Spark and Small Files

In my previous post I have showed this short code example: And I asked what may be the problem with that code, assuming that the input ( my_website_visits ) is very big and that we filter most of it using the ‘where’ clause. Well the answer is of course, is that that piece of code may result in a large amount of small files. Why? Because we are reading a large input, the number of tasks will be quite large. When filtering out most of the data and then writing it, the number of tasks will remain the same, since no shuffling was done. This means that each task will write only a small amount of data, which means small files on the target path. If in the example above Spark created 165 tasks to handle our input. That means that even after filtering most of the data, the output of this process will be at least 165 files with only a few kb in each. What is the problem with a lot of small files?Well, first of all, the writing itself is inefficient. More files means unneeded overhead in resources and time. If you’re storing your output on the Continue reading Spark and Small Files

Quick tip: Easily find data on the data lake when using AWS Glue Catalog

Finding data on the data lake can sometimes be a challenge. At my current workplace (ZipRecruiter) we have hundreds of tables on the data lake and it’s growing each day. We store the data on AWS S3 and we use AWS Glue Catalog as meta data for our Hive tables. But even with Glue Catalog, finding data on the data lake can still be a hustle. Let’s say I am trying to find a certain type of data, like ‘clicks’ for example. It would be very nice to have an easy way to get all the clicks related tables (including aggregation tables, join tables and so on..) so i could choose from. Or perhaps I would like to know which tables were generated by a specific application. There is no easy way to find these table by default. But here is something pretty cool that I recently found about Glue Catalog that can help.If you add properties to glue tables, then you can search tables based on those properties. For example, if you would add the property “clicks” to all the job related tables, then you can get all of those tables as a result by searching the phrase “clicks” Continue reading Quick tip: Easily find data on the data lake when using AWS Glue Catalog