I am trying to figure out how to write directly from a EMR map task to the s3 bucket. I would like to run a python streaming job which would get some data from the internet and save it to s3 - without returning it back to reduce job. Can anyone help me with that?
Why don't you just set the output of your MR job to be a s3 directory and tell it that there is no reducer:
./elastic-mapreduce ..... --output s3n://bucket/outputfiles --reducer NONE
That should do what you want it to.
Then your script can do something like this (sorry, ruby):
STDIN.each do |url|
puts extract_data(url)
end
Related
I am trying to save my Glue job output to S3 using following code snippet
output_table = glueContext.write_dynamic_frame.from_options(
frame=table,
connection_type="s3",
format="json",
connection_options={"path": "s3://brand-code-mappings", "partitionKeys": []},
transformation_ctx="S3bucket_node3",
)
I want to overwrite all the objects that are already present in S3 bucket instead of appending to them.
I have tried making following changes, but nothing seems to work.
table.toDF()
.write
.mode("overwrite")
.format("parquet")
.partitionBy()
.save('s3://brand-code-mappings')
table.toDF()
.write
.mode("overwrite")
.parquet("s3://brand-code-mappings")
Please help on how i can overwrite already existing objects in the S3 bucket with the Glue output.
I am using Glue 3.0- which supports Spark 3.1, Scala 2 and Python 3.
Thanks,
Anamika
I'm currently exploring implementing hooks in some of my DAGs. For instance, in one dag, I'm trying to connect to s3 to send a csv file to a bucket, which then gets copied to a redshift table.
I have a custom module written which I import to run this process. I am trying to currently set up an S3Hook to undergo this process instead. But I'm a little confused in setting up the connection, and how everything works.
First, I input the hook
from airflow.hooks.S3_hook import S3Hook
Then I try to make the hook instance
s3_hook = S3Hook(aws_conn_id='aws-s3')
Next I try to set up the client
s3_client = s3_hook.get_conn()
However when I run the client line above, I received this error
OperationalError: (sqlite3.OperationalError)
no such table: connection
[SQL: SELECT connection.password AS connection_password, connection.extra AS connection_extra, connection.id AS connection_id, connection.conn_id AS connection_conn_id, connection.conn_type AS connection_conn_type, connection.description AS connection_description, connection.host AS connection_host, connection.schema AS connection_schema, connection.login AS connection_login, connection.port AS connection_port, connection.is_encrypted AS connection_is_encrypted, connection.is_extra_encrypted AS connection_is_extra_encrypted
FROM connection
WHERE connection.conn_id = ?
LIMIT ? OFFSET ?]
[parameters: ('aws-s3', 1, 0)]
(Background on this error at: http://sqlalche.me/e/13/e3q8)
I'm trying to diagnose the error, but the tracebook is long. I'm a little confused on why sqlite3 is involved here, when I'm trying to utilize s3 here. Can anyone unpack this? Why is this error being thrown when trying to set up the client?
Thanks
Airflow is not just a library - it's also an application.
To execute Airflow code you must have airflow instance running this mean also having a database with the needed schema.
To create the tables you must execute airflow init db.
Edit:
After the discussion in comments. Your issue is that you have working Airflow application inside docker but your DAGs are written on your local disk. Docker is closed environment if you want Airflow to recognize your dags you must move the files to the DAG folder in the docker.
I really liked BigQuery's Data Transfer Service. I have flat files in the exact schema sitting to be loaded into BQ. It would have been awesome to just setup DTS schedule that picked up GCS files that match a pattern and load the into BQ. I like the built in option to delete source files after copy and email in case of trouble. But the biggest bummer is that the minimum interval is 60 minutes. That is crazy. I could have lived with a 10 min delay perhaps.
So if I set up the DTS to be on demand, how can I invoke it from an API? I am thinking create a cronjob that calls it on demand every 10 mins. But I can’t figure out through the docs how to call it.
Also, what is my second best most reliable and cheapest way of moving GCS files (no ETL needed) into bq tables that match the exact schema. Should I use Cloud Scheduler, Cloud Functions, DataFlow, Cloud Run etc.
If I use Cloud Function, how can I submit all files in my GCS at time of invocation as one bq load job?
Lastly, anyone know if DTS will lower the limit to 10 mins in future?
So if I set up the DTS to be on demand, how can I invoke it from an API? I am thinking create a cronjob that calls it on demand every 10 mins. But I can’t figure out through the docs how to call it.
StartManualTransferRuns is part of the RPC library but does not have a REST API equivalent as of now. How to use that will depend on your environment. For instance, you can use the Python Client Library (docs).
