I need to move my delta lake files to a new blobstore on a different subscription. Any ideas whats the best way to do this?
Im moving them to an ADLS Gen2 Storage, I think the previous storage was just blob storage. This delta lake is updated on an hourly basis by databricks jobs (but I can pause those if necessary). Size is around 3TB-5TB, I'm initially thinking of pausing all jobs and using azcopy to move the files and point the jobs there afterwards. But I want to check other options that may be better in terms of speed of transfer and cost.
The best way would just to use Azure Data Factory. There you can point to your different locations and move the files really quick.
Related
We are running a Delta lake on ADLS Gen2 with plenty of tables and Spark jobs. The Spark jobs are running in Databricks and we mounted the ADLS containers into DBFS (abfss://delta#<our-adls-account>.dfs.core.windows.net/silver). There's one container for each "tier", so bronze, silver, gold.
This setup has been stable for some months now, but last week, we've seen a sudden increase in transactions within our storage account, particularly in the ListFilesystemDir operations:
We've added some smaller jobs that read and write some data in that time frame, but turning them off did not reduce the amount of transactions back to the old level.
Two questions regarding this:
Is there some sort of documentation that explains which operation on a Delta table causes which kind of ADLS transactions?
Is it possible to find out which container/directory/Spark job/... causes this amount of transactions, without turning off the Spark jobs one by one?
If you go into logs from your data lake (if you have log analytics enabled) you can view the exact timestamp, caller and target of the spike. Take that data and go into your databricks cluster and navigate to Spark UI. In there you should be able to see timestamps and jobs. There you can find what notebook is causing it.
I'm beginning my journey into Delta Tables and one thing that is still confusing me is where is the best place to save your delta tables if you need to query them later.
For example I'm migrating several tables from on-prem to azure databricks into individual delta tables. My question is, should I save the individual delta tables which could be significant in size into the DBFS databricks internal storage, or should I mount a blob storage location and save the delta lake tables there? What do people normally do in these situations?
I usually recommend people to store data in a separate storage account (either mounted, or used directly), and don't use the internal storage of workspace for that tasks. Primary reason - it's easier to share this data with other workspaces, or other systems if it's necessary. Internal storage should be primarily used for temp files, libraries, init scripts, etc.
There is a number of useful guides available that can help:
Azure Databricks Best Practices, and it's specifically says about internal storage
About securing access to Azure Data Lake
Currently, I have a large set of text files which contain (historical) raw data from various sensors. New files are received and processed every day. I'd like to move this off of an on-premises solution to the cloud.
Would Azure's Blob storage be an appropriate mechanism for this volume of small(ish) private files? or is there another Azure solution that I should be pursuing?
Relevent Data (no pun intended) & Requirements-
The data set contains a millions files of mostly small files, for a total of near 400gb. The average file size is around 50kb, but some files could exceed 40mb.
I need to maintain the existing data set for posterity's sake.
New files would be uploaded daily, and then processed once. Processing would be handled by Background Workers reading files off a queue.
Certain files would be downloaded / reviewed / reprocessed after the initial processing.
Let me elaborate more on David's comments.
As David mentioned, there's no limit on number of objects (files) that you can store in Azure Blob Storage. The limit is of the size of the storage account which currently is 500TB. As long as you stay in this limit you will be good. Further, you can have 100 storage accounts in an Azure Subscription so essentially the amount of data that you will be able to store is practically limitless.
I do want to mention one more thing though. It seems that the files that are uploaded in blob storage are once processed and then kind of archived. For this I suggest you take a look at Azure Cool Blob Storage. It is essentially meant for this purpose only where you want to store objects that are not frequently accessible yet when you need those objects they are accessible almost immediately. The advantage of using Cool Blob Storage is that writes and storage is cheaper as compared to Hot Blob Storage accounts however the reads are expensive (which makes sense considering their intended use case).
So a possible solution would be to save the files in your Hot Blob Storage accounts. Once the files are processed, they are moved to Cool Blob Storage. This Cool Blob Storage account can be in the same or different Azure Subscription.
I'm guessing it CAN be used as a file system, is the right (best) tool for the job.
Yes, Azure Blobs Storage can be used as cloud file system.
The data set contains a millions files of mostly small files, for a total of near 400gb. The average file size is around 50kb, but some files could exceed 40mb.
As David and Gaurav Mantri mentioned, Azure Blob Storage could meet this requirement.
