Is there any way to manually kick off an Amazon S3 Inventory report job?
I'm working on a project that creates daily inventory reports to another account but I can't seem to find a way to manually kick off the run. We're in the design / development phase of a data telemetry project and are tweaking our inventory configurations but having to wait for the daily job to run to see if the configuration satisfies our requirements is really inconvenient and slowing us down.
Is there a way to manually kick off an inventory report run after making a configuration change? I've tried looking in the api documentation as well as the boto3 documentation and all I have found is a call to create a bucket inventory configuration but nothing to actually perform a run.
Thanks,
Bill
As far as I know the inventory report does not run on-demand. It's quite a heavy operation for AWS for many buckets have billions of objects, so I can understand why they don't provide that service for free.
The aws cli can be used of course to get an inventory but it's incredibly slow (takes HOURS if not days just to list all objects in a bucket of a few million objects). Basically the only real options for large buckets is custom scripting with parallel execution. There are quite some open source projects out there that do this.
But since your original question is about the inventory report itself I'm afraid there is no real alternative.
Related
I have a Saas billing-model and each user has their own GCP Project. This is similar to this reddit thread, which asks:
I’m thinking about selling a saas service. I’ve decided every customer will get their own gcp project every customer will have a bunch of cloud run services, a cloud sql database and some users in Identity platform. I know the default project limit is around 12 and it can be increased by filling a form.
This works for something like BigQuery, where each user's Dataset or Table will be created within their own GCP project, and thus their billing (and data) will be segmented under their project.
However, I also have some shared endpoints on Google Cloud Functions, for example let's say I have general/shared endpoints to do something like "export data". Now of course the query to grab the data will hit the correct GCP project, but if the export (or some other data processing task) is doing something that is very expensive -- some exports might take over an hour to write the data, if dealing with billions of rows, what would be the suggested way to set that up so the end user is paying for their computation, since I imagine an endpoint such as www.example.com/api/export is just going to be on the main Project account, and we wouldn't have, for example, 1000 different cloud functions that do the same thing just to have each one under their respective project.
What might be a solution to this? In a way I'm looking for something like this I suppose where the requestor pays.
You would probably need to record how long each function call took, and save that data somewhere before exiting the shared function.
The only alternative would be to split the function for each client, and use billing labels to help with allocation.
We are using Spark history 3.2.1 to monitor our Spark applications.
We have thousands of daily jobs (running on Kubernetes) that writes event logs to S3 bucket (in a dedicated folder).
We are using history-server to analyze and compare completed jobs (incomplete running jobs never appeared in the UI but it's not a requirement now).
Recently I've noticed increase in our ListBucket API Operation in AWS billing cost explorer. This cost is higher than the cost of the StandardStorage (the price we pay for storing the data itself). It's up to few hundreds per month!
Running history-server with DEBUG log level exposed the "problem": every 10s the the history-server list the bucket to get all logs and then it iterate over each folder to get it's content. So if I want to keep the last 10,000 jobs, I'll have to pay for 10,101 ListBucket requests every 10s!
Here is one example (out of the 10k) reproduced locally with minio as S3:
22/02/20 06:44:31 DEBUG wire: http-outgoing-57 << "<ListBucketResult xmlns="http://s3.amazonaws.com/doc/2006-03-01/"><Name>local-audience</Name><Prefix>history-logs/eventlog_v2_spark-ffffdf5903c841259f28b53981746b76/</Prefix><KeyCount>2</KeyCount><MaxKeys>5000</MaxKeys><Delimiter>/</Delimiter><IsTruncated>false</IsTruncated><Contents><Key>history-logs/eventlog_v2_spark-ffffdf5903c841259f28b53981746b76/appstatus_spark-ffffdf5903c841259f28b53981746b76</Key><LastModified>2022-02-12T17:00:15.304Z</LastModified><ETag>"d41d8cd98f00b204e9800998ecf8427e"</ETag><Size>0</Size><Owner><ID></ID><DisplayName></DisplayName></Owner><StorageClass>STANDARD</StorageClass></Contents><Contents><Key>history-logs/eventlog_v2_spark-ffffdf5903c841259f28b53981746b76/events_1_spark-ffffdf5903c841259f28b53981746b76</Key><LastModified>2022-02-12T17:00:15.136Z</LastModified><ETag>"f91cc774d92c6f6c2ca4d0e1a1e76e13"</ETag><Size>868837</Size><Owner><ID></ID><DisplayName></DisplayName></Owner><StorageClass>STANDARD</StorageClass></Contents></ListBucketResult>"
To ensure that the cost comes from history-server I turned it off for a day and there was no charge per ListBucket since then:
To mitigate the problem (because we still need the history-server), I can set the spark.history.fs.update.interval to higher number (such as 3600s or so). As we are checking the history-server once a day it is overkill and doesn't worth it (cost wise).
