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How can one set up an Execution Class = FLEX on a Jupyter Job Run , im using the %magic on my %%configure cell like below and also setting the input arguments with --execution_class = FLEX
But still the jobs are quicking as STANDARD
%%configure
{
"region": "us-east-1",
"idle_timeout": "480",
"glue_version": "3.0",
"number_of_workers": 10,
"execution_class": "FLEX",
"worker_type": "G.1X"
}
so I have 2 streams and what I am doing is making 2 tables in studio and then just merging the 2 tables using inner-join and putting all that data in s3, what this issue seems to be is that because of the time being different aws is not able to join the data based on the given key.
the following is the code for joining the 2 tables previously when I used simple data everything was working but now there is no error, only the tables are not merging
```
%flink.ssql(type=update)
INSERT INTO s3_join
SELECT * FROM ExampleInputStream1
INNER JOIN ExampleInputStream
ON ExampleInputStream1.seq_num = ExampleInputStream.seq_num
```
I have a string type for date and in that column, it has the word 'None'
My query for casting the date is below - *getting only the Month and Year on it*,
date_format(cast(c.enddate as date), '%M') as "Month",
date_format(cast(c.enddate as date), '%Y') as "Year"
ERROR prompted
INVALID_CAST_ARGUMENT: Value cannot be cast to date: None-
Can somebody help me with this problem, so that I can still get the Month and Year only?
Thank you in advance!
Hi, I'd appreciate AWS Athena support for TIMESTAMP data type with microsecond precision for all row formats and table engines. Currently, the support is very inconsistent. See the SQL script below.
```
drop table if exists test_csv;
create external table if not exists test_csv (
id int,
created_time timestamp
)
row format serde 'org.apache.hadoop.hive.serde2.OpenCSVSerde'
with serdeproperties('separatorChar'=',', 'quoteChar'='"', 'escapeChar'='\\')
location 's3://my-bucket/tmp/timestamp_csv_test/';
-- result: OK
drop table if exists test_parquet;
create external table if not exists test_parquet (
id int,
created_time timestamp
)
row format serde 'org.apache.hadoop.hive.ql.io.parquet.serde.ParquetHiveSerDe'
stored as inputformat 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetInputFormat'
outputformat 'org.apache.hadoop.hive.ql.io.parquet.MapredParquetOutputFormat'
location 's3://my-bucket/tmp/timestamp_parquet_test/'
tblproperties ('parquet.compress' = 'snappy');
-- result: OK
drop table if exists test_iceberg;
create table if not exists test_iceberg (
id int,
created_time timestamp
)
location 's3://my-bucket/tmp/timestamp_iceberg_test/'
tblproperties ( 'table_type' ='iceberg');
-- result: OK
insert into test_csv values (1, timestamp '2023-03-22 11:00:00.123456');
/*
result: ERROR [HY000][100071] [Simba][AthenaJDBC](100071) An error has been thrown from the AWS Athena client. GENERIC_INTERNAL_ERROR: class org.apache.hadoop.hive.serde2.objectinspector.primitive.WritableIntObjectInspector cannot be cast to class org.apache.hadoop.hive.serde2.objectinspector.primitive.StringObjectInspector (org.apache.hadoop.hive.serde2.objectinspector.primitive.WritableIntObjectInspector and org.apache.hadoop.hive.serde2.objectinspector.primitive.StringObjectInspector are in unnamed module of loader io.trino.server.PluginClassLoader @1df1bd44). If a data manifest file was generated at 's3://my-bucket/athena_results/ad44adee-2a80-4f41-906a-17aa5dc27730-manifest.csv', you may need to manually clean the data from locations specified in the manifest. Athena will not delete data in your account. [Execution ID: ***]
*/
insert into test_parquet values (1, timestamp '2023-03-22 11:00:00.123456');
-- result: OK
select * from test_parquet;
-- result: OK DATA: 1,2023-03-22 11:00:00.123000 BUT THE TIMESTAMP VALUE IS TRUNCATED TO MILLISECONDS!
insert into test_iceberg values (1, timestamp '2023-03-22 11:00:00.123456');
-- result: OK
select * from test_csv;
select * from test_iceberg;
-- result: OK DATA: 1,2023-03-22 11:00:00.123456 THIS IS FINE
```
Hi community,
I am trying to perform an ETL job using AWS Glue.
Our data is stored in MongoDB Atlas, inside a VPC.
Our AWS is connected to our MongoDB Atlas using VPC peering.
To perform the ETL job in AWS Glue I have first created a connection using the VPC details and the mongoDB Atlas URI along with the password and username. The connection is used by the AWS Glue crawlers to extract the schema to AWS Data Catalog Tables.
