I'm trying to load json files that are partitioned like this in an S3 storage :
|-json-data
|-x=something
|-y=something
|-data.json
I'm loading them like this in my ETL job
datasource0 = glueContext.create_dynamic_frame_from_options('s3',
{
'paths': ['s3://bucket/json-data/'],
'recurse': True,
'groupFiles': 'inPartition',
'partitionKeys':['x', 'y']
},
format='json',
transformation_ctx = 'datasource0')
However when I try to read the schema using datasource0.printSchema() I don't have any partition in the schema. I need to have those partitions in the schema to do the transformations. After some research I'm not sure if this is a supported feature of create_dynamic_frame_from_options. Does someone know how to do this ?
You can only pass partitionKeys in write_dynamic_frame.from_options and not while reading from s3.For you to load specific partitions or filter them you need these partitions to be present already in source.
So you need to either crawl using Glue crawler or create table in Athena with partitions. Once the table is available in Glue metadata you can load the table's partitions in to Glue ETL as shown below:
glue_context.create_dynamic_frame.from_catalog(
database = "my_S3_data_set",
table_name = "catalog_data_table",
push_down_predicate = my_partition_predicate)
Please refer to below link on how you can leverage predicate pushdown:
https://docs.aws.amazon.com/glue/latest/dg/aws-glue-programming-etl-partitions.html
Related
I am having a problem, where i have set enableUpdateCatalog=True and also updateBehaviour=LOG to update my glue table which has 1 partition key. After the job, runs there are no new partitions added on my glue catalog table, but data in S3 is separated by the partition key i have used, how do i get the job to automatically partition my glue catalog table?
Currently i have to manually run boto3 create_partition to create partitions on my glue catalog table. I want my job to automatically be able to create partitions as it discovers in S3 path separated by partition Keys
Code:
additionalOptions = {
"enableUpdateCatalog": True,
"updateBehavior": "LOG"}
additionalOptions["partitionKeys"] = ["partition_key0", "partition_key1"]
my_df = glueContext.write_dynamic_frame_from_catalog(frame=last_transform, database=<dst_db_name>,
table_name=<dst_tbl_name>, transformation_ctx="DataSink1",
additional_options=additionalOptions)
job.commit()
PS: I am currently using PARQUET format
Am i missing any Rights that has to be added to my job so that it can create partitions from the job itself?
I got it to work by adding useGlueParquetWriter: 'true' to the CATALOG table properties. And also I have added
format_options = {
'useGlueParquetWriter': True
}
in the write_dynamic_frame.from_catalog calls.
These steps got it to start working :)
I'm creating a Glue ETL job that transfers data from BigQuery to S3. Similar to this example, but with my own dataset.
n.b.: I use BigQuery Connector for AWS Glue v0.22.0-2 (link).
The data in BigQuery is already partitioned by date, and I would like to have every Glue job run fetches a specific date only (WHERE date = ...) and group them into 1 CSV file output. But I don't find any clue where to insert the custom WHERE query.
In BigQuery source node configuration options, the options are only these:
Also in the generated script, it uses create_dynamic_frame.from_options which does not accommodate custom query (per documentation).
# Script generated for node Google BigQuery Connector 0.22.0 for AWS Glue 3.0
GoogleBigQueryConnector0220forAWSGlue30_node1 = (
glueContext.create_dynamic_frame.from_options(
connection_type="marketplace.spark",
connection_options={
"parentProject": args["BQ_PROJECT"],
"table": args["BQ_TABLE"],
"connectionName": args["BQ_CONNECTION_NAME"],
},
transformation_ctx="GoogleBigQueryConnector0220forAWSGlue30_node1",
)
)
So, is there any way I can write a custom query? Or is there any alternative method?
Quoting this AWS sample project, we can use filter in Connection Options:
filter – Passes the condition to select the rows to convert. If the table is partitioned, the selection is pushed down and only the rows in the specified partition are transferred to AWS Glue. In all other cases, all data is scanned and the filter is applied in AWS Glue Spark processing, but it still helps limit the amount of memory used in total.
