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Design approach
The designs depend on the work done in the below Jira, where data node is conceptualized as a collection of heterogeneous storage with different durability and performance requirements.
https://issues.apache.org/jira/browse/HDFS-2832
Design 1
1) Hot data with partitions that are wholly hosted by HDFS.
2) Cold data with partitions that are wholly hosted by S3.
3) A view that unions these two tables which is the live table that we expose to end users.
Design 2
1) Hot data with partitions that are wholly hosted by HDFS.
2) Cold data with partitions that are wholly hosted by S3.
3) Both hot and cold data are in the same table
Design 2 is chosen over Design 1 because Design 1 is not transparent to the application layer. The change from old table to the view would inherently transfer some level of porting/integration extra work to the application.
Architecture Diagram
High Level Design
Automation Flow Diagram
Code
Automation tool codebase
https://github.com/RajdeepBiswas/HybridArchiveStorage/blob/master/hive_hybrid_storage.sh
Example configuration file
https://github.com/RajdeepBiswas/HybridArchiveStorage/blob/master/test_table.conf
Setup & Run
Setup
- cd /root/scripts/dataCopy
- vi hive_hybrid_storage.sh ##Put the script here
- chmod 755 hive_hybrid_storage.sh
- cd /root/scripts/dataCopy/conf
- vi test_table.conf ##This is where the cold partition names are placed
Run
Option1
Retain the hdfs partition and delete it manually after data verification.
./hive_hybrid_storage.sh schema_name.test_table test_table.conf retain
Option2
Delete the hdfs partition as part of the script. It will delete after data is copied to s3. So there is an option to copy it back to hdfs if you want to revert the location of the partition to hdfs.
./hive_hybrid_storage.sh schema_name.test_table test_table.conf delete
For part 1 of the article refer to the following link:
