TypedTables.jl
Simple, column-based storage
TypedTables.jl provides two column-based storage container Table and FlexTable, which
represent an array of NamedTuples. This package is designed to be lightweight,
easy-to-use and fast, and presents a very minimal new interface to learn.
Data manipulation is possible throught the tools built into Julia (such as map, filter,
and reduce) and those provide by SplitApplyCombine.jl
(like group and innerjoin). The package is integrated the Tables.jl interface and therefore the rest of
the data ecosystem such as Query.jl.
Quick Start
It's simple to get started and create a table!
julia> using TypedTables
julia> t = Table(a=[1, 2, 3], b = [2.0, 4.0, 6.0])
3-element Table{NamedTuple{(:a, :b),Tuple{Int64,Float64}},1,NamedTuple{(:a, :b),Tuple{Array{Int64,1},Array{Float64,1}}}}:
(a = 1, b = 2.0)
(a = 2, b = 4.0)
(a = 3, b = 6.0)
julia> t[1] # Get first row
(a = 1, b = 2.0)
julia> t.a # Get column `a`
3-element Array{Int64,1}:
1
2
3The Table type is a simple AbstractArray where each element ("row") is a NamedTuple.
Upon construction, it retains references to it's columns rather than creating copies.
Strong typing means that you can iterate through the rows of the Table at blazing speed,
allowing you to write your own algorithms in native Julia. To achieve this, the outermost
Table structure is immutable and the names and types of the columns are fixed (while the
data itself can be mutated).
A more flexible table FlexTable is also provided. In a FlexTable, columns can be added, removed,
renamed or replaced. This comes at the cost of type-inferability - it will be slower to iterate the
rows of a FlexTable in a for loop. However, all the higher-level functions and queries will
still execute at full speed!
Notes
This rewrite of TypedTables.jl is still quite young, and more functionality will be added
over time. Be assured that the current provided interface is fully stabilized as it is
simply the interface provided by an AbstractVector{<:NamedTuple}.