I have an Athena db that I would like to duplicate (with a different name of course). Right now, I don't have an effective way to do this. My approach right now is just to execute CTAS queries per table but this obviously doesn't work for databases with 100s of tables. I've also looked into using crawlers but unfortunately, I do not always have a reliable S3 export for this.
Is there a way for me to duplicate Athena databases? Perhaps a shell script?
Related
Based on my research, the easiest and the most straight forward way to get metadata out of Glue's Data Catalog, is using Athena and querying the information_schema database. The article below has come up frequently in my research and is written by Amazon's team:
Querying AWS Glue Data Catalog
However, under the section titled Considerations and limitations the following is written:
Querying information_schema is most performant if you have a small to moderate amount of AWS Glue metadata. If you have a large amount of metadata, errors can occur.
Unfortunately, in this article, there do not seem to be any indications or suggestion regarding what constitutes as "large amount of metadata" and exactly what errors could occur when the metadata is large and one needs to query the metadata.
My question is, how to deal with the issue related to the ever growing size of data catalog's metadata so that one would never encounter errors when using Athena to query the metadata?
Is there a best practice for this? Or perhaps a better solution for getting the same metadata that querying the catalog using Athena provides without multiple or great many API calls (using boto3, Hive DDL etc)?
I talked to AWS Support and did some research on this. Here's what I gathered:
The information_schema is built at query execution time, there doesn't seem to be any caching.
If you access information_schema.tables, it will make separate calls for each schema you have to the Hive Metastore (Glue Data Catalog).
If you access information_schema.columns, it will make separate calls for each schema and each table in that schema you have to the Hive Metastore.
These queries are affected by the general service quotas. In this case, DML queries like your select must finish within 30 minutes.
If your Glue Data Catalog has many thousands of schemas, tables, and columns all of this may result in slow performance. As a rough guesstimate support told me that you should be fine as long as you have less than ~ 10000 tables, which should be the case for most people.
I am copying an entire snowflake DB into S3 to be viewed through Athena. I would like to preserve the schema/hierarchy so that the corresponding queries do not change. All the files are organized properly for this in S3 as follows
DataBase/Schema/Folder/Table/{parquet files}
When I crawl with Glue they all end up in one DB at the same level. Is it possible to have a similar folder structure in Athena?
Right now all queries in Athena are like
Select *
FROM database.table
I would like to have
Select *
FROM database.schema.folder.table
The only logical grouping of tables available in Athena is a database, and as you have indicated, there is no concept of hierarchy, schemas, or folders in Athena.
Database and schema comprise a namespace in Snowflake. If your intention is to simply have a similar namespace, what you can do is combine the Snowflake database d1 and schema name s1 to create a flattened logical grouping in Athena d1_s1. Then you can do:
SELECT * FROM d1_s1.table
Also, the only special character you can have in the database name is an underscore, so there really is no other way to preserve the structure or the existing queries. At least, this way the format is close enough that it should be easy enough to programmatically fix the existing queries (e.g., using regex to replace a.b.c with a_b.c).
However, there will still be differences. For example, grants are managed differently for Snowflake databases and schemas. Schemas also have a concept of managed access. This will not be possible in Athena.
At my organization, we are using a stack of AWS S3, AWS Glue, and Athena to drive some reporting of internal metrics. In general, this stack is great for quick set up for reporting off of raw data (stored in S3). The problem we've come against is what to do if we notice we need to somehow update the data that's already stored in S3. For example, we want to update values in a column that have a certain string to update that value.
Unlike a database, we can't just run a query to update all the existing data. I've tried to see if we can utilize Glue Jobs to accomplish this, but from my limited understanding, it doesn't seem like it's meant to do ETL from a bucket back to the same bucket.
The only thing I can think is to write a custom tool that iterates through an S3 bucket, loads a file, provides the transformation, and puts it back, overwriting the original. It seems there has to be a better way though.
Updates are not handled in a native way in a traditional hive-like warehousing solution, which I deem Athena to be. A common solution is a kind of engineering workaround where you do "insert overwrite" a partition (borrowing Hive syntax, possible in Presto and hopefully also possible in Athena, which is based on Presto).
Other solutions include creating new tables and atomically replacing a view, which users are supposed to query, instead of querying the underlying table(s) directly.
As this is a common problem, there are also some ready to use solutions to it, but I do not know whether which/whether they are possible with Athena. They are certainly possible with Presto (Presto SQL):
Hive ACID transactional tables (updates currently required Hive runtime)
Data Lake (open sourced by Databricks; updates currently require Spark runtime)
Hudi (I know little about this one)
We have a very large number of folders and files in S3, all under one particular folder, and we want to crawl for all the CSV files, and then query them from one table in Athena. The CSV files all have the same schema. The problem is that the crawler is generating a table for every file, instead of one table. Crawler configurations have a checkbox option to "Create a single schema for each S3 path" but this doesn't seem to do anything.
Is what I need possible? Thanks.
Glue crawlers claims to solve many problems, but in fact solves few. If you're slightly outside the scope of what they designed for you're out of luck. There might be a way to configure it to do what you want, but in my experience trying to make Glue crawlers do things that aren't perfectly aligned with it is not worth the effort.
It sounds like you have a good idea of what the schema of your data is. When that is the case Glue crawlers also provide very little value. You probably have a better idea of what the schema should look than Glue will ever be able to figure out.
I suggest that you manually create the table, and write a one off script that lists all the partition locations on S3 that you want to include in the table and generate ALTER TABLE ADD PARTITION … SQL, or Glue API calls to add those partitions to the table.
To keep the table up to date when new partition locations are added, have a look at this answer for guidance: https://stackoverflow.com/a/56439429/1109
One way to do what you want is to use just one of the tables created by the crawler as an example, and create a similar table manually (in AWS Glue->Tables->Add tables, or in Athena itself, with
CREATE EXTERNAL TABLE `tablename`(
`column1` string,
`column2` string, ...
using existing table as an example, you can see the query used to create that table in Athena when you go to Database -> select your data base from Glue Data Catalog, then click on 3 dots in front of the one "automatically created by crawler table" that you choose as an example, and click on "Generate Create table DDL" option. It will generate a big query for you, modify it as necessary (I believe you need to look at LOCATION and TBLPROPERTIES parts, mostly).
When you run this modified query in Athena, a new table will appear in Glue data catalog. But it will not have any information about your s3 files and partitions, and crawler most likely will not update metastore info for you. So you can in Athena run "MSCK REPAIR TABLE tablename;" query (it's not very efficient, but works for me), and it will add missing file information, in the Result tab you will see something like (in case you use partitions on s3, of course):
Partitions not in metastore: tablename:dt=2020-02-03 tablename:dt=2020-02-04
Repair: Added partition to metastore tablename:dt=2020-02-03
Repair: Added partition to metastore tablename:dt=2020-02-04
After that you should be able to run your Athena queries.
I created a database and some tables with Data on AWS Athena and would like to rename the database without deleting and re-creating the tables and database. Is there a way to do this? I tried the standard SQL alter database but it doesn't seem to work.
thanks!
I'm afraid there is no way to do this according to this official forum thread. You would need to remove the database and re-create it. However, since Athena does not store any data by itself, deleting a table or a database won't impact your data stored on S3. Therefore, if you kept all the scripts that create external tables, re-creating a database should be fairly quick thing to do.
Athena doesn't support renaming database. You need to recreate database with a new name.
You can use Presto which is an open source version of Athena and Presto supports more DDL queries.