Tag Archives: General Programming

Getting Started with DataFrames.jl: A Beginner’s Guide

By: Joel Nelson

Re-posted from: https://blog.glcs.io/julia-dataframes

When doing any sort of development one will often find themselves in need of working with data in atabular format. This is especially true for those of us in data science, or data analysis, fields.In the Julia programming language one of the more popular libraries for this type of datawrangling is DataFrames.jl. In this blog post we’ll explore the beginnings of working with thispackage.

Introduction

The great thing about a package like Dataframes.jl is that it bridges the gap between traditionalprogramming and SQL (Structured Query Language). Databases are great tools for easily gaining insightsinto your data by joining, filtering, aggregating, sorting, etc… Dataframes.jl brings those goodies rightinto your hands by simply adding the package into your julia session. So, lets get started!

Getting Started

Adding the package is a few simple steps.

julia> using Pkgjulia> Pkg.add("DataFrames")julia> using DataFrames

The constructor for a DataFrame provides flexibility to create from arrays, tuples, constants, or files. The documentation covers all these, but for this post we’ll just explore one of the more common ways.

julia> df = DataFrame(a = 1:4, b = rand(4), c = "My first DataFrame")43 DataFrame Row  a      b         c                        Int64  Float64   String                1      1  0.141874  My first DataFrame   2      2  0.432084  My first DataFrame   3      3  0.47098   My first DataFrame   4      4  0.414639  My first DataFrame

You’ll notice in the code above we use a mix of datatypes including range, array, and scalar. The underlying vectors must be of the same sizeand the scalar gets broadcasted, or repeated, for each row. Also, pay attention that the types of each column are inferredbased on the arrays passed into the constructor.

Now, to access a column of a DataFrame there are also a few different possibilities. Here are a few examples of accessing thefirst column “a”.

julia> df.a4-element Vector{Int64}: 1 2 3 4julia> df."a"4-element Vector{Int64}: 1 2 3 4julia> df[!, "a"]4-element Vector{Int64}: 1 2 3 4julia> df[!, :a]4-element Vector{Int64}: 1 2 3 4julia> df[:, :a]4-element Vector{Int64}: 1 2 3 4

In these examples columns can be access directly with literals such as df.a, or more dynamically using brackets (since variables could be substituted.) You may also findyourself wondering the difference between ! and :, which is an important distinction!

The ! returns the underlying vector and : returns a copy. This can be showcased in anexample where we will attempt to change the description of the second value in column cto “I love Julia!”

julia> df[:, :c][2] = "I love Julia!"3julia> df43 DataFrame Row  a      b         c                        Int64  Float64   String                1      1  0.394165  My first DataFrame   2      2  0.809883  My first DataFrame   3      3  0.124035  My first DataFrame   4      4  0.886781  My first DataFramejulia> df[!, :c][2] = "I love Julia!"3julia> df43 DataFrame Row  a      b         c                        Int64  Float64   String                1      1  0.394165  My first DataFrame   2      3  0.809883  I love Julia!   3      3  0.124035  My first DataFrame   4      4  0.886781  My first DataFrame

Notice how the change will only persist to df when we access the column with !.

There is often a tradeoff between returning copies versus the actual underlying vectors. Returning a copy is generally considered safer since if the copy is later mutated the underlyingDataFrame remains unchanged. However, with very large DataFrames copying every column access willresult in an increase in memory. It is best to weigh those considerations and figure out whatapproach will work best for a given program.

Data Wrangling

Import / Export

Another great feature of the Julia programming language is that many different packages will interact wellwhen used together. For instance, DataFrames.jl and CSV.jl can be used to very easily import and exportdata.

First, we can save the DataFrame from above to CSV.

julia> using CSVjulia> path = joinpath(homedir(), "my_df.csv")julia> CSV.write(path, df)

And, reading in the DataFrame from file is just as easy!

julia> CSV.read(path, DataFrame)43 DataFrame Row  a      b         c                        Int64  Float64   String31              1      1  0.601361  My first DataFrame   2      2  0.178065  My first DataFrame   3      3  0.729591  My first DataFrame   4      4  0.280314  My first DataFrame

There are many keyword arguments to explore when handling csv files and the documentation is best forcovering all of these CSV.jl.

