Shuffle is an expensive operation whether you do it with plain old MapReduce programs or with Spark. Shuffle is he process of bringing Key Value pairs from different mappers (or tasks in Spark) by Key in to a single reducer (task in Spark). So all key value pairs of the same key will end up in one task (node). So we can loop through the key value pairs and do the needed aggregation.
Since production jobs usually involve a lot of tasks in Spark, the key value pairs movement between nodes during shuffle (from one task to another) will cause a significant bottleneck. In some cases Shuffle is not avoidable but in many instances you could avoid shuffle by structuring your data little differently. Avoiding shuffle will have an positive impact on performance.
Read the whole thing. Getting partitions right is critical to writing scalable Spark jobs.
If we were to got all Spark developers to vote, out of memory (OOM) conditions would surely be the number one problem everyone has faced. This comes as no big surprise as Spark’s architecture is memory-centric. Some of the most common causes of OOM are:
* Incorrect usage of Spark
* High concurrency
* Inefficient queries
* Incorrect configuration
Definitely worth the read.
Databricks is a managed Spark framework, similar to what we saw with HDInsight in the previous post. The major difference between the two technologies is that HDInsight is more of a managed provisioning service for Hadoop, while Databricks is more like a managed Spark platform. In other words, HDInsight is a good choice if we need the ability to manage the cluster ourselves, but don’t want to deal with provisioning, while Databricks is a good choice when we simply want to have a Spark environment for running our code with little need for maintenance or management.
Azure Databricks is not a Microsoft product. It is owned and managed by the company Databricks and available in Azure and AWS. However, Databricks is a “first party offering” in Azure. This means that Microsoft offers the same level of support, functionality and integration as it would with any of its own products. You can read more about Azure Databricks here, hereand here.
Click through for a demonstration of the product.
We’re delighted to release the Azure Toolkit for IntelliJ support for SQL Server Big Data Cluster Spark job development and submission. For first-time Spark developers, it can often be hard to get started and build their first application, with long and tedious development cycles in the integrated development environment (IDE). This toolkit empowers new users to get started with Spark in just a few minutes. Experienced Spark developers also find it faster and easier to iterate their development cycle.
The toolkit extends IntelliJ support for the Spark job life cycle starting from creation, authoring, and debugging, through submission of jobs to SQL Server Big Data Clusters. It enables you to enjoy a native Scala and Java Spark application development experience and quickly start a project using built-in templates and sample code. The integration with SQL Server Big Data Cluster empowers you to quickly submit a job to the big data cluster as well as monitor its progress. The Spark console allows you to check schemas, preview data, and validate your code logic in a shell-like environment while you can develop Spark batch jobs within the same toolkit.
It looks pretty good from my vantage point.
Spark session is a unified entry point of a spark application from Spark 2.0. It provides a way to interact with various spark’s functionality with a lesser number of constructs. Instead of having a spark context, hive context, SQL context, now all of it is encapsulated in a Spark session.
Read on to learn more about SparkSession and how you can use it.
To make the above possible, we provide a Bring Your Own VNET (also called VNET Injection) feature, which allows customers to deploy the Azure Databricks clusters (data plane) in their own-managed VNETs. Such workspaces could be deployed using Azure Portal, or in an automated fashion using ARM Templates, which could be run using Azure CLI, Azure Powershell, Azure Python SDK, etc.
With this capability, the Databricks workspace NSG is also managed by the customer. We manage a set of inbound and outbound NSG rules using a Network Intent Policy, as those are required for secure, bidirectional communication with the control/management plane.
This is a good article if the defaults won’t get past corporate security.
There is a Jira ticket for the Apache Spark project, SPARK-27006. The gist of this ticket is to bring .NET support to Spark, specifically by supporting DataFrames in C# (and hopefully F#). No support for Datasets or RDDs is included in here, but giving .NET developers DataFrame access would make it easy for us to write code which interacts with Spark SQL and a good chunk of the SparkSession object.
You an click through and read everything I have to say, but do go to the Spark ticket and vote for .NET support.
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.
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.
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.
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.