Tag Archives: DataFrames

DataFrames.jl Style Linear Relations

Re-posted from: https://www.philipzucker.com/dataframes-jl-style-linear-relations/

DataFrames.jl is a Julia library to store, retrieve and manipulate tabular data. It is the analog of Pandas for python or related tools in R. It implements an interface that is a mish-mash of numpy-like slicing and SQL-like queries and can be used as lightweight flexible relational database. It has proven to be a popular and intuitive interface.

Relational Databases are computer implementations of the mathematical concept of finite relations, but there are other classes of relations. This blog post is about using (abusing?) the DataFrame.jl interface to implement linear relations. Linear relations are useful for describing physical systems whose behavior is constrained by linear equations, such as electrical circuits.

It all comes out quite cute.

Databases and Relations

A finite relation over sets A,B,C,D is a subset of $ A\times B \times C \times D $ . In the database, each of these sets corresponds to a column of the database, and each element of the subset corresponds to a row of the database.

Much of the time, we think of storing discrete data in databases, things like names, ids, occupations, ages. However, DataFrame.jl is certainly also capable of storing floats. In this case, the DataFrame could be interpreted as storing a finite number of points in the continuous space $ R^n $, one dimension for each column, one point for each row.

In continuous space, it is desirable to not only describe discrete sets of points, but also blobs. Conceptually, blobs are only a subset of $ R^n$, and are still a mathematical relation. In some sense a blob could be thought of as the contents of a database holding an uncountable number of rows, but then the enumerative storage schemes of an actual physical database doesn’t cut it anymore. We need an extremely compressed storage scheme.

Describing Linear Subspaces

Trying to store arbitrary blobs is a bit too unruly, but if we constrain ourselves to certain classes of blobs, we can still make headway. In this case, we are going to stick to linear blobs, i.e. hyperplanes.

One can concretely describe a hyperplane of $ R^n$ by two main representations. First one can list a set of vectors $ {v_i }$ that span the hyperplane. From this list, we are implicitly describing any linear combination of these vectors $ v = \sum \lambda_i v_i $. A second distinct representation is to list the coefficient vectors $ {c_i^T }$ of a set of linear equations $ { c_i^T v = 0 }$ describing the hyperplane. I will call these the span and constraint representations.

An alternative name for these two representations might be the range and nullspace representation, the generator and equation representation, or by analogy to polyhedral computation, the v-rep and h-rep.

These two representations are dual in some sense. One way in which they feel dual is that adding a new non independent vector to the span increases the dimensionality of the linear subspace by 1. Adding a new independent linear equation to the constraint rep decreases the dimension of the subspace by 1.

It is important to have both representations because different operations on linear subspaces are easy in each. The intersection of two subspaces is easy in the constraint representation. It corresponds to just concatenating two constraint sets. Projection is easy in the span representation where it corresponds to just taking a slice out of each individual generator vector.

As more evidence for the two representation’s duality, two other important operations that are easy in a particular representation are the image and preimage operations under linear maps. The image is easy to compute in the span representation (matrix multiply $ {Av_i}$) and the preimage is easy in the constraint representation (matrix multiply maybe with a transpose thrown in there $ {c_i^T A}$).

The Julia Implementation

The interface of DataFrames still makes sense for linear relations and we can slightly abuse its already existent machinery to make fast headway.

First the constructors of the two representations. As a slight hack, to differentiate between the two representations, I will tag a special blank column to differentiate which representation we are in. Otherwise I forward all arguments to the ordinary DataFrames constructor so you can build these as you would any DataFrame. Each row of the dataframe is either one vector from the span set or one constraint from the constraint set. Column are components of these vectors

using DataFrames
using LinearAlgebra

function DataFrameSpan( x...; kwargs...)
    df = DataFrame(x...; kwargs...)
    df[:spanrep] = 0 # missing
    df
end

# representing linear relations via constraints
function DataFrameCons( x...; kwargs...)
    df = DataFrame(x...; kwargs...)
    df[:consrep] = 0 # missing
    df
end

Because each individual vector in the span obeys the constraint equations, we can numerically convert between them by calling the Julia function nullspace. There is a pleasing symmetry between the two conversion directions.

