everyone!
I'm working on a solution that intends to use Amazon Athena to run SQL queries from Parquet files on S3.
Those filed will be generated from a PostgreSQL database (RDS). I'll run a query and export data to S3 using Python's Pyarrow.
My question is: since Athena is schema-on-read, add or delete of columns on database will not be a problem...but what will happen when I get a column renamed on database?
Day 1: COLUMNS['col_a', 'col_b', 'col_c']
Day 2: COLUMNS['col_a', 'col_beta', 'col_c']
On Athena,
SELECT col_beta FROM table;
will return only data from Day 2, right?
Is there a way that Athena knows about these schema evolution or I would have to run a script to iterate through all my files on S3, rename columns and update table schema on Athena from 'col_a' to 'col_beta'?
Would AWS Glue Data Catalog help in any way to solve this?
I'll love to discuss more about this!
I recommend reading more about handling schema updates with Athena here. Generally Athena supports multiple ways of reading Parquet files (as well as other columnar data formats such as ORC). By default, using Parquet, columns will be read by name, but you can change that to reading by index as well. Each way has its own advantages / disadvantages dealing with schema changes. Based on your example, you might want to consider reading by index if you are sure new columns are only appended to the end.
A Glue crawler can help you to keep your schema updated (and versioned), but it doesn't necessarily help you to resolve schema changes (logically). And it comes at an additional cost, of course.
Another approach could be to use a schema that is a superset of all schemas over time (using columns by name) and define a view on top of it to resolve changes "manually".
You can set a granularity based on 'On Demand' or 'Time Based' for the AWS Glue crawler, so every time your data on the S3 updates a new schema will be generated (you can edit the schema on the data types for the attributes). This way your columns will stay updated and you can query on the new field.
Since AWS Athena reads data in CSV and TSV in the "order of the columns" in the schema and returns them in the same order. It does not use column names for mapping data to a column, which is why you can rename columns in CSV or TSV without breaking Athena queries.
Related
I have an existing Athena table (w/ hive-style partitions) that's using the Avro SerDe. When I first created the table, I declared the Athena schema as well as the Athena avro.schema.literal schema per AWS instructions. Everything has been working great.
I now wish to add new columns that will apply going forward but not be present on the old partitions. I tried a basic ADD COLUMNS command that claims to succeed but has no impact on SHOW CREATE TABLE. I then wondered if I needed to change the Avro schema declaration as well, which I attempted to do but discovered that ALTER TABLE SET SERDEPROPERTIES DDL is not supported in Athena.
AWS claims I should be able to add columns when using Avro, but at this point I'm unsure how to do it. Even if I'm willing to drop the table metadata and redeclare all of the partitions, I'm not sure how to do it right since the schema is different on the historical partitions.
Looking for high-level guidance on the steps to be taken. Documentation is scant and Athena seems to be lacking support for commands that are referenced in this same scenario in vanilla Hive world. Thanks for any insights.
I am uploading CSV files in the s3 bucket and creating tables through glue crawler and seeing the tables in Athena, making connection between Athena and Quicksight, and showing the result graphically there in quicksight.
But what I need to do now is keep the history of the files uploaded, instead of a new CSV file being uploaded and crawler updating the table, can I have crawler save each record separately? or is it even a reasonable thing to do? since I wonder it would then create so many tables and it'll be a mess?
I'm just trying to figure out a way to keep a history of previous records. how can i achieve this?
When you run an Amazon Athena query, Athena will look at the location parameter defined in the table's DDL. This specifies where the data is stored in an Amazon S3 bucket.
Athena will include all files in that location when it runs the query on that table. Thus, if you wish to add more data to the table, simply add another file in that S3 location. To replace data in that table, you can overwrite the file(s) in that location. To delete data, you can delete files from that location.
There is no need to run a crawler on a regular basis. The crawler can be used to create the table definition and it can be run again to update the table definition if anything has changed. But you typically only need to use the crawler once to create the table definition.
If you wish to preserve historical data in the table while adding more data to the table, simply upload the data to new files and keep the existing data files in place. That way, any queries will include both the historical data and the new data because Athena simply looks at all the files in that location.
I need to pull two companies' data from their respective AWS S3 buckets, map their columns in Glue, and export them to a specific schema in a Microsoft SQL database. The schema is to have one table, with the companies' data being distinguished with attributes for each of their sites (each company has multiple sites).
I am completely new to AWS and SQL, would someone mind explaining to me how to add an attribute to the data, or point me to some good literature on this? I feel like manipulating the .csv in the Python script I'm already running to automatically download the data from another site then upload it to S3 could be an option (deleting NaN columns and adding a column for site name), but I'm not entirely sure.
I apologize if this has already been answered elsewhere. Thanks!
I find this website to generally be pretty helpful with figuring out SQL stuff. I've linked to the ALTER TABLE commands that would allow you to do this through SQL.
If you are running a python script to edit the .csv to start, then I would edit the data there, personally. Depending on the size of the data sets, you can run your script as a Lambda or Batch job to grab, edit, and then upload to s3. Then you can run your Glue crawler or whatever process you're using to map the columns.
Today, I saw myself with a simple problem, renaming column of an Athena glue table from old to new name.
First thing, I search here and tried some solutions like this, this, and many others. Unfortunately, none works, so I decided to use my knowledge and imagination.
I'm posting this question with the intention of share, but also, with the intention to get how others did and maybe find out I reinvented the wheel. So please also share your way if you know how to do it.
My setup is, a Athena JSON table partitioned by day with valuable and enormous amount of data, the infrastructure is defined and updated through Cloudformation.
How to rename an Athena column and still keep the data?
Explaining without all the cloudformation infrastructure.
Imagine a table containing:
userId
score
otherColumns
eventDateUtc
dt_utc
Partitioned by dt_utc and stored using JSON format. Wee need to change the column score to deltaScore.
Keep in mind, although I haven't tested with others format/configurations, this should apply to any configuration supported by athena as we are going to use athena algorithm to do the job for us.
How to do
if you run the cloudformation migration first, you gonna "lose" access to the dropped column.
but you can simply rename the column back and the data appears.
Those are the steps required for rename a AWS Athena table:
Create a temporary table mapping the old column name to the new one:
This can be done by use of CREATE TABLE AS, read more in the aws docs
With this command, we use Athena engine to apply the transformation on the files of the original table for us and save at s3://bucket_name/A_folder/temp_table_rename/.
CREATE TABLE "temp_table_rename"
WITH(
format = 'JSON',
external_location = 's3://bucket_name/A_folder/temp_table_rename/',
partitioned_by = ARRAY['dt_utc']
)
AS
SELECT DISTINCT
userid,
score as deltascore,
otherColumns,
eventDateUtc,
"dt_utc"
FROM "my_database"."original_table"
Apply the database rename by running the cloudformation with the changes or on the way you have.
At this point, you can even drop the original_table, and create again using the right column name.
After rename, you will notice that the renamed column have no data.
Remove the data of the original table by deleting it's s3 source.
Copy the data from the temp table source to the original table source
I prefer to use a aws command as, there can be thousands of files to copy
aws s3 cp s3://bucket_name/A_folder/temp_table_rename/ s3://bucket_name/A_folder/original_table/ --recursive
Restore the index of the original table
MSCK REPAIR TABLE "my_database"."original_table"
done.
Final notes:
Using CREATE TABLE AS to do the transformation job, allow you to do much more than only renaming the column, for example split the data of a column into 2 new columns, or merge it to a single 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.