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P4 (4% of grade): SQL and HDFS

Overview

In this project, you will depoly a data system consisting of an SQL server, an HDFS cluster, a gRPC server, and a client. You will need to read and filter data from a SQL database and persist it to HDFS. Then, you will partition the data to enable efficient request processing. Additionally, you will need to write a fault-tolerant application that works even when an HDFS DataNode fails (we will test this scenario).

Learning objectives:

• communicate with the SQL Server using SQL queries
• use the WebHDFS API
• utilize PyArrow to interact with HDFS and process Parquet files
• handle data loss scenario

Before starting, please review the general project directions.

Corrections/Clarifications

• none yet

Introduction

You'll need to deploy a system including 5 docker containers like this:

We have provided the other components; what you only need is to complete the work within the gRPC server and its Dockerfile.

Client

This project will use docker exec -it to run the client on the gRPC server's container. Usage of client.py is as follows:

#Inside the server container
python3 client.py DbToHdfs
python3 client.py BlockLocations -f <file_path>
python3 client.py PartitionByCounty 
python3 client.py CalcAvgLoan -c <county_code>

Docker Compose

Take a look at the provided Docker compose file. There are several services, including 3 datanodes, a namenode, a SQL server, a gRPC Server. The NameNode service will serve at the host of boss within the docker compose network.

gRPC

You are required to write a server.py and Dockerfile.server based on the provided proto file (you may not modify it).

The gRPC interfaces have already been defined (see lender.proto for details). There are no constraints on the return values of DbToHdfs and PartitionByCounty, so you may return what you think is helpful.

Docker image naming

You need to build the Docker image following this naming convention.

docker build . -f Dockerfile.hdfs -t p4-hdfs
docker build . -f Dockerfile.namenode -t p4-nn
docker build . -f Dockerfile.datanode -t p4-dn
docker build . -f Dockerfile.mysql -t p4-mysql
docker build . -f Dockerfile.server -t p4-server

PROJECT environment variable

Note that the compose file assumes there is a "PROJECT" environment variable. You can set it to p4 in your environment:

export PROJECT=p4

Hint 1: The command docker logs <container-name> -f might be very useful for troubleshooting. It allows you to view real-time output from a specific container.

Hint 2: Think about whether there is any .sh script that will help you quickly test code changes. For example, you may want it to rebuild your Dockerfiles, cleanup an old Compose cluster, and deploy a new cluster.

Hint 3: If you're low on disk space, consider running docker system prune -a --volumes -f

Part 1: DbToHdfs gRPC Call

In this part, your task is to implement the DbToHdfs gRPC call (you can find the interface definition in the proto file).

DbToHdfs: To be more specific, you need to:

1. Connect to the SQL server, with the database name as CS544 and the password as abc. There are two tables in databse: loans ,and loan_types. The former records all information related to loans, while the latter maps the numbers in the loan_type column of the loans table to their corresponding loan types. There should be 447367 rows in table loans. It's like:

   mysql> show tables;
   +-----------------+
   | Tables_in_CS544 |
   +-----------------+
   | loan_types      |
   | loans           |
   +-----------------+
   mysql> select count(*) from new_table;
   +----------+
   | count(*) |
   +----------+
   |   426716 |
   +----------+

2. What are the actual types for those loans? Perform an inner join on these two tables so that a new column loan_type_name added to the loans table, where its value is the corresponding loan_type_name from the loan_types table based on the matching loan_type_id in loans.
3. Filter all rows where loan_amount is greater than 30,000 and less than 800,000. After filtering, this table should have only 426716 rows.
4. Upload the generated table to /hdma-wi-2021.parquet in the HDFS, with 2x replication and a 1-MB block size, using PyArrow (https://arrow.apache.org/docs/python/generated/pyarrow.fs.HadoopFileSystem.html).

To check whether the upload was correct, you can use docker exec -it to enter the gRPC server's container and use HDFS command hdfs dfs -du -h <path>to see the file size. The expected result is:

15.3 M  30.5 M  hdfs://nn:9000/hdma-wi-2021.parquet

Hint 1: We used similar tables in lecture: https://git.doit.wisc.edu/cdis/cs/courses/cs544/s25/main/-/tree/main/lec/15-sql

Hint 2: To get more familiar with these tables, you can use SQL queries to print the table schema or retrieve sample data. A convenient way to do this is to use docker exec -it to enter the SQL Server, then run mysql client mysql -p CS544 to access the SQL Server and then perform queries.

