Is there an option to take full snapshot using the ES snapshot api. we would like to take full snapshot every 3 days.
You can refer to the following document: https://docs.aws.amazon.com/opensearch-service/latest/developerguide/managedomains-snapshots.html
I used to do something the same in my previous company where we had a lambda trigger the backup every week via cron and a Full backup used to happen of all Documents and indexes. I have thought tried to restore once which failed the first time, but the second time it worked though, the issue was the instance was small and it needed a bigger one to restore the data, so please those setting as well.
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I have a use case where I have a AWS Step function where is each task is a Lambda. One of the lambda expects a particular version file to be present in an S3 location. The particular version of file is uploaded by an external service. The only way to know if version recently uploaded is the one we are interested in, is by looking for a data attribute inside the file.
If data attribute is missing, then I am not interested in that version and in that case I want to wait for an hour and re-execute the same lambda to check if newer version uploaded is the version we are interested in, until either we find the correct version or exhaust retries.
If at any point within retry limit I find the data attribute, the next task should be executed.
Any advice is much appreciated on how to tackle this.
Use a choice state after your lambda. If the lambda output indicates the version wasn't found, then redirect to a wait state set for 1 hour which feeds back into the lambda. If the lambda output indicates the version was found, then continue with processing.
Hope this helps!
I have written a cloud storage trigger based cloud function. I have 10-15 files landing at 5 secs interval in cloud bucket which loads data into a bigquery table(truncate and load).
While there are 10 files in the bucket I want cloud function to process them in sequential manner i.e 1 file at a time as all the files accesses the same table for operation.
Currently cloud function is getting triggered for multiple files at a time and it fails in BIgquery operation as multiple files trying to access the same table.
Is there any way to configure this in cloud function??
Thanks in Advance!
You can achieve this by using pubsub, and the max instance param on Cloud Function.
Firstly, use the notification capability of Google Cloud Storage and sink the event into a PubSub topic.
Now you will receive a message every time that a event occur on the bucket. If you want to filter on file creation only (object finalize) you can apply a filter on the subscription. I wrote an article on this
Then, create an HTTP functions (http function is required if you want to apply a filter) with the max instance set to 1. Like this, only 1 function can be executed in the same time. So, no concurrency!
Finally, create a PubSub subscription on the topic, with a filter or not, to call your function in HTTP.
EDIT
Thanks to your code, I understood what happens. In fact, BigQuery is a declarative system. When you perform a request or a load job, a job is created and it works in background.
In python, you can explicitly wait the end on the job, but, with pandas, I didn't find how!!
I just found a Google Cloud page to explain how to migrate from pandas to BigQuery client library. As you can see, there is a line at the end
# Wait for the load job to complete.
job.result()
than wait the end of the job.
You did it well in the _insert_into_bigquery_dwh function but it's not the case in the staging _insert_into_bigquery_staging one. This can lead to 2 issues:
The dwh function work on the old data because the staging isn't yet finish when you trigger this job
If the staging take, let's say, 10 seconds and run in "background" (you don't wait the end explicitly in your code) and the dwh take 1 seconds, the next file is processed at the end of the dwh function, even if the staging one continue to run in background. And that leads to your issue.
The architecture you describe isn't the same as the one from the documentation you linked. Note that in the flow diagram and the code samples the storage events triggers the cloud function which will stream the data directly to the destination table. Since BigQuery allow for multiple streaming insert jobs several functions could be executed at the same time without problems. In your use case the intermediate table used to load with write-truncate for data cleaning makes a big difference because each execution needs the previous one to finish thus requiring a sequential processing approach.
I would like to point out that PubSub doesn't allow to configure the rate at which messages are sent, if 10 messages arrive to the topic they all will be sent to the subscriber, even if processed one at a time. Limiting the function to one instance may lead to overhead for the above reason and could increase latency as well. That said, since the expected workload is 15-30 files a day the above maybe isn't a big concern.
If you'd like to have parallel executions you may try creating a new table for each message and set a short expiration deadline for it using table.expires(exp_datetime) setter method so that multiple executions don't conflict with each other. Here is the related library reference. Otherwise the great answer from Guillaume would completely get the job done.
Please help me to understand what am I doing wrong.
I'm trying to restore db instance from snapshot.
Seems like restoring happens fast but modifying phase gets stuck for hours.
As I can see it cannot apply Option Group for new instance - it gets stuck with status Pending-Apply for hours.
As I understand it should take only minutes to complete restoring.
Have you read the log files? what kind of database is? I had a similar issue with mysql and it was because a innodb datafile was corrupt. I solved it forcing to recover it setting the parameter force_innodb_recovery=1, but be careful because you can lost data.
Actually it didn't get stuck. It was converting storage type from IOPS to GP2. It's time consuming operation. When I left storage type without changes restoring operation took about 15 minutes.
I want to use AWS elasticsearch to store the log of my application. Since there a huge amount of data to input to AWS elasticsearch ( ~30GB daily), so i would only keep 3 days of data. Are there any way to schedule data removal from AWS elasticsearch or do a log rotation? What happen if the AWS elasticsearch storage is full?
Thanks for the help
A possible way is to specify the index parameter in elasticsearchoutput to something like logstash-%{appname}-%{date_format}". Hence you can then use curator plugin in order to delete the old indices by number of days or so.
