I am trying to deploy (in CDK) scheduled Python ETL scripts as Batch jobs (Fargate?) to parse data from AWS and other tools we utilize. A Spunk Forwarder consumes this data and sends it to our Splunk Index. Am I going to need an S3 bucket for the output of logs from my ETL scripts? How can I deploy the Splunk Forwarder alongside these scripts?
There are about 5-6 scripts that I would like to deploy via CDK.
AWS Batch jobs can send STDERR and STDOUT to CloudWatch Logs. Depends on how logging is configured in your Python scripts, that may be the easy answer. If logging is configured to write to a file, then yes I would recommend you upload the file to S3 after the ETL is finished.
Output from the scripts (the ETL results) will need to land someplace, and S3 is a great choice for that. Your Splunk Forwarder can be set up to monitor the bucket for new data and ingest. If the scripts directly send data to the forwarder you should not need a S3 bucket, but I personally would recommend that you decouple the ETL data from ingestion of the result into Splunk.
Splunk Forwarders (stable servers) would be deployed separate from AWS Batch resources.
Related
We have ETL jobs i.e. a java jar(performs etl operations) is run via shell script. The shell script is passed with some parameters as per the job being run. These shell scripts are run via crontab as well as manually depending on the requirements. Sometimes there is need of running some sql commands/scripts on posgresql RDS DB too, before the shell script run.
We have everything on AWS i.e. Ec2 talend server, Postgresql RDS, Redshift, ansible etc.
How can we automate this process? How to deploy and handle passing custom parameters etc. Pointers are welcome.
I would prefer to go with AWS Data pipeline, and add steps to perform any pre / post operations on your ETL job, like running shell scripts, or any hql etc.
AWS Glue runs on Spark engine, and it has other features as well as such AWS Glue Development Endpoint, Crawler, Catalog, Job schedulers. I think AWS Glue would be ideal if you are starting afresh, or plan to move your ETL to AWS Glue. Please refer here on price comparison.
AWS Pipeline: For details on AWS Pipeline
AWS Glue FAQ:For details on supported languages for AWS Glue
Please note according to AWS Glue FAQ:
Q: What programming language can I use to write my ETL code for AWS
Glue?
You can use either Scala or Python.
Edit: As Jon scott commented, Apache Airflow is another option for job scheduling, but I have not used it.
You can use Aws Glue for performing serverless ETL. Glue also has triggers which lets you automate their jobs.
We would like to process AWS ELB access logs and write them into InfluxDB
to be used for application metrics and monitoring (ex. Grafana).
We configured ELB to store access logs into S3 bucket.
What would be the best way to process those logs and write them to InfluxDB?
What we tried so far was to mount S3 bucket to filesystem using s3fs and then use Telegraf agent for processing. But this approach has some issues: s3fs mounting looks like a hack, and all the files in the bucket are compressed and need to be unzipped before telegraf can process them which makes this task overcomplicated.
Is there any better way?
Thanks,
Oleksandr
Can you just install the telegraf agent on the AWS instance that is generating the logs, and have them sent directly to InfluxDB in real-time?
We have a large extended network of users that we track using badges. The total traffic is in the neighborhood of 60 Million impressions a month. We are currently considering switching from a fairly slow, database-based logging solution (custom-built on PHP—messy...) to a simple log-based alternative that relies on Amazon S3 logs and Splunk.
After using Splunk for some other analyisis tasks, I really like it. But it's not clear how to set up a source like S3 with the system. It seems that remote sources require the Universal Forwarder installed, which is not an option there.
Any ideas on this?
Very late answer but I was looking for the same thing and found a Splunk app that does what you want, http://apps.splunk.com/app/1137/. I have yet not tried it though.
I would suggest logging j-son preprocessed data to a documentdb database. For example, using azure queues or simmilar service bus messaging technologies that fit your scenario in combination with azure documentdb.
So I'll keep your database based approach and modify it to be a schemaless easy to scale document based DB.
I use http://www.insight4storage.com/ from AWS Marketplace to track my AWS S3 storage usage totals by prefix, bucket or storage class over time; plus it shows me the previous versions storage by prefix and per bucket. It has a setting to save the S3 data as splunk format logs that might work for your use case, in addition to its UI and webservice API.
You use Splunk Add-On for AWS.
