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.
Related
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 have an Athena database set up pointing at an S3 bucket containing ALB logs, and it all works correctly. I partition the table by a column called datetime and the idea is that it has the format YYYY/MM/DD.
I can manually create partitions through the Athena console, using the following command:
ALTER TABLE alb_logs ADD IF NOT EXISTS PARTITION (datetime='2019-08-01') LOCATION 's3://mybucket/AWSLogs/myaccountid/elasticloadbalancing/eu-west-1/2019/08/01/'
I have created a lambda to run daily to create a new partition, however this doesn't seem to work. I use the boto3 python client and execute the following:
result = athena.start_query_execution(
QueryString = "ALTER TABLE alb_logs ADD IF NOT EXISTS PARTITION (datetime='2019-08-01') LOCATION 's3://mybucket/AWSLogs/myaccountid/elasticloadbalancing/eu-west-1/2019/08/01/'",
QueryExecutionContext = {
'Database': 'web'
},
ResultConfiguration = {
"OutputLocation" : "s3://aws-athena-query-results-093305704519-eu-west-1/Unsaved/"
}
)
This appears to run successfully without any errors and the query execution even returns a QueryExecutionId as it should. However if I run SHOW PARTITIONS web.alb_logs; via the Athena console it hasn't created the partition.
I have a feeling it could be down to permissions, however I have given the lambda execution role full permissions to all resources on S3 and full permissions to all resources on Athena and it still doesn't seem to work.
Since Athena query execution is asynchronous your Lambda function never sees the result of the query execution, it just gets the result of starting the query.
I would be very surprised if this wasn't a permissions issue, but because of the above the error will not appear in the Lambda logs. What you can do is to log the query execution ID and look it up with the GetQueryExecution API call to see that the query succeeded.
Even better would be to rewrite your code to use the Glue APIs directly to add the partitions. Adding a partition is a quick and synchronous operation in Glue, which means you can make the API call and get a status in the same Lambda execution. Have a look at the APIs for working with partitions: https://docs.aws.amazon.com/glue/latest/dg/aws-glue-api-catalog-partitions.html
Since I'm not allowed to ask my question in the same thread where another person have the same problem (but not using a template) I'm creating this new thread.
The problem: Im creating a dataflow job from a template in gcp to ingest data from pub/sub into BQ. This works fine until the job executes. The job gets "stuck" and does not write anything to BQ.
I cant do so much because I cant choose the beam version in the template. This is the error:
Processing stuck in step WriteSuccessfulRecords/StreamingInserts/StreamingWriteTables/StreamingWrite for at least 01h00m00s without outputting or completing in state finish
at sun.misc.Unsafe.park(Native Method)
at java.util.concurrent.locks.LockSupport.park(LockSupport.java:175)
at java.util.concurrent.FutureTask.awaitDone(FutureTask.java:429)
at java.util.concurrent.FutureTask.get(FutureTask.java:191)
at org.apache.beam.sdk.io.gcp.bigquery.BigQueryServicesImpl$DatasetServiceImpl.insertAll(BigQueryServicesImpl.java:803)
at org.apache.beam.sdk.io.gcp.bigquery.BigQueryServicesImpl$DatasetServiceImpl.insertAll(BigQueryServicesImpl.java:867)
at org.apache.beam.sdk.io.gcp.bigquery.StreamingWriteFn.flushRows(StreamingWriteFn.java:140)
at org.apache.beam.sdk.io.gcp.bigquery.StreamingWriteFn.finishBundle(StreamingWriteFn.java:112)
at org.apache.beam.sdk.io.gcp.bigquery.StreamingWriteFn$DoFnInvoker.invokeFinishBundle(Unknown Source)
Any ideas how to get this to work?
The issue is coming from the step WriteSuccessfulRecords/StreamingInserts/StreamingWriteTables/StreamingWrite which suggest a problem while writing the data.
Your error can be replicated by (using either Pub/Sub Subscription to BigQuery or Pub/Sub Topic to BigQuery):
Configuring a template with a table that doesn't exist.
Starting the
template with a correct table and delete it during the job execution.
In both cases the stuckness message happens because the data is being read from Pubsub but it is waiting for the table availability to insert the data. The error is being reported each 5 minutes and it gets resolved when the table is created.
To verify the table configured in your template, see the property outputTableSpec in the PipelineOptions in the Dataflow UI.
I had the same issue before. The problem was that I used NestedValueProviders to evaluate the Pub/Sub topic/subscription and this is not supported in case of templated pipelines.
I was getting the same error and reason was that I created an empty BigQuery table without specifying an schema. Make sure to create a BQ table with a schema before you can load data via Dataflow.
