I'm writing a Dataflow Pipeline. It deserializes a confluent Avro PubSub subscription, and writes it to Google Big Query. Confluent Avro has a schema registry that we connect to in order to get the schema definition. We use Private Service Connect to have an IP of the form 192.168.x.x to connect to it.
I have this code:
| "Write records to BigQuery" >> beam.io.Write(
beam.io.WriteToBigQuery(
table=output_table,
dataset=output_dataset,
project=output_project,
schema=out_schema
create_disposition=beam.io.BigQueryDisposition.
CREATE_IF_NEEDED,
write_disposition=beam.io.BigQueryDisposition.WRITE_APPEND)))
out_schema should be fetched from the SchemaRegistry using a function fetchSchema(). I want to create the table with out_schema when the pipeline starts if the table does not exist. I need to be able to fetch the schema before the pipeline starts in order to do that.
I can connect to the Schema Registry inside the pipeline, but I can't connect to it outside of the pipeline's functions and ParDo classes. When I try, I get 'connection refused'. I believe this is because the subnetwork I'm using is specified in the pipeline options.
Is there a way to use the subnetwork outside of the pipeline so I can connect to the schema registry before starting the pipeline?
def helper(record):
logging.getInfo(fetchSchema()) #Works
return record
fetchSchema() # Does not work
with beam.Pipeline(options=options) as pipeline:
(pipeline | ... | beam.Map(lambda r: helper(r)))
If I understand your problem correctly, I think a good approach to handle this would be to use Flex Templates.
When you create a job from a Flex Template, a GCE VM is created to run your program that creates the actual pipeline (which has to be packaged as a Docker container).
That launcher VM will be inside your VPC/network and should be able to talk to the schema registry.
Related
I am trying to create a datashare in Redshift by following this documentation. When I type this command:
CREATE DATASHARE datashare_name
I get this message:
ERROR: CREATE DATASHARE is not enabled.
I also tried to make it using console, but same issue.
So how to enable data sharing in Redshift ?
From the documents:here
Data sharing via datashare is only available for ra3 instance types
The document lists ra3.16xlarge, ra3.4xlarge, and ra3.xlplus instance types for producer and consumer clusters.
So, if I were in your place - I would first go back and check my instance type. If still not sure, drop a simple CS ticket and ask them if anything has changed recently & documentation is not updated
As the runtime of my flink application I use managed flink by AWS (Kinesis Data Analytics Application)
I added functionality (sink) for write processed events from kinesis queue in S3 in a parquet format.
Locally everything works for me, but when I try to run the application in the cloud I get the following exception:
"throwableInformation": [
"com.esotericsoftware.kryo.KryoException: Error constructing instance of class: org.apache.avro.Schema$LockableArrayList",
"Serialization trace:",
"types (org.apache.avro.Schema$UnionSchema)",
"schema (org.apache.avro.Schema$Field)",
"fieldMap (org.apache.avro.Schema$RecordSchema)",
After finding a solution to the problem, I found that I need to change following property (checked this on a local cluster):
classloader.resolve-order: child-first -> classloader.resolve-order: parent-first
Is it possible to change this configuration when using AWS managed Fink (not EMR, Kinesis Data Analytics applications) in any way?
aws support answer: No. This property cannot be changed.
I have successfully scheduled my query in BigQuery, and the result is saved as a table in my dataset. I see a lot of information about scheduling data transfer in to BigQuery or Cloud Storage, but I haven't found anything regarding scheduling an export from a BigQuery table to Cloud Storage yet.
Is it possible to schedule an export of a BigQuery table to Cloud Storage so that I can further schedule having it SFTP-ed to me via Google BigQuery Data Transfer Services?
There isn't a managed service for scheduling BigQuery table exports, but one viable approach is to use Cloud Functions in conjunction with Cloud Scheduler.
The Cloud Function would contain the necessary code to export to Cloud Storage from the BigQuery table. There are multiple programming languages to choose from for that, such as Python, Node.JS, and Go.
Cloud Scheduler would send an HTTP call periodically in a cron format to the Cloud Function which would in turn, get triggered and run the export programmatically.