DataFrames.jl also supports writing and reading to multiple files types such as Arrow, JSON, Parquet, and others.

Joins

A join is a way to merge data from two DataFrames into a single DataFrame. There are several typesand they generally mimic the same types that a database would support.

  • innerjoin
  • leftjoin
  • rightjoin
  • outerjoin
  • semijoin
  • antijoin
  • crossjoin

Definitions of each can be found in either the documentation, or docstrings, but lets take a look at a fewexamples. Say we have the following DataFrame sets containing information from a school.

julia> student_df = DataFrame(student_id = 1:10, student_name = ["Joe", "Sally", "Jim", "Sandy", "Beth", "Alex", "Tom", "Liz", "Bill", "Carl"], teacher_id = repeat([1,2],5))103 DataFrame Row  student_id  student_name  teacher_id       Int64       String        Int64         1           1  Joe                    1   2           2  Sally                  2   3           3  Jim                    1   4           4  Sandy                  2   5           5  Beth                   1   6           6  Alex                   2   7           7  Tom                    1   8           8  Liz                    2   9           9  Bill                   1  10          10  Carl                   2julia> teacher_df = DataFrame(teacher_id = 1:2, teacher_name = ["Mr. Jackson", "Ms. Smith"])22 DataFrame Row  teacher_id  teacher_name       Int64       String          1           1  Mr. Jackson   2           2  Ms. Smithjulia> grade_df = DataFrame(exam_id = 1, student_id = vcat(1:3, 5:10), grade = [0.95, 0.93, 0.81, 0.85, 0.73, 0.88, 0.77, 0.75, 0.93])93 DataFrame Row  exam_id  student_id  grade         Int64    Int64       Float64    1        1           1     0.95   2        1           2     0.93   3        1           3     0.81   4        1           5     0.85   5        1           6     0.73   6        1           7     0.88   7        1           8     0.77   8        1           9     0.75   9        1          10     0.93

If we look at the grade_df we can see there are 9 results, but in the student_df we have 10 students.So, someone must have missed the exam! Let’s find out who that way we can alert the teacher to schedulea makeup.

Let’s do a leftjoin, which means every row will persist from the first DataFrame regardless if there isa match to the second DataFrame. The leftjoin function also takes an on keyword argumentto signify what column needs to be used to find matches.

julia> student_grade_df = leftjoin(student_df, grade_df, on=:student_id)105 DataFrame Row  student_id  student_name  teacher_id  exam_id  grade            Int64       String        Int64       Int64?   Float64?      1           1  Joe                    1        1        0.95   2           2  Sally                  2        1        0.93   3           3  Jim                    1        1        0.81   4           5  Beth                   1        1        0.85   5           6  Alex                   2        1        0.73   6           7  Tom                    1        1        0.88   7           8  Liz                    2        1        0.77   8           9  Bill                   1        1        0.75   9          10  Carl                   2        1        0.93  10           4  Sandy                  2  missing  missing

We notice Sandy has a missing value for both the exam_id and grade fields. missing is a special datatype in Julia that is similar to a null value in databases. This would signify tous that there was no match in the grade_df meaning Sandy missed the exam. We can add one more join to get the respective teacher’s name.

julia> result_df = innerjoin(student_grade_df, teacher_df, on=:teacher_id)106 DataFrame Row  student_id  student_name  teacher_id  exam_id  grade       teacher_name       Int64       String        Int64       Int64?   Float64?    String          1           1  Joe                    1        1        0.95  Mr. Jackson   2           2  Sally                  2        1        0.93  Ms. Smith   3           3  Jim                    1        1        0.81  Mr. Jackson   4           5  Beth                   1        1        0.85  Mr. Jackson   5           6  Alex                   2        1        0.73  Ms. Smith   6           7  Tom                    1        1        0.88  Mr. Jackson   7           8  Liz                    2        1        0.77  Ms. Smith   8           9  Bill                   1        1        0.75  Mr. Jackson   9          10  Carl                   2        1        0.93  Ms. Smith  10           4  Sandy                  2  missing  missing     Ms. Smith

We used an innerjoin this time since we know that every student would have a teacher assigned.Now, we can let Ms. Smith know that she needs to reach out to Sandy to re-schedule her exam.

