Computers, Software

Moving Data in AWS

I have been a long time user of Amazon Web Services, but I only recently started using the Data Pipeline service they offer to handle ETL needs. The service provides a cloud ready, low cost, turn key (in some cases) solution to moving data within your services. I had a difficult time getting up and running, partly due to the lack of discussion online about this service, so I want to share my experience, offer some best practices, walk through how I developed our pipelines.

Anyone with an AWS account can use Data Pipelines. But be careful there there is no BAA agreement if you are in the healthcare industry and are passing around PHI data. Fortunately, our needs do not yet require us to move around PHI information.

The first step in my ETL journey was first formalizing what data needed to go where. Before I even opened up the Data Pipeline service, I had to understand our specific needs. I identified two use cases.

Archiving Old Data

RDS instances have a max allowable size for the database and ours was getting full. The approach I took was to look at our largest tables, and focus on reducing those first. I ran some queries to understand what is using the most space: http://stackoverflow.com/a/9620273/802407

Once I had a list of my largest tables, I could classify them and assign retention rules. Some tables I decided to leave in place, and others I decided were transient data, and could be archived. (HIPAA mandates a 7 year data retention policy, so no luck just deleting). We decided as a business that different classifications could live within our application for differing time frames. Once timeframes were established, I could then write a data pipeline, and move any data older than our cut off date for that table to a storage solution outside of the database. We chose to house MySQL backups on S3 in encrypted buckets.

Fortunately the Data Pipeline service provides a number of templates to help you get started. Navigate to https://console.aws.amazon.com/datapipeline .  I found the templates good starting point, but there are some frustrations that I will mention below in the “Quirks” section. Click “Create Pipeline”. I used the template “Full Copy of RDS MySQL table to S3”. I filled in the parameters, and edited the pipeline in “Edit in Architect” mode.

Since I wanted to archive old data, I modified the SqlDataNode’s Select Query to be only records older than my retention policy:

select * from #{table} 
where #{myRDSTableLastModifiedCol} <= 
date_sub('#{format(@scheduledStartTime, 'YYYY-MM-dd HH-mm-ss')}' , 
  interval 6 month)

This will select records only older than 6 months from the pipeline scheduled start time. The template then moves these to S3. There are two parameters, defined in the parameters section for “#{myRDSTableName}”, and “#{myRDSTableLastModifiedCol}”. I supplied my table name, and the updated_at datetime column for my records.

I added a new SqlActivity dependent on the CopyActivity named “Delete old records”. Once they move to S3, I want to delete them from the database table. This activity “Depends on: RDSToS3CopyActivity” so if saving to S3 fails, the records are left untouched. I added the following script to mirror my select query above, but deleting the records instead:

delete from #{myRDSTableName} 
where #{myRDSTableLastModifiedCol} <= 
date_sub('#{format(@scheduledStartTime, 'YYYY-MM-dd HH-mm-ss')}' , 
  interval 6 month)

I would recommend doing this on a test database first before you delete production records while testing your script. Because the timestamp is the same as from the select, this will be the same record set, provided you have an updated_at column that shows when a record was last touched.

Expose Data for Reporting

My other use case was getting data to the reporting server which is in Redshift. Again, there is a nice template to get started. Click “Create Pipeline” and then select “Incremental copy of RDS MySQL table to Redshift”, then “Edit in Architect”.

The first run will need to be a full copy if I want all the data in the table. After that, I can use delta copies to only move over the new data fitting your criteria. This is driven by a SQL select query, so it is easy to modify. In the SqlDataNode I can edit the Select Query to my liking. Note that I removed the conditions from the query to get all records on the first run. I changed the instance type to something more substantial (t1.micro to m1.small), and upped the timeout from 2 hours (to 8 hours). I then went in before the next run and modified the query to put back the conditions that selected the delta data, then downgraded the instance type, and timeout values to their smaller defaults.

I then ran into an infrastructure quirk where our Redshift instance was inside a VPC, and our RDS database was inside a classic instance (non-VPC). This meant that the same EC2 instance would not be able to talk to both databases since it had to be on one side or the other. Because of this limitation, I had to modify parts of the pipeline that assumed a single EC2 instance would be talking to both databases. Note that I had to edit the JSON as the Data Pipeline UI does not allow changing the resources that activities run on from the template. I created two EC2 instances – one for talking to RDS and S3, and one for talking to S3 and Redshift.

