Using Dataflow vs. Cloud Composer

问题:

I apologize for this naive question, but I'd like to get some clarification on whether Cloud Dataflow or Cloud Composer is the right tool for the job, and I wasn't clear from the Google Documentation.

Currently, I'm using Cloud Dataflow to read a non-standard csv file -- do some basic processing -- and load it into BigQuery.

Let me give a very basic example:

# file.csv
type\x01date
house\x0112/27/1982
car\x0111/9/1889

From this file we detect the schema and create a BigQuery table, something like this:

`table`
type (STRING)
date (DATE)

And, we also format our data to insert (in python) into BigQuery:

DATA = [
    ("house", "1982-12-27"),
    ("car", "1889-9-11")
]

This is a vast simplification of what's going on, but this is how we're currently using Cloud Dataflow.

My question then is, where does Cloud Composer come into the picture? What additional features could it provide on the above? In other words, why would it be used "on top of" Cloud Dataflow?

回答1:

Cloud composer(which is backed by Apache Airflow) is designed for tasks scheduling in small scale.

Here is an example to help you understand:

Say you have a CSV file in GCS, and using your example, say you use Cloud Dataflow to process it and insert formatted data into BigQuery. If this is a one-off thing, you have just finished it and its perfect.

Now let's say your CSV file is overwritten at 01:00 UTC every day, and you want to run the same Dataflow job to process it every time when its overwritten. If you don't want to manually run the job exactly at 01:00 UTC regardless of weekends and holidays, you need a thing to periodically run the job for you (in our example, at 01:00 UTC every day). Cloud Composer can help you in this case. You can provide a config to Cloud Composer, which includes what jobs to run (operators), when to run (specify a job start time) and run in what frequency (can be daily, weekly or even yearly).

It seems cool already, however, what if the CSV file is overwritten not at 01:00 UTC, but anytime in a day, how will you choose the daily running time? Cloud Composer provides sensors, which can monitor a condition (in this case, the CSV file modification time). Cloud Composer can guarantee that it kicks off a job only if the condition is satisfied.

There are a lot more features that Cloud Composer/Apache Airflow provide, including having a DAG to run multiple jobs, failed task retry, failure notification and a nice dashboard. You can also learn more from their documentations.

回答2:

For the basics of your described task, Cloud Dataflow is a good choice. Big data that can be processed in parallel is a good choice for Cloud Dataflow.

The real world of processing big data is usually messy. Data is usually somewhat to very dirty, arrives constantly or in big batches and needs to be processed in time sensitive ways. Usually it takes the coordination of more than one task / system to extract desired data. Think of load, transform, merge, extract and store types of tasks. Big data processing is often glued together using using shell scripts and / or Python programs. This makes automation, management, scheduling and control processes difficult.

Google Cloud Composer is a big step up from Cloud Dataflow. Cloud Composer is a cross platform orchestration tool that supports AWS, Azure and GCP (and more) with management, scheduling and processing abilities.

Cloud Dataflow handles tasks. Cloud Composer manages entire processes coordinating tasks that may involve BigQuery, Dataflow, Dataproc, Storage, on-premises, etc.

My question then is, where does Cloud Composer come into the picture? What additional features could it provide on the above? In other words, why would it be used "on top of" Cloud Dataflow?

If you need / require more management, control, scheduling, etc. of your big data tasks, then Cloud Composer adds significant value. If you are just running a simple Cloud Dataflow task on demand once in a while, Cloud Composer might be overkill.