# nullconvert turns span rep to constraint rep or vice versa
function nullconvert(df)
    cols = names(df)
    A = nullspace(Matrix(df))

    DataFrame(mapslices(x->[x], A, dims=2)[:] , cols ) 
end

#https://discourse.julialang.org/t/converting-a-matrix-into-an-array-of-arrays/17038/2


# We tag somewhat inelegantly whether we're inb the span rep or constrain rep via a column tag
isspanrep(x) = "spanrep" ∈ names(x)
isconsrep(x) = "consrep" ∈ names(x)


function spanrep(x) 
    if isspanrep(x)
        return x
    else
        df = select(x, Not(:consrep))
        df = nullconvert(df)
        df[:spanrep] = 0 #add tag
        return df
    end
end

function consrep(x) 
    if isconsrep(x)
        return x
    else
        df = select(x, Not(:spanrep)) #remove tag
        df = nullconvert(df)
        df[:consrep] = 0 #add tag 
        return df
     end
end

The ability to name columns and the analog of the join operation from database gives us a way to compositionally construct systems of linear equations. We also define a projection operations which in some sense solves these equations.

function linearjoin(df,dg)  #should we add makefresh flag?
        df = consrep(df)
        dg = consrep(dg)
        coalesce.(vcat(df,dg, cols=:union), 0)
end


function linearproject(df, cols)
        df = spanrep(df)
        df = df[ [cols ; :spanrep]]
end

And shockingly, that’s about it. A very elegant implementation compared to my previous efforts.

Here as an example we join two resistors in series. We then project out the inner voltage, which returns a relation describing the

resistor(R, i , v1, v2) = DataFrameCons( [i => [R], v1 => [1], v2 => [-1] ] )
vsource(V, v1, v2) =  DataFrameCons( [:one => [V], v1 => [1], v2 => [-1] ] )
isource(I, i) =  DataFrameCons( [:one => [I], i => [-1] ] )

r1 = resistor(10, :i1, :v1, :v2)
r2 = resistor(10, :i1, :v2, :v3)


sys = consrep(linearproject(linearjoin(r1,r2), [:i1, :v1, :v3]) ) 

1×4 DataFrame
│ Row │ i1       │ v1        │ v3         │ consrep │
│     │ Float64  │ Float64   │ Float64    │ Int64   │
├────┼──────────┼───────────┼────────────┼─────────┤
│ 1   │ 0.997509 │ 0.0498755 │ -0.0498755 │ 0       │

This solution is linear proportional to [20,1,-1] hence representing the linear relation of a 20ohm resistor.

For more examples of usage of linear relations, check out these blog posts.

That one could use pandas or Dataframes.jl in this way is something I realized about a year ago when I was writing these posts, but I didn’t realize how pleasantly it would all fall out when I actually sat down to do it.

Bits and Bobbles

One often wants not only linear subspaces, but affine subspaces. The convenient and standard trick to convert affine subspaces to linear subspaces is the method of homogenous coordinates. We can increase our linear space with an extra dimension called :one. Whenever we need a scalar occurring in an equation, it now goes in front of this new variable. In the span representation, it is convenient to normalize this entry to 1 by scaling the each span vector. Then the span representation representing the affine subspace is $ \sum \lambda_i v_i$ with the constraint that $ \sum \lambda_i = 1$.
Linear relations are not the only sensible relations. Other classes of relations that have computational teeth include

Polyhedral relations https://github.com/JuliaPolyhedra/Polyhedra.jl
Polynomial Relations (aka algebraic or semialgebraic relations) https://www.juliahomotopycontinuation.org/
Convex relations https://juliareach.github.io/LazySets.jl/dev/

It seems reasonable that each of these could also use the DataFrames interface as a way of modelling themselves.

Linear relations elegantly express operations I would ordinarily phrase in terms of Schur complements, one of my favorite pieces of linear algebra. Projecting a linear relation is finding the effective or reduced system of equations.

In this manner, linear relations are related to Gaussian integrals, which are the canonical example of “integrating out” dynamics in physics. Multidimensional Gaussian integrals are ultimately basically linear algebra engines, giving a way to formulate the Schur complement operation. I think linear relations would form a good core for an “exact” Gaussian integral library.

Quadratic Optimization with linear equality constraints is reducible to a linear relation. I used this for example in my LQR control example above. This is also related to Gaussians, since the method of steepest descent approximates an integration problem by an optimization problem. Gaussian integrals in the presence of linear delta functions $ \delta(Ax-b)$ (which are important) convert under the steepest descent method to a quadratic optimization problem with linear equality constraints. All of this is completely ubiquitous in the connection between classical and quantum mechanics, and between statistical and thermodynamics. The Feynman integration over all paths becomes the principle of least action. The partition function integrals become free energy minimizations.

