning of those s3 files then go with the firehose option. Also if you dont have any partitioning key requirement that makes many small s3 files then firehose is a good solution. If you are doing more cleaning up the FH files than you would have created those s3 files yourself then FH isnt a good option.
Also depends on what do you with those s3 files. You need to find out if you are saving any work/money because of using Firehose against the manual creation of S3 files. Remember you cant reorder the content of the s3 files.
Related
I am trying to write some IOT data to the S3 bucket and so I know 2 options so far.
1) Use AWS CLI and put data directly to the S3.
The downside of this approach is that I would have to parse out the data and figure out how to write it to S3. So there would be some dev required here. The upside is that there isn't additional cost associated to this.
2) Use Kinesis firehose
The downside of this approach is that it costs more money. It might be wasteful because the data doesn't have to be transferred in the real time, and it's not a huge amount of data. The upside is that I don't have to write any code for this data to be written in the S3 bucket.
Is there another alternative that I can explore?
If you're looking at keeping costs low, can you use some sort of cron functionality on your IoT device to POST data to a Lambda function that writes to S3, possibly?
Option 2 with Kinesis Data Firehose has the least administrative overhead.
You may also want to look into the native IoT services. It may be possible to use IoT Core and put the data directly in S3.
My use-case is as follows:
I have JSON data coming in which needs to be stored in S3 in parquet format. So far so good, I can create a schema in Glue and attach a "DataFormatConversionConfiguration" to my firehose stream. BUT the data is coming from different "topics". Each topic has a particular "schema". From my understanding I will have to create multiple firehose streams, as one stream can only have one schema. But I have thousands of such topics with very high volume high throughput data incoming. It does not look feasible to create so many firehose resources (https://docs.aws.amazon.com/firehose/latest/dev/limits.html)
How should I go about building my pipeline.
IMO you can:
ask for upgrade of your Firehose limit and do everything with 1 Firehose/stream + add Lambda transformation to convert the data into a common schema - IMO not cost-efficient but you should see with your load.
create a Lambda for each Kinesis data stream, convert each event to the schema managed by a single Firehose and at the end can send the events directly to your Firehose stream with Firehose API https://docs.aws.amazon.com/firehose/latest/APIReference/API_PutRecord.html (see "Q: How do I add data to my Amazon Kinesis Data Firehose delivery stream?" here https://aws.amazon.com/kinesis/data-firehose/faqs/) - but also, check the costs before because even though your Lambdas are invoked "on demand", you may have a lot of them invoked during long period of time.
use one of data processing frameworks (Apache Spark, Apache Flink, ...) and read your data from Kinesis in batches of 1 hour, starting every time when you terminated last time --> use available sinks to convert the data and write it in Parquet format. The frameworks use the notion of checkpointing and store the last processed offset in an external storage. Now, if you restart them every hour, they will start to read the data directly from the last seen entry. - It may be cost-efficient, especially if you consider using spot instances. On the other side, it requires more coding that 2 previous solutions and obviously may have higher latency.
Hope that helps. You could please give a feedback about chosen solution ?
We wish to build data pipeline system which tracks website interactions/events.
The goal is to track user behavior in a website so we would like to choose the right architecture to implement it having the following two constraints :
1) the system is Amazon
2) this is budgetary project so we cannot use redshift for this purpose
Based on the above two constraints my plan is to implement the following architecture:
website-javascript --> AWS-S3 -->(AWS-Lambda)--> AWS-RDS
website javascript client -
aws-firehose data delivery system to S3 - tracking user interaction and load them to aws-firehose which eventually write them in aws-S3.
AWS Lambda (Python) - Periodically task which pulls daily events from AWS-S3 and load them to AWS-RDS.
The reason I have chosen AWS-RDS is due to its cost-effectiveness for this objective
Appreciate any comment to the above mentioned implementation or any other architecture proposal that you may recommend to use instead of the above
If I understand your question correctly, you are proposing below solution to perform web analytics for your application:
WebServer --> Firehose --> AWS-S3 --> AWS-Lambda --> AWS-RDS
I see below pros and cons with above design
Pros:
low cost
easy to implement
Cons:
RDS may not be salable enough to handle analytics on massive amounts of web-streaming data, which tend to grow rapidly
Need to handle load balancing, failure scenarios and other complexities for lambda
You need to handle data transformation for RDS as it expects structured data to be ingested into relational tables
Proposal to store the data in S3 through Firehose sounds a good solution. But please keep in mind that minimum interval for Firehose is one minute, so your application needs to tolerate this minor latency. You may use Kinesis Streams to have millisecond latency, but then you need to manage your own application code and instances to handle Streams.
