Is there any way possible to have a single history server showing Spark applications running on different emr clusters?
According to this link - https://docs.aws.amazon.com/emr/latest/ManagementGuide/emr-cluster-application-history.html
Section Off-cluster access to persistent application user interfaces states that the persistent application UIs are run off-cluster, but can it be configured such that every spark application (running on different clusters) can be pointed to single application UI? Or is it cluster specific only?
I have tried figuring it out from the aws docs but can't find anything relevant. Any reference/suggestion will be appreciated.
Thanks.
I want to understand if it's possible to use flask application connected to Spark master node implemented in Amazon EMR. The goal is to call Flask from a web app to retrieve spark outputs. Ports are open in amazon EMR cluster's security group but I can't reach it from outside on his port.
What do you think about it? Are there any other solutions?
While it is totally possible to call Flask (or anything) running on EMR, depending on what you are doing you might find Apache Livy handy. The good thing is Livy is fully supported by EMR. You can use Livy to submit jobs and to retrieve results synchronously or asynchronously. It gives you a rest API to interact with Spark.
I have HBase Spark job running at AWS EMR cluster. Recently we moved to GCP. I transferred all HBase data to BigTable. Now I am running same Spark - Java/Scala job in Dataproc. Spark job failing as it is looking spark.hbase.zookeeper.quorum setting.
Please let me know, how without code change I can make my spark job to run successfully with BigTable.
Regards,
Neeraj Verma
While BigTable shares same principles and same Java API is available as HBase, it is not sharing its wire protocol. So standard HBase Client won't work (zookeeper error looks like you are trying to connect to BigTable via HBase client). Instead, you need to modify your program to use BigTable-specific client. It implements same Java interfaces as HBase, but requires custom google jars in classpath and few property overrides to enable it.
I am using Google Data Flow to implement an ETL data ware house solution.
Looking into google cloud offering, it seems DataProc can also do the same thing.
It also seems DataProc is little bit cheaper than DataFlow.
Does anybody know the pros / cons of DataFlow over DataProc
Why does google offer both?
Yes, Cloud Dataflow and Cloud Dataproc can both be used to implement ETL data warehousing solutions.
An overview of why each of these products exist can be found in the Google Cloud Platform Big Data Solutions Articles
Quick takeaways:
Cloud Dataproc provides you with a Hadoop cluster, on GCP, and access to Hadoop-ecosystem tools (e.g. Apache Pig, Hive, and Spark); this has strong appeal if you are already familiar with Hadoop tools and have Hadoop jobs
Cloud Dataflow provides you with a place to run Apache Beam based jobs, on GCP, and you do not need to address common aspects of running jobs on a cluster (e.g. Balancing work, or Scaling the number of workers for a job; by default, this is automatically managed for you, and applies to both batch and streaming) -- this can be very time consuming on other systems
Apache Beam is an important consideration; Beam jobs are intended to be portable across "runners," which include Cloud Dataflow, and enable you to focus on your logical computation, rather than how a "runner" works -- In comparison, when authoring a Spark job, your code is bound to the runner, Spark, and how that runner works
Cloud Dataflow also offers the ability to create jobs based on "templates," which can help simplify common tasks where the differences are parameter values
Here are three main points to consider while trying to choose between Dataproc and Dataflow
Provisioning
Dataproc - Manual provisioning of clusters
Dataflow - Serverless. Automatic provisioning of clusters
Hadoop Dependencies
Dataproc should be used if the processing has any dependencies to tools in the Hadoop ecosystem.
Portability
Dataflow/Beam provides a clear separation between processing logic and the underlying execution engine. This helps with portability across different execution engines that support the Beam runtime, i.e. the same pipeline code can run seamlessly on either Dataflow, Spark or Flink.
This flowchart from the google website explains how to go about choosing one over the other.
https://cloud.google.com/dataflow/images/flow-vs-proc-flowchart.svg
Further details are available in the below link
https://cloud.google.com/dataproc/#fast--scalable-data-processing
Same reason as why Dataproc offers both Hadoop and Spark: sometimes one programming model is the best fit for the job, sometimes the other. Likewise, in some cases the best fit for the job is the Apache Beam programming model, offered by Dataflow.
In many cases, a big consideration is that one already has a codebase written against a particular framework, and one just wants to deploy it on the Google Cloud, so even if, say, the Beam programming model is superior to Hadoop, someone with a lot of Hadoop code might still choose Dataproc for the time being, rather than rewriting their code on Beam to run on Dataflow.
The differences between Spark and Beam programming models are quite large, and there are a lot of use cases where each one has a big advantage over the other. See https://cloud.google.com/dataflow/blog/dataflow-beam-and-spark-comparison .
Cloud Dataflow is a serverless data processing service that runs jobs written using the Apache Beam libraries. When you run a job on Cloud Dataflow, it spins up a cluster of virtual machines, distributes the tasks in your job to the VMs, and dynamically scales the cluster based on how the job is performing. It may even change the order of operations in your processing pipeline to optimize your job.
So use cases are ETL (extract, transfer, load) job between various data sources / data bases. For example load big files from Cloud Storage into BigQuery.
Streaming works based on subscription to PubSub topic, so you can listen to real time events (for example from some IoT devices) and then further process.
Interesting concrete use case of Dataflow is Dataprep. Dataprep is cloud tool on GCP used for exploring, cleaning, wrangling (large) datasets. When you define actions you want to do with your data (like formatting, joining etc), job is run under the hood on Dataflow.
Cloud Dataflow also offers the ability to create jobs based on "templates," which can help simplify common tasks where the differences are parameter values.