Example if used in script:
# Script generated for node Google BigQuery Connector 0.22.0 for AWS Glue 3.0
GoogleBigQueryConnector0220forAWSGlue30_node1 = (
glueContext.create_dynamic_frame.from_options(
connection_type="marketplace.spark",
connection_options={
"parentProject": "...",
"table": "...",
"connectionName": "...",
"filter": "date = 'yyyy-mm-dd'" #put condition here
},
transformation_ctx="GoogleBigQueryConnector0220forAWSGlue30_node1",
)
)
I created a table in Athena without a crawler from S3 source. It is showing up in my datacatalog. However, when I try to access it through a python job in Glue ETL, it shows that it has no column or any data. The following error pops up when accessing a column: AttributeError: 'DataFrame' object has no attribute '<COLUMN-NAME>'.
I am trying to access the dynamic frame following the glue way:
datasource = glueContext.create_dynamic_frame.from_catalog(
database="datacatalog_database",
table_name="table_name",
transformation_ctx="datasource"
)
print(f"Count: {datasource.count()}")
print(f"Schema: {datasource.schema()}")
The above logs output: Count: 0 & Schema: StructType([], {}), where the Athena table shows I have around ~800,000 rows.
Sidenotes:
The ETL job concerned has AWSGlueServiceRole attached.
I tried Glue Visual Editor as well, it showed the datacatalog database/table concerned but sadly, same error.
It looks like the S3 bucket has multiple nested folders inside it. For Glue to read these folders you need to add a flag adding additional_options = {"recurse": True} to your from_catalog(). This will help to recursively read records from s3 files.
I'm trying to figure out how to migrate a use case from EMR to AWS Glue involving Hive views.
In EMR today, I have Hive external tables backed by Parquet in S3, and I have additional views like create view hive_view as select col from external_table where col = x
Then in Spark on EMR, I can issue statements like df = spark.sql("select * from hive_view") to reference my Hive view.
I am aware I can use the Glue catalog as a drop-in replacement for the Hive metastore, but I'm trying to migrate the Spark job itself off of EMR to Glue. So in my end state, there is no longer a Hive endpoint, only Glue.
Questions:
How do I replace the create view ... statement if I no longer have an EMR cluster to issue Hive commands? What's the equivalent AWS Glue SDK call?
How do I reference those views from within a Glue job?
What I've tried so far: using boto3 to call glue.create_table like this
glue = boto3.client('glue')
glue.create_table(DatabaseName='glue_db_name',
TableInput = {'Name': 'hive_view',
'TableType': 'VIRTUAL_VIEW',
'ViewExpandedText': 'select .... from ...'
})
I can see the object created in the Glue catalog but the classification shows as "Unknown" and the references in the job fail with a corresponding error:
py4j.protocol.Py4JJavaError: An error occurred while calling o56.getCatalogSource. :
java.lang.Error: No classification or connection in bill_glue_poc.view_test at ...
I have validated that I can use Hive views with Spark in EMR with the Glue catalog as the metastore -- I see the view in the Glue catalog, and Spark SQL queries succeed, but I cannot reference the view from within a Glue job.
You can create a temporary view in Spark and query it like a Hive table (Scala):
val dataDyf = glueContext.getSourceWithFormat(
connectionType = "s3",
format = "parquet",
options = JsonOptions(Map(
"paths" -> Array("s3://bucket/external/folder")
))).getDynamicFrame()
// Convert DynamicFrame to Spark's DataFrame and apply filtering
val dataViewDf = dataDyf.toDF().where(...)
dataViewDf.createOrReplaceTempView("hive_view")
val df = spark.sql("select * from hive_view")
I have a Spark batch job which is executed hourly. Each run generates and stores new data in S3 with the directory naming pattern DATA/YEAR=?/MONTH=?/DATE=?/datafile.
After uploading the data to S3, I want to investigate it using Athena. Also, I would like to visualize them in QuickSight by connecting to Athena as a data source.
The problem is that after each run of my Spark batch, the newly generated data stored in S3 will not be discovered by Athena, unless I manually run the query MSCK REPAIR TABLE.
Is there a way to make Athena update the data automatically, so that I can create a fully automatic data visualization pipeline?
There are a number of ways to schedule this task. How do you schedule your workflows? Do you use a system like Airflow, Luigi, Azkaban, cron, or using an AWS Data pipeline?