When I set up a re-occuring backup via the export function in the DynamoDB console, the task it creates automatically creates a new EMR cluster when it runs. Some of my tables need to be backed up but are fairly small. What I end up with is a huge number of large servers running to back up some relatively small tables. Is there any easy way to chain these tasks to run on one server group in series or parallel?
Yes, it is possible. There is not a direct way but needs some additional tweaking in the Data-Pipeline end. You are required to understand how Data-Pipeline actually runs your export job by default.
When you click on export button on DDB console, it takes you to Data-Pipelines console to create a Pipeline for the export.
After filling out the template, instead of running, you can use Edit in Architect feature to alter the current template which only works with one table.
On the architect page, if you observe the Activities section ,you will find EmrAcvity running a EMR STEP using the following param's . This EMR STEP will run the export job using parameters that you initially passed on the template. Note that it will also RunsOn EMRclusterforBackup resource which you can find in resource section.
s3://dynamodb-emr-#{myDDBRegion}/emr-ddb-storage-handler/2.1.0/emr-ddb-2.1.0.jar,org.apache.hadoop.dynamodb.tools.DynamoDbExport,#{output.directoryPath},#{input.tableName},#{input.readThroughputPercent}
To run export on other DDB tables using same EMR resource, you simply need to create another EMRActivity object by clicking Add and then add EMRActivity on architect. On this activity , you can use the same RunsOn as previous activity is using and in the STEP param's you can manually edit to to include other table name and its export path
like
s3://dynamodb-emr-#{myDDBRegion}/emr-ddb-storage-handler/2.1.0/emr-ddb-2.1.0.jar,org.apache.hadoop.dynamodb.tools.DynamoDbExport,s3://myexport-bucket/table2/,table2,0.9
You can extend it for multiple tables.
Note: This can easily be done for multiple tables using a JSON file as Data-Pipeline definition , editing it to add more activities and parameters and then exporting it to Run later.
I have a problem with connection to DynamoDB. I get this exception:
com.amazonaws.services.dynamodb.model.ResourceNotFoundException:
Requested resource not found (Service: AmazonDynamoDB; Status Code:
400; Error Code: ResourceNotFoundException; Request ID: ..
But I have a table and region is correct.
From the docs it's either you don't have a Table with that name or it is in CREATING status.
I would double check to verify that the table does in fact exist, in the correct region, and you're using an access key that can reach it
My problem was stupid but maybe someone has the same... I changed recently the default credentials of aws (~/.aws/credentials), I was testing in another account and forgot to rollback the values to the regular account.
I spent 1 day researching the problem in my project and now I should repay a debt to humanity and reduce the entropy of the universe a little.
Usually, this message says that your client can't reach a table in your DB.
You should check the next things:
1. Your database is running
2. Your accessKey and secretKey are valid for the database
3. Your DB endpoint is valid and contains correct protocol ("http://" or "https://"), and correct hostname, and correct port
4. Your table was created in the database.
5. Your table was created in the database in the same region that you set as a parameter in credentials. Optional, because some
database environments (e.g. Testcontainers Dynalite) don't have an incorrect value for the region. And any nonempty region value will be correct
In my case problem was that I couldn't save and load data from a table in tests with DynamoDB substituted by Testcontainers and Dynalite. I found out that in our project tables creates by Spring component marked with #Component annotation. And in tests, we are using a global setting for lazy loading components to test, so our component didn't load by default because no one call it in the test explicitly. ¯_(ツ)_/¯
If DynamoDB table is in a different region, make sure to set it before initialising the DynamoDB by
AWS.config.update({region: "your-dynamoDB-region" });
This works for me:)
Always ensure that you do one of the following:
The right default region is set up in the AWS CLI configuration files on all the servers, development machines that you are working on.
The best choice is to always specify these constants explicitly in a separate class/config in your project. Always import this in code and use it in the boto3 calls. This will provide flexibility if you were to add or change based on the enterprise requirements.
If your resources are like mine and all over the place, you can define the region_name when you're creating the resource.
I do this for all my instantiations as it forces me to think about what I'm putting/calling where.
boto3.resource("dynamodb", region_name='us-east-2')
I was getting this issue in my .NetCore Application.
Following fixed the issue for me in Startup class --> ConfigureServices method
services.AddDefaultAWSOptions(
new AWSOptions
{
Region = RegionEndpoint.GetBySystemName("eu-west-2")
});
I got Error warning Lambda : lifecycleIteration=0 lambda handler returned an error: ResourceNotFoundException: Requested resource not found
I spent 1 week to fix the issue.
And so its root cause and steps to find issue is mentioned in below Git Issue thread and fixed it.
https://github.com/soto-project/soto/issues/595