NimData

Overview

NimData is a data manipulation and analysis library for the Nim programming language. It combines Pandas-like syntax with the type-safe, lazy APIs of distributed frameworks like Spark/Flink/Thrill. Although NimData is currently non-distributed, it harnesses the power of Nim to perform out-of-core processing at native speed.

NimData's core data type is the generic DataFrame[T]. All DataFrame methods are based on the MapReduce paradigm and fall into two categories:

• Transformations: Operations like map or filter transform one DataFrame into another. Transformations are lazy, meaning that they are not executed until an action is called. They can also be chained.
• Actions: Operations like count, min, max, sum, reduce, fold, collect, or show perform an aggregation on a DataFrame. Calling an action triggers the processing pipeline.

For a complete list of NimData's supported operations, see the module docs.

Installation

1. Install Nim and ensure that both Nim and Nimble (Nim's package manager) are added to your PATH.
2. From the command line, run $ nimble install NimData (this will download NimData's source from GitHub to ~/.nimble/pkgs).

Quickstart

Hello, World!

Once NimData is installed, we'll write a simple program to test it. Create a new file named test.nim with the following contents:

import nimdata

echo DF.fromRange(0, 10).collect()

From the command line, use $ nim c -r test.nim to compile and run the program (c for compile, and -r to run directly after compilation). It should print this sequence:

# => @[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]

Pandas users: This is roughly equivalent to print(pd.DataFrame(range(10))[0].values)

Reading raw text data

Next we'll use this German soccer data set to explore NimData's main functionality.

To create a DataFrame which simply iterates over the raw text content of a file, we can use DF.fromFile():

let dfRawText = DF.fromFile("examples/Bundesliga.csv")

Note that fromFile is a lazy operation, meaning that NimData doesn't actually read the contents of the file yet. To read the file, we need to call an action on our dataframe. Calling count, for example, triggers a line-by-line reading of the file and returns the number of rows:

echo dfRawText.count()
# => 14018

We can chain multiple operations on dfRawText. For example, we can use take to filter the file down to its first five rows, and show to print the result:

dfRawText.take(5).show()
# =>
# "1","Werder Bremen","Borussia Dortmund",3,2,1,1963,1963-08-24 09:30:00
# "2","Hertha BSC Berlin","1. FC Nuernberg",1,1,1,1963,1963-08-24 09:30:00
# "3","Preussen Muenster","Hamburger SV",1,1,1,1963,1963-08-24 09:30:00
# "4","Eintracht Frankfurt","1. FC Kaiserslautern",1,1,1,1963,1963-08-24 09:30:00
# "5","Karlsruher SC","Meidericher SV",1,4,1,1963,1963-08-24 09:30:00

Pandas users: This is equivalent to print(dfRawText.head(5)).

Note, however, that every time an action is called, the file is read from scratch, which is inefficient. We'll improve on that in a moment.

Type-safe schema parsing

At this stage, dfRawText's data type is a plain DataFrame[string]. It also doesn't have any column headers, and the first field isn't a proper index, but rather contains string literals. Let's transform our dataframe into something more useful for analysis:

const schema = [
  strCol("index"),
  strCol("homeTeam"),
  strCol("awayTeam"),
  intCol("homeGoals"),
  intCol("awayGoals"),
  intCol("round"),
  intCol("year"),
  dateCol("date", format="yyyy-MM-dd hh:mm:ss")
]
let df = dfRawText.map(schemaParser(schema, ','))
                  .map(record => record.projectAway(index))
                  .cache()

This code does three things:

1. The schemaParser macro constructs a specialized parsing function for each field, which takes a string as input and returns a type-safe named tuple corresponding to the type definition in schema. For instance, dateCol("date") tells the parser that the last column is named "date" and contains datetime values. We can even specify the datetime format by passing a format string to dateCol() as a named parameter. A key benefit of defining the schema at compile time is that the parser produces highly optimized machine code, resulting in very fast performance.

2. The projectAway macro transforms the results of schemeParser into a new dataframe with the "index" column removed (Pandas users: this is roughly equivalent to dfRawText.drop(columns=['index'])). See also projectTo, which instead keeps certain fields, and addFields, which extends the schema by new fields.

3. The cache method stores the parsing result in memory. This allows us to perform multiple actions on the data without having to re-read the file contents every time. Spark users: In contrast to Spark, cache is currently implemented as an action.