As an example and more specifically, you can follow these steps:
Create a Cloud Function using Python with an HTTP trigger. To interact with BigQuery from within the code you need to use the BigQuery client library. Import it with from google.cloud import bigquery. Then, you can use the following code in main.py to create an export job from BigQuery to Cloud Storage:
# Imports the BigQuery client library
from google.cloud import bigquery
def hello_world(request):
# Replace these values according to your project
project_name = "YOUR_PROJECT_ID"
bucket_name = "YOUR_BUCKET"
dataset_name = "YOUR_DATASET"
table_name = "YOUR_TABLE"
destination_uri = "gs://{}/{}".format(bucket_name, "bq_export.csv.gz")
bq_client = bigquery.Client(project=project_name)
dataset = bq_client.dataset(dataset_name, project=project_name)
table_to_export = dataset.table(table_name)
job_config = bigquery.job.ExtractJobConfig()
job_config.compression = bigquery.Compression.GZIP
extract_job = bq_client.extract_table(
table_to_export,
destination_uri,
# Location must match that of the source table.
location="US",
job_config=job_config,
)
return "Job with ID {} started exporting data from {}.{} to {}".format(extract_job.job_id, dataset_name, table_name, destination_uri)
Specify the client library dependency in the requirements.txt file
by adding this line:
google-cloud-bigquery
Create a Cloud Scheduler job. Set the Frequency you wish for
the job to be executed with. For instance, setting it to 0 1 * * 0
would run the job once a week at 1 AM every Sunday morning. The
crontab tool is pretty useful when it comes to experimenting
with cron scheduling.
Choose HTTP as the Target, set the URL as the Cloud
Function's URL (it can be found by selecting the Cloud Function and
navigating to the Trigger tab), and as HTTP method choose GET.
Once created, and by pressing the RUN NOW button, you can test how the export
behaves. However, before doing so, make sure the default App Engine service account has at least the Cloud IAM roles/storage.objectCreator role, or otherwise the operation might fail with a permission error. The default App Engine service account has a form of YOUR_PROJECT_ID#appspot.gserviceaccount.com.
If you wish to execute exports on different tables,
datasets and buckets for each execution, but essentially employing the same Cloud Function, you can use the HTTP POST method
instead, and configure a Body containing said parameters as data, which
would be passed on to the Cloud Function - although, that would imply doing
some small changes in its code.
Lastly, when the job is created, you can use the Cloud Function's returned job ID and the bq CLI to view the status of the export job with bq show -j <job_id>.
Not sure if this was in GA when this question was asked, but at least now there is an option to run an export to Cloud Storage via a regular SQL query. See the SQL tab in Exporting table data.
Example:
EXPORT DATA
OPTIONS (
uri = 'gs://bucket/folder/*.csv',
format = 'CSV',
overwrite = true,
header = true,
field_delimiter = ';')
AS (
SELECT field1, field2
FROM mydataset.table1
ORDER BY field1
);
This could as well be trivially setup via a Scheduled Query if you need a periodic export. And, of course, you need to make sure the user or service account running this has permissions to read the source datasets and tables and to write to the destination bucket.
Hopefully this is useful for other peeps visiting this question if not for OP :)
You have an alternative to the second part of the Maxim answer. The code for extracting the table and store it into Cloud Storage should work.
But, when you schedule a query, you can also define a PubSub topic where the BigQuery scheduler will post a message when the job is over. Thereby, the scheduler set up, as described by Maxim is optional and you can simply plug the function to the PubSub notification.
Before performing the extraction, don't forget to check the error status of the pubsub notification. You have also a lot of information about the scheduled query; useful is you want to perform more checks or if you want to generalize the function.
So, another point about the SFTP transfert. I open sourced a projet for querying BigQuery, build a CSV file and transfert this file to FTP server (sFTP and FTPs aren't supported, because my previous company only used FTP protocol!). If your file is smaller than 1.5Gb, I can update my project for adding the SFTP support is you want to use this. Let me know
I need to automate a process to extract data from Google Big Query and exported to an external CSV in a external server outside of the GCP.
I just researching how to to that I found some commands to run from my External Server. But I prefer to do everything in GCP to avoid possible problems.
To run the query to CSV in Google storage
bq --location=US extract --compression GZIP 'dataset.table' gs://example-bucket/myfile.csv
To Download the csv from Google Storage
gsutil cp gs://[BUCKET_NAME]/[OBJECT_NAME] [OBJECT_DESTINATION]
But I would like to hear your suggestions
If you want to fully automatize this process, I would do the following:
Create a Cloud Function to handle the export:
This is the more lightweight solution, as Cloud Functions are serverless, and provide flexibility to implement code with the Client Libraries. See the quickstart, I recommend you to use the console to create the functions to start with.