In an attempt to make things easier, Amazon provides some Python scripts that get called under the hood to reflect on your MySQL table structure and convert it to a PostgreSQL CREATE TABLE command. This didn’t work for me because of my VPC permissions issues, so I provided my own CREATE TABLE Sql in the S3StagingDataNode. This was generated using the Python script by Amazon, but I supplied the inputs manually:

#!/bin/bash
aws s3 cp s3://datapipeline-us-east-1/sample-scripts/mysql_to_redshift.py \
/tmp/mysql_to_redshift.py

for target in table1 table2 table3
do
  mysqldump -h <host> --port=3306 -u <username> --password=<password>  \
--compatible=postgresql --default-character-set=utf8 -n -d \
-r "/tmp/${target}_structure.sql" <database> $target

  python /tmp/mysql_to_redshift.py --input_file=/tmp/${target}_structure.sql \
--output_file=/tmp/${target}_create.psql --table_name=$target \
--insert_mode=KEEP_EXISTING
done

This Bash script will pull down the public file mysql_to_redshift.py. Then it loops over the target tables you want to setup pipelines for. For each table (table1, table2, table3, etc) it does a mysqldump of the table structure. It then feeds this table structure file into the python conversion utility to produce the PostgreSQL version of the table structure. The contents of the table1_create.psql file is what I copied into my “Create Table Sql” field in the Data Pipeline UI.

Note that the “Create Table SQL” is interpreted literally, and has no schema context in Redshift. Therefor if I want to create the database table in another schema, the CREATE TABLE contents need to be modified to prepend this table name with a schema qualifier. e.g. “table1” would become “staging.table” (without the quotes). The Python utility will double quote the table name if given a table name with a period. This will incorrectly create a table in the public schema: public.”staging.table”, which is probably not what desired. Check the contents of the CREATE TABLE for accuracy.

I also changed the “Insert Mode” in the S3ToRedshiftCopyActivity” to be OVERWRITE_EXISTING . This uses the primary key of the table to detect duplicate rows. Since we might modify existing records, I wanted to replace those records in Redshift when they are modified in the application.

Quirks

The data pipeline services has a number of quirks that I stumbled upon. I hope Amazon works to refine the service, and that one day these are no longer issues. But for now I observed the following:

  • I cannot add everything via the UI. Things like parameters, and EC2 resources can only be added via editing the JSON. Don’t be afraid to open it up – it is reasonably easy to follow. Hopefully support to add these objects will come to the UI in the future.
  • The default templates are a great place to understand the pipelines, but are very messy. The JSON definitions are painful to manage. Some information are stored in parameters, others are done inline. Some activities cannot have parameterized variables. Sometimes the parameter names MUST start with “my”, e.g. “myDatabaseUsername”. I found this arbitrary and frustrating. Also some parameters have a “watermark” value, a “help” value, others don’t. At least one variable started with a “*” character. No explanation why.
  • When testing a pipeline I cannot change between a scheduled pipeline, and an on demand pipeline. I have to export the JSON definition and create a new pipeline as the other type. This makes testing painful.
  • The “Execution Details” screen is hard to interpret. The filter defaults to “Activities” only, but all of my pipeline definitions start with an EC2 resource being instantiated which is filtered out. The timeframes are also strange. I needed to change the “Schedule Interval” to be “Executed start” while testing an on demand pipeline. Finally the dates need changing from the default. It will default to 2 days ago, and will include a lot of previous test runs if you are developing. They don’t seem to be sorted in any logical way either, making tracing the pipeline execution difficult at a glance.
  • While debugging, check S3 for the logs. I found log data was contained in S3 that was not referenced at all in the Data Pipeline UI. This was critical for understanding failures.
  • The visualization in the Architect mode is not particularly helpful. The only thing I can do is click on a node and see the properties on the right. I cannot view two node’s properties at once. Worse is the parameters are in a completely different section so I can only see the variable name, or the variable value at any time. I found it only useful to see the flow of execution. For editing, I would export the data pipeline to JSON and modify in a text editor outside of the UI.

Conclusion

The Data Pipeline service offers an affordable way to automate ETLs in a cloud friendly way. The proof of concept that I did under represented the amount of work it would take to get these running in production. I frequently battled with permissions, UI quirks, and timeouts. I was persistent, and eventually got these pipelines running. One running, I stripped out the passwords and committed the JSON exports into version control for peace of mind.

I want to look into the SNS failure reporting next to be alerted when pipelines fail. I am most concerned with upstream changes to the table schemas. If these don’t match the downstream schema, the pipelines will fail. To guard against this I wrote unit tests that inspect the tables and ensure no changes have been introduced upstream. If a change is introduced, the test fails with the message that it was expecting something else. This should trigger the developer to alert the data services team to modify the pipeline downstream to prepare for upstream changes. This policy has not yet been tested.

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