In the past, I’ve modeled relations in a Categorical style. This is still I think an excellent internal representation, but perhaps not the best user facing interface.

Query optimization is a big topic in database theory. The ordering of the joins and projections and the chosen points at which to change representations or indices can greatly change the time of the query. It is intriguing that such disparate seeming calculations may benefit from a unified relational query optimizer. I suggest that a categorical IR and the tools of Catlab https://github.com/AlgebraicJulia/Catlab.jl might be a way to do this.

Using sparse matrices would be a boon to linear relations (they tend to be very sparse), but we may have to roll our own DataFrame implementation to do so.
Should we allow columns to have width? Then variables can represent blocks of the matrix rather than just a single component. It feels like this has nice modelling power. It was also not convenient to do so with the stock DataFrame implementation

A Tour of the Data Ecosystem in Julia

By: Jacob Quinn

Re-posted from: https://quinnj.home.blog/2019/07/21/a-tour-of-the-data-ecosystem-in-julia/

Julia 1.0 was released at JuliaCon 2018 and it’s been a quick year for the package ecosystem to build upon the first long-term stable release. In a lot of ways, pre-1.0 for packages involved a lot of experimentation; a lot of trying out various ideas, shotgun-style and seeing what sticks, in addition to trying to keep up with the evolving core language. One of my favorite things about Julia is the great efforts that have been made to collaborate, coordinate, and modularize not just the base language and standard libraries, but the entire package ecosystem. Julia was born in the age of GitHub, Discourse, and Slack, which has led to an exceptional amount of public communication from and with core developers, targeted efforts to automatically update package code and test the impact of core changes to the ecosystem, and a proliferation of user-group meetups and domain-specific GitHub organizations. I don’t feel dishonest in saying I think Julia is the most collaborative programming language that exists today.

With all this context, one might be wondering: so what is the current status of working with data in Julia? How do various packages like CSV.jl, DataFrames.jl, JuliaDB.jl, and Query.jl play together. And so I present, “A Tour of the Data Ecosystem in Julia”. Let’s begin…

Data I/O: How do I get data in and out of Julia?

Text Files
I once heard Jeff Bezanson say tongue-in-cheek, “It doesn’t really matter what fancy features you put in a programming language, all people really want to do is read csv files. That’s it, just csv file reading.” Julia has grown a lot since the earliest days of dlmread, with several excellent packages for reading csv and other delimited files, each growing out of a unique idea for approaching this old problem or addressing a specific integration need.

CSV.jl
As blog author, you’ll have to forgive the shameless plug for my own packages: I started CSV.jl in 2015 to try and make a strong effort to match csv parsing functionality provided by other popular languages (in particular, Pandas and fread in R). Since then, it’s accumulated some ~400 commits from over 25 collaborators, and recently hit issue #464. It also released a significant upgrade with the recent 0.5 release, bringing performance inline with the fastest parsers in other languages, while providing some powerful and unique features not available elsewhere, including: “perfect” column typing without needing to restart parsing at all, auto delimiter detection, automatic handling of invalid rows, and several options/layers for lazily parsing files, all with unparalleled performance (a full feature comparison with pandas and R can be found in the latest discourse announcement post). Part of CSV.jl’s speed comes from the hand-tuned type parsers made available in the Parsers.jl package, which includes extremely performant parsing for ints, floats, bools, and dates/datetimes, all in pure Julia.

Tables.jl
We’re going to take a quick detour from I/O packages here to mention a key building block in the I/O story. The Tables.jl package was born during the 2018 JuliaCon hack-a-thon. It was a collaboration between several data-related package authors to come up with essentially two access patterns for “table” like data: “rows” and “columns”. The core idea is simple, yet powerful: any table-like data format that can implement one of the access patterns (via row iteration, or column access), can “automatically” integrate with any downstream package that then uses one of the access patterns, all without needing to take bi-directional dependencies. Even being a relatively new package, the power of this interface can be seen already in the integrations already available:

In-memory datastructures
- DataFrames.jl
- TypedTables.jl
- IndexedTables.jl/JuliaDB.jl
- FunctionalTables.jl
Data Formating/Processing Packages
- DataKnots.jl
- FreqTables.jl
- Mustache.jl
- FormattedTables.jl
- PrettyTables.jl
- TableView.jl
- BrowseTables.jl
Data File Format Packages
- CSV.jl
- Feather.jl
- StataDTAFiles.jl
- Taro.jl
- XLSX.jl
Database Packages
- ODBC.jl
- SQLite.jl
- MySQL.jl
- LibPQ.jl
- JDBC.jl
Statistics Packages
- StatsModels.jl
- MLJ.jl
- GLM.jl
Plotting Packages
- StatsMakie.jl
- StatsPlots.jl
- TableWidgets.jl

Ok, back to data I/O. We also have the wonderful TextParse.jl, which started as the data ingestion engine for JuliaDB.jl. While blazing fast, it doesn’t have quite the maturity or breadth of functionality compared to CSV.jl, like this long-standing issue of incorrect float parsing. The CSVFiles.jl package also exists to provide FileIO.jl integration, meaning you can just do load("data.csv") and FileIO.jl automatically recognizes the .csv extension and knows how to load the data. (A full feature comparison between CSV.jl and CSVFiles.jl can be found here). Another interesting approach to csv reading popped up recently in the form of TableReader.jl from the ever-talented Kenta Sato, using finite state machines. A pretty decent collection of benchmarks can be found here.

In addition to the excellent packages for handling text-based, delimited files, there are also some great files for managing/handling data formats in general.

RData.jl: for reading .rda files from R into Julia
DBFTables.jl: for reading .dbf files into Julia
StatFiles.jl: for reading SAS, SPSS, and Stata files into Julia
StataDTAFiles.jl: for reading and writing Stata files
DataDeps.jl: for declaring data dependencies and managing reproducible setups for with data in Julia
ExcelFiles.jl: for reading excel files into Julia and integration with FileIO.jl

Another popular option for data storage is binary-based formats, including feather, apache arrow, parquet, orc, avro, and BSON. Julia has pretty good coverage of these formats, including native Julia implementations in Feather.jl (and FileIO.jl integration in FeatherFiles.jl), Arrow.jl, and Parquet.jl (again, with FileIO.jl integration with ParquetFiles.jl). There’s also the BSON.jl package for binary JSON support. Beginning support for avro has been started in Avro.jl, and I’m personally interested in diving into the ORC format.

Database Support
As mentioned above in Tables.jl integrations, there are also great packages in place to support extracting data from databases, including:

ODBC.jl: provides generic support for any database that provides a compatible ODBC driver. Supports parameterized queries for inserting data, as well as extracting data from a query, and with Tables.jl support, “exporting” the results to any Tables.jl-compatible sink. It does require setting up an ODBC administrator tool on OSX and linux (windows has builtin support), and then proper setup/installation of the specific database’s ODBC driver, but once setup, things generally work pretty seamlessly.
JDBC.jl: the JDBC.jl package also provides access for databases that support JDBC access. It does so via JavaCall.jl, which does require an active JDK to work, which can also be tricky to setup, but database vendors tend to have pretty good support for a JDBC driver
MySQL.jl/LibPQ.jl: specific packages for mysql/postgres databases, respectively. Both provide integration with Tables.jl and aim to provide support for database-specific types and functionality (beyond the more generic interfaces of ODBC/JDBC). They can be easier to setup since there’s no “middle man” interface library and you just need to interact with the database libraries directly.
SQLite.jl: a library providing integration with the excellent sqlite database; supports in-memory databases as well as file-based. Supports parameterized queries and Tables.jl integration for loading data and extracting query results.

Data Processing: what can I do with my data once it’s in Julia?

Once you identify the right I/O package for reading your data, you then have a choice to make with regards to what you need/want to do with it. Clean it? Reshape it? Filter/calculate/group/sort it? Just browse around for a little while? Do advanced statistics, model training, or other machine learning techniques? Here we take a tour of the current landscape of packages that provide various types of functionality with regards to data processing, including table-like structures, query functionality, and statistics/machine learning.