Sorting

Another helpful function for analysis is sort. Let’s sort our result_df by the grade column.

julia> sort(result_df, [:grade])106 DataFrame Row  student_id  student_name  teacher_id  exam_id  grade       teacher_name       Int64       String        Int64       Int64?   Float64?    String          1           6  Alex                   2        1        0.73  Ms. Smith   2           9  Bill                   1        1        0.75  Mr. Jackson   3           8  Liz                    2        1        0.77  Ms. Smith   4           3  Jim                    1        1        0.81  Mr. Jackson   5           5  Beth                   1        1        0.85  Mr. Jackson   6           7  Tom                    1        1        0.88  Mr. Jackson   7           2  Sally                  2        1        0.93  Ms. Smith   8          10  Carl                   2        1        0.93  Ms. Smith   9           1  Joe                    1        1        0.95  Mr. Jackson  10           4  Sandy                  2  missing  missing     Ms. Smith

The function takes the DataFrame and an array of columns to sort on. Our result of sort is putting the lowestgrade first, but if we wanted it descending we can pass a rev keyword argument.

julia> sort(result_df, [:grade], rev=true)106 DataFrame Row  student_id  student_name  teacher_id  exam_id  grade       teacher_name       Int64       String        Int64       Int64?   Float64?    String          1           4  Sandy                  2  missing  missing     Ms. Smith   2           1  Joe                    1        1        0.95  Mr. Jackson   3           2  Sally                  2        1        0.93  Ms. Smith   4          10  Carl                   2        1        0.93  Ms. Smith   5           7  Tom                    1        1        0.88  Mr. Jackson   6           5  Beth                   1        1        0.85  Mr. Jackson   7           3  Jim                    1        1        0.81  Mr. Jackson   8           8  Liz                    2        1        0.77  Ms. Smith   9           9  Bill                   1        1        0.75  Mr. Jackson  10           6  Alex                   2        1        0.73  Ms. Smith

In both these cases a copy of the DataFrame is returned and the result_df is left unchanged. But, if we wanted tosort in-place we can also use the sort! function that will update the passed DataFrame.

julia> sort!(result_df, [:grade], rev=true)106 DataFrame Row  student_id  student_name  teacher_id  exam_id  grade       teacher_name       Int64       String        Int64       Int64?   Float64?    String          1           4  Sandy                  2  missing  missing     Ms. Smith   2           1  Joe                    1        1        0.95  Mr. Jackson   3           2  Sally                  2        1        0.93  Ms. Smith   4          10  Carl                   2        1        0.93  Ms. Smith   5           7  Tom                    1        1        0.88  Mr. Jackson   6           5  Beth                   1        1        0.85  Mr. Jackson   7           3  Jim                    1        1        0.81  Mr. Jackson   8           8  Liz                    2        1        0.77  Ms. Smith   9           9  Bill                   1        1        0.75  Mr. Jackson  10           6  Alex                   2        1        0.73  Ms. Smith

Split-apply-combine

Now that we have some basics down, it’s time to dive into aggregating results. In DataFrames.jl this isreferred to as a split-apply-combine strategy. It is a bit of a mouthful, but let’s walk through whatexactly this is referring to.

Split is simply breaking the DataFrame into groups using the groupby function. In our example lets splitour DataFrame by the teacher_name column.

