IndexedTables.jl

IndexedTables provides tabular data structures where some of the columns form a sorted index. It provides the backend to JuliaDB, but can be used on its own for efficient in-memory data processing and analytics.

Data Structures

IndexedTables offers two data structures: IndexedTable and NDSparse.

• Both types store data in columns.
• IndexedTable and NDSparse differ mainly in how data is accessed.
• Both types have equal performance for Table operations (select, filter, etc.).

Quickstart

using Pkg
Pkg.add("IndexedTables")
using IndexedTables

t = table((x = 1:100, y = randn(100)))

select(t, :x)

filter(row -> row.y > 0, t)

IndexedTable vs. NDSparse

First let's create some data to work with.

using Dates

city = vcat(fill("New York", 3), fill("Boston", 3))

dates = repeat(Date(2016,7,6):Day(1):Date(2016,7,8), 2)

vals = [91, 89, 91, 95, 83, 76]

IndexedTable

• (Optionally) Sorted by primary key(s), pkey.
• Data is accessed as a Vector of NamedTuples.

using IndexedTables

julia> t1 = table((city = city, dates = dates, values = vals); pkey = [:city, :dates])
Table with 6 rows, 3 columns:
city        dates       values
──────────────────────────────
"Boston"    2016-07-06  95
"Boston"    2016-07-07  83
"Boston"    2016-07-08  76
"New York"  2016-07-06  91
"New York"  2016-07-07  89
"New York"  2016-07-08  91

julia> t1[1]
(city = "Boston", dates = 2016-07-06, values = 95)

NDSparse

• Sorted by index variables (first argument).
• Data is accessed as an N-dimensional sparse array with arbitrary indexes.

julia> t2 = ndsparse((city=city, dates=dates), (value=vals,))
2-d NDSparse with 6 values (1 field named tuples):
city        dates      │ value
───────────────────────┼──────
"Boston"    2016-07-06 │ 95
"Boston"    2016-07-07 │ 83
"Boston"    2016-07-08 │ 76
"New York"  2016-07-06 │ 91
"New York"  2016-07-07 │ 89
"New York"  2016-07-08 │ 91

julia> t2["Boston", Date(2016, 7, 6)]
(value = 95)

Get started

For more information, check out the JuliaDB Documentation.