Goodsync with aws s3 is slow11/23/2023 ![]() ![]() Surprisingly i found one more issue even though i set my compression as snappy the output table size is 108GB more that raw input text file which is 100 GB. It takes time for moving data from hive staging directory to destination table directory. Users from other places in the world will experience delay when requesting data from these buckets. Unfortunately, GoodSync may experience slow speeds over long distance connections (wide-area networks). This is the place where your files are physically stored. For your cluster to use canned ACLs when it writes files to Amazon S3, set the fs.s3.canned.acl cluster configuration option to the canned ACL to use. 1 Answer Sorted by: 22 As you already said, your S3 buckets are situated in a specific location, for example us-east, europe, us-west etc. so all slowness to that point has been the overhead of instantiating the AmazonS3Client, including fetching the temporary credentials from STS. INSERT into TABLE orders_parq partition(O_ORDERDATE) For information about how the other AWS user can grant you permissions to write files to the other users Amazon S3 bucket, see Editing bucket permissions in the Amazon Simple Storage Service User Guide. Whether you’re building your next application or upgrading your cloud object storage on an existing app, Storj is S3 compatible, making it simple to switch over, and compatible with your current S3 tooling. You said 'slowness in the response from S3' but it occurs to me, now, that you may not realize the fact that when your code reaches return url.toString(), no interaction with S3 has yet occurred. Size of table is 100 GB.so destination table is expected to have 2400 partitions. ![]() The source table contains orders data for 2400 days. Below is my source table structure CREATE EXTERNAL TABLE ORDERS (īelow is the structure of destination table. But looks like the setting is not available in Hive provided in EMR or Apache Hive. The main issue is that Hive first writes data to a staging directory and then moves the data to the original location.ĭoes anyone have a better solution for this? Using S3 is really slowing down our jobs.Ĭloudera recommends to use the setting hive.mv.files.threads. The "INSERT INTO TABLE" and "INSERT OVERWRITE" statements are very slow when using destination table as external table pointing to S3. I have created external tables pointing to S3 location.
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