julia> grouped_df = groupby(result_df, :teacher_name)GroupedDataFrame with 2 groups based on key: teacher_nameFirst Group (5 rows): teacher_name = "Mr. Jackson" Row  student_id  student_name  teacher_id  exam_id  grade     teacher_name       Int64       String        Int64       Int64?   Float64?  String          1           1  Joe                    1        1      0.95  Mr. Jackson   2           3  Jim                    1        1      0.81  Mr. Jackson   3           5  Beth                   1        1      0.85  Mr. Jackson   4           7  Tom                    1        1      0.88  Mr. Jackson   5           9  Bill                   1        1      0.75  Mr. JacksonLast Group (5 rows): teacher_name = "Ms. Smith" Row  student_id  student_name  teacher_id  exam_id  grade       teacher_name       Int64       String        Int64       Int64?   Float64?    String          1           2  Sally                  2        1        0.93  Ms. Smith   2           6  Alex                   2        1        0.73  Ms. Smith   3           8  Liz                    2        1        0.77  Ms. Smith   4          10  Carl                   2        1        0.93  Ms. Smith   5           4  Sandy                  2  missing  missing     Ms. Smith

The result of calling groupby is of type GroupedDataFrame, which is basically a wrapperaround one, or many, groups of a DataFrame. In our example we have two teachers and so the resultGroupedDataFrame has two groups.

Now, lets try to get an average exam grade for our two teachers. This will introduce the combinefunction that takes a GroupedDataFrame and any number of aggregation functions. Let’s also addthe Statistics.jl package, so we can take advantage of the mean function.

julia> using Statisticsjulia> combine(grouped_df, :grade => mean)22 DataFrame Row  teacher_name  grade_mean        String        Float64?       1  Mr. Jackson         0.848   2  Ms. Smith     missing

The result is a DataFrame where the first column(s) will match our GroupedDataFrame key(s) and the subsequent column(s) will match the function(s) we pass for aggregation. However, Ms. Smith has a grade_mean of missing!?

In our earlier discussion we found that Sandy missed the exam, so her grade was set to missing. A missing valuebehaves differently than normal numbers, which is problematic in our aggregation function. Take a look at a very simpleexample.

julia> 1 + missingmissing

We notice that adding a value of 1 to missing equals missing. This is a necessary evil and you may be wonderingwhy don’t we just treat it as 0? Let’s see what happens to our results if we replace missing with 0.

julia> combine(grouped_df, :grade => (x -> mean(coalesce.(x, 0))))22 DataFrame Row  teacher_name  grade_function       String        Float64           1  Mr. Jackson            0.848   2  Ms. Smith              0.672

In the above example, instead of just passing mean as the function we create an anonymous function. This allows us to get a littlemore clever with adding a coalesce to replace the missing values with 0. We see from the results that Ms. Smith has a much lowerscoring average than Mr. Jackson. But, if we think about it the results are getting incorrectly skewed. We know Sandy didn’t actuallyscore a 0, but rather didn’t take the test at all. Treating her result as a 0 is skewing the average much lower than it should be.

In some cases replacing with a 0 would make sense, but not in this scenario. Here are a few better options:

We could just drop the rows that contain missing values prior to aggregation. DataFrames.jl provides a dropmissing functionspecifically for this.

julia> result_no_missing_df = dropmissing(result_df)96 DataFrame Row  student_id  student_name  teacher_id  exam_id  grade    teacher_name       Int64       String        Int64       Int64    Float64  String          1           1  Joe                    1        1     0.95  Mr. Jackson   2           2  Sally                  2        1     0.93  Ms. Smith   3           3  Jim                    1        1     0.81  Mr. Jackson   4           5  Beth                   1        1     0.85  Mr. Jackson   5           6  Alex                   2        1     0.73  Ms. Smith   6           7  Tom                    1        1     0.88  Mr. Jackson   7           8  Liz                    2        1     0.77  Ms. Smith   8           9  Bill                   1        1     0.75  Mr. Jackson   9          10  Carl                   2        1     0.93  Ms. Smithjulia> grouped_no_missing_df = groupby(result_no_missing_df, :teacher_name)GroupedDataFrame with 2 groups based on key: teacher_nameFirst Group (5 rows): teacher_name = "Mr. Jackson" Row  student_id  student_name  teacher_id  exam_id  grade    teacher_name       Int64       String        Int64       Int64    Float64  String          1           1  Joe                    1        1     0.95  Mr. Jackson   2           3  Jim                    1        1     0.81  Mr. Jackson   3           5  Beth                   1        1     0.85  Mr. Jackson   4           7  Tom                    1        1     0.88  Mr. Jackson   5           9  Bill                   1        1     0.75  Mr. JacksonLast Group (4 rows): teacher_name = "Ms. Smith" Row  student_id  student_name  teacher_id  exam_id  grade    teacher_name       Int64       String        Int64       Int64    Float64  String          1           2  Sally                  2        1     0.93  Ms. Smith   2           6  Alex                   2        1     0.73  Ms. Smith   3           8  Liz                    2        1     0.77  Ms. Smith   4          10  Carl                   2        1     0.93  Ms. Smithjulia> combine(grouped_no_missing_df, :grade => mean)22 DataFrame Row  teacher_name  grade_mean       String        Float64       1  Mr. Jackson        0.848   2  Ms. Smith          0.84