Now we can perform the same operations as before, but this time our dataframe contains the parsed tuples:

echo df.count()
# => 14018

df.take(5).show()
# =>
# +------------+------------+------------+------------+------------+------------+------------+
# | homeTeam   | awayTeam   |  homeGoals |  awayGoals |      round |       year | date       |
# +------------+------------+------------+------------+------------+------------+------------+
# | "Werder B… | "Borussia… |          3 |          2 |          1 |       1963 | 1963-08-2… |
# | "Hertha B… | "1. FC Nu… |          1 |          1 |          1 |       1963 | 1963-08-2… |
# | "Preussen… | "Hamburge… |          1 |          1 |          1 |       1963 | 1963-08-2… |
# | "Eintrach… | "1. FC Ka… |          1 |          1 |          1 |       1963 | 1963-08-2… |
# | "Karlsruh… | "Meideric… |          1 |          4 |          1 |       1963 | 1963-08-2… |
# +------------+------------+------------+------------+------------+------------+------------+

Note that instead of starting the pipeline from dfRawText and using caching, we could always write the pipeline from scratch:

DF.fromFile("examples/Bundesliga.csv")
  .map(schemaParser(schema, ','))
  .map(record => record.projectAway(index))
  .take(5)
  .show()

Filter

Data can be filtered by using filter. For instance, we can filter the data to get games of a certain team only:

import strutils

df.filter(record =>
    record.homeTeam.contains("Freiburg") or
    record.awayTeam.contains("Freiburg")
  )
  .take(5)
  .show()
# =>
# +------------+------------+------------+------------+------------+------------+------------+
# | homeTeam   | awayTeam   |  homeGoals |  awayGoals |      round |       year | date       |
# +------------+------------+------------+------------+------------+------------+------------+
# | "Bayern M… | "SC Freib… |          3 |          1 |          1 |       1993 | 1993-08-0… |
# | "SC Freib… | "Wattensc… |          4 |          1 |          2 |       1993 | 1993-08-1… |
# | "Borussia… | "SC Freib… |          3 |          2 |          3 |       1993 | 1993-08-2… |
# | "SC Freib… | "Hamburge… |          0 |          1 |          4 |       1993 | 1993-08-2… |
# | "1. FC Ko… | "SC Freib… |          2 |          0 |          5 |       1993 | 1993-09-0… |
# +------------+------------+------------+------------+------------+------------+------------+

Note: Without the strutils module, contains will throw a type error here.

Or search for games with many home goals:

df.filter(record => record.homeGoals >= 10)
  .show()
# =>
# +------------+------------+------------+------------+------------+------------+------------+
# | homeTeam   | awayTeam   |  homeGoals |  awayGoals |      round |       year | date       |
# +------------+------------+------------+------------+------------+------------+------------+
# | "Borussia… | "Schalke … |         11 |          0 |         18 |       1966 | 1967-01-0… |
# | "Borussia… | "Borussia… |         10 |          0 |         12 |       1967 | 1967-11-0… |
# | "Bayern M… | "Borussia… |         11 |          1 |         16 |       1971 | 1971-11-2… |
# | "Borussia… | "Borussia… |         12 |          0 |         34 |       1977 | 1978-04-2… |
# | "Borussia… | "Arminia … |         11 |          1 |         12 |       1982 | 1982-11-0… |
# | "Borussia… | "Eintrach… |         10 |          0 |          8 |       1984 | 1984-10-1… |
# +------------+------------+------------+------------+------------+------------+------------+

Note that we can now fully benefit from type-safety: The compiler knows the exact fields and types of a record. No dynamic field lookup and/or type casting is required. Assumptions about the data structure are moved to the earliest possible step in the pipeline, allowing to fail early if they are wrong. After transitioning into the type-safe domain, the compiler helps to verify the correctness of even long processing pipelines, reducing the risk of runtime errors.

Other filter-like transformation are:

• take, which takes the first N records as already seen.
• drop, which discard the first N records.
• filterWithIndex, which allows to define a filter function that take both the index and the elements as input.

Collecting data

A DataFrame[T] can be converted easily into a seq[T] (Nim's native dynamic arrays) by using collect:

echo df.map(record => record.homeGoals)
       .filter(goals => goals >= 10)
       .collect()
# => @[11, 10, 11, 12, 11, 10]

Numerical aggregation

A DataFrame of a numerical type allows to use functions like min/max/mean. This allows to get things like:

echo "Min date: ", df.map(record => record.year).min()
echo "Max date: ", df.map(record => record.year).max()
echo "Average home goals: ", df.map(record => record.homeGoals).mean()
echo "Average away goals: ", df.map(record => record.awayGoals).mean()
# =>
# Min date: 1963
# Max date: 2008
# Average home goals: 1.898130974461407
# Average away goals: 1.190754743900699

# Let's find the highest defeat
let maxDiff = df.map(record => (record.homeGoals - record.awayGoals).abs).max()
df.filter(record => (record.homeGoals - record.awayGoals) == maxDiff)
  .show()
# =>
# +------------+------------+------------+------------+------------+------------+------------+
# | homeTeam   | awayTeam   |  homeGoals |  awayGoals |      round |       year | date       |
# +------------+------------+------------+------------+------------+------------+------------+
# | "Borussia… | "Borussia… |         12 |          0 |         34 |       1977 | 1978-04-2… |
# +------------+------------+------------+------------+------------+------------+------------+