Why does it scan the completed jobs every time (over and over again) and not only new jobs? is there a way to configure such behavior to avoid those ListBucket operations?
If I care only for completed jobs, and assuming I can wait few minutes to see the list, is there a mode that can load the list only when I login to the UI? (rather than periodically doing it for nothing).
P.S - I'm using AWS lifecycle rules to clean this folder every few few days (and not the server cleaning feature), by expiration objects after few days.
treewalking in s3 is (a) expensive and (b) horribly slow, especially given that a deep tree scan exists. If you want to fix this and can write scala code, see if you can fix the server to switch to a deep listing by moving to FileSystem.listFiles(path, true). Yes that involves coding, but the OSS community depends on everyone fixing their own personal issues and sharing the outcome
After digging into this issue, I decided to stop using the "rolling" feature for now - as my application jobs are relatively small.
I removed the:
spark.eventLog.rolling.enabled: true
spark.eventLog.rolling.maxFileSize: 16m
from the spark-submit command and the cost is now back to normal...
I also wrote about it here.
#stevel thanks for your answer - I will try to contribute and fix that! :)
I am looking to get all of the Activity and Lead data in Marketo to be mirrored in an AWS S3 bucket so that I can build dashboards on it in Quicksight, so preferably I'd like to stream the data from Marketo into S3 in real-time, and then use Glue and Athena to connect the data to Quicksight. However, the only way to get large volumes of data out of Marketo appears to be their Bulk Extract tool (one for Leads, one for Activity data).
The problem is that these API interfaces make any attempt at near real-time streaming really clunky. Currently, I have Lambda functions being triggered every hour to pull the most recent hour of Lead/Activity data and saving it as a gzipped CSV in S3. But Marketo's Bulk Extract tool has a request queue and requests often take longer than 15 minutes to process (15 minutes being Lambda's max timeout length). So at least once a day my requests are getting dropped.
The solution seems to be to instead run this on an EC2 instance that can juggle multiple requests and patiently wait for Marketo's queue. But I'd rather not get into all the async and error-handling issues that that approach may entail if there is an easier way to accomplish this.
As an alternative solution, Amazon Appflow integrates with Marketo. But last I checked, it only works with Lead data, not Activity data. And there are restrictions on the filters you have to apply to the Lead data that make it clunky to work with anyway.
On Google I have found several companies that claim to offer seamless, reliable Marketo-to-S3 ETL, but I haven't yet researched their pricing or quality.
If anyone knows of a good approach to set up reliable and cost-efficient ETL between Marketo and S3 in a short period of time, I would very much appreciate it.
In a case like this, I would be tempted to recommend using an EC2 instance to run Singer with a Marketo input and CSV output, then set up something to move the CSV over to S3 as needed. That would be the absolute cheapest ETL solution, but this does suppose you have some comfort and familiarity with Python.
Also worth noting is that Stitch, Singers's paid product equivalent, supports native S3 export--you could always first test with a non-Marketo data source and see if that performs the way you'd like if you prefer money over time.
I've been reading some articles regarding this topic and have preliminary thoughts as what I should do with it, but still want to see if anyone can share comments if you have more experience with running machine learning on AWS. I was doing a project for a professor at school, and we decided to use AWS. I need to find a cost-effective and efficient way to deploy a forecasting model on it.