We now see that the two teachers average test scores are very similar. This approach would work wellif we never again needed the rows containing missing values.

But, if we wanted to keep those rows around and rather just exclude them from certain calculations. We can make use of another function, skipmissing, which will simply skip over the missing values.

julia> combine(grouped_df, :grade => (x -> mean(skipmissing(x))))22 DataFrame Row  teacher_name  grade_function       String        Float64           1  Mr. Jackson            0.848   2  Ms. Smith              0.84

One last thing to note on missing values is that it is easy to identify if one, or more, of your DataFramecolumns contains missing values. We talked earlier that DataFrames.jl infers the types for eachcolumn and displays them in the output. You’ll notice in result_df that the column teacher_id is of datatypeInt64 and exam_id is of Int64?. Here the ? denotes that missing values were found, so be careful!

Conclusion

We’ve touched on some of the topics that makes DataFrames.jl such a great general purpose package. It is a helpfultool to quickly interact for data exploration, or to be used in production code to manipulate tabular data. I hopeyou’ve enjoyed today’s reading and be sure to check out the rest of our blog posts on blog.glcs.io!

A Million Text Files And A Single Laptop

By: randyzwitch - Articles

Re-posted from: http://randyzwitch.com/gnu-parallel-medium-data/

GNU Parallel Cat Unix

Wait…What? Why?

More often that I would like, I receive datasets where the data has only been partially cleaned, such as the picture on the right: hundreds, thousands…even millions of tiny files. Usually when this happens, the data all have the same format (such as having being generated by sensors or other memory-constrained devices).

The problem with data like this is that 1) it’s inconvenient to think about a dataset as a million individual pieces 2) the data in aggregate are too large to hold in RAM but 3) the data are small enough where using Hadoop or even a relational database seems like overkill.

Surprisingly, with judicious use of GNU Parallel, stream processing and a relatively modern computer, you can efficiently process annoying, “medium-sized” data as described above.

Data Generation

For this blog post, I used a combination of R and Python to generate the data: the “Groceries” dataset from the arules package for sampls ing transactions (with replacement), and the Python Faker (fake-factory) package to generate fake customer profiles and for creating the 1MM+ text files.

The contents of the data itself isn’t important for this blog post, but the data generation code is posted as a GitHub gist should you want to run these commands yourself.

Problem 1: Concatenating (cat * >> out.txt ?!)

The cat utility in Unix-y systems is familiar to most anyone who has ever opened up a Terminal window. Take some or all of the files in a folder, concatenate them together….one big file. But something funny happens once you get enough files…

$ cat * >> out.txt
-bash: /bin/cat: Argument list too long

That’s a fun thought…too many files for the computer to keep track of. As it turns out, many Unix tools will only accept about 10,000 arguments; the use of the asterisk in the cat command gets expanded before running, so the above statement passes 1,234,567 arguments to cat and you get an error message.