Table Structures
Yes, in Julia we can have an entire section where we talk about table structures, plural. Sometimes when I mention to people that there are several dataframe-like packages in Julia, they are initially confused: why does Julia need more than one table package? Why doesn’t everyone just work on a single package to focus on quality over quantity? My most common response goes something like this: for a project like Pandas, or data.table, or data.frame, most of the actual code is written in what language? Python? Or R? It’s actually C/C++. The common belief is that because R/Python are dynamic, “high-level” languages, they can’t be fast, but that’s no worry, you just write the parts that need to be fast in C. But therein lies a core issue: for languages like Python and R, where the user-to-developer ratio is so high, having core pieces of functionality written in a lower-level language like C makes the code much less accessible to someone who wants to contribute, let alone just take a peek under the hood to see how things work. It automatically splits the code into a lower-level “black box” component, and a higher-level “wrapper” component. I believe it stunts innovation due to such a high “barrier to entry” to take a new approach to a table structure. I might be a budding R or Python user, getting into developing things, but shoot, if I want to make a meaningful contribution to Pandas or data.table, all the sudden I’m wading into deep make/cmake/build issues, compiler versions, and manual memory management that I’ve never dealt with before. The other alternative is I write something in pure R/Python which will just surely be doomed to performance issues, regardless of how novel my interfaces or APIs might be.
Julia, however, is a famously declared solution to this “two-language problem”. No longer do budding developers need to fear plain for-loops, or vectorize every operation, or rely on C/C++ for the “core stuff”. You can just write plain Julia, down in the trenches, and up at the highest-level user APIs. Julia, all the way down.
I firmly believe this has led to greater innovation in Julia for experimenting with unique table structures, as well as making packages more accessible to those hoping to contribute.
Ok, enough soap-boxing, let’s talk packages.

DataFrames.jl
The DataFrames.jl package is one of the very oldest packages in the Julia ecosystem. This tenure and naming proximity with its cousins in Pandas and R have also made it one of the most popular packages for those hoping to give Julia a try. The package has evolved quite a bit since its early days, and is rapidly approaching its own 1.0 release (expected around JuliaCon 2019). Development over the last year or two has focused on core performance, safety of APIs, and overall consistency with Base APIs. The amount of thought, effort, discussion, and documentation by numerous collaborators makes it the most mature “table” package in my opinion. So what’s unique about DataFrames? I’ll try to give what I deem to be notable highlights of how DataFrames.jl approaches representing tabular data:

A DataFrame stores columns internally as a Vector{AbstractVector}; but wait, you might ask, isn’t that type unstable (since we’re essentially lumping all columns, regardless of individual column type, as AbstractVector)? Yes! And on purpose! Experienced Julia developers are quick to point out that sometimes code can get “overly typed”, leading to “compilation overdrive”, where the compiler is having to generate very specialized code for every operation, with compiled code reuse rare. A DataFrame can represent any number of columns, with any combination of column types, so it’s a natural scenario where you may want to “hide” too much type information from the compiler by slapping the lowest common denominator abstract type as the type label (AbstractVector in this case). This design decision has certainly been extensively discussed, but remains as-is, if not as a more compiler-friendly option than other “strongly typed table” types.
DataFrames.jl includes specialized subtypes for representing SubDataFrames and GroupedDataFrames, as opposed to returning full DataFrames; these “lazy” structures are mostly used in intermediate operations and is a useful way to avoid too much unnecessary data copying/movement
Core manipulation operations included in the package itself include grouping, joining, and indexing; in particular, supporting column indexing via regex matches, functional selecting, Not indexing (inverted indices), and flexible interfaces similar to Base.Arrays in terms of filtering/selecting specific indices for rows or columns.
A lot of work in recent years has also been to simplify and move code out of the DataFrames.jl package, to focus on the core types, functionality, and reduce the dependency burden, being a common dependency for packages around the ecosystem; this has included notably the creation of the DataFramesMeta.jl package to support other common query/filter/manipulation operations
DataFrames.jl supports the Tables.jl interface, which means any I/O package also supporting it can automatically convert it’s table format into an in-memory DataFrame, and convert back to the format for output

IndexedTables.jl / JuliaDB.jl

JuliaDB.jl splashed onto the Julia scene in early 2017, touting a new approach to query/table operations that utilized type stability and Julia’s built in parallelism to provide “out of core” functionality like a database. JuliaDB.jl’s core table type actually lives in the IndexedTables.jl package, which in turn uses the clever StructArrays.jl package to turn NamedTuple rows into an efficient struct-of-arrays structure more suitable for columnar analytics. JuliaDB.jl itself then, with the use of Dagger.jl, adds the “parallel” layer on top of IndexedTables.jl. The benefits of “type stability” come from a table being essentially encoded as Table{Col1T, Col2T, Col3T, ...}, where the full type of each column is encoded in the top-level table type. This allows operations like selecting columns, filtering, and aggregating to be extremely efficient due to the compiler knowing the exact types it’s dealing with (as opposed to having to do “runtime” checks like in the DataFrames.jl case). Now, as discussed in the DataFrames.jl section, this doesn’t come without a cost; indeed, there’s a long-standing issue for dealing with the compilation cost for tables with a large number of columns. But, in the case of a manageable number of columns, and with the ability to scale tables up beyond a single machine’s memory limits is powerful functionality. JuliaDB.jl is also sponsored by JuliaComputing, which gives it a nice stamp of support and stability. While it may lack some of the maturity of DataFrames.jl long-discussed APIs, I’m excited by the unique, “Julian” approach JuliaDB.jl provides in the big data analytics space. Go check out the docs here and give it a spin. An additional package providing experimental manipulation functions for JuliaDB is JuliaDBMeta.jl, which mostly mirrors the before-mentioned DataFramesMeta.jl, but for JuliaDB tables.

