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Data Skew is a real problem in Spark. It seems like the sort of thing which should be solved automatically by the job manager but unfortunately, it isn’t. Skew is where we have a given partition which contains a huge amount more data than others – leaving one executor to process a lot of data, […]
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Apache Spark provides us with a framework to crunch a huge amount of data efficiently by leveraging parallelism which is great! However, with great power, comes great responsibility; because, optimising your scripts to run efficiently, is not so easy. Within our scripts, we need to look to minimize the data we bring in; avoid UDF’s […]
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Window functions are incredibly useful. Within a single query, you can find out things which may have otherwise been tricky. In this article, I will cover all of the key window functions in Pyspark. First off, we need to define our dataframe – you can get the data to play along here. Now, we have […]
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Okay, so the first thing I should note is that you should avoid UDF’s (User Defined Functions) like the plague unless you absolutely have to; I have spoken about why that is here. Now that I’ve warned you, let’s talk about what a UDF is. A UDF is a User Defined Function, it’s a function […]
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When you’re dealing with tonnes of different datasources, issues crop up all the time: the datasource hasn’t been populated; the schema has changed; the schedule changes or the file format changes. The key with our jobs is to build in enough checks to at the very least warn us if something fails, so we can […]
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Welcome to this weeks Starter Sunday – which is where I cover off some basic concepts of Spark, Python or Hive. Today, we’re looking at selecting and filtering data from our dataframes in Spark, specifically, Pyspark. Select specific columns Below, we have a dataframe called df_select which will take just two columns from the dataframe […]
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In Spark Streaming, we have the concept of sources and sinks. A source is very much what you would expect it to be – it’s the source of the data we will be ingesting into our stream. A sink, is the destination for that data in the L part of the ETL process. Sources Looking […]
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Streaming applications need to be fault tolerant. They need to be able to cope with system failures, JVM crashes and all that sort of horrible stuff. To cope with that, Spark Streaming uses the concept of checkpointing. That is, it stores metadata about which files it’s processed so far – so if it fails, it […]
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In my last article, I showed an example of a Spark Streaming application for ingesting application log files. Today, I am going to cover off an overview of DStreams in Spark Streaming. In subsequent articles, we’ll really look in detail at some of the main Spark Streaming concepts. We can source data from loads of […]
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In this article I am going to cover how to create a simple streaming log monitor using Spark Streaming. In subsequent posts, I’ll go deeper on some of these aspects. The first thing we need to do, as always is to create a Spark Session. There isn’t much new here, except that we import the […]
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Often, we develop ETL pipelines and they just work. They do exactly what we expect them to do and produce the correct outputs. One day though, your script stops working and the error points to it being a corrupted file. That’s pretty annoying – it took you away from doing something you enjoy and now […]
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Melting dataframes is the process of taking a short, wide table and making it into a long thin one, using column headings as categorical data within the resulting dataframe. In the below example we have a dataframe which shows the total kilometres walked and cycled per person. NAME BIKEKM WALKKM Kieran 77 178 Bobby 79 […]
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