This connection works!
However, I am then attempting to perform the actual ETL job using the following pySpark code:
#My Temp Variables
source_database="d*********a"
source_table_name="main_businesses
source_mongodb_db_name = "main"
source_mongodb_collection = "businesses"
glueContext.create_dynamic_frame.from_catalog(database=source_database,table_name=source_table_name,additional_options = {"database": source_mongodb_db_name,"collection":source_mongodb_collection})
However the connection times out and for some reason mongodb atlas is blocking the connection from the ETL job.
It's as if the ETL Job is using the connection differently than the crawler does. Maybe the ETL Job is not able to run the job inside our AWS VPC that is connected to the MongoDB Atlas VPC (VPC Peering is not possible?).
Does anyone have any idea what might be going on or how I can fix this?
Thank you!
Hi,
I'm creating a dashboard for operators to download the athena query results.
The ID column values contain hyphens `-` and
For example, if table contains the following data
| id | name |
| --- | --- |
| `-xyz` | `First example` |
| `a-b-c` | `Second example` |
The generated csv contains a extra single quote in the id column at the first row
```csv
"id","name"
"'-xyz","First example"
"a-b-c","Second example"
```
Is there any way to avoid it?
I have a KPI visual that displays the count of records I have from a dataset. Is it possible for me to make that KPI show me all the records that were included in this count?
In Redshift, I'm trying to update a table using another table from another database. The error details:
SQL Error [XX000]: ERROR: Assert
Detail:
-----------------------------------------------
error: Assert
code: 1000
context: scan->m_src_id == table_id -
query: 17277564
location: xen_execute.cpp:5251
process: padbmaster [pid=30866]
The context is not helpful. I have used a similar join based approach for other tables and there the update statement has been working fine.
Update syntax used:
```
UPDATE ods.schema.tablename
SET
"TimeStamp" = GETDATE(),
"col" = S."col",
FROM ods.schema.tablename T
INNER JOIN stg.schema.tablename S
ON T.Col = S.Col;
```
Hello, I am running a job to apply an ETL on a semi-colon-separated CSV on S3, however, when I read the file using the DynamicFrame feature of AWS and try to use any method like `printSchema` or `toDF`, I get the following error:
```
py4j.protocol.Py4JJavaError: An error occurred while calling o77.schema.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 1.0 failed 1 times, most recent failure: Lost task 0.0 in stage 1.0 (TID 1) (52bff5da55da executor driver): com.amazonaws.services.glue.util.FatalException: Unable to parse file: s3://my-bucket/my-file.csv
```
I have already verified the codification, it is UTF-8 so there should be no problem. When I read the CSV using `spark.read.csv`, it works fine, and the Crawlers can also recognize the schema. The data has some special characters that shouldn't be there, and that's part of the ETL I am looking to perform. Neither using the `from_catalog` nor `from_options` function from AWS Glue works, the problem is the same whether I run the job locally on docker or Glue Studio. My data have a folder date partition so I would prefer to avoid using directly Spark to read the data and take advantage of the Glue Data Catalog as well.
Thanks in advance.
We have been able to connect to a Microsoft SQL Server DB using both Glue's DynamicFrame and Spark's own JDBC write option due to the Glue connection option. However, considering the transactional nature of the data, we wanted to switch this to sending data programmatically using a python library, thus allowing us to move from Glue ETL Job to Python Shell Job. Our initial option was pyodbc, however, due to our inability to integrate the required driver with Glue, we were unsuccessful. Another option that we looked at was pymssql. Our experience with connecting to Microsoft SQL server using pymssql was seamless. However, it was restricted to Python 3.6 and we were unable to import it with Python 3.9. Our requirement for Python 3.9 was due to the boto3 version compatibility with the said python version as we are also trying to access the boto3 redshift-data client in the same script. Having considered all of the above, is it possible to query Microsoft SQL Server using a python library with Python 3.9 in AWS Glue Python Shell Job?
Hello.
I'm trying to update my stack on cloud formation to add custom vocabulary for AWS transcribe.
every time I update the stack it rollback changes and the status changed to UPDATE_ROLLBACK_COMPLETE
how can I know the reason of rolling back? I need to apply my change Can anyone tell me what should I do?
regards,
Hello
I have JSON files for jobs created and extracted from Amazon transcribe and I need to add it to cloud formation stack of PCA
so I found that on my PCA(post call analytics) dashboard there is not option to create a new data source to add the JSON file to it
How can I add new data source (JSON files) from transcribe to PCA??
and How can I display my new jobs which created manually within transcribe on PCA as a data source?