/RethinkDB Python

Useful RethinkDB commands for exploring data

Data Exploration Illustration

Akshay Chougule, a biologist working with large data sets, wrote a blog post about Useful Unix commands for exploring data, showing ways to “query” comma-delimited plain text data sets with common shell commands. We thought it’d be interesting to do a similar article using ReQL in the Data Explorer, showing how it can be used for ad hoc queries.

Akshay created a fictitious data set of movies; we’ll use data from the IMDb Top 250. (Note that we captured this on August 26, 2014, so the data will likely be different if you check it now.) The plain-text data from IMDb isn’t in any format, but we’ve turned it into a JSON file available at http://rethinkdb.com/sample/top-250-ratings.json. (For the record, we converted it to a tab-delimited file first, used rethinkdb import to get it into a database, fixed the column types and re-exported it. See Importing your data for details.)

Since it’s available online, you can import our sample data set just by creating a table and importing it with http. Just execute these commands directly in the Data Explorer.


The Data Explorer will return information about the inserted rows.

	"deleted": 0 ,
	"errors": 0 ,
	"generated_keys": [
	"bbf81f4d-2a6d-40bb-9b5d-b6e288cc8795" ,
	"0d6054f4-12b0-4c2e-b221-881441c779c4" ,
	] ,
	"inserted": 253 ,
	"replaced": 0 ,
	"skipped": 0 ,
	"unchanged": 0

The table created has six fields: an automatically generated primary key (id), rank (the IMDb rank, 1–250), rating (on a 1–10 scale), title, votes, and year.

Get the top movie

So we can see IMDb’s number one movie with filter({rank: 1}):

r.table('movies').filter({rank: 1})

		"id":  "bbf81f4d-2a6d-40bb-9b5d-b6e288cc8795" ,
		"rank": 1 ,
		"rating": 9.2 ,
		"title":  "The Shawshank Redemption" ,
		"votes": 1262930 ,
		"year": 1994

(“Shawshank” has been the most-loved movie on IMDb for many years. Take that, Orson Welles.)

Removing duplicate documents

You might have caught that there were 253 documents inserted, not 250. Either we have the top 253 movies, or there are a few duplicate records lurking in there. We can use distinct to get a count of unique rows, but we need to remove the id column from the query, since all ID values are unique.



To get the list without duplicates, we can simply leave off count.


To put these into a new table, wrap that query with insert. We’ll get new IDs generated automatically. This is also an example of using subqueries with ReQL: it’s easy to pass the results of one query into another. (One of ReQL’s other nice properties, which we’ve already seen, is command chaining: the input of one command is often the output of the command before it, similar to Unix piping.)


Now with a “clean” data set we can run simple reports right in the Data Explorer. (You might want to switch to Table View for some of these, and you can also add .without('id') in the command chain to “prettify” the table display if you wish.)

Display the top 10 movies…


…and the bottom 10


(Those are the bottom 10 of the top 250, so they’re still pretty good.)

Get the 1st, 2nd ,6th and last records

r.table('moviesUnique').filter(function (doc) {
  return r.expr([1, 2, 6, r.table('moviesUnique').max('rank')('rank')]).

Find the average number of votes for the top 25 movies


Find the most recent movie in the top 25


Find the highest-ranked movie with under 100,000 votes


You can read more about ReQL in the Introduction to ReQL article, or go into greater depth with the API documentation.

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