TypedTables.jl
The TypedTables.jl package is one that started out in “experimentation” mode for a while until recently being declared “ready” by its primary author, the venerable Andy Ferris. Andy has long been known in the Julia community for his eye for API consistency and being able to strike that elusive balance between theoretical ideals and practical use. In TypedTables.jl, the “fully type stable” approach is taken, similar to IndexedTables.jl/JuliaDB.jl, with a Table being defined literally as <: AbstractVector{T} where {T <: NamedTuple}, that is, a collection of “rows” or NamedTuples. While TypedTables.jl includes two @Select and @Compute macros for simple manipulations, some of the more interesting promise comes from the TypedTables.jl “supporting cast” packages:

AcceleratedArrays.jl: a tidy package to turn any array into an “indexed” array (in the database sense), to provide optimized “search” functions: findall, findfirst, filter, unique, group, join, etc. An AcceleratedArray can thus be used in a TypedTable to provide powerful indexing behavior for an entire table (though note that it works on any AbstractArray, which means these indexed columns could even be used in a DataFrame).
SplitApplyCombine.jl: this package provides a powerful set of functions to perform common split, apply, and combine operations on generic collections like mapmany, group, product, groupreduce, and innerjoin. The aim is to provide the basic building blocks of relational algebra functions that work on any kind of collection, obviously including a TypedTable as a specialized type of AbstractVector of NamedTuples.

CSV.File
One more honorable mention for table structures (and another shameless plug) actually comes from the CSV.jl package. While CSV.read materializes a file as a DataFrame, a CSV.File, which supports all the same keyword arguments as CSV.read, can be used to transfer data to any other Tables.jl sink, or used itself as a table directly. It supports getproperty for column view access and iterates a CSV.Row type (which acts like a NamedTuple). I think as packages continue to evolve, we’ll see more and more cases of customized structures like this, which allow for certain efficiencies or specialized views into raw data formats, and with sufficiently general interfaces like Tables.jl and SplitApplyCombine.jl, users won’t need to worry as much about conforming to a single table structure for everything, but can focus on understanding more generic interfaces, and using data structures optimized for specific use-cases, data formats, and workflows.

Query.jl
Another package (set of packages really) that must be discussed in the Julia data ecosystem is Query.jl. Pioneered by David Anthoff, Query.jl provides a LINQ implementation for Julia. Query.jl and its sister package QueryOperators.jl provide a custom “query dsl” by a set of macros that allow convenient “query context” syntax for common manipulation tasks: selection and projection, filtering, grouping, and joining. These “query verbs” also are able to operate on any iterator, in true LINQ fashion, which makes the processing functions extremely versatile. While currently Query.jl/QueryOperators.jl hold the sole implementations of the query verbs, the grander scheme of having a custom dsl is the ability to represent entire queries in an AST (abstract syntax tree), which could then allow custom implementations that “execute” a structured query. This is most immediately useful when one considers being able to use a single “query dsl” to operate on both DataFrames and database tables, having a query translated into a vendor-specific SQL syntax. While not currently fully fleshed out, the ambitious undertaking is exciting to track.

DataValues.jl
One note for users on the use of Query.jl is the current reliance on the DataValues.jl package to represent missing data. What that means is that query functions in Query.jl/QueryOperators.jl aren’t integrated with the Base-builtin representation of missing, but rely on the DataValues.jl package, which defines a DataValue{T} wrapper type that also holds whether a value is missing or not (along with what “type” of missing value it is). While the history of missing data in Julia is long and storied, Andy Ferris wisely noted that no missing value representation is perfect. missing was included in Base largely due to the ease of working with a single sentinel value, and compiler support for code generation involving Union{T, Missing}. Inherent in the use of Union{T, Missing}, however, is a current compiler complexity involving inference of Union values that are stored in a parametric struct field. This affects the @map macro in Query.jl with NamedTuple inputs to NamedTuple outputs, hence, Query.jl relies on the use of DataValue{T} to more conveniently pass type information through projections. There are also active efforts explore ways the core language can avoid the need for an explicit wrapper type while still propagating the type information soundly through projections.