From any of these, you should be able to fire off the following CLI command.
$ aws athena start-query-execution --query-string "MSCK REPAIR TABLE some_database.some_table" --result-configuration "OutputLocation=s3://SOMEPLACE"
Another option would be AWS Lambda. You could have a function that calls MSCK REPAIR TABLE some_database.some_table in response to a new upload to S3.
An example Lambda Function could be written as such:
import boto3
def lambda_handler(event, context):
bucket_name = 'some_bucket'
client = boto3.client('athena')
config = {
'OutputLocation': 's3://' + bucket_name + '/',
'EncryptionConfiguration': {'EncryptionOption': 'SSE_S3'}
}
# Query Execution Parameters
sql = 'MSCK REPAIR TABLE some_database.some_table'
context = {'Database': 'some_database'}
client.start_query_execution(QueryString = sql,
QueryExecutionContext = context,
ResultConfiguration = config)
You would then configure a trigger to execute your Lambda function when new data are added under the DATA/ prefix in your bucket.
Ultimately, explicitly rebuilding the partitions after you run your Spark Job using a job scheduler has the advantage of being self documenting. On the other hand, AWS Lambda is convenient for jobs like this one.
You should be running ADD PARTITION instead:
aws athena start-query-execution --query-string "ALTER TABLE ADD PARTITION..."
Which adds a the newly created partition from your S3 location
Athena leverages Hive for partitioning data.
To create a table with partitions, you must define it during the CREATE TABLE statement. Use PARTITIONED BY to define the keys by which to partition data.
There's multiple ways to solve the issue and get the table updated:
Call MSCK REPAIR TABLE. This will scan ALL data. It's costly as every file is read in full (at least it's fully charged by AWS). Also it's painfully slow. In short: Don't do it!
Create partitions by your own by calling ALTER TABLE ADD PARTITION abc .... This is good in a sense no data is scanned and costs are low. Also the query is fast, so no problems here. It's also a good choice if you have very cluttered file structure without any common pattern (which doesn't seem it's your case as it's a nicely organised S3 key pattern). There's also downsides to this approach: A) It's hard to maintain B) All partitions will to be stored in GLUE catalog. This can become an issue when you have a lot of partitions as they need to be read out and passed to Athena and EMRs Hadoop infrastructure.
Use partition projection. There's two different styles you might want to evaluate. Here's the variant with does create the partitions for Hadoop at query time. This means there's no GLUE catalog entries send over the network and thus large amounts of partitions can be handled quicker. The downside is you might 'hit' some partitions that might not exist. These will of course be ignored, but internally all partitions that COULD match your query will be generated - no matter if they are on S3 or not (so always add partition filters to your query!). If done correctly, this option is a fire and forget approach as there's no updates needed.
CREATE EXTERNAL TABLE `mydb`.`mytable`
(
...
)
PARTITIONED BY (
`YEAR` int,
`MONTH` int,
`DATE` int)
...
LOCATION
's3://DATA/'
TBLPROPERTIES(
"projection.enabled" = "true",
"projection.account.type" = "integer",
"projection.account.range" = "1,50",
"projection.YEAR.type" = "integer",
"projection.YEAR.range" = "2020,2025",
"projection.MONTH.type" = "integer",
"projection.MONTH.range" = "1,12",
"projection.DATE.type" = "integer",
"projection.DATE.range" = "1,31",
"storage.location.template" = "s3://DATA/YEAR=${YEAR}/MONTH=${MONTH}/DATE=${DATE}/"
);
https://docs.aws.amazon.com/athena/latest/ug/partition-projection.html
Just to list all options: You can also use GLUE crawlers. But it doesn't seemed to be a favourable approach as it's not as flexible as advertised.
You get more control on GLUE using Glue Data Catalog API directly, which might be an alternative to approach #2 if you have a lot of automated scripts
that do the preparation work to setup your table.
In short:
If your application is SQL centric, you like the leanest approach with no scripts, use partition projection
If you have many partitions, use partition projection
If you have a few partitions or partitions do not have a generic pattern, use approach #2
If you're script heavy and scripts do most of the work anyway and are easier to handle for you, consider approach #5
If you're confused and have no clue where to start - try partition projection first! It should fit 95% of the use cases.