As an example, I used the following code (you'll need to run pip install google-cloud-bigquery-datatransfer for the depencencies):
import time
from google.cloud import bigquery_datatransfer_v1
from google.protobuf.timestamp_pb2 import Timestamp
client = bigquery_datatransfer_v1.DataTransferServiceClient()
PROJECT_ID = 'PROJECT_ID'
TRANSFER_CONFIG_ID = '5e6...7bc' # alphanumeric ID you'll find in the UI
parent = client.project_transfer_config_path(PROJECT_ID, TRANSFER_CONFIG_ID)
start_time = bigquery_datatransfer_v1.types.Timestamp(seconds=int(time.time() + 10))
response = client.start_manual_transfer_runs(parent, requested_run_time=start_time)
print(response)
Note that you'll need to use the right Transfer Config ID and the requested_run_time has to be of type bigquery_datatransfer_v1.types.Timestamp (for which there was no example in the docs). I set a start time 10 seconds ahead of the current execution time.
You should get a response such as:
runs {
name: "projects/PROJECT_NUMBER/locations/us/transferConfigs/5e6...7bc/runs/5e5...c04"
destination_dataset_id: "DATASET_NAME"
schedule_time {
seconds: 1579358571
nanos: 922599371
}
...
data_source_id: "google_cloud_storage"
state: PENDING
params {
...
}
run_time {
seconds: 1579358581
}
user_id: 28...65
}
and the transfer is triggered as expected (nevermind the error):
Also, what is my second best most reliable and cheapest way of moving GCS files (no ETL needed) into bq tables that match the exact schema. Should I use Cloud Scheduler, Cloud Functions, DataFlow, Cloud Run etc.
With this you can set a cron job to execute your function every ten minutes. As discussed in the comments, the minimum interval is 60 minutes so it won't pick up files less than one hour old (docs).
Apart from that, this is not a very robust solution and here come into play your follow-up questions. I think these might be too broad to address in a single StackOverflow question but I would say that, for on-demand refresh, Cloud Scheduler + Cloud Functions/Cloud Run can work very well.
Dataflow would be best if you needed ETL but it has a GCS connector that can watch a file pattern (example). With this you would skip the transfer, set the watch interval and the load job triggering frequency to write the files into BigQuery. VM(s) would be running constantly in a streaming pipeline as opposed to the previous approach but a 10-minute watch period is possible.
If you have complex workflows/dependencies, Airflow has recently introduced operators to start manual runs.
If I use Cloud Function, how can I submit all files in my GCS at time of invocation as one bq load job?
You can use wildcards to match a file pattern when you create the transfer:
Also, this can be done on a file-by-file basis using Pub/Sub notifications for Cloud Storage to trigger a Cloud Function.
Lastly, anyone know if DTS will lower the limit to 10 mins in future?
There is already a Feature Request here. Feel free to star it to show your interest and receive updates
Now your can easy manual run transfer Bigquery data use RESTApi:
HTTP request
POST https://bigquerydatatransfer.googleapis.com/v1/{parent=projects/*/locations/*/transferConfigs/*}:startManualRuns
About this part > {parent=projects//locations//transferConfigs/*}, check on CONFIGURATION of your Transfer then notice part like image bellow.
Here
More here:
https://cloud.google.com/bigquery-transfer/docs/reference/datatransfer/rest/v1/projects.locations.transferConfigs/startManualRuns
following the Guillem's answer and the API updates, this is my new code:
import time
from google.cloud.bigquery import datatransfer_v1
from google.protobuf.timestamp_pb2 import Timestamp
client = datatransfer_v1.DataTransferServiceClient()
config = '34y....654'
PROJECT_ID = 'PROJECT_ID'
TRANSFER_CONFIG_ID = config
parent = client.transfer_config_path(PROJECT_ID, TRANSFER_CONFIG_ID)
start_time = Timestamp(seconds=int(time.time()))
request = datatransfer_v1.types.StartManualTransferRunsRequest(
{ "parent": parent, "requested_run_time": start_time }
)
response = client.start_manual_transfer_runs(request, timeout=360)
print(response)
For this to work, you need to know the correct TRANSFER_CONFIG_ID.
