@@ -157,19 +157,10 @@ In this part, your task is to implement the `PartitionByCounty` and `CalcAvgLoan
Imagine a scenario where there could be many queries differentiated by `county`, and one of them is to get the average loan amount for a county. In this case, it might be much more efficient to generate a set of 1x Parquet files filtered by county, and then read data from these partitioned, relatively much smaller tables for computation.
**PartitionByCounty:** To be more specific, you need to categorize the contents of that parquet file just stored in HDFS using `county_id` as the key. For each `county_id`, create a new parquet file that records all entries under that county, and then save them with a **1x replication**. Files should be written into folder `/partitioned/` and name for each should be their `county_id`.
**PartitionByCounty:** To be more specific, you need to categorize the contents of that parquet file just stored in HDFS using `county_id` as the key. For each `county_id`, create a new parquet file that records all entries under that county, and then save them with a **1x replication**. Files should be written into folder `/partitioned/`.
**CalcAvgLoan:** To be more specific, for a given `county_id` , you need to return a int value, indicating the average `loan_amount` of that county. **Note:** You are required to perform this calculation based on the partitioned parquet files generated by `FilterByCounty`. `source` field in proto file can ignored in this part.
The inside of the partitioned directory should look like this:
```
├── partitioned/
│ ├── 55001.parquet
│ ├── 55003.parquet
│ └── ...
```
The root directory on HDFS should now look like this:
```
14.4 M 43.2 M hdfs://boss:9000/hdma-wi-2021.parquet