Hint 3: After docker compose up, the SQL Server needs some time to load the data before it is ready. Therefore, you need to wait for a while, or preferably, add a retry mechanism for the SQL connection.

Hint 4: For PyArrow to be able to connect to HDFS, you'll need to configure some env variables carefully. Look at how Dockerfile.namenode does this for the start CMD, and do the same in your own Dockerfile for your server.

Part 2: BlockLocations gRPC Call

In this part, your task is to implement the BlockLocations gRPC call (you can find the interface definition in the proto file).

BlockLocations: To be more specific, for a given file path, you need to return a Python dictionary (that is, a map in proto), recording how many blocks are stored by each DataNode (key is the DataNode location and value is number of blocks on that node).

For example, running docker exec -it p4-server-1 python3 /client.py BlockLocations -f /hdma-wi-2021.parquet should show something like this:

{'7eb74ce67e75': 15, 'f7747b42d254': 6, '39750756065d': 11}

Note: DataNode location is the randomly generated container ID for the container running the DataNode, so yours will be different, and the distribution of blocks across different nodes will also likely vary.

The documents here describe how we can interact with HDFS via web requests. Many examples show these web requests being made with the curl command, but you'll adapt those examples to use requests.get. By default, WebHDFS runs on port 9870. So use port 9870 instead of 9000 to access HDFS for this part.

Use a GETFILEBLOCKLOCATIONS operation to find the block locations.

Part 3: CalcAvgLoan gRPC Call

In this part, your task is to implement the CalcAvgLoan gRPC call (you can find the interface definition in the proto file).

The call should read hdma-wi-2021.parquet, filtering to rows with the specified county code. One way to do this would be to pass a ("column", "=", ????) tuple inside a filters list upon read: https://arrow.apache.org/docs/python/generated/pyarrow.parquet.read_table.html

The call should return the average loan amount from the filtered table.

As an optimization, your code should also write the filtered data to a file named partitions/<county_code>.parquet. If there are later calls for the same county_code, your code should use the smaller, county-specific Parquet file (instead of filtering the big Parquet file with all loan applications). The county-specific Parquet file should have 1x replication. When CalcAvgLoan returns the average, it should also use the "source" field to indicate whether the data came from big Parquet file (source="create" because a new county-specific file had to be created) or a county-specific file was previously created (source="reuse").

One easy way to check if the county-specific file already exists is to just try reading it with PyArrow. You should get an FileNotFoundError exception if it doesn't exist.

After a DbToHdfs call and a few CalcAvgLoan calls, your HDFS directory structure will look something like this:

  ```
  ├── hdma-wi-2021.parquet
  ├── partitioned/
  │   ├── 55001.parquet
  │   ├── 55003.parquet
  │   └── ...
  ```

Part 4: Fault Tolerance

In this part, your task is to modify the CalcAvgLoan gRPC calls you implemented in Part 3.

Imagine a scenario where one (or even two) of the three DataNodes fail and go offline. In this case, can the logic implemented in Part 3 still function correctly? If we still want our gRPC server to correctly respond to requests for calculating the average loan, what should we do?

Keep in mind that /hdma-wi-2021.parquet has a replication factor of 3, while /partitioned only has a replication factor of 1.

CalcAvgLoan: To be more specific, modify this gRPC call to support the server in correctly handling responses even when one (or even two) DataNode is offline. You may try reading the partitioned parquet file first. If unsuccessful, then go back to the large hdma-wi-2021.parquet file and complete the computation. What's more, you have to return with the field source filled:

1. "partitioned": calculation performed on parquet partitioned before
2. "unpartitioned": parquet partitioned before is lost, and calculation performed on the initial unpartitioned table

To simulate a data node failure, you should use docker kill to terminate a node and then wait until you confirm that the number of live DataNodes has decreased using the hdfs dfsadmin -fs <hdfs_path> -report command.

Submission

Read the directions here about how to create the repo.

You have some flexibility about how you write your code, but we must be able to run it like this:

docker build . -f Dockerfile.hdfs -t p4-hdfs
docker build . -f Dockerfile.namenode -t p4-nn
docker build . -f Dockerfile.datanode -t p4-dn
docker build . -f Dockerfile.mysql -t p4-mysql
docker build . -f Dockerfile.server -t p4-server
docker compose up -d

We will copy in the all the dockerfiles except "Dockerfile.server", "docker-compose.yml", "client.py", "lender.proto", and "hdma-wi-2021.sql.gz", overwriting anything you might have changed.

Please make sure you have client.py copied into p4-server-1. We will run client.py in p4-server-1 to test your code.

Tester

Not released yet.