Each key value pair is formatted as a key, in double quotes, followed by a colon, followed by the value.
Values can be strings, numbers, Boolean values, Null values, other objects, or arrays. Arrays are collections of objects or values encased on square brackets and separated by commas.
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For example, in the above example there are 2 objects within the teams array, each containing an array of players that belong specifically to that team. JSON paths are similar to XML paths, in that they are the sequence of objects through which you need to navigate to get to the object that you are interested in.
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Unlike XML the path is separated by periods. When querying an array you need to specify which object within the array you are referencing by specifying the position number of the relevant item in square brackets.
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Array positions are zero based, so the first item will be position 0, the second position 1, etc. The following example returns all data from the second player, from the second team:.
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Whilst the above queries are fine for visualising data from JSON, you are generally not interested in the key and type of your data. What you want is a query akin to a query against a table.
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A query where each value is held within its own field. Adding a WITH statement allows us to define those fields However; by specifying a JSON path within the field definitions you can specify which key value you want to return. The following example returns the data relating to the first player from the first team, but specifies JSON paths to allow renaming of the resulting fields:. Obviously, this method of extracting data is very limited, and would be unworkable if the JSON contained players rather than just 4.
Thankfully there is another method which allows you to query entire arrays.
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The results of this query are identical to the prior query. This example demonstrates how to use sqlContext. The next steps use the DataFrame API to filter the rows for salaries greater than , from one of the tables and shows the resulting DataFrame. Then the two DataFrames are joined to create a third DataFrame.
Finally the new DataFrame is saved to a Hive table. The equivalent program in Python, that you could submit using spark-submit , would be:.
Quick start - how to use
If the cluster is running low on storage space and it is important to free space immediately, rather than waiting for the HDFS trashcan to be periodically emptied. If the underlying data files reside on the Amazon S3 filesystem. If the underlying data files contain sensitive information and it is important to remove them entirely, rather than leaving them to be cleaned up by the periodic emptying of the trashcan.
This restriction primarily applies to CDH 5. With CDH 5.