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Category: Spark

The Benefits of Delta Lake

Kaushik Nath explains what a Delta Lake is and why it is beneficial:

Data lakes have generated a large amount of publicity as the new storage technology for our big data era. Because something new is always better, right? 

All this hype around data lakes has ignored their inherent drawbacks and limitations. Well, I’m Not Here to create a debate by saying that no one should ever use data lakes. But I am saying that companies should enter into the data lake investment with eyes wide open. Otherwise it might lead to some serious complications.

Delta Lake is a concept intended to mitigate some of the issues with data lakes in general, turning them into data swamps.

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PySpark DataFrame Joining

Monika Rathor shows the various ways you can join DataFrames with PySpark:

PySpark provides multiple ways to combine dataframes i.e. join, merge, union, SQL interface, etc. In this article, we will take a look at how the PySpark join function is similar to SQL join, where two or more tables or dataframes can be combined based on conditions. 

One join type you don’t directly get in SQL Server is the left anti join. We can build something quite similar with NOT EXISTS, though.

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Financial Time Series Analysis in Databricks

Ricardo Portilla shares a demo of financial time series analysis in Databricks:

We’ve shown a merging technique above, so now let’s focus on a standard aggregation, namely Volume-Weighted Average Price (VWAP), which is the average price weighted by volume. This metric is an indicator of the trend and value of the security throughout the day.  The vwap function within our wrapper class (in the attached notebook) shows where the VWAP falls above or below the trading price of the security. In particular, we can now identify the window during which the VWAP (in orange) falls below the trade price, showing that the stock is overbought.

Click through for the article, as well as a notebook you can try out.

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Differences in Spark RDDs and DataSets

Brad Llewellyn looks at some of the differences between RDDs and DataSets in Spark:

We see that there are some differences between filtering RDDsData Frames and Datasets.  The first major difference is the same one we keep seeing, RDDs reference by indices instead of column names.  There’s also an interesting difference of using 2 =’s vs 3 =’s for equality operators. Simply put, “==” tries to directly equate two objects, whereas “===” tries to dynamically define what “equality” means.  In the case of filter(), it’s typically used to determine whether the value in one column (income, in our case) is equal to the value of another column (string literal “<=50K”, in our case).  In other words, if you want to compare values in one column to values in another column, “===” is the way to go.

Interestingly, there was another difference caused by the way we imported our data.  Since we custom-built our RDD parsing algorithm to use <COMMA><SPACE> as the delimiter, we don’t need to trim our RDD values.  However, we used the built-in sqlContext.read.csv() function for the Data Frame and Dataset, which doesn’t trim by default.  So, we used the ltrim() function to remove the leading whitespace.  This function can be imported from the org.apache.spark.sql.functions library.

Read on for more, including quite a few code samples.

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An Introduction to Apache Livy

Guy Shilo explains why we should care about Apache Livy:

Apache Livy is an open source server that exposes Spark as a service. Its backend connects to a Spark cluster while the frontend enables REST API. This enables running it as the organization’s Spark gateway and even run in in docker containers.

Not only it enables running Spark jobs from anywhere, but it also enables shared Spark context and a shared RDD cache among all it’s users which is time and memory saving.

I will demonstrate here how to setup Apache Livy on one of the cluster’s nodes and on a separate server.

Click through for the demonstration.

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Migrating Databricks Workspaces

Gerhard Brueckl has made DatabricksPS better:

I do not know what is/was the problem here but I did not have time to investigate but instead needed to come up with a proper solution in time. So I had a look what needs to be done for a manual export. Basically there are 5 types of content within a Databricks workspace:

– Workspace items (notebooks and folders)
– Clusters
– Jobs
– Secrets
– Security (users and groups)

For all of them an appropriate REST API is provided by Databricks to manage and also exports and imports. This was fantastic news for me as I knew I could use my existing PowerShell module DatabricksPS to do all the stuff without having to re-invent the wheel again.

I’ve used DatabricksPS and really like it for cases where I’d have to loop with the Databricks REST API—for example, when uploading files.

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Resource Allocation in Spark Applications

The folks at Beginner’s Hadoop take us through resource allocation in Spark applications:

Tiny executors essentially means one executor per core. Following table depicts the values of our spar-config params with this approach:

Analysis: With only one executor per core, as we discussed above, we’ll not be able to take advantage of running multiple tasks in the same JVM. Also, shared/cached variables like broadcast variables and accumulators will be replicated in each core of the nodes which is 16 times. Also, we are not leaving enough memory overhead for Hadoop/Yarn daemon processes and we are not counting in ApplicationManager. NOT GOOD!

Read on for the full analysis.

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Delta Lake Schema Enforcement

Burak Yavuz, et al, explain the concept of schema enforcement with Databricks Delta Lake:

Schema enforcement, also known as schema validation, is a safeguard in Delta Lake that ensures data quality by rejecting writes to a table that do not match the table’s schema. Like the front desk manager at a busy restaurant that only accepts reservations, it checks to see whether each column in data inserted into the table is on its list of expected columns (in other words, whether each one has a “reservation”), and rejects any writes with columns that aren’t on the list.

Something something “relational database” something something. They also walk us through some examples in a Databricks notebook, so check that out.

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Spark Streaming DStreams

Manish Mishra explains the fundamental abstraction of Spark Streaming:

Before going into details of the operations available on the DStream API, let us look at the input sources from which we can start a Stream. There are multiple ways in which we can get the inputs from e.g. Kafka, Flume, etc. Or simple Idle files. To get the details on the available input sources supported by Spark, you can refer to this section. As part of this blog, we will take the example of Kafka.

Read on to see an example of pulling data from Kafka and converting inputs into microbatches.

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Diagnosing TCP SACKs-Related Slowdown in Databricks

Chris Stevens, et al, walk us through troubleshooting a slowdown after using Linux images which have been patched for the TCP SACKs vulnerabilities:

In order to figure out why the straggler task took 15 minutes, we needed to catch it in the act. We reran the benchmark while monitoring the Spark UI, knowing that all but one of the tasks for the save operation would complete within a few minutes. Sorting the tasks in that stage by the Status column, it did not take long for there to be only one task in the RUNNING state. We had found our skewed task and the IP address in the Host column pointed us at the executor experiencing the regression.

This is a nice case study of network troubleshooting, so of course there are Wireshark screenshots in it.

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