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Category: Spark

Databricks Dashboards

Published 2019-03-27 by Kevin Feasel

Megan Quinn takes us through building dashboards with Apache Zeppelin on Databricks:

The first step in any type of analysis is to understand the dataset itself. A Databricks dashboard can provide a concise format in which to present relevant information about the data to clients, as well as a quick reference for analysts when returning to a project.

To create this dashboard, a user can simply switch to Dashboard view instead of Code view under the View tab. The user can either click on an existing dashboard or create a new one. Creating a new dashboard will automatically display any of the visualizations present in the notebook. Customization of the dashboard is easily achieved by clicking on the chart icon in the top right corner of the desired command cells to add new elements.

This isn’t quite a step-by-step guide but does spur on ideas.

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Working with Columns in Spark

Published 2019-03-26 by Kevin Feasel

Achilleus has a two-parter on working with columns in Spark. Part 1 covers some of the basic syntax and several functions:

Also, we can have typed columns which is basically a column with an expression encoder specified for the expected input and return type.

scala> val name = $"name".as[String]
name: org.apache.spark.sql.TypedColumn[Any,String] = name
scala> val name = $"name"
name: org.apache.spark.sql.ColumnName = name

There are more than 50 methods(67 the last time I counted ) that can be used for transformations on the column object. We will be covering some of the important methods that are generally used.

Part 2 covers other functions including window functions:

17) over
This is one of the most important function that is used in many of the window operations.We can talk about the window function in detail when discuss about aggregation in spark but for now, it will be fair enough to say that over method provides a way to apply an aggregation over a window specification which in turn can be used to specify partition, order and frame boundaries of the aggregation.

Check out both of these posts for useful tidbits.

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Batch Consumption from Kafka with Spark

Published 2019-03-14 by Kevin Feasel

Swapnil Chougule shares a few tips on performing batch processing of a Kafka topic using Apache Spark:

Spark as a compute engine is very widely accepted by most industries. Most of the old data platforms based on MapReduce jobs have been migrated to Spark-based jobs, and some are in the phase of migration. In short, batch computation is being done using Spark. As a result, organizations’ infrastructure and expertise have been developed around Spark.

So, the now question is: can Spark solve the problem of batch consumption of data inherited from Kafka? The answer is yes.

The advantages of doing this are: having a unified batch computation platform, reusing existing infrastructure, expertise, monitoring, and alerting.

Click through to get to the starting point on this as well as a few tips to avoid stumbling blocks.

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Securely Accessing External Resources From Databricks AWS

Published 2019-03-13 by Kevin Feasel

Itai Weiss shows how you can securely hit external data sources when using Databricks for AWS:

For security purposes, Databricks Apache Spark clusters are deployed in an isolated VPC dedicated to Databricks within the customer’s account. In order to run their data workloads, there is a need to have secure connectivity between the Databricks Spark Clusters and the above data sources.

It is straightforward for Databricks clusters located within the Databricks VPC to access data from AWS S3 which is not a VPC specific service. However, we need a different solution to access data from sources deployed in other VPCs such as AWS Redshift, RDS databases, streaming data from Kinesis or Kafka. This blog will walk you through some of the options you have available to access data from these sources securely and their cost considerations for deployments on AWS. In order to establish a secure connection to these data sources, we will have to configure the Databricks VPC with either one of the following two available options :

Read on for those two options.

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Monitoring Kafka Streams with JMX Metrics

Published 2019-03-07 by Kevin Feasel

Rishi Khandelwal provides a reference architecture for monitoring a Kafka Streams application using JMX Metrics and pushing the results into Graphite:

Service (application) exposes the JMX metrics at some port which will be captured by Jolokia java agent. Then Jolokia exposes those metrics at some port which is easily accessible through a rest endpoint (we call it Jolokia URL). Then we have JMX2Graphte which polls the metrics from Jolokia URL and push it to Graphite. Then Grafana reads the Graphite metrics and creates a beautiful dashboard for us along with the alerts.

So this is the working of the proposed monitoring solution. Now let’s discuss the components of the monitoring solution.

There’s a bit of code/configuration in here as well, so check it out.

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Sample Spark-Submit Config Settings

Published 2019-03-04 by Kevin Feasel

Leela Prasad shares a few sample configuration settings for Spark-Submit jobs:

Before going further let’s discuss on the below parameters which I have given for a Job.
spark.executor.cores=5 
spark.executor.instances=3
spark.executor.memory=20g
spark.driver.memory=5g 
spark.dynamicAllocation.enabled=true 
spark.dynamicAllocation.maxExecutors=10 

Click through to see what these do and why Leela chose these settings. The Spark documentation has the full list of settings but it’s good to hear explanations from practitioners.

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Datasets In Spark

Published 2019-03-04 by Kevin Feasel

Ayush Hooda explains the differences between DataFrames and Datasets in Apache Spark:

The Datasets API provides the benefits of RDDs (strong typing, ability to use powerful lambda functions) with the benefits of Spark SQL’s optimized execution engine. You can define Dataset objects and then manipulate them using functional transformations (map, flatMap, filter, and so on) similar to an RDD. The benefits are that, unlike RDDs, these transformations are now applied on a structured and strongly typed distributed collection that allows Spark to leverage Spark SQL’s execution engine for optimization.

Read on for more details and a few examples of how to operate DataFrames and Datasets.

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Improving Spark Auto-Scaling On ElasticMapReduce

Published 2019-02-27 by Kevin Feasel

Udit Mehrotra explains some of the ways Amazon ElasticMapReduce reduces the pain of node loss in Spark jobs:

The Automatic Scaling feature in Amazon EMR lets customers dynamically scale clusters in and out, based on cluster usage or other job-related metrics. These features help you use resources efficiently, but they can also cause EC2 instances to shut down in the middle of a running job. This could result in the loss of computation and data, which can affect the stability of the job or result in duplicate work through recomputing.

To gracefully shut down nodes without affecting running jobs, Amazon EMR uses Apache Hadoop‘s decommissioning mechanism, which the Amazon EMR team developed and contributed back to the community. This works well for most Hadoop workloads, but not so much for Apache Spark. Spark currently faces various shortcomings while dealing with node loss. This can cause jobs to get stuck trying to recover and recompute lost tasks and data, and in some cases eventually crashing the job. 

Auto-scaling doesn’t always mean scaling up.

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SparkSession and its Component Contexts

Published 2019-02-27 by Kevin Feasel

The folks at Hadoop in Real World explain the difference between SparkSession, SparkContext, SQLContext, and HiveContext:

SQLContext is your gateway to SparkSQL. Here is how you create a SQLContext using the SparkContext.
// sc is an existing SparkContext.
val sqlContext = new org.apache.spark.sql.SQLContext(sc)

Once you have the SQLContext you can start working with DataFrame, DataSet etc.

Knowing the right entry point is important.

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