Additional Data Efforts

Other efforts integrating with the data ecosystem include (but are certainly not limited to):

StatsModels.jl: for specifying (in familiar “formula” notation), fitting and evaluating statistical models; can operate on any Tables.jl-compatible
GLM.jl for working specifically with generalized linear models
LightQuery.jl: another budding approach to type-stable querying capabilities
Flux.jl for an extremely extensible approach to machine learning modelling, GPU integration, and pure Julia, all the way down
Distributions.jl: for sampling, moments, and density/mass functions for a wide variety of distributions
StatsMakie.jl for GPU-enabled statistical plotting goodness
MLJ.jl: another new holistic approach to representing a variety of machine learning models sponsored by the Alan Turing Institute
ScikitLearn.jl for Julia access to the scikit-learn APIs for machine learning, including pure Julia implementations and integration with python models via PyCall.jl
OnlineStats.jl: a mature, fully featured statistical package for “online” statistical algorithms, including well-documented source code for published algorithms
TextAnalysis.jl: providing algorithms, statistical support, and feature engineering for text analysis
Dagger.jl: a dask-like Julia framework for distributed, parallel computation graphs
MultivariateStats.jl: stats, but for multiple variables!
RCall.jl: package that allows integrating with the R statistical language; transfer objects between languages, call R functions/libraries, etc.
StatsPlots.jl: another strong statistical plotting package

Future of Data in Julia

So now JuliaCon 2019 is upon us and we have to wonder: what’s next for working with data in Julia? As I’ve tried to illustrate in this long showcase of Julia packages, the data ecosystem has come along way since the official 1.0 release of the language itself. Support for the most common data formats is about as mature as any other language, but there’s always room to improve. The in-memory processing is an exciting space to watch in Julia due to the number of approaches being fleshed out, with varying degrees of maturity. DataFrames.jl is solid and should be a main utility knife for any Julia programmer, but one of the wonders of Julia, as mentioned above, is the ease of developing high-level, performant solutions in the language itself, so it’s exciting to see alternative approaches that can offer trade-offs or additional features that may enable better workflows depending on the environment. But given all that, here’s a shortlist of things swimming in my head around the future of the data ecosystem in Julia:

Ensure the performance and usability of Union{T, Missing} to represent missing data in Julia; currently, 95% of uses and workflows work amazingly well, but we always want to track down corner cases and do everything we can to improve the compiler, core language, or APIs to improve
Data format support: the job is never done here, but on my mind are an officially blessed (and integrated) implementation of apache arrow, write support for Parquet.jl, and a Julia package for supporting the ORC data format
Working towards a common API package/definitions for common table processing tasks; while exploratory efforts are always encouraged, it could be immensely convenient to users of various table types if there were a common set of operations that “just worked”. While Query.jl currently provides the best solution for this, it has other integration issues in the ecosystem; so working to resolve those or define a new common “table operations” type package
Relatedly, defining a full “structured query graph” model could be one way to provide a lower-level shared representation of querying tasks. This could catalyze “frontend” efforts (custom DSLs like Query.jl’s, or new dplyr-like verbs, or even a plain SQL parsing package) to “lower” to this common representation, while allowing similar types of backend innovation in *how* these structured query graphs are executed (with parallel support, out-of-core, etc.). I’ve recently been studying an old effort to do something like this for inspiration

For those who made it this far, kudos! As always, follow and ping me on twitter to chat data in Julia. And if you’ll be at JuliaCon 2019 in Baltimore, hit me up in the official Julia slack to meet up and chat.

Generated constant propagation; hold on to your britches!

By: Jacob Quinn

Re-posted from: https://quinnj.home.blog/2019/06/20/generated-constant-propagation-hold-on-to-your-britches/

In a recent JuliaLang Discourse post, OP was looking for a simple way to convert their Vector{CustomType} to a DataFrame, treating each CustomType element as a “row”, with the fields of CustomType as fields in the row. The post piqued my interest, given my work on the Tables.jl interface over the last year. Tables.jl is geared specifically towards creating generic access patterns to “table” like sources, specifically providing Tables.rows and Tables.columns as two ways to access table data from any source. Tables.columns returns a “property-accessible object” of iterators, while Tables.rows returns the dual: an iterator of “property-accessible objects” (property-accessible object here is defined as any object that supports both propertynames(obj) and getproperty(obj, prop), which includes all custom structs by default).