Sorting

A DataFrame can be transformed into a sorted DataFrame by the sort() method. Without specifying any arguments, the operation would sort using default comparison over all columns. By specifying a key function and the sort order, we can for instance rank the games by the number of away goals:

df.sort(record => record.awayGoals, SortOrder.Descending)
  .take(5)
  .show()
# =>
# +------------+------------+------------+------------+------------+------------+------------+
# | homeTeam   | awayTeam   |  homeGoals |  awayGoals |      round |       year | date       |
# +------------+------------+------------+------------+------------+------------+------------+
# | "Tasmania… | "Meideric… |          0 |          9 |         27 |       1965 | 1966-03-2… |
# | "Borussia… | "TSV 1860… |          1 |          9 |         29 |       1965 | 1966-04-1… |
# | "SSV Ulm"  | "Bayer Le… |          1 |          9 |         25 |       1999 | 2000-03-1… |
# | "Rot-Weis… | "Eintrach… |          1 |          8 |         32 |       1976 | 1977-05-0… |
# | "Borussia… | "Bayer Le… |          2 |          8 |         10 |       1998 | 1998-10-3… |
# +------------+------------+------------+------------+------------+------------+------------+

Unique values

The DataFrame[T].unique() transformation filters a DataFrame to unique elements. This can be used for instance to find the number of teams that appear in the data:

echo df.map(record => record.homeTeam).unique().count()
# => 52

Pandas user note: In contrast to Pandas, there is no differentiation between a one-dimensional series and multi-dimensional DataFrame (unique vs drop_duplicates). unique works the same in for any hashable type T, e.g., we might as well get a DataFrame of unique pairs:

df.map(record => record.projectTo(homeTeam, awayTeam))
  .unique()
  .take(5)
  .show()
# =>
# +------------+------------+
# | homeTeam   | awayTeam   |
# +------------+------------+
# | "Werder B… | "Borussia… |
# | "Hertha B… | "1. FC Nu… |
# | "Preussen… | "Hamburge… |
# | "Eintrach… | "1. FC Ka… |
# | "Karlsruh… | "Meideric… |
# +------------+------------+

Value counts

The DataFrame[T].valueCounts() transformation extends the functionality of unique() by returning the unique values and their respective counts. The type of the transformed DataFrame is a tuple of (key: T, count: int), where T is the original type.

In our example, we can use valueCounts() for instance to find the most frequent results in German soccer:

df.map(record => record.projectTo(homeGoals, awayGoals))
  .valueCounts()
  .sort(x => x.count, SortOrder.Descending)
  .map(x => (
    homeGoals: x.key.homeGoals,
    awayGoals: x.key.awayGoals,
    count: x.count
  ))
  .take(5)
  .show()
# =>
# +------------+------------+------------+
# |  homeGoals |  awayGoals |      count |
# +------------+------------+------------+
# |          1 |          1 |       1632 |
# |          2 |          1 |       1203 |
# |          1 |          0 |       1109 |
# |          2 |          0 |       1092 |
# |          0 |          0 |        914 |
# +------------+------------+------------+

This transformation first projects the data onto a named tuple of (homeGoals, awayGoals). After applying valueCounts() the data frame is sorted according to the counts. The final map() function is purely for cosmetics of the resulting table, projecting the nested (key: (homeGaols: int, awayGoals: int), counts: int) tuple back to a flat result.

DataFrame viewer

DataFrames can be opened and inspected in the browser by using df.openInBrowser(), which offers a simple Javascript based data browser:

Note that the viewer uses static HTML, so it should only be applied to small or heavily filtered DataFrames.

Benchmarks

More meaningful benchmarks are still on the todo list. This just shows a few first results. The benchmarks will be split into small (data which fits into memory so we can compare against Pandas or R easily) and big (where we can only compare against out-of-core frameworks).

All implementations are available in the benchmarks folder.

Basic operations (small data)

The test data set is 1 million rows CSV with two int and two float columns. The test tasks are:

• Parse/Count: Just the most basic operations -- iterating the file, applying parsing, and return a count.
• Column Averages: Same steps, plus an additional computation of all 4 column means.

The results are average runtime in seconds of three runs:

Note that Spark internally caches the file over the three runs, so the first iteration is much slower (with > 3 sec) while it reaches run times of 0.6 sec in the last iterations (obviously the data is too small to justify the overhead anyway).

Next steps

• More transformations:
  • map
  • filter
  • flatMap
  • sort
  • unique
  • valueCounts
  • groupBy (reduce)
  • groupBy (transform)
  • join (inner)
  • join (outer)
  • concat/union
  • window
• More actions:
  • numerical aggergations (count, min, max, sum, mean)
  • collect
  • show
  • openInBrowser
• More data formats/sources
  • csv
  • gzipped csv
  • parquet
  • S3
• REPL or Jupyter kernel?
• Plotting (maybe in the form of Bokeh bindings)?

License

This project is licensed under the terms of the MIT license.