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Spark Module 2 SparkSQL and DataFrames
featuring SQL and DataFrames.
This course is around 5 hours long.
- The second module in the Spark series moves on to explore the SparkSQL and DataFrames API. This allows us to concentrate on the Data Science and work at a much higher level of abstraction, working with SQL style syntax instead of worrying about RDDs.
- This course is designed for all Java developers who want to explore Spark. No previous data science experience is assumed, so every concept is explained in detail.
Contents
Having problems? check the errata
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Introduction 6m 29s What do DataFrames and SparkSQL offer compared to SparkCore (RDDs)? |
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Getting Started 20m 10s We'll read in a DataSet (DataFrame) to get started |
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Working with DataSets 29m 3s For our first real task with SparkSQL, we'll see how do filters |
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Full SQL Syntax 13m 45s How to query Spark using the full SQL syntax |
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In Memory Data 15m 4s In Module 1 we used parallelize to use in memory data - useful for unit tests. This is how to do it using DataFrames. |
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Grouping and Aggregating 12m 59s Understanding the Group By clause in SparkSQL |
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Date Formatting 6m 30s How to use the date_format function in SparkSQL |
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Multiple Groupings 13m 59s More than one group by column? |
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Ordering 16m 36s How to use the order by clause |
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DataFrames API 28m 4s We've concentrated on the SQL syntax so far, but we can also use a Java API to do everything (and more) that SQL can. |
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Pivot Tables 21m 21s In DataFrames, we can produce Pivot Tables as with spreadsheets and databases. But for Big Data! |
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General Aggregations 18m 49s The agg method is the most flexible aggregating function, so we'll see how to use it. |
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Practical Session 8m 12s A short exercise |
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User Defined Functions 23m 55s How to use lambdas to add your own functions to the SQL syntax and DataFrame API |
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Performance 25m 56s Using the SparkUI to analyse tasks. We ask the question: is the SQL syntax slower than the DataFrame API? Answers will follow in the next video... |
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HashAggregation 39m 21s Spark has two strategies for grouping - HashAggregation is extremely efficient but can only be used in restricted circumstances. Find out how to make sure HashAggegration is used instead of the (usually) slower SortAggregate routine. |
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SparkSQL vs SparkRDD 6m 55s Which performs "better"? |
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Update - Tuning the spark.sql.shuffle.partitions Property 8m 18s An update - by default you will have a large number of partitions when shuffling (such as when grouping) - this can kill performance on small jobs. This is how to fix the problem. |
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Module Summary 2m 24s Coming up later in 2018 is a module on SparkML. |
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