In this example I recommend you to trigger the Cloud Function from an HTTP request, i.e. when the function URL is called, it will run the code inside of it.
An example Cloud Function code in Python, that creates the export when a HTTP request is made:
main.py
from google.cloud import bigquery
def hello_world(request):
project_name = "MY_PROJECT"
bucket_name = "MY_BUCKET"
dataset_name = "MY_DATASET"
table_name = "MY_TABLE"
destination_uri = "gs://{}/{}".format(bucket_name, "bq_export.csv.gz")
bq_client = bigquery.Client(project=project_name)
dataset = bq_client.dataset(dataset_name, project=project_name)
table_to_export = dataset.table(table_name)
job_config = bigquery.job.ExtractJobConfig()
job_config.compression = bigquery.Compression.GZIP
extract_job = bq_client.extract_table(
table_to_export,
destination_uri,
# Location must match that of the source table.
location="US",
job_config=job_config,
)
return "Job with ID {} started exporting data from {}.{} to {}".format(extract_job.job_id, dataset_name, table_name, destination_uri)
requirements.txt
google-cloud-bigquery
Note that the job will run asynchronously in the background, you will receive a return response with the job ID, which you can use to check the state of the export job in the Cloud Shell, by running:
bq show -j <job_id>
Create a Cloud Scheduler scheduled job:
Follow this documentation to get started. You can set the Frequency with the standard cron format, for example 0 0 * * * will run the job every day at midnight.
As a target, choose HTTP, in the URL put the Cloud Function HTTP URL (you can find it in the console, inside the Cloud Function details, under the Trigger tab), and as HTTP method choose GET.
Create it, and you can test it in the Cloud Scheduler by pressing the Run now button in the Console.
Synchronize your external server and the bucket:
Up until now you only have scheduled exports to run every 24 hours, now to synchronize the bucket contents with your local computer, you can use the gsutil rsync command. If you want to save the imports, lets say to the my_exports folder, you can run, in your external server:
gsutil rsync gs://BUCKET_WITH_EXPORTS /local-path-to/my_exports
To periodically run this command in your server, you could create a standard cron job in your crontab inside your external server, to run each day as well, just at a few hours later than the bigquery export, to ensure that the export has been made.
Extra:
I have hard-coded most of the variables in the Cloud Function to be always the same. However, you can send parameters to the function, if you do a POST request instead of a GET request, and send the parameters as data in the body.
You will have to change the Cloud Scheduler job to send a POST request to the Cloud Function HTTP URL, and in the same place you can set the body to send the parameters regarding the table, dataset and bucket, for example. This will allow you to run exports from different tables at different hours, and to different buckets.
I am having a problem importing data from Excel sheet to a Amazon DynamoDB table. I have the Excel sheet in an Amazon S3 bucket and I want to import data from this sheet to a table in DynamoDB.
Currently I am following Import and Export DynamoDB Data Using AWS Data Pipeline but my pipeline is not working normally.
It gives me WAITING_FOR_RUNNER status and after sometime the status changed to CANCELED. Please suggest what I am doing wrong or is there any other way to import data from an Excel sheet to a DynamoDB table?
The potential reasons are as follows:-
Reason 1:
If your pipeline is in the SCHEDULED state and one or more tasks
appear stuck in the WAITING_FOR_RUNNER state, ensure that you set a
valid value for either the runsOn or workerGroup fields for those
tasks. If both values are empty or missing, the task cannot start
because there is no association between the task and a worker to
perform the tasks. In this situation, you've defined work but haven't
defined what computer will do that work. If applicable, verify that
the workerGroup value assigned to the pipeline component is exactly
the same name and case as the workerGroup value that you configured
for Task Runner.
Reason 2:-
Another potential cause of this problem is that the endpoint and
access key provided to Task Runner is not the same as the AWS Data
Pipeline console or the computer where the AWS Data Pipeline CLI tools
are installed. You might have created new pipelines with no visible
errors, but Task Runner polls the wrong location due to the difference
in credentials, or polls the correct location with insufficient
permissions to identify and run the work specified by the pipeline
definition.