After ingesting data in Kinesis Firehose or Streams, you may also explore below alternatives:
Use Kinesis Analytics to track web users activity in real-time if it's available in your AWS region. It's only available in selected AWS regions currently
Within Firehose, transform your data using lambda and store it in S3 in optimized format for further analysis with AWS Athena
Use Elastic Search as a destination and perform web analytics with ELK stack instead of RDS
Though you mentioned that you can not use RedShift, it still may be the best solution for time series analysis. Exploring RedShift, RedShift Spectrum and formatted data stored in S3 may still be a cost effective solution with better cababilities
Adding few references from AWS, which you may go through before deciding on the solution:
Real-Time Web Analytics with Kinesis Data Analytics Solution
Near Real-time Analytics on Streaming Data with Amazon Kinesis and Amazon Elasticsearch
Schema-On-Read Analytics Pipeline Using Amazon Athena
Amazon Redshift Spectrum Extends Data Warehousing Out to Exabytes—No Loading Required
Hey folky this is a getting more and more common.
Generally the pattern is click events to Kinesis streams then you can monitor user interaction with the website in real time using Kinesis analytics. You can connect the stream to firehose to offload data in to an S3 bucket as well as incorporate Lambdas to transform the data.
There is some major complexity around handling Lambdas and Kinesis streams in parallel so this solution might not be as scalable as using AWS Kafka. Or perhaps run a job to move your s3 data into rds for whatever reporting you might need that is adhoc.
Here is a pattern AWS already has real-time-web-analytics-with-kinesis
Question
I've read this and this and this articles. But they provide contradictory answers to the question: how to customize partitioning on ingesting data to S3 from Kinesis Stream?
More details
Currently, I'm using Firehose to deliver data from Kinesis Streams to Athena. Afterward, data will be processed with EMR Spark.
From time to time I have to handle historical bulk ingest into Kinesis Streams. The issue is that my Spark logic hardly depends on data partitioning and order of event handling. But Firehouse supports partitioning only by ingestion_time (into Kinesis Stream), not by any other custom field (I need by event_time).
For example, under Firehouse's partition 2018/12/05/12/some-file.gz I can get data for the last few years.
Workarounds
Could you please help me to choose between the following options?
Copy/partition data from Kinesis Steam with help of custom lambda. But this looks more complex and error-prone for me. Maybe because I'm not very familiar with AWS lambdas. Moreover, I'm not sure how well it will perform on bulk load. At this article it was said that Lambda option is much more expensive than Firehouse delivery.
Load data with Firehouse, then launch Spark EMR job to copy the data to another bucket with right partitioning. At least it sounds simpler for me (biased, I just starting with AWS Lambas). But it has the drawback of double-copy and additional spark Job.
At one hour I could have up to 1M rows that take up to 40 MB of memory (at compressed state). From Using AWS Lambda with Amazon Kinesis I know that Kinesis to Lambda event sourcing has a limitation of 10,000 records per batch. Would it be effective to process such volume of data with Lambda?
While Kinesis does not allow you to define custom partitions, Athena does!
The Kinesis stream will stream into a table, say data_by_ingestion_time, and you can define another table data_by_event_time that has the same schema, but is partitioned by event_time.
Now, you can make use of Athena's INSERT INTO capabilities to let you repartition data without needing to write Hadoop or a Spark job and you get the serverless scale-up of Athena for your data volume. You can use SNS, cron, or a workflow engine like Airflow to run this at whatever interval you need.
We dealt with this at my company and go in-to more depth details of the trade-offs of using EMR or a streaming solution, but now you don't need to introduce anymore systems like Lambda or EMR.
https://radar.io/blog/custom-partitions-with-kinesis-and-athena
you may use the kinesis stream, and create the partitions like you wants.
you create a producer, and in your consumer, create the partitions.
https://aws.amazon.com/pt/kinesis/data-streams/getting-started/
Background
I have found that Amazon Kinesis Data Analytics can be used for streaming data as well as data present in an S3 bucket.
However, there are some parts of the Kinesis documentation that make me question whether Amazon Kinesis Analytics can be used for a huge amount of existing data in an S3 bucket:
Authoring Application Code
We recommend the following:
In your SQL statement, don't specify a time-based window that is longer than one hour for the following reasons:
Sometimes an application needs to be restarted, either because you updated the application or for Kinesis Data Analytics internal reasons. When it restarts, all data included in the window must be read again from the streaming data source. This takes time before Kinesis Data Analytics can emit output for that window.
Kinesis Data Analytics must maintain everything related to the application's state, including relevant data, for the duration. This consumes significant Kinesis Data Analytics processing units.
Question
Will Amazon Kinesis Analytics be good for this task?
The primary use case for Amazon Kinesis Analytics is stream data processing. For this reason, you attach an Amazon Kinesis Analytics application to a streaming data source. You can optionally include reference data from S3, which is limited in size to 1 GB at this time. We will load data from an S3 object into a SQL table that you can use to enrich the incoming stream.
It sounds like want a more general purpose tool for querying data from S3, not a stream data processing solution. I would recommend looking at Presto and Amazon EMR instead of using Amazon Kinesis Analytics.
Disclaimer: I work for the Amazon Kinesis team.