无法在外部Hadoop集群执行命令，如何迁移数据至Amazon S3？

Great question—let's walk through each of your options since you're restricted to pulling data from the external Hadoop cluster (no command execution allowed there) to AWS:

1. Is using Sqoop to extract data from Hive feasible? #

Absolutely, this is a solid approach, but it relies on a few key prerequisites:

• Network accessibility: Your AWS environment (where you’ll run Sqoop) needs to reach the source Hive Metastore (typically port 9083) and the source HDFS NameNode/DataNode ports. This usually requires setting up VPC peering, a VPN, or adjusting inbound security group rules on the source cluster.
• Sqoop setup:
  • Point Sqoop to the source Hive Metastore using --connect jdbc:hive2://<source-metastore-host>:9083/ (for HiveServer2) or by placing the source cluster’s hive-site.xml in your Sqoop configuration directory.
  • To export directly to S3, use the --target-dir s3://<your-bucket>/target-path parameter. Ensure your Sqoop runtime has AWS credentials configured—using an IAM role (if running on EC2/EMR) is far more secure than hardcoding keys.
• Data format support: If your Hive tables use columnar formats like Parquet or ORC, use Sqoop’s format-specific flags (e.g., --as-parquetfile) to preserve structure and performance. Tune the --num-mappers parameter to optimize parallelism for your dataset size.

Note: Sqoop excels with structured/semi-structured data, but it works for unstructured HDFS-backed Hive external tables too—though file-based tools might be more efficient for raw unstructured data.

2. What configurations are needed to run S3-distcp on AWS? #

S3-distcp (preinstalled on Amazon EMR clusters) is built for large-scale file transfers between HDFS-compatible storage and S3. Here’s what you need:

• EMR cluster deployment: Launch an EMR cluster with Hadoop included (all modern EMR releases come with S3-distcp). Make sure the cluster is in a VPC that can reach the source Hadoop cluster’s HDFS endpoints.
• Network access: Confirm your EMR cluster can connect to the source HDFS NameNode (port 8020 or 9000, depending on Hadoop version) and DataNodes.
• IAM permissions: Attach an IAM role to your EMR cluster that grants s3:PutObject, s3:GetObject, and s3:ListBucket permissions on your target S3 bucket. Avoid manual credential configuration—IAM roles are the secure default.
• Source path lookup: Since S3-distcp operates on raw HDFS files, you’ll need the storage path of your Hive tables from the source Metastore. If you can query the source Hive, run DESCRIBE FORMATTED <table-name> to get this path; otherwise, retrieve it directly from the Metastore database.
• Sample S3-distcp command:

  s3-distcp --src hdfs://<source-namenode>:<port>/path/to/hive/table/data --dest s3://<your-bucket>/target/path --num-mappers 20
  

  Add flags like --sync to copy only new/updated files, or --delete to remove files in S3 that no longer exist in the source.

3. Are there other feasible solutions? #

Yes—here are alternatives tailored to different scale and use cases:

• AWS Glue: Use Glue Crawlers to connect to the source Hive Metastore and auto-discover table schemas, then build serverless ETL jobs to pull data from the source HDFS to S3. Glue handles both structured and unstructured data without requiring you to manage clusters.
• Apache Spark on EMR/EC2: Spin up a Spark cluster on EMR or EC2, configure it to access the source Hive Metastore, then run a Spark job to read Hive tables and write directly to S3. Spark is highly efficient for large datasets and supports all major data formats.
• Hadoop FS commands: For smaller datasets, use standard Hadoop filesystem commands from your AWS environment (if you have Hadoop installed) to copy files directly:

  hadoop fs -cp hdfs://<source-namenode>:<port>/path/to/data s3://<your-bucket>/target/path
  

  This is simpler but less performant for large-scale transfers compared to S3-distcp or Spark.
• AWS DataSync: If your source cluster is on-premises, use DataSync to transfer data from HDFS to S3. DataSync is optimized for high-speed transfers and supports incremental syncs, but requires installing a DataSync agent in your on-premises environment.

内容的提问来源于stack exchange，提问作者John Thomas