I decided to respond by showing how simple it can be to “hook in” to the Tables.jl world of interop; my code looked like:

using DataFrames, Random, Tables


struct Mine
    a::Int
    b::Float64
    c::Matrix{Float64}
end
Tables.istable(::Type{Vector{Mine}}) = true 
Tables.rowaccess(::Type{Vector{Mine}}) = true 
Tables.rows(x::Vector{Mine}) = x 
Tables.schema(x::Vector{Mine}) = Tables.Schema((:a, :b), Tuple{Int, Float64})

Random.seed!(999) v = [Mine(rand(1:10), rand(), rand(2,2)) for i ∈ 1:10^6]; df = DataFrame(v)

But when doing a quick benchmark of the conversion from Vector{Mine} to DataFrame, it was 3x-5x slower than other methods already proposed. Huh? I started digging. One thing I noticed was by ***not*** defining Tables.schema, the code was a bit faster! That led me to assume something was up in our schema-based row-to-column code.

Part of the challenge with this functionality is wrestling with providing a fast, type-stable method for building up strongly-typed columns for a set of rows with arbitrary number of columns and types. Just iterating over a row in a for-loop, accessing each field programmatically can be disastrous; because each extracted field value in the hot for-loop could be a number of types, the compiler can’t do much more than create a Core.Box to put arbitrary values in, then pull out again for inserting into the vector we’re building up. Yuck, yuck, yuck; if you see Core.Boxs in your @code_typed output, or values inferred as Any, it’s an indication the compiler just couldn’t figure out anything more specific, and in a hot for-loop called over and over and over again, any hope of performance is toast.

Enter meta-programming: Tables.jl tries to judiciously employ the use of @generated functions to help deal w/ the issue here. Specifically, when doing this rows-to-columns conversion, with a known schema, Tables.eachcolumn can take the column names/types as input, and generate a custom function with this aforementioned “hot for-loop” unrolled. That is, instead of:

for rownumber = 1:number_of_rows
row = rows[rownumber] for i = 1:ncols column[i][rownumber] = getfield(row, i) end
end

For a specific table input’s schema, the generated code looks like:

for rownumber = 1:number_of_rows row = rows[rownumber] column[1][rownumber] = getfield(row, 1) column[2][rownumber] = getfield(row, 2) # etc. end

What makes this code ***really*** powerful is the compiler’s ability to do “constant propagation”, which means that, while compiling, when it encounters getfield(row, 1), it can realize, “hey, I know that row is a Mine type, and I see that they’re calling getfield to get the 1 field, and I happen to know that the 1 field of Mine is an Int64, so I’ll throw that information into dataflow analysis so things can get further inlined/optimized”. It’s really the difference between the scenario I described before of the compiler just having to treat the naive for-loop values as Any vs. recognizing that it can figure out the exact types to expect which can make the operation we’re talking about here boil down to a very simple “load”, then “store”, without needing to box/unbox or rely on runtime dynamic dispatch based on types.

Back to the original issue: why was this code slower than the unknown schema case? Well, it turns out that our generated code was actually trying to be clever by calculating a run-length encoding of consecutive types for a schema, then generating mini type-stable for-loops; i.e. the generated code looked like:

for rownumber = 1:number_of_rows row = rows[rownumber] # if columns 1 & 2 are Int64 for col = 1:2 column[col][rownumber] = getfield(row, col) end # column 3 is a Float64 for col = 3:3 column[col][rownumber] = getfield(row, col) end # columns 4 & 5 are Int64 again for col = 4:5 column[col][rownumber] = getfield(row, col) end # etc. end

While this is clever, and can generate less code when schema types tend to be “sticky” (i.e. lots of the same type consecutively), the problem is in the getfield(row, col) call. Even though the call to each getfield ***should*** be type-stable, as calculated from our run-length encoding, the compiler unfortunately can’t figure that out inside these mini type-stable for-loops. This means no super-inlining/optimizations, so things end up being slower.

So what’s the conclusion here? What can be done? The answer was pretty simple, instead of trying to be extra clever with run-length encodings, if the schema is small enough, just generate the getfield calls directly for each column. The current heuristic is for schemas with less than 100 columns, the code will be generated directly, and for larger schema cases, we’ll try the run-length encoding approach (since it’s still more efficient than the initial naive approach).

Hope you enjoyed a little dive under the hood of Tables.jl and some of the challenges the data ecosystem faces in the powerful type system of Julia. Ping me on twitter with any thoughts or questions.

Cheers.

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