Google Dataflow vs Apache Spark

Question

I am surveying Google Dataflow and Apache Spark to decide which one is more suitable solution for our bigdata analysis business needs.

I found there are Spark SQL and MLlib in the spark platform to do structured data query and machine learning.

I wonder is there any corresponding solution in the Google Dataflow platform?

Tilney

for machine learning maybe your dataflow can output a cloud storage file that you later feed to prediction api – Balmung

@ZigMandel It sounds like spark is the more integrated solution in machine learning part – Tilney

I am also shaping the same opinion with @BrownyLin, but I don't know for sure, maybe he could point to that survey? :) – Columelliform

Answer 1

It would help if you could expand a bit on your specific use case(s). What are you trying to accomplish in relation to "Bigdata analysis"? The short answer... it depends :-)

Here are some key architectural points to consider in relation to Google Cloud Dataflow v. Spark and Hadoop MR.

• Resource Mgmt: Cloud Dataflow is a completely on demand execution environment. Specifically - when you execute a job in Dataflow the resources are allocated on demand for that job only. There is no sharing/contention of resources across jobs. In comparison to a Spark or MapReduce cluster you would typically deploy a cluster of X nodes and then submit jobs and then tune the node resources across jobs. Of course you can build up and tear down these clusters, but the Dataflow model is geared towards hands free dev ops in relation to resource management. If want to optimize resource usage to job demands Dataflow is a solid model to control cost and nearly forget about resource tuning. If you prefer a multi-tenant style cluster I'd suggest you look at Google Cloud Dataproc as it provides the on demand cluster management aspects like Dataflow, but focused on class Hadoop workloads like MR, Spark, Pig, ...

• Interactivity: Currently Cloud Dataflow does not provide an interactive mode. Meaning once you submit a job the work resources are bound to the graph that was submitted AND the majority of the data is loaded into resources as needed. Spark can be a better model if you want to load data into the cluster via in memory RDD's and then dynamically execute queries. The challenge is that as your data sizes and query complexity increases you will have to handle the devOps. Now if most of your queries can be expressed in SQL syntax you may want to look at BigQuery. BigQuery provides the "on demand" aspects of Dataflow and enables you to interactively execute queries over massive amounts of data e.g petabytes. The biggest advantage in my opinion of BigQuery is that you do not have think/worry about hardware allocation to deal with your data sizes. Meaning as your data sizes grow you don't have to think about hardware (memory and disk size) configuration.

• Programming Model: Dataflow's programming model is functionally biased vs. a classic MapReduce model. There are many similarities between Spark and Dataflow in terms of API primitives. Things to consider: 1) Dataflow's primary programming language is Java. There is a Python SDK in the works. The Dataflow Java SDK in open sourced and has been ported to Scala. Today, Spark has more SDK surface choice with GraphX, Streaming, Spark SQL, and ML. 2) Dataflow is a unified programming model for batch and streaming based DAG development. The goal was to remove the complexity and cost switching when moving between batch and streaming models. The same graph can seamlessly run in either mode. 3) Today, Cloud Dataflow does not support converging/iterative based graph execution. If you need the power of something like MLib then Spark is the way to go. Keep in mind this is the state of things today.

• Streaming & Windowing: Dataflow (building on top of the unified programming model) was architected to be a highly reliable, durable, and scalable execution environment for streaming. One of the key differences between Dataflow and Spark is that Dataflow enables you to easily process data in terms of its true event time vs. solely processing it at it's arrival time into the graph. You can window data into fixed, sliding, session or custom windows based on event time or arrival time. Dataflow also provides Triggers (applied to Windows) that enable you to control how you want to handle late arriving data. Net-net you dial in the level of correctness control to meet the needs of your analysis. For example, lets say you have a mobile game that interacts with a 100 edge nodes. These nodes create 10000's events second related to game play. Let's say a group of nodes can't communicate with your back end streaming analysis system. In the case of Dataflow - once that data does arrive - you can control how you'd like to handle the data in relation to your query correctness needs. Dataflow also provides the ability to upgrade your streaming jobs while they are in flight. For example, let's say you discover a logical bug in a transform. You can upgrade your in flight job without losing your existing Windowed state. Net-net you can keep you business running.

Net-net: - if you are really primarily doing ETL style work (filtering, shaping, joining, ...) or batch style MapReduce Dataflow is a great path if you want minimal devOps.
- if you need to implement ML style graphs, go the Spark path and give Dataproc a try - if you are doing ML and you first need to do ETL to clean up your training data implement a hybrid with Dataflow and Dataproc - if you need interactivity Spark is a solid choice, but so is BigQuery if you are/can express your queries in SQL - if you need to process your ETL and or MR jobs over streams, Dataflow is a solid choice.

---

So... what are you scenarios?

Ohalloran

Thanks for your clear explanation, my scenario is like some kind of realtime recommendation system – Tilney

Answer 2

I've tried both :

Dataflow is still very young, the is no "out-of-the-box" solution for doing ML with it (even though you could implement algorithms in transforms), you could output the processes data to cloud storage and read it later with another tool.

Spark would be recommended but you would have to manage your cluster yourself. However there is a good alternative: Google Dataproc

You can develop analysis tools with spark and deploy them with one command on your cluster, dataproc will manage the cluster itself without having to tweak the configuration.

Doall

Answer 3

I have built code using spark,DataFlow .Let me put my thoughts.

Spark/DataProc: I have used spark (Pyspark) a lot for ETL. You can use SQL and any programming language of your choice. Lot of functions are available (Including Window functions). Build your dataframe and write your transformation and it can be super fast. Once data is cached , any operation on the Dataframe will quick.

You can simply build hive external table on the GCS. Then you can use Spark for ETL and Load data into Big Query. This is for Batch processing.

For streaming you can use spark Streaming and load data into Big query.

Now if you have cluster allready then you have think whether to move to Google cloud or not. I found Data proc (Google Cloud Hadoop/Spark) offering is better as you don't have to worry many cluster managements..

DataFlow : It's know as apache beam. Here you can write your code in Java/Python or any other language. You can execute the code in any framework (Spark/MR/Flink).This is a unified model. Here you can do both batch processing and Stream Data processing.

Cenacle

Answer 4

google now offers both programming models- mapreduce and spark. Cloud DataFlow and Cloud DataProc they are respectively

Dissatisfied