One (naive) solution would be to loop over every file (a completely serial operation):

for f in *; do cat "$f" >> ../transactions_cat/transactions.csv; done

Roughly 10,093 seconds later, you’ll have your concatenated file. Three hours is quite a coffee break…

Solution 1: GNU Parallel & Concatenation

Above, I mentioned that looping over each file gets you past the error condition of too many arguments, but it is a serial operation. If you look at your computer usage during that operation, you’ll likely see that only a fraction of a core of your computer’s CPU is being utilized. We can greatly improve that through the use of GNU Parallel:

ls | parallel -m -j $f "cat {} >> ../transactions_cat/transactions.csv"

The $f argument in the code is to highlight that you can choose the level of parallelism; however, you will not get infinitely linear scaling, as shown below (graph code, Julia):

Given that the graph represents a single run at each level of parallelism, it’s a bit difficult to say exactly where the parallelism gets maxed out, but at roughly 10 concurrent jobs, there’s no additional benefit. It’s also interesting to point out what the -m argument represents; by specifying m, you allow multiple arguments (i.e. multiple text files) to be passed as inputs into parallel. This alone leads to an 8x speedup over the naive loop solution.

Problem 2: Data > RAM

Now that we have a single file, we’ve removed the “one million files” cognitive dissonance, but now we have a second problem: at 19.93GB, the amount of data exceeds the RAM in my laptop (2014 MBP, 16GB of RAM). So in order to do analysis, either a bigger machine is needed or processing has to be done in a streaming or “chunked” manner (such as using the “chunksize” keyword in pandas).

But continuing on with our use of GNU Parallel, suppose we wanted to answer the following types of questions about our transactions data:

  1. How many unique products were sold?
  2. How many transactions were there per day?
  3. How many total items were sold per store, per month?

If it’s not clear from the list above, in all three questions there is an “embarrassingly parallel” portion of the computation. Let’s take a look at how to answer all three of these questions in a time- and RAM-efficient manner:

Q1: Unique Products

Given the format of the data file (transactions in a single column array), this question is the hardest to parallelize, but using a neat trick with the `tr` (transliterate) utility, we can map our data to one product per row as we stream over the file:

# Serial method (i.e. no parallelism)
# This is a simple implementation of map & reduce; tr statements represent one map, sort -u statements one reducer
# cut -d ' ' -f 5- transactions.csv | \ - Using cut, take everything from the 5th column and over from the transactions.csv file
# tr -d \" | \ - Using tr, trim off double-quotes. This leaves us with a comma-delimited string of products representing a transaction
# sort -u | \ - Using sort, put similar items together, but only output the unique values
# wc -l - Count number of unique lines, which after de-duping, represents number of unique products
$ time cut -d ' ' -f 5- transactions.csv | tr -d \" | tr ',' '\n' | sort -u | wc -l
331
real 292m7.116s
# Parallelized version, default chunk size of 1MB. This will use 100% of all CPUs (real and virtual)
# Also map & reduce; tr statements a single map, sort -u statements multiple reducers (8 by default)
$ time cut -d ' ' -f 5- transactions.csv | tr -d \" | tr ',' '\n' | parallel --pipe --block 1M sort -u | sort -u | wc -l
331
# block size performance - Making block size smaller might improve performance
# Number of jobs can also be manipulated (not evaluated)
# --500K: 73m57.232s
# --Default 1M: 75m55.268s (3.84x faster than serial)
# --2M: 79m30.950s
# --3M: 80m43.311s
view raw unique_products.sh hosted with ❤ by GitHub

The trick here is that we swap the comma-delimited transactions with the newline character; the effect of this is taking a single transaction row and returning multiple rows, one for each product. Then we pass that down the line, eventually using sort-u to de-dup the list and wc-l to count the number of unique lines (i.e. products).

In a serial fashion, it takes quite some time to calculate the number of unique products. Incorporating GNU Parallel, just using the defaults, gives nearly a 4x speedup!

Q2. Transactions By Day

If the file format could be considered undesirable in question 1, for question 2 the format is perfect. Since each row represents a transaction, all we need to do is perform the equivalent of a SQL GroupBy on the date and sum the rows:

# Data is at transaction level, so just need to do equivalent of 'group by' operation
# Using cut again, we choose field 3, which is the date part of the timestamp
# sort | uniq -c is a common pattern for doing a 'group by' count operation
# Final tr step is to trim the leading quotation mark from date string
time cut -d ' ' -f 3 transactions.csv | sort | uniq -c | tr -d \"
real 76m51.223s
# Parallelized version
# Quoting can be annoying when using parallel, so writing a Bash function is often much easier than dealing with escaping quotes
# To do 'group by' operation using awk, need to use an associative array
# Because we are doing parallel operations, need to pass awk output to awk again to return final counts
awksub () { awk '{a[$3]+=1;}END{for(i in a)print i" "a[i];}';}
export -f awksub
time parallel --pipe awksub < transactions.csv | awk '{a[$1]+=$2;}END{for(i in a)print i" "a[i];}' | tr -d \" | sort
real 8m22.674s (9.05x faster than serial)
view raw group-by-awk.sh hosted with ❤ by GitHub