What we want to achieve is:
read the data from S3 bucket monthly (there will be new data coming in every month),
run a few python files (.py) for custom-built packages and install dependencies (including the files, no more than 30kb),
produce predicted results into a file back in S3 (JSON or CSV works), or push to other endpoints (most likely to be some BI tools - tableau etc.) - but really this step can be flexible (not web for sure)
First thought I have is AWS sagemaker. However, we'll be using "fb prophet" model to predict the results, and we built a customized package to use in the model, therefore, I don't think the notebook instance is gonna help us. (Please correct me if I'm wrong) My understanding is that sagemaker is a environment to build and train the model, but we already built and trained the model. Plus, we won't be using AWS pre-built models anyways.
Another thing is if we want to use custom-built package, we will need to create container image, and I've never done that before, not sure about the efforts to do that.
2nd option is to create multiple lambda functions
one that triggers to run the python scripts from S3 bucket (2-3 .py files) every time a new file is imported into S3 bucket, which will happen monthly.
one that trigger after the python scripts are done running and produce results and save into S3 bucket.
3rd option will combine both options:
- Use lambda function to trigger the implementation on the python scripts in S3 bucket when the new file comes in.
- Push the result using sagemaker endpoint, which means we host the model on sagemaker and deploy from there.
I am still not entirely sure how to put pre-built model and python scripts onto sagemaker instance and host from there.
I'm hoping whoever has more experience with AWS service can help give me some guidance, in terms of more cost-effective and efficient way to run model.
Thank you!!
I would say it all depends on how heavy your model is / how much data you're running through it. You're right to identify that Lambda will likely be less work. It's quite easy to get a lambda up and running to do the things that you need, and Lambda has a very generous free tier. The problem is:
Lambda functions are fundamentally limited in their processing capacity (they timeout after max 15 minutes).
Your model might be expensive to load.
If you have a lot of data to run through your model, you will need multiple lambdas. Multiple lambdas means you have to load your model multiple times, and that's wasted work. If you're working with "big data" this will get expensive once you get through the free tier.
If you don't have much data, Lambda will work just fine. I would eyeball it as follows: assuming your data processing step is dominated by your model step, and if all your model interactions (loading the model + evaluating all your data) take less than 15min, you're definitely fine. If they take more, you'll need to do a back-of-the-envelope calculation to figure out whether you'd leave the Lambda free tier.
Regarding Lambda: You can literally copy-paste code in to setup a prototype. If your execution takes more than 15min for all your data, you'll need a method of splitting your data up between multiple Lambdas. Consider Step Functions for this.
SageMaker is a set of services that each is responsible for a different part of the Machine Learning process. What you might want to use is the hosted version of Jupyter notebooks in SageMaker. You get a lot of freedom in the size of the instance that you are using (CPU/GPU, memory, and disk), and you can install various packages on that instance (such as FB Prophet). If you need it once a month, you can stop and start the notebook instances between these times and "Run all" the cells in your notebooks on this instance. It will only cost you the minutes of execution.
regarding the other alternatives, it is not trivial to run FB Prophet in Lambda due to the size limit of the libraries that you can install on Lambda (to avoid too long cold start). You can also use ECS (container Service) where you can have much larger images, but you need to know how to build a Docker image of your code and endpoint to be able to call it.
All the examples i've seen are with Java programs?
I want to be able to track the a user's behaviour while navigating my website by looking at all the API calls made by that user. All the API calls are based on data stored in a SQL database.
I also for example want to check all the keywords passed to my search API to have a list of most search terms.
I thought about using Oozie but does anyone have any other suggestions ?
There are several option for analyzing the data in your database.
Normal SQL experimentation
I'd suggest starting with normal SQL statements against your database to experiment with finding what data is of interest. This might be a little slow if you have millions of records, but gives you full flexibility to play around with the data.
Amazon EMR
Once you have identified the types of analysis you'd like to run on a regular basis (eg daily or weekly), you could launch an EMR cluster to perform analysis. Please note that this is a powerful but rather complex toolset and the time required to fully utilize it might not be worthwhile.
You can launch a transient cluster, which means that the cluster terminates once it has finished the jobs it has been given. Thus, the cluster can be triggered via a scheduled API call and will automatically terminate.
Amazon Athena
Amazon Athena provides an SQL interface to data stored in Amazon S3. The common use-case is to analyze log files that are in S3 without having to load them into a database. Athena is powerful and processes data in parallel to give results back very quickly.
Bottom line: Start simple. Play with the existing data to figure out what you'd like to discover. Then optimize.