I am currently trying to run a daily snapshot (I schedule it to run every day) of an RDS (Postgres) database using AWS DMS. My destination endpoint is S3 and I am using Full Load Replication. My goal is to write every snapshot into a separate date partition. For example, I would like to write today's snapshot into an S3 folder partition that looks like:
database_schema_name/table_name/2021/11/06/13/LOAD00000001.csv.
However, it seems like I am unable to achieve the folder partitioning with full loads. Reading the docs on date-based folder partitioning (here):
You can enable date-based folder partitioning when you create an S3
target endpoint. You can enable it when you either migrate existing
data and replicate ongoing changes (full load + CDC), or replicate
data changes only (CDC only).
To my understanding of the docs date-based folder partitioning seems to be only available for CDC or CDC + full loads. I have also tried using it myself but without success.
To summarize, my goal is to be able to run full-loads via DMS and to place each day's load into a date-partitioned folder structure within S3. I am aware that I can write to S3 then place the written files into folder using Lambdas, but I was hoping to achieve that cleanly using DMS and without adding a further complexity to the system.
Related
I'm currently building an application with Apache Spark (pyspark), and I have the following use case:
Run pyspark with local mode (using spark-submit local[*]).
Write the results of my spark job to S3 in the form of partitioned Parquet files.
Ensure that each job overwrite the particular partition it is writing to, in order to ensure idempotent jobs.
Ensure that spark-staging files are written to local disk before being committed to S3, as staging in S3, and then committing via a rename operation, is very expensive.
For various internal reasons, all four of the above bullet points are non-negotiable.
I have everything but the last bullet point working. I'm running a pyspark application, and writing to S3 (actually an on-prem Ceph instance), ensuring that spark.sql.sources.partitionOverwriteMode is set to dynamic.
However, this means that my spark-staging files are being staged in S3, and then committed by using a delete-and-rename operation, which is very expensive.
I've tried using the Spark Directory Committer in order to stage files on my local disk. This works great unless spark.sql.sources.partitionOverwriteMode.
After digging through the source code, it looks like the PathOutputCommitter does not support Dynamic Partition Overwriting.
At this point, I'm stuck. I want to be able to write my staging files to local disk, and then commit the results to S3. However, I also need to be able to dynamically overwrite a single partition without overwriting the entire Parquet table.
For reference, I'm running pyspark=3.1.2, and using the following spark-submit command:
spark-submit --repositories https://repository.cloudera.com/artifactory/cloudera-repos/ --packages com.amazonaws:aws-java-sdk:1.11.375,org.apache.hadoop:hadoop-aws:3.2.0,org.apache.spark:spark-hadoop-cloud_2.12:3.1.1.3.1.7270.0-253
I get the following error when spark.sql.sources.partitionOverwriteMode is set to dynamic:
java.io.IOException: PathOutputCommitProtocol does not support dynamicPartitionOverwrite
My spark config is as follows:
self.spark.conf.set("spark.sql.files.ignoreCorruptFiles", "true")
self.spark.conf.set("spark.sql.execution.arrow.pyspark.enabled", "true")
self.spark.conf.set("spark.hadoop.fs.s3a.committer.name", "magic")
self.spark.conf.set("spark.sql.sources.commitProtocolClass",
"org.apache.spark.internal.io.cloud.PathOutputCommitProtocol")
self.spark.conf.set("spark.sql.parquet.output.committer.class",
"org.apache.spark.internal.io.cloud.BindingParquetOutputCommitter")
self.spark.conf.set(
"spark.sql.sources.partitionOverwriteMode", "dynamic"
)
afraid the s3a committers don't support the dynamic partition overwrite feature. That actually works by doing lots of renaming, so misses the entire point of zero rename committers.
the "partioned" committer was written by netflix for their use case of updating/overwriting single partitions in an active table. it should work for you as it is the same use case.
consult the documentation
We're looking at moving away from Splunk as our datastore and looking at AWS Data Lake backed by S3.
What would be the process of migrating data from Splunk to S3? I've read lots of documents talking about archiving data from Splunk to S3 but not sure if this archives the data as a usable format OR if its in some archive format that needs to be restored to splunk itself?
Check out Splunk's SmartStore feature. It moves your non-hot buckets to S3 so you save storage costs. Running SmartStore on AWS only makes sense, however, if you run Splunk on AWS. Otherwise, the data export charges will bankrupt you. Data export applies when Splunk needs to search a bucket that's stored in S3 and so copies that bucket to an indexer. See https://docs.splunk.com/Documentation/Splunk/8.0.0/Indexer/AboutSmartStore for more information.
From what I've read there are a couple of ways to do it:
Export using the Web UI
Export using REST API Endpoint
Export using CLI
Copy certain files in the filesystem
So far I've tried using the CLI to export and I've managed to export around 500,000 events at a time using
splunk search "index=main earliest=11/11/2019:00:00:01 latest=11/15/2019:23:59:59" -output rawdata -maxout 500000 > output2.dmp
However - I'm not sure how I can accurately repeat this step to make sure I include all 100 million+ events. IE search from DATE A to DATE B for 500,000 records, then search from DATE B to DATE C for the next 500,000 - without missing any events inbetween.
