Is there a way to get CloudWatch Metrics directly into S3? I don't need logs but ELB Metrics. I would like them logged to S3 on a regular basis (ideally as CSV).
Right now, I'm thinking of writing my own script to do it, but maybe there's there's an automatic way to put it in S3 (or Redshift)?
CloudWatch itself does not have a native export feature that will send data periodically to S3.
As you suggest, you would need to develop a scrip tthat pulls the CloudWatch metrics that you wish to store (in this case ELB metrics) using the AWS CLI and copy those metrics to your S3 bucket on a regular basis.
Using the get-metric-statistics command, the script would get the statistics for the specified metric, and store the data to your S3 bucket
See also Elastic Load Balancing Dimensions and Metrics
Related
AWS allows us to capture all kinds of metrics and logs through CloudWatch. Are these data accessible outside the AWS cloud environment (assuming proper permissions and policies are set to allow it to be so)?
For example, could these data be backed up and stored on-prem?
I imagine a Lambda function could be created to access say S3 data and fetch it through the Gateway API, but are CloudWatch data stored in S3?
Log data on CloudWatch is stored in S3 which we can not access. However, you can export log to S3.
Doc says..
You can export log data from your log groups to an Amazon S3 bucket
and use this data in custom processing and analysis, or to load onto
other systems.
...
To begin the export process, you must create an S3 bucket to store the
exported log data. You can store the exported files in your Amazon S3
bucket and define Amazon S3 lifecycle rules to archive or delete
exported files automatically.
Then you can simply download from S3 or use services as you like.
The raw metrics stored in CloudWatch Metrics are not accessible.
For example, when each Amazon EC2 instance sends CPUUtilization to CloudWatch.
Instead, aggregated metrics can be queried, such as "Average CPU Utilization over a 5-minute period".
This is different to CloudWatch Logs, which can be exported to Amazon S3.
I have a CloudWatch Log Group, this log group continuously receives logging information from my AWS services.
I want to extract some of the logging information from this log-group and want to store that data into S3 in some format (CSV, PARQUET).
I will then use Athena to query this logging data.
I want some sort of automatic mechanism to send these logs continuously to S3.
Can anyone suggest solution for this?
It looks like Athena is able to communicate directly with cloudwatch as shown here. Not sure how performant this is and how costly this turns out.
The other option is to configure Cloudwatch to send data to Firehose via Subscriptions which then dumps it to S3.
It is straightforward enough to pull data for how much is in a particular S3 bucket. I am trying to connect Geckoboard to monitor S3 data usage, is there a way to use cloudwatch metrics to access how much storage is in use overall on our account? We have many buckets and I just need a total, not a number for each bucket, which is as far as I have gotten.
Thanks
Chris "AWS console is frustratingly unintuitive" H.
It isn't quite the solution I was hoping for, but it is possible to collect this data using the graph metrics in Cloudwatch.
In our infrastructure we have a bunch of pipelines for ETL data before pushing them into Redshift. We use s3 bucket for logs and SNS alerting for activities. Most of that activities are standard CopyActivity, RedshiftCopyActivity and SqlActivity.
We want to get all available metrics for this activities to dashboard them (E.g.: Cloudwatch) so we know what's going on that side in one single place. Unfortunately I didn't find much information on AWS documentation for that and have to do all that manually in code.
What is the most common way for monitoring AWS Data Pipeline?
Amazon Cloudwatch provides some very useful metrics for monitoring my EC2s, load balancers, elasticache and RDS databases, etc and allows me to set alarms for a whole range of criteria; but is there any way to configure it to monitor my S3s as well? Or are there any other monitoring tools (besides simply enabling logging) that will help me monitor the numbers of POST/GET requests and data volumes for my S3 resources? And to provide alarms for thresholds of activity or increased datastorage?
AWS S3 is a managed storage service. The only metrics available in AWS CloudWatch for S3 are NumberOfObjects and BucketSizeBytes. In order to understand your S3 usage better you need to do some extra work.
I have recently written an AWS Lambda function to do exactly what you ask for and it's available here:
https://github.com/maginetv/s3logs-cloudwatch
It works by parsing S3 Server side log files and aggregates/exports metrics to AWS Cloudwatch (CloudWatch allows you to publish custom metrics).
Example graphs that you will get in AWS CloudWatch after deploying this function on your AWS account are:
RestGetObject_RequestCount
RestPutObject_RequestCount
RestHeadObject_RequestCount
BatchDeleteObject_RequestCount
RestPostMultiObjectDelete_RequestCount
RestGetObject_HTTP_2XX_RequestCount
RestGetObject_HTTP_4XX_RequestCount
RestGetObject_HTTP_5XX_RequestCount
+ many others
Since metrics are exported to CloudWatch, you can easily set up alarms for them as well.
CloudFormation template is included in GitHub repo and you can deploy this function very quickly to gain visibility into your S3 bucket usage.
EDIT 2016-12-10:
In November 2016 AWS has added extra S3 request metrics in CloudWatch that can be enabled when needed. This includes metrics like AllRequests, GetRequests, PutRequests, DeleteRequests, HeadRequests etc. See Monitoring Metrics with Amazon CloudWatch documentation for more details about this feature.
I was also unable to find any way to do this with CloudWatch. This question from April 2012 was answered by Derek#AWS as not having S3 support in CloudWatch. https://forums.aws.amazon.com/message.jspa?messageID=338089
The only thing I could think of would be to import the S3 access logs to a log service (like Splunk). Then create a custom cloud watch metric where you post the data that you parse from the logs. But then you have to filter out the polling of the access logs and…
And while you were at it, you could just create the alarms in Splunk instead of in S3.
If your use case is to simply alert when you are using it too much, you could set up an account billing alert for your S3 usage.
I think this might depend on where you are looking to track the access from. I.e. if you are trying to measure/watch usage of S3 objects from outside http/https requests then Anthony's suggestion if enabling S3 logging and then importing into splunk (or redshift) for analysis might work. You can also watch billing status on requests every day.
If trying to guage usage from within your own applications, there are some AWS SDK cloudwatch metrics:
http://docs.aws.amazon.com/AWSJavaSDK/latest/javadoc/com/amazonaws/metrics/package-summary.html
and
http://docs.aws.amazon.com/AWSJavaSDK/latest/javadoc/com/amazonaws/services/s3/metrics/S3ServiceMetric.html
S3 is a managed service, meaning that you don't need to take action based on system events in order to keep it up and running (as long as you can afford to pay for the service's usage). The spirit of CloudWatch is to help with monitoring services that require you to take action in order to keep them running.
For example, EC2 instances (which you manage yourself) typically need monitoring to alert when they're overloaded or when they're underused or else when they crash; at some point action needs to be taken in order to spin up new instances to scale out, spin down unused instances to scale back in, or reboot instances that have crashed. CloudWatch is meant to help you do the job of managing these resources more effectively.
To enable Request and Data transfer metrics in your bucket you can run the below command. Be aware that these are paid metrics.
aws s3api put-bucket-metrics-configuration \
--bucket YOUR-BUCKET-NAME \
--metrics-configuration Id=EntireBucket
--id EntireBucket
This tutorial describes how to do it in AWS Console with point and click interface.