I need to maintain the existing data set for posterity's sake.
Data in Azure Blob Storage is durable. You could reference the SERVICE LEVEL AGREEMENTS of Storage.
New files would be uploaded daily, and then processed once. Processing would be handled by Background Workers reading files off a queue.
You can use Azure Function to do the file processing work. Since it will do once a day, you could add a TimerTrigger Function.
//This function will be executed once a day
public static void TimerJob([TimerTrigger("0 0 0 * * *")] TimerInfo timerInfo)
{
//write the processing job here
}
Certain files would be downloaded / reviewed / reprocessed after the initial processing.
Blobs can be downloaded or updated at anytime you want.
In addition, if your data processing job is very complicated, you also could store your data in Azure Data Lake Store and do the data processing job using Hadoop analytic frameworks such as MapReduce or Hive. Microsoft Azure HDInsight clusters can be provisioned and configured to directly access data stored in Data Lake Store.
Here are the differences between Azure Data Lake Store and Azure Blob Storage.
Comparing Azure Data Lake Store and Azure Blob Storage
What would cause Polybase performance to degrade when querying larger datasets in order to insert records into Azure Data Warehouse from Blob storage?
For example, a few thousand compressed (.gz) CSV files with headers partitioned by a few hours per day across 6 months worth of data. Querying these files from an external table in SSMS is not exactly optimial and it's extremely slow.
Objectively, I'm loading data into Polybase in order to transfer data into Azure Data Warehouse. Except, it seems with large datasets, Polybase is pretty slow.
What options are available to optimize Polybase here? Wait out the query or load the data after each upload to blob storage incrementally?
In your scenario, Polybase has to connect to the files in the external source, uncompress them, then ensure they fit your external table definition (schema) and then allow the contents to be targeted by the query. When you are processing large amounts of text files in a one-off import fashion, there is nothing to really cache either, since it is dealing with new content every time. In short, your scenario is compute heavy.
Azure Blob Storage will (currently) max out at around 1,250MB/sec, so if your throughput is not near maxing this, then the best way to improve performance is to upgrade your DWU on your SQL data warehouse. In the background, this will spread your workload over a bigger cluster (more servers). SQL Data Warehouse DWU can be scaled either up and down in a matter of minutes.
If you have huge volumes and are maxing the storage, then use multiple storage accounts to spread the load.
Other alternatives include relieving Polybase of the unzip work as part of your upload or staging process. Do this from within Azure where the network bandwidth within a data center is lightning fast.
You could also consider using Azure Data Factory to do the work. See here for supported file formats. GZip is supported. Use the Copy Activity to copy from the Blob storage in to SQL DW.
Also look in to:
CTAS (Create Table as Select), the fastest way to move data from external tables in to internal storage in Azure Data Warehouse.
Creating statistics for your external tables if you are going to query them repeatedly. SQL Data Warehouse does not create statistics automatically like SQL Server and you need to do this yourself.
Currently my team is creating a solution that would use HDInsight. We will be getting 5TB of data daily and will need to do some map/reduce jobs on this data. Would there be any performance/cost difference if our data will be stored in Azure Table Storage instead of Azure HBase?
The main differences will be in both functionality and cost.
Azure Table Storage doesn't have a map reduce engine attached to it in itself, though of course you could use the map reduce approach to write your own.
You can use Azure HDInsight to connect Map Reduce to table storage. There are a couple of connectors around, including one written by me which is hive focused and requires some configuration, and may not suit your partition scheme (http://www.simonellistonball.com/technology/hadoop-hive-inputformat-azure-tables/) and a less performance focused, but more complete version from someone at Microsoft (http://blogs.msdn.com/b/mostlytrue/archive/2014/04/04/analyzing-azure-table-storage-data-with-hdinsight.aspx).
The main advantage of Table Storage is that you aren't constantly taking processing cost.
If you use HBase, you will need to run a full cluster all the time, so there is a cost disadvantage, however, you will get some functionality and performance gains, plus you will have something a bit more portable, should you wish to use other hadoop platforms. You would also have access to a much greater range of analytic functionality with the HBase option.
HDInsight (HBase/Hadoop) uses Azure Blob storage not ATS. For your data-storage you will charged only applicable blob storage cost, based on your subscription.
P.S. Don't forget to delete your cluster once job has completed, to avoid charges. Your data will persist in BLOB storage and can be used by next cluster you build.