I see on the Lambda support pages there are examples of scripts to create thumbnail images in a separate bucket any time an image is uploaded. But I'm looking at using S3 to upload customer image files for multiple customers. We will likely use something like dropzone.js for handling the uploads and I've already built a working example to upload to an existing bucket.
But since we will be dealing with multiple customers, I'm wondering what the best-practices for handling different customer files is when used in conjunction with S3 and especially with the need to display thumbnails to the customer.
I note the Lambda solution appears to use a pre-configured bucket including all of the necessary permissions and event triggers to run the script. I'm not as familiar with node.js and have done very little in Java or python, and I'm new to the aws environment.
Should I create a new bucket for each customer? Can I? Do I have to add new lambda createThumbnail permissions/event-triggers every time a new bucket is created for a new customer?
Is there a better way to do this?
I would also be curious to know (being new to node.js and aws) how difficult it would be to build a cached thumbnail only when it was requested as opposed to trying to build one whenever a file is uploaded.
SW
You can use the same bucket with each sub-folder containing thumbnails images for each customer/user (You can name each folder with ${user_id} or something similar)
The workflow could be
Full image is uploaded to S3 to customer sub-folder with from your UI (dropzone.js or whatever)
Upon success upload. Use S3 object creation event to trigger your Lambda to process & generate a thumbnail. (putting it in thumbnail sub-fol
dr is an option).
Ex:
YOUR_NEW_BUCKET
|
----customer_1
|
___Image1.png
___Image2.jpg
___Thumbnails
|
___Image1.png
___Image2.jpg
We are using DMS to get data from SQL Server and load it in S3 bucket, after which the data is finally loaded into Snowflake DB using Snowpipe for Full Load.
Now, in order for Snowpipe to know there is new data in S3 bucket, the filename needs to be different than the last one. Have tried all the task setting options available (DROP_AND_CREATE, DO_NOTHING, TRUNCATE) to have the file name different, but still not working. It loads the file name as LOAD00000001.csv
In documentation it shows that file name will be incremental (eg. LOAD00000001.csv, LOAD00000002.csv .. and so on) but it's not happening. Which is why the Snowpipe is not able to register the changes.
https://docs.aws.amazon.com/dms/latest/userguide/CHAP_Target.S3.html
Can someone please help?
For DMS the incremental counter is started over from 1 each time the task is run. It does not have a "Don't override existing objects" feature.
Your best bet may be to handle the load yourself by looking for updated object timestamps in your folder or setting up S3 event notifications.
I have a few GCP projects with log sinks to different storage buckets. I'd like to combine them into a single bucket. But the stackdriver export doesn't add any distinguishing information to the object names it creates; they all look like cloudaudit.googleapis.com/activity/2017/11/14/00:00:00_00:59:59_S0.json
What will happen if I start pushing them all to a single bucket? Will the different project sinks overwrite each other's objects? Is there any way to distinguish which project created the logs just from the object?
If not, I guess I should switch to pubsub sinks, and then write some code that produces objects with more desirable names. Are there any established patterns or examples for doing this?
Update: I filed https://issuetracker.google.com/issues/69371200 for this issue.
To enable this, just select custom destination on the sink and point to the bucket with this format: storage.googleapis.com/[BUCKET_ID].
I've just enabled this in a couple of my projects, as I'm curious to see the results when exporting to a bucket. However, I have been using a single BQ sink for all my projects, and the tables created have all the logs mixed, so no logs lost when using a single BQ sink.
I'm assuming for a GCS sink will work in the same way, but I'll tell you in a couple of days.
If a single bucket sink does not work, you can always use a single BQ sink (that will help in analyzing the logs), and when you no longer want to have them in BQ, export them and store the files wherever you want.
Also, since you'll be writing to your sink constantly, you can't use nearline or coldline, so the storage pricing is better in BQ than a regional bucket (0.02 USD/GB in BQ vs somewhere between 0.02 and 0.35 USD/GB for regional storage, depending on the region; BQ has 10GB free monthly, GCS 5GB).
I would generally recommend using a BQ sink, but I'll tell you what happens with my bucket logs.
Update:
A few hours later, and I've verified that shared bucket sinks work pretty much as you would expect. It concatenates logs chronologically regardless of the project origin, and only creates a single file for each time window. Hope this helps! (I still prefer BQ as a log sink...)
Update 2:
For the behavior you seek in the feature request, I would use BQ, but you could just as easily grep the project ID and separate the logs:
grep '"logName":"projects/<your-project-id>/' mixed-log.json > single-project-log.json
Or just get a cloud function triggered by bucket updates (so, every time you receive a log file in the sink) to run this for you.
Or namespace you buckets and have a cloud function moving them to wherever you need as soon as they are written.
The possibilities are endless!
If you have an organization or folder which includes all the projects that you want to collect logs from, then you can create a sink that collects from all projects in that org/folder.
Unfortunatlely, you cannot do this from the Cloud Console. Instead you must use gcloud with the --organization or --folder option or the API.