This is what I understand,
Create a Splunk instance. Use the website version or the on-premise
AMI of splunk to create an EC2 where splunk is running.
Install Splunk Add-On for AWS application on the EC2.
Based on the input logs type (e.g. Cloudtrail logs, Config logs, generic logs, etc) configure the Add-On and supply AWS account id or IAM Role, etc parameters.
The Add-On will automatically ping AWS S3 source and fetch the latest logs after specified amount of time (default to 30 seconds).
For generic use case (like ours), you can try and configure Generic S3 input for Splunk
We have a site where users upload files, some of them quite large. We've got multiple EC2 instances and would like to load balance them. Currently, we store the files on an EBS volume for fast access. What's the best way to replicate the files so they can be available on more than one instance?
My thought is that some automatic replication process that uploads the files to S3, and then automatically downloads them to other EC2 instances would be ideal.
EBS snapshots won't work because they replicate the entire volume, and we need to be able to replicate the directories of individual customers on demand.
You could write a shell script that would spawn s3cmd to sync your local filesystem with a S3 bucket whenever a new file is uploaded (or deleted). It would look something like:
s3cmd sync ./ s3://your-bucket/
Depends on what OS you are running on your EC2 instances:
There isn't really any need to add S3 to the mix unless you want to store them there for some other reason (like backup).
If you are running *nix the classic choice might be to run rsync and just sync between instances.
On Windows you could still use rsync or else SyncToy from Microsoft is a simple free option. Otherwise there are probably hundreds of commercial applications in this space...
If you do want to sync to S3 then I would suggest one of the S3 client apps like CloudBerry or JungleDisk, which both have sync functionality...
If you are running Windows it's also worth considering DFS (Distributed File System) which provides replication and is part of Windows Server...
The best way is to use the Amazon Cloud Front service. All of the replication is managed as part of the AWS. Content is served from several different availability zones, but does not require you to have EBS volumes in those zones.
Amazon CloudFront delivers your static and streaming content using a global network of edge locations. Requests for your objects are automatically routed to the nearest edge location, so content is delivered with the best possible performance.
http://aws.amazon.com/cloudfront/
Two ways:
Forget EBS, transfer the files to S3 and use S3 as your file-manager than EBS, add cloudfront and use the common-link everywhere.
Mount S3 bucket on any machines.
1. Amazon CloudFront is a web service for content delivery. It delivers your static and streaming content using a global network of edge locations.
http://aws.amazon.com/cloudfront/
2. You can mount S3 bucket on your linux machine. See below:
s3fs -
http://code.google.com/p/s3fs/wiki/InstallationNotes
- this did work for me. It uses FUSE file-system + rsync to sync the files
in S3. It kepes a copy of all
filenames in the local system & make
it look like a FILE/FOLDER.
That way you can share the S3 bucket on different machines.
I am working on a Java MapReduce app that has to be able to provide an upload service for some pictures from the local machine of the user to an S3 bucket.
The thing is the app must run on an EC2 cluster, so I am not sure how I can refer to the local machine when copying the files. The method copyFromLocalFile(..) needs a path from the local machine which will be the EC2 cluster...
I'm not sure if I stated the problem correctly, can anyone understand what I mean?
Thanks
You might also investigate s3distcp: http://docs.amazonwebservices.com/ElasticMapReduce/latest/DeveloperGuide/UsingEMR_s3distcp.html
Apache DistCp is an open-source tool you can use to copy large amounts of data. DistCp uses MapReduce to copy in a distributed manner—sharing the copy, error handling, recovery, and reporting tasks across several servers. S3DistCp is an extension of DistCp that is optimized to work with Amazon Web Services, particularly Amazon Simple Storage Service (Amazon S3). Using S3DistCp, you can efficiently copy large amounts of data from Amazon S3 into HDFS where it can be processed by your Amazon Elastic MapReduce (Amazon EMR) job flow. You can also use S3DistCp to copy data between Amazon S3 buckets or from HDFS to Amazon S3.
You will need to get the files from the userMachine to at least 1 node before you will be able to use them through a MapReduce.
The FileSystem and FileUtil functions refer to paths either on the HDFS or the local disk of one of the nodes in the cluster.
It cannot reference the user's local system. (Maybe if you did some ssh setup... maybe?)