This SO pretty much explains the same. Hope it helps!
I assume you are using the AWS Amazon Elasticsearch Service?
The storage type is an EBS volume with a fixed size of disk space. If you want to keep only the last three days, I assume you have 3 indices then, like that
my-index-2017.01.30
my-index-2017.01.31
my-index-2017.02.01
Basically you can write some simple script which deletes indices older than 3 days. With the REST API it just is in Sense DELETE my-index-2017.01.30.
I recommend to use Elasticsearch Curator for the job. See https://www.elastic.co/guide/en/elasticsearch/client/curator/current/delete_indices.html
I'm not sure if the Service interface itself has an option for that. But Elasticsearch Curator should do the job for you.
Update for 2020:
AWS ES has now support for Index state management which lets you define custom management policies to automate routine tasks and apply them to indices and index patterns. You no longer need to set up and manage external processes to run your index operations.
For example, you can define a policy that moves your index into a read_only state after 30 days and then ultimately deletes it after 90 days.
Index State Management - https://docs.aws.amazon.com/elasticsearch-service/latest/developerguide/ism.html
S3 FAQ mentions that "Amazon S3 buckets in all Regions provide read-after-write consistency for PUTS of new objects and eventual consistency for overwrite PUTS and DELETES." However, I don't know how long it takes to get eventual consistency. I tried to search for this but couldn't find an answer in S3 documentation.
Situation:
We have a website consists of 7 steps. When user clicks on save in each step, we want to save a json document (contains information of all 7 steps) to Amazon S3. Currently we plan to:
Create a single S3 bucket to store all json documents.
When user saves step 1 we create a new item in S3.
When user saves step 2-7 we override the existing item.
After user saves a step and refresh the page, he should be able to see the information he just saved. i.e. We want to make sure that we always read after write.
The full json document (all 7 steps completed) is around 20 KB.
After users clicked on save button we can freeze the page for some time and they cannot make other changes until save is finished.
Question:
How long does it take for AWS S3 to save and load an item? (We can freeze our website when document is being saved to S3)
Is there a function to calculate save/load time based on item size?
Is the save/load time gonna be different if I choose another S3 region? If so which is the best region for Seattle?
I wanted to add to #error2007s answers.
How long does it take for AWS S3 to save and load an item? (We can freeze our website when document is being saved to S3)
It's not only that you will not find the exact time anywhere - there's actually no such thing exact time. That's just what "eventual consistency" is all about: consistency will be achieved eventually. You can't know when.
If somebody gave you an upper bound for how long a system would take to achieve consistency, then you wouldn't call it "eventually consistent" anymore. It would be "consistent within X amount of time".
The problem now becomes, "How do I deal with eventual consistency?" (instead of trying to "beat it")
To really find the answer to that question, you need to first understand what kind of consistency you truly need, and how exactly the eventual consistency of S3 could affect your workflow.
Based on your description, I understand that you would write a total of 7 times to S3, once for each step you have. For the first write, as you correctly cited the FAQs, you get strong consistency for any reads after that. For all the subsequent writes (which are really "replacing" the original object), you might observe eventual consistency - that is, if you try to read the overwritten object, you might get the most recent version, or you might get an older version. This is what is referred to as "eventual consistency" on S3 in this scenario.
A few alternatives for you to consider:
don't write to S3 on every single step; instead, keep the data for each step on the client side, and then only write 1 single object to S3 after the 7th step. This way, there's only 1 write, no "overwrites", so no "eventual consistency". This might or might not be possible for your specific scenario, you need to evaluate that.
alternatively, write to S3 objects with different names for each step. E.g., something like: after step 1, save that to bruno-preferences-step-1.json; then, after step 2, save the results to bruno-preferences-step-2.json; and so on, then save the final preferences file to bruno-preferences.json, or maybe even bruno-preferences-step-7.json, giving yourself the flexibility to add more steps in the future. Note that the idea here to avoid overwrites, which could cause eventual consistency issues. Using this approach, you only write new objects, you never overwrite them.
finally, you might want to consider Amazon DynamoDB. It's a NoSQL database, you can securely connect to it directly from the browser or from your server. It provides you with replication, automatic scaling, load distribution (just like S3). And you also have the option to tell DynamoDB that you want to perform strongly consistent reads (the default is eventually consistent reads; you have to change a parameter to get strongly consistent reads). DynamoDB is typically used for "small" records, 20kB is definitely within the range -- the maximum size of a record would be 400kB as of today. You might want to check this out: DynamoDB FAQs: What is the consistency model of Amazon DynamoDB?
How long does it take for AWS S3 to save and load an item? (We can freeze our website when document is being saved to S3)
You will not find the exact time anywhere. If you ask AWS they will give you approx timings. Your file is 20 KB so as per my experience from S3 usage the time will be more or less 60-90 Sec.
Is there a function to calculate save/load time based on item size?
No there is no any function using which you can calculate this.
Is the save/load time gonna be different if I choose another S3 region? If so which is the best region for Seattle?
For Seattle US West Oregon Will work with no problem.
You can also take a look at this experiment for comparison https://github.com/andrewgaul/are-we-consistent-yet