I have a DMS replication task using full load + cdc mode, using mysql table as source and s3 as target. I'm using in the target conection to s3 the parameter DatePartitionEnabled to true that creates the folder in the bucket based on the actual date: s3://bucket/table/2023/01/01/file.CSV. The origin table has a column created_at. Is it possible to partitioned the table in s3 based on the table column ?
Related
Unable to select "Update the table definition in the data catalog" in AWS Glue crawler settings.
I'm using s3 as target. Any idea why it is not supported ?
Note: I'm able to run the crawler but adding new data to the s3 location with more columns never get detected.
I have a table in Amazon Redshift and my use case would be to unload this table on row number partition to multiple S3 file locations daily. The files from S3 locations will be loaded to DynamoDB by means of S3DataNode of Datapipeline with parameters of DirectoryPath that is pointing towards the S3 locations updated in above step.
unload('select * from test_table where (row_num%100) >= 0 and (row_num%100)<=30')
to 's3://location1/file_'
iam_role 'arn:aws:iam::xxxxxxxxxx'
parallel off
ALLOWOVERWRITE;
unload('select * from test_table where (row_num%100) >= 31 and (row_num%100)<=100')
to 's3://location2/file_'
iam_role 'arn:aws:iam::xxxxxxxxxx'
parallel off
ALLOWOVERWRITE;
Day1: there are huge number of rows in Redshift and it created file_000, file_001, file_002 and file_003 in one of S3 location (say location2).
Day2: there is not much of rows in Redshift and it created file_000. Because of ALLOWOVERWRITE option that I'm using while unload to S3, there is only overwrite of file_000. but still the other files exists(file_001, file_002, file_003 that were created on Day1) and this is resulting in updating already existing items from files_002 and file_003 to DynamoDB.
How should I copy only overwrite files to DynamoDB?
(Note: If I use MANIFEST on unload, Can the datapipeline S3 DataNode only copies the manifest generated files to DynamoDB ?)
I am new to AWS and trying to figure out how to populate a table within an external schema, residing in Amazon Redshift. I used Amazon Glue to create a table from a .csv file that sits in a S3 bucket. I can query the newly created table via Amazon Athena.
Here is where I am stuck because my task is to take the data and populate a table living in an RedShift external schema. I tried created a Job within Glue, but had no luck.
This is where I am stuck. Am I supposed to first create an empty destination table that mirrors the table that I can query using Athena?
Thank you to anyone in advance who might be able to assist!!!
Redshift Spectrum and Athena both use the Glue data catalog for external tables. When you create a new Redshift external schema that points at your existing Glue catalog the tables it contains will immediately exist in Redshift.
-- Create the Redshift Spectrum schema
CREATE EXTERNAL SCHEMA IF NOT EXISTS my_redshift_schema
FROM DATA CATALOG DATABASE 'my_glue_database'
IAM_ROLE 'arn:aws:iam:::role/MyIAMRole'
;
-- Review the schema info
SELECT *
FROM svv_external_schemas
WHERE schemaname = 'my_redshift_schema'
;
-- Review the tables in the schema
SELECT *
FROM svv_external_tables
WHERE schemaname = 'my_redshift_schema'
;
-- Confirm that the table returns data
SELECT *
FROM my_redshift_schema.my_external_table LIMIT 10
;
SELECT * FROM "sampledb"."parquetcheck" limit 10;
Trying to use Parquet file in S3 and created a table in AWS Athena and it is created perfectly.
However when I run the select query, it says "Zero Records Returned."
Although My Parquet file in S3 has data.
I have created partition too. IAM has full access on Athena.
If your specified columns names are correct, then you may need to load the partitions using: MSCK REPAIR TABLE EnterYourTableName;. This will add new partitions to the Glue Catalog.
If any of the above fails, you can create a temporary Glue Crawler to crawl your table and then validate the metadata in Athena by clicking the 3 dots next to the table name. Then select Generate Create Table DDL. You can then compare any differences in DDL.
I have a defined a partitioned table which points to a S3 bucket which uses date partitioning. I have data for the past 3 months in the S3 bucket. I have loaded the partitions for the 1st month. However I haven't loaded the data in partition using msck repair table or alter table commands for the past 2 months. When I try to query the table , data for the past 2 months are not loaded from S3 , only the most recent partitioned data is showing up in the query results. Is this expected? If so , why?
I tried to create another partitioned table for the same s3 bucket but this time around I did not load any of the partitions. When I query this table , I get the most recent records.
Yes it is expected.
Athena uses metadata to recognize data in S3. Most important metadata used to detect data in S3 is partition. Athena keeps details about all partitions in metadata. Using this partition info, it reaches to corresponding folder in S3 to fetch data.
If you add more files to same partition: If partition is already added in athena metadata, all new files will be detected automatically because athena reads all files from folder in S3 by using partition metadata and s3 location.
If u add files in new partition: if partition is not in athena metadata, athana doesn't know how to locate corresponding folder in S3. Therefore, it doesn't access data from that folder.
There are three ways to recognize new partitions:
1. Run Glue crawler over S3 bucket and it will refresh partition metadata.
2. Use alter table command in athana to add new partitions
3. Use msck repair table if your partition has different schema than table schema.