Spark Analysis on a Large File has free gazetteer data by country or for the world, provided in tab-separated text files.  In this post I show you how to do some simple analysis using DataFrames in Spark.  As the global file is 280M compressed and 1.2G uncompressed.  This size of file makes it difficult to do data analysis until you have it into a database or similar analysis platform.  Enter Spark…

The file I’m using is allCountries.txt but I suggest grabbing one of the smaller files just for your country of interest while testing.  I’ve unzipped it manually before doing the analysis.  As a homework exercise, the reader is left to figure out how to use one of the other binary readers to interact with the .zip file directly.

Spark Shell Install & Startup

I’m running on a MacBook and installed Spark using the command:

brew install spark

As my allCountries file is quite large and I’m not using other machines to help with the analysis, I need to give the Spark shell lots of memory for processing.  So to start the shell:

spark-shell --driver-memory 2G

Setting RDD Schema

There are many columns in the geonames dataset, but I’m only interested in the second one, the place name itself.  As there is no schema information stored in the text file itself, we have to define it up front:

case class geoname(locname: String)

I do it by defining a class called “geoname” that has a single (string) column in it called “locname”.

Now when we tell it how to read the text file, we can focus on mapping only the second column into the schema we’ve defined:

val locnames = sc.textFile("allCountries.txt").map(_.split("\t")).map(p => geoname(p(1)))

Note that this likely has done nothing on your computer and was instantaneous – because Spark won’t do anything with the actual data until it has to.  It does not just load it all into memory and then wait.  Instead it waits until you do a grouping or sort or output stage and then executes.  We can initiate this by just asking for the first record to be printed or doing a record count:

OUTPUT: res1: geoname = geoname(Pic de Font Blanca)

OUTPUT: res4: Long = 10131643

Querying a DataFrame in Spark

Lots can be done in the RDD version of the data but to do more analytics we want to work with DataFrames.

On Windows I had to use the following two lines before creating the DataFrame, or you’ll get “value toDF is not a member of org.apache.spark.rdd.RDD”.
On OSX I did not have to use it.  It may be a version difference, but I didn’t check:

 val sqlContext= new org.apache.spark.sql.SQLContext(sc)
import sqlContext.implicits._

As we already have a schema defined, it’s as simple as a single statement:

val locdf = locnames.toDF()

Now that we have a DataFrame we can do use some more functions.  In this case I want to count distinct usages of the place names and then print the top 20 most popular ones (sort descending on count).


locname              count
First Baptist Church 2631
San Antonio          2244
The Church of Jes... 2178
San José             1914
Church of Christ     1657
Santa Rosa           1619
Mill Creek           1531
Spring Creek         1506
La Esperanza         1464
San Francisco        1464
San Isidro           1407
San Juan             1367
First Presbyteria... 1338
Dry Creek            1260
San Miguel           1214
Krajan               1151
Mount Zion Church    1100
Stormyra             1097
San Pedro            1091
Bear Creek           1081

So there you have it – the most popular place names in the world!  Odd are you have one of them near you 🙂

I ran the same analysis but just on the Canadian place names and found it, socially, interesting to see that the results where all about physical geography and not about cultural locations.

locname         count
Long Lake       199
Mud Lake        166
Long Pond       148
Lac Long        131
Green Island    121
Long Point      116
Big Island      116
The Narrows     110
Otter Lake      105
Round Lake      105
Lac Rond        103
Little Lake     99
Moose Lake      99
Black Rock      95
Long Island     94
Lac à la Truite 92
Gull Pond       92
Burnt Island    92
Twin Lakes      90
Island Lake     87

NOTE: For a similar but more in-depth Spark tutorial check out this great one from MapR – “Using Apache Spark DataFrames for Processing of Tabular Data“.

About Tyler Mitchell

Director Product Marketing @ GPU-accelerate data analytics | Sr. Product Manager @ - next generation Data Platform for System of Engagement! Former Eng. Director, author and technology writer in NoSQL, big data, graph analytics, geospatial and Internet of Things. Follow me @1tylermitchell or get my book from