I'm doing some testing to integrate the Delta lake format into AWS Athena, currently, I have some delta tables already in Athena by manually creating the symlink format manifest.
I was reading a recent article that allows you to create AWS Delta Glue Crawlers to crawl the metadata found within the _delta_log folder to stop using the symlink format manifest, but after some testing I found out that it is not necessary to run the crawler every time a change is made to the delta table (if only, it's just necessary to run the crawler to create the table within the Glue catalog).
My question is: Are the crawlers really necessary to keep reading the metadata?
Related
We know that,
the procedure of writing from pyspark script (aws glue job) to AWS data catalog is to write in s3 bucket (eg.csv) use a crawler and schedule it.
Is there any other way of writing to aws glue data catalog?
I am looking for a direct way to do this.Eg. writing as a s3 file and sync to the aws glue data catalog.
You may manually specify the table. The crawler only discovers the schema. If you set the schema manually, you should be able to read your data when you run the AWS Glue Job.
We have had this same problem for one of our customers who had millions of small files within AWS S3. The crawler practically would stall and not proceed and continue to run infinitely. We came up with the following alternative approach :
A Custom Glue Python Shell job was written which leveraged AWS Wrangler to fire queries towards AWS Athena.
The Python Shell job would List the contents of folder s3:///event_date=<Put the Date Here from #2.1>
The queries fired :
alter table add partition (event_date='<event_date from above>',eventname=’List derived from above S3 List output’)
4. This was triggered to run post the main Ingestion Job via Glue Workflows.
If you are not expecting schema to change, use Glue job directly after creating manually tables using Glue Database and Table.
I am trying to create external table in Amazon Redshift using statement
mentioned at this link.
In my case I want location To be parameterized instead of static value
I am using dB Weaver for Amazon redshift
If your partitions are hive compatible(<partition_column_name>=<partition_column_value>) and your table is defined via Glue or Athena, then you can run MSCK REPAIR TABLE on the Athena table directly, which would add them. Read this thread for more info: https://forums.aws.amazon.com/thread.jspa?messageID=800945
You can also try using partition projections, if you don't use hive compatible partitions, where you define the structure of the files location in relation to the partitions and parameters.
If those don't work with you, you can use AWS Glue Crawlers which supposedly automatically detect partitions: https://docs.aws.amazon.com/glue/latest/dg/add-crawler.html
If that doesn't work for you, well then your problem is very specific. I suggest pulling up your sleeves and write some code, deploy on Lambda or AWS Glue Python Shell Job. Here's a bunch of examples where other people tried that:
https://medium.com/swlh/add-newly-created-partitions-programmatically-into-aws-athena-schema-d773722a228e
https://medium.com/#alsmola/partitioning-cloudtrail-logs-in-athena-29add93ee070
I have a few AWS Glue crawlers setup to crawl CSV's in S3 to populate my tables in Athena.
My scenario and question:
I replace the .csv files in S3 daily with updated versions. Do I have to run the existing crawlers again perhaps on a schedule to update the tables on Athena with the latest content? Or is the crawler only required to run if schema changes such as additional columns added? I just want to ensure that my tables in Athena always output all of the data as per the updated CSV's - I rarely do any schema changes to the table structures. If the crawlers are only required to run when actual structure changes take place then I would prefer to run them a lot less frequently
When a glue crawler runs, the following actions take place:
It classifies data to determine the format, schema, and associated properties of the raw data
Groups data into tables or partitions
Writes metadata to the Data Catalog
The schema of tables created in the Data Catalog is referenced by Athena to query the specified S3 datasource. So, if the schema remains constant, scheduling the crawler runs can be reduced.
You can also refer the documentation here to understand working with glue crawlers and csv files in Athena: https://docs.aws.amazon.com/athena/latest/ug/glue-best-practices.html
I've DMS configured to continuously replicate data from MySQL RDS to S3. This creates two type of CSV files: a full load and change data capture (CDC). According to my tests, I have the following files:
testdb/addresses/LOAD001.csv.gz
testdb/addresses/20180405_205807186_csv.gz
After DMS is running properly, I trigger a AWS Glue Crawler to build the Data Catalog for the S3 Bucket that contains the MySQL Replication files, so the Athena users will be able to build queries in our S3 based Data Lake.
Unfortunately the crawlers are not building the correct table schema for the tables stored in S3.
For the example above It creates two tables for Athena:
addresses
20180405_205807186_csv_gz
The file 20180405_205807186_csv.gz contains a one line update, but the crawler is not capable of merging the two informations (taking the first load from LOAD001.csv.gz and making the updpate described in 20180405_205807186_csv.gz).
I also tried to create the table in the Athena console, as described in this blog post:https://aws.amazon.com/pt/blogs/database/using-aws-database-migration-service-and-amazon-athena-to-replicate-and-run-ad-hoc-queries-on-a-sql-server-database/.
But it does not yield the desired output.
From the blog post:
When you query data using Amazon Athena (later in this post), you
simply point the folder location to Athena, and the query results
include existing and new data inserts by combining data from both
files.
Am I missing something?
The AWS Glue crawler is not able to reconcile the different schemas in the initial LOAD csvs and incremental CDC csvs for each table. This blog post from AWS and its associated cloudformation templates demonstrate how to use AWS Glue jobs to process and combine these two type of DMS target outputs.
Athena will combine the files in am S3 if they are the same structure. The blog speaks to only inserts of new data in the cdc files. You'll have to build a process to merge the CDC files. Not what you wanted to hear, I'm sure.
From the blog post:
"When you query data using Amazon Athena (later in this post), due to the way AWS DMS adds a column indicating inserts, deletes and updates to the new file created as part of CDC replication, we will not be able to run the Athena query by combining data from both files (initial load and CDC files)."
I am new to AWS Glue and am having difficulty fully understanding the AWS docs, but am struggling through the following use case:
We have an s3 bucket with a number of Avro files. We have decided to use Avro due to having extensive support for data schema changes overtime, allowing new fields to be applied to old data with no problem.
With AWS Glue, I understand that a new table is created by a crawler whenever there is a schema change. When our schema has changed, this has caused a number of new tables to be created by the crawler, as expected, but not quite as we desire...
Ultimately, we would like the crawler to detect the most recent schema and apply this schema to all the data that we are crawling in the s3 bucket, outputting only one table. We had (perhaps incorrectly) assumed that by using Avro, this would not be an issue as the crawler could apply new schema fields with a given default or null value to older data (the benefit of using Avro), and only output one table that we then could query using AWS Athena.
Is there a way in AWS Glue to use a given schema for all data in the s3 bucket, enabling us to leverage the Avro benefit of schema evolution, so that all data is output into one table?
I haven't worked with Avro files specifically but AWS Glue lets you configure the crawler in several ways.
If you create a new crawler, you'll be prompted with a few options under the "Configure the crawler's output" section.
Based on your situation, I think you'll need to tick the box that says Update all new and existing partitions with metadata from the table.
This is how that sub-menu looks like.