Dataproc is a managed Spark and Hadoop service that lets you take advantage of open source data tools for batch processing, querying, streaming, and machine learning. Dataproc automation helps you create clusters quickly, manage them easily, and save money by turning clusters off when you don't need them. With less time and money spent on administration, you can focus on your jobs and your data.
Super fast — Without using Dataproc, it can take from five to 30
minutes to create Spark and Hadoop clusters on-premises or through
IaaS providers. By comparison, Dataproc clusters are quick to start,
scale, and shutdown, with each of these operations taking 90 seconds
or less, on average. This means you can spend less time waiting for
clusters and more hands-on time working with your data.
Integrated — Dataproc has built-in integration with other Google
Cloud Platform services, such as BigQuery, Cloud Storage, Cloud
Bigtable, Cloud Logging, and Cloud Monitoring, so you have more than
just a Spark or Hadoop cluster—you have a complete data platform.
For example, you can use Dataproc to effortlessly ETL terabytes of
raw log data directly into BigQuery for business reporting.
Managed — Use Spark and Hadoop clusters without the assistance of an
administrator or special software. You can easily interact with
clusters and Spark or Hadoop jobs through the Google Cloud Console,
the Cloud SDK, or the Dataproc REST API. When you're done with a
cluster, you can simply turn it off, so you don’t spend money on an
idle cluster. You won’t need to worry about losing data, because
Dataproc is integrated with Cloud Storage, BigQuery, and Cloud
Bigtable.
Simple and familiar — You don’t need to learn new tools or APIs to
use Dataproc, making it easy to move existing projects into Dataproc
without redevelopment. Spark, Hadoop, Pig, and Hive are frequently
updated, so you can be productive faster.
If you want to migrate from your existing Hadoop/Spark cluster to the cloud, or take advantage of so many well-trained Hadoop/Spark engineers out there in the market, choose Cloud Dataproc; if you trust Google's expertise in large scale data processing and take their latest improvements for free, choose DataFlow.
Here are three main points to consider while trying to choose between Dataproc and Dataflow
Provisioning
Dataproc - Manual provisioning of clusters
Dataflow - Serverless. Automatic provisioning of clusters
Hadoop Dependencies
Dataproc should be used if the processing has any dependencies to tools in the Hadoop ecosystem.
Portability
Dataflow/Beam provides a clear separation between processing logic and the underlying execution engine. This helps with portability across different execution engines that support the Beam runtime, i.e. the same pipeline code can run seamlessly on either Dataflow, Spark or Flink.
Cloud Dataproc and Cloud Dataflow can both be used for data processing, and there’s overlap in their batch and streaming capabilities. You can decide which product is a better fit for your environment.
Cloud Dataproc is good for environments dependent on specific Apache big data components:
- Tools/packages
- Pipelines
- Skill sets of existing resources
Cloud Dataflow is typically the preferred option for green field environments:
- Less operational overhead
- Unified approach to development of batch or streaming pipelines
- Uses Apache Beam
- Supports pipeline portability across Cloud Dataflow, Apache Spark, and Apache Flink as runtimes.
See more details here https://cloud.google.com/dataproc/
Pricing comparision:
DataProc
Dataflow
If you want to calculate and compare cost of more GCP resources, please refer this url https://cloud.google.com/products/calculator/
One of the other important difference is:
Cloud Dataproc:
Data mining and analysis in datasets of known size
Cloud Dataflow:
Manage datasets of unpredictable size
see
Cloud Dataflow
Is a serverless data processing service that runs jobs written using
the Apache Beam libraries.
When you run a job on Cloud Dataflow it gets operated like this:
It spins up a cluster of virtual machines
Distributes the tasks in your job to the VMs, and dynamically scale the cluster based on how the job is performing
Dataflow may even change the order of operations in your processing pipeline to optimize your job.
It supports both batch and streaming Jobs. So use cases are ETL (extract, transfer, load) jobs between various data sources/databases.
For example, load big files from Cloud Storage into Big Query.
Streaming works based on subscription to Pub-Sub topic, so you can listen to real-time events (for example from some IoT devices) and then further process the data.
An interesting concrete use case of Dataflow is Data prep.
Data prep is a cloud tool on GCP used for exploring, cleaning, and wrangling (large) datasets. When you define the actions you want to perform on your data (like formatting, joining etc.), the job run under the hood on Dataflow.
Cloud Dataflow also offers the ability to create jobs based on "templates" which can help simplify common tasks where the differences are parameter values.
Data proc
Is a managed Spark and Hadoop service that lets you take advantage of
open-source data tools for batch processing, querying, streaming, and
machine learning.
Data proc automation helps you create clusters quickly, manage them easily, and save money by turning clusters off when you don't need them. With less time and money spent on administration, you can focus on your jobs and your data.
I have a conceptual question. I downloaded Apache Spark and Hortonworks Hadoop Sandbox. As far as I know, we analyze big data by distributing the tasks to multiple machines or clusters. Amazon Web Services provide customers clusters when they pay for their services. But in the case of Spark or Hadoop, whose clusters I am using when I simply download these environments? They say that these environments provide a single-node clusters, which is, I assume my computer itself. But then, how can I analyze big data if I am limited to my computer itself? In brief, what is the logic of using Spark on my own laptop?
The environments are exactly what they say they are, a sandbox. It can be used to test functionality but not performance because as you rightly said, they are running out of your laptop. The VM comes configured with all the software neccesary for you to test exactly this.
If you wish to get the true performance potential of spark, then you will need to install spark on a cluster of servers using the procedures that they describe here and then you will be truly using the computational power from the servers that you just installed spark on.
Hope that helps!