amazon-s3

问题 I have installed Spark 2.4.3 with Hadoop 3.2 on an AWS EC2 instance. I’ve been using spark (mainly pyspark) in local mode with great success. It is nice to be able to spin up something small and then resize it when I need power, and do it all very quickly. When I really need to scale I can switch to EMR and go to lunch. It all works smoothly apart from one issue: I can’t get the local spark to reliably write to S3 (I've been using local EBS space). This is clearly something to do with all the

问题 I have a use case in which I have to verify that the payloads sent to Kinesis firehose are indeed being sent. In order to do that I came up with the chain Firehose -> Firehose Data transformation(using lambda) -> DDB -> Check for payload in DDB (the payload is the hashkey in the DDB). I have to define this entire chain in one shot programatically. The data transformation is the same as http://docs.aws.amazon.com/firehose/latest/dev/data-transformation.html. I am doing all this since I cannot

问题 I have a use case in which I have to verify that the payloads sent to Kinesis firehose are indeed being sent. In order to do that I came up with the chain Firehose -> Firehose Data transformation(using lambda) -> DDB -> Check for payload in DDB (the payload is the hashkey in the DDB). I have to define this entire chain in one shot programatically. The data transformation is the same as http://docs.aws.amazon.com/firehose/latest/dev/data-transformation.html. I am doing all this since I cannot

问题 I'm currently importing postgres data to hdfs. I'm planning to move the storage from hdfs to S3. When i'm trying to provide S3 location, the sqoop job is failing. I'm running it on EMR(emr-5.27.0) cluster and I've read/write access to that s3 bucket from all nodes in the cluster. sqoop import \ --connect "jdbc:postgresql://<machine_ip>:<port>/<database>?sslfactory=org.postgresql.ssl.NonValidatingFactory&ssl=true" \ --username <username> \ --password-file <password_file_path> \ --table

问题 I'm currently importing postgres data to hdfs. I'm planning to move the storage from hdfs to S3. When i'm trying to provide S3 location, the sqoop job is failing. I'm running it on EMR(emr-5.27.0) cluster and I've read/write access to that s3 bucket from all nodes in the cluster. sqoop import \ --connect "jdbc:postgresql://<machine_ip>:<port>/<database>?sslfactory=org.postgresql.ssl.NonValidatingFactory&ssl=true" \ --username <username> \ --password-file <password_file_path> \ --table

问题 We are using Amazon connect which uses the "Get customer input" interact block to use the Amazon Lex which internally connects to Lambda so the flow will be like Amazon Connect -> (using Get Customer Input, It connects to Amazon Lex) Amazon Lex -> (In Amazon Lex, the intent is pointed to Amazon Lambda) Amazon Lambda Amazon connect -> Amazon Lex -> AWS Lambda The flow works for all the intents as expected. but for a particular flow, We need to upload the file from lambda to S3 and return the

问题 I imported the contents of a SQL Server table into an S3 CSV file. A year_month column was formatted as datetime in SQL Server, and appeared as 2020-01-01 00:00:00.000 . That same record appears as 1/1/2020 12:00:00 AM in the S3 csv file. I am using this dataset in AWS Forecast, which requires 2020-01-01 00:00:00.000 formatting. What is the best tool within AWS to adjust the format? Or conversely, can I tackle the problem in SQL Server? 来源： https://stackoverflow.com/questions/61992855

问题 I have successfully configured my S3 bucket to serve a static website and also redirect to a lambda function if a file is not found in the bucket. <RoutingRules> <RoutingRule> <Condition> <KeyPrefixEquals/> <HttpErrorCodeReturnedEquals>404</HttpErrorCodeReturnedEquals> </Condition> <Redirect> <Protocol>https</Protocol> <HostName>mylambda.execute-api.us-east-1.amazonaws.com</HostName> <ReplaceKeyPrefixWith>/?key=</ReplaceKeyPrefixWith> <HttpRedirectCode>307</HttpRedirectCode> </Redirect> <

问题 I am trying to get a Google Cloud Function in Python 3.7 to take a file from Google Cloud Storage and upload it into AWS S3. In the command line, I would authenticate with awscli and then use the gsutil cp command to copy the file across. I have translated this process into python as: import subprocess def GCS_to_s3(arg1, arg2): subprocess.call(["aws configure set aws_access_key_id AKIA********"], shell=True) subprocess.call(["aws configure set aws_secret_access_key EgkjntEFFDVej"], shell

问题 I have a postdata presigned URL of Amazon S3. I want to use it in a Karate feature file to upload a file (say: pdf) Here is a sample Curl request that I need to perform Using Karate POST request curl --location --request POST '<s3bucketURL>' \ --form 'key=some_key_fileName' \ --form 'x-amz-meta-payload={JsonObject}' \ --form 'Content-Type=application/pdf' \ --form 'bucket=<BucketName>' \ --form 'X-Amz-Algorithm=AWS4-HMAC-SHA256' \ --form 'X-Amz-Credential=<AWS_Credential>' \ --form 'X-Amz