Using GNU Parallel starts to become complicated here, but you do get a 9x speed-up by calculating rows by date in chunks, then “reducing” again by calculating total rows by date (a trick I picked up at this blog post).

Q3. Total items Per store, Per month

For this example, it could be that my command-line fu is weak, but the serial method actually turns out to be the fastest. Of course, at a 14 minute run time, the real-time benefits to parallelization aren’t that great.

# Serial method uses 40-50% all available CPU prior to sort step. Assuming linear scaling, best we could achieve is halving the time.
# Grand Assertion: this pipeline actually gives correct answer! This is a very complex way to calculate this, SQL would be so much easier...
# cut -d ' ' -f 2,3,5 - Take fields 2, 3, and 5 (store, timestamp, transaction)
# tr -d '[A-Za-z\"/\- ]' - Strip out all the characters and spaces, to just leave the store number, timestamp, and commas to represent the number of items
# awk '{print (substr($1,1,5)"-"substr($1,6,6)), length(substr($1,14))+1}' - Split the string at the store, yearmo boundary, then count number of commas + 1 (since 3 commas = 4 items)
# awk '{a[$1]+=$2;}END{for(i in a)print i" "a[i];}' - Sum by store-yearmo combo
# sort - Sort such that the store number is together, then the month
time cut -d ' ' -f 2,3,5 transactions.csv | tr -d '[A-Za-z\"/\- ]' | awk '{print (substr($1,1,5)"-"substr($1,6,6)), length(substr($1,14))+1}' | awk '{a[$1]+=$2;}END{for(i in a)print i" "a[i];}' | sort
real 14m5.657s
# Parallelize the substring awk step
# Actually lowers processor utilization!
awksub2 () { awk '{print (substr($1,1,5)"-"substr($1,6,6)), length(substr($1,14))+1}';}
export -f awksub2
time cut -d ' ' -f 2,3,5 transactions.csv | tr -d '[A-Za-z\"/\- ]' | parallel --pipe -m awksub2 | awk '{a[$1]+=$2;}END{for(i in a)print i" "a[i];}' | sort
real 19m27.407s (worse!)
# Move parallel to aggregation step
awksub3 () { awk '{a[$1]+=$2;}END{for(i in a)print i" "a[i];}';}
export -f awksub3
time cut -d ' ' -f 2,3,5 transactions.csv | tr -d '[A-Za-z\"/\- ]' | awk '{print (substr($1,1,5)"-"substr($1,6,6)), length(substr($1,14))+1}' | parallel --pipe awksub3 | awksub3 | sort
real 19m24.851s (Same as other parallel run)
view raw transactions-month-store.sh hosted with ❤ by GitHub

It may be possible that one of you out there knows how to do this correctly, but an interesting thing to note is that the serial version already uses 40-50% of the available CPU available. So parallelization might yield a 2x speedup, but seven minutes extra per run isn’t worth spending hours trying to the optimal settings.

But, I’ve got MULTIPLE files…

The three examples above showed that it’s possible to process datasets larger than RAM in a realistic amount of time using GNU Parallel. However, the examples also showed that working with Unix utilities can become complicated rather quickly. Shell scripts can help move beyond the “one-liner” syndrome, when the pipeline gets so long you lose track of the logic, but eventually problems are more easily solved using other tools.

The data that I generated at the beginning of this post represented two concepts: transactions and customers. Once you get to the point where you want to do joins, summarize by multiple columns, estimate models, etc., loading data into a database or an analytics environment like R or Python makes sense. But hopefully this post has shown that a laptop is capable of analyzing WAY more data than most people believe, using many tools written decades ago.