In my case, I wanted to list all the BigQuery Scheduled queries, to get a specific ID. You can do it like that :
# Put your projetID here
PROJECT_ID = 'PROJECT_ID'
from google.cloud import bigquery_datatransfer_v1
bq_transfer_client = bigquery_datatransfer_v1.DataTransferServiceClient()
parent = bq_transfer_client.project_path(PROJECT_ID)
# Iterate over all results
for element in bq_transfer_client.list_transfer_configs(parent):
# Print Display Name for each Scheduled Query
print(f'[Schedule Query Name]:\t{element.display_name}')
# Print name of all elements (it contains the ID)
print(f'[Name]:\t\t{element.name}')
# Extract the IDs:
TRANSFER_CONFIG_ID= element.name.split('/')[-1]
print(f'[TRANSFER_CONFIG_ID]:\t\t{TRANSFER_CONFIG_ID}')
# You can print the entire element for debug purposes
print(element)
I created my first notebook instance on Amazon SageMaker.
Next I opened the Jupyter notebook and I used the SageMaker Example in the section Reinforcement Learning rl_deepracer_coach_robomaker.ipynb. The question is addressed principally to those who are familiar with this notebook.
There you can launch a training process and a RoboMaker simulation application to start the learning process for an autonomous car.
When a simulation job is launched, one can access to the log file, which is visualised by default in a CloudWatch console. Some of the informations that appear in the log file can be modified in the script deepracer_env.py in /src/robomaker/environments subdirectory.
I would like to "bypass" the CloudWatch console, saving the log file informations like episode, total reward, number of steps, coordinates of the car, steering and throttle etc. in a dataframe or csv file to be written somewhere on the S3 at the end of the simulation.
Something similar has been done in the main notebook rl_deepracer_coach_robomaker.ipynb to plot the metrics for a training job, namely the training reward per episode. There one can see that
csv_file_name = "worker_0.simple_rl_graph.main_level.main_level.agent_0.csv"
is called from the S3, but I simply cannot find where this csv is generated to mimic the process.
You can create a csv file in the /opt/ml/output/intermediate/ folder, and the file will be saved in the following directory:
s3://<s3_bucket>/<s3_prefix>/output/intermediate/<csv_file_name>
However, it is not clear to me where exactly you will create such a file. DeepRacer notebook uses two machines, one for training (SageMaker instance) and one for simulations (RoboMaker instance). The above method will only work in a SageMaker instance, but much of what you would like to log such as ("Total rewards" in an episode) is actually in RoboMaker instance. For RoboMaker instances, the intermediate folder feature doesn't exist, and you'll have to save the file to s3 yourself using the boto library. Here is an example of doing that: https://qiita.com/hengsokvisal/items/329924dd9e3f65dd48e7
There is a way to download the CloudWatch logs to a file. This way you can just print, save the logs and parse it. Assuming you are executing from a notebook cell:
STREAM_NAME= <your stream name as given by RoboMaker CloudWatch logs>
task = !aws logs create-export-task --task-name "copy_deepracer_logs" --log-group-name "/aws/robomaker/SimulationJobs" --log-stream-name-prefix $STREAM_NAME --destination "<s3_bucket>" --destination-prefix "<s3_prefix>" --from <unix timestamp in milliseconds> --to <unix timestamp in milliseconds>
task_id = json.loads(''.join(task))['taskId']
The export is an asynchronous call, so give it a few minutes to download. If you can print the task_id, then the export is done.
I have data in Spark which I want to save to S3. The recommended method is to save is using the saveAsTextFile method on the SparkContext, which is successful. I expect that the data will be saved as 'parts'.
My problem is that when I go to S3 to look at my data it has been saved in a folder name _temporary, with a subfolder 0 and then each part or task saved in its own folder.
For example,
data.saveAsTextFile("s3:/kirk/data");
results in file likes
s3://kirk/data/_SUCCESS
s3://kirk/data/_temporary/0/_temporary_$folder$
s3://kirk/data/_temporary/0/task_201411291454_0001_m_00000_$folder$
s3://kirk/data/_temporary/0/task_201411291454_0001_m_00000/part-00000
s3://kirk/data/_temporary/0/task_201411291454_0001_m_00001_$folder$
s3://kirk/data/_temporary/0/task_201411291454_0001_m_00001/part-00001
and so on. I would expect and have seen something like
s3://kirk/data/_SUCCESS
s3://kirk/data/part-00000
s3://kirk/data/part-00001
Is this a configuration setting, or do I need to 'commit' the save to resolve the temporary files?
I had the same problem with spark streaming, that was because my Sparkmaster was set up with conf.setMaster("local") instead of conf.SetMaster("local[*]")
Without the [*], spark can't execute saveastextfile during the stream.
Try using coalesce() to reduce the rdd to 1 partition before you export.
Good luck!