This vignette assumes that the reader is familiar with data.table's [i, j, by] syntax, and how to perform fast key based subsets. If you're not familar with these concepts, please read the “Introduction to data.table”, “Reference semantics” and “Keys and fast binary search based subset” vignettes first.
We will use the same flights data as in the “Introduction to data.table” vignette.
flights <- fread("flights14.csv")
head(flights)
# year month day dep_delay arr_delay carrier origin dest air_time distance hour
# 1: 2014 1 1 14 13 AA JFK LAX 359 2475 9
# 2: 2014 1 1 -3 13 AA JFK LAX 363 2475 11
# 3: 2014 1 1 2 9 AA JFK LAX 351 2475 19
# 4: 2014 1 1 -8 -26 AA LGA PBI 157 1035 7
# 5: 2014 1 1 2 1 AA JFK LAX 350 2475 13
# 6: 2014 1 1 4 0 AA EWR LAX 339 2454 18
dim(flights)
# [1] 253316 11
In this vignette, we will
discuss secondary indices and provide rationale as to why we need them by citing cases where setting keys is not necessarily ideal,
perform fast subsetting, once again, but using the new on argument, which computes secondary indices internally for the task (temporarily), and reuses if one already exists,
and finally look at auto indexing which goes a step further and creates secondary indices automatically, but does so on native R syntax for subsetting.
Secondary indices are similar to keys in data.table, except for two major differences:
It doesn't physically reorder the entire data.table in RAM. Instead, it only computes the order for the set of columns provided and stores that order vector in an additional attribute called index.
There can be more than one secondary index for a data.table (as we will see below).
origin as a secondary index in the data.table flights?setindex(flights, origin)
head(flights)
# year month day dep_delay arr_delay carrier origin dest air_time distance hour
# 1: 2014 1 1 14 13 AA JFK LAX 359 2475 9
# 2: 2014 1 1 -3 13 AA JFK LAX 363 2475 11
# 3: 2014 1 1 2 9 AA JFK LAX 351 2475 19
# 4: 2014 1 1 -8 -26 AA LGA PBI 157 1035 7
# 5: 2014 1 1 2 1 AA JFK LAX 350 2475 13
# 6: 2014 1 1 4 0 AA EWR LAX 339 2454 18
## alternatively we can provide character vectors to the function 'setindexv()'
# setindexv(flights, "origin") # useful to program with
# 'index' attribute added
names(attributes(flights))
# [1] "names" "row.names" "class" ".internal.selfref"
# [5] "index"
setindex and setindexv() allows adding a secondary index to the data.table.
Note that flights is not phyiscally reordered in increasing order of origin, as would have been the case with setkey().
Also note that the attribute index has been added to flights.
setindex(flights, NULL) would remove all secondary indices.
flights?indices(flights)
# [1] "origin"
setindex(flights, origin, dest)
indices(flights)
# [1] "origin" "origin__dest"
The function indices() returns all current secondary indices in the data.table. If none exists, NULL is returned.
Note that by creating another index on the columns origin, dest, we do not lose the first index created on the column origin, i.e., we can have multiple secondary indices.
Consider the case where you would like to perform a fast key based subset on origin column for the value “JFK”. We'd do this as:
## not run
setkey(flights, origin)
flights["JFK"] # or flights[.("JFK")]
setkey() requires: {.bs-callout .bs-callout-info}a) computing the order vector for the column(s) provided, here, origin, and
b) reordering the entire data.table, by reference, based on the order vector computed.
Computing the order isn't the time consuming part, since data.table uses true radix sorting on integer, character and numeric vectors. However reordering the data.table could be time consuming (depending on the number of rows and columns).
Unless our task involves repeated subsetting on the same column, fast key based subsetting could effectively be nullified by the time to reorder, depending on our data.table dimensions.
key at the mostNow if we would like to repeat the same operation but on dest column instead, for the value “LAX”, then we have to setkey(), again.
## not run
setkey(flights, dest)
flights["LAX"]
And this reorders flights by dest, again. What we would really like is to be able to perform the fast subsetting by eliminating the reordering step.
And this is precisely what secondary indices allow for!
Since there can be multiple secondary indices, and creating an index is as simple as storing the order vector as an attribute, this allows us to even eliminate the time to recompute the order vector if an index already exists.
on argument allows for cleaner syntax and automatic creation and reuse of secondary indicesAs we will see in the next section, the on argument provides several advantages:
on argument {.bs-callout .bs-callout-info}enables subsetting by computing secondary indices on the fly. This eliminates having to do setindex() every time.
allows easy reuse of existing indices by just checking the attributes.
allows for a cleaner syntax by having the columns on which the subset is performed as part of the syntax. This makes the code easier to follow when looking at it at a later point.
Note that on argument can also be used on keyed subsets as well. In fact, we encourage to provide the on argument even when subsetting using keys for better readability.
on argument and secondary indicesionflights["JFK", on = "origin"]
# year month day dep_delay arr_delay carrier origin dest air_time distance hour
# 1: 2014 1 1 14 13 AA JFK LAX 359 2475 9
# 2: 2014 1 1 -3 13 AA JFK LAX 363 2475 11
# 3: 2014 1 1 2 9 AA JFK LAX 351 2475 19
# 4: 2014 1 1 2 1 AA JFK LAX 350 2475 13
# 5: 2014 1 1 -2 -18 AA JFK LAX 338 2475 21
# ---
# 81479: 2014 10 31 -4 -21 UA JFK SFO 337 2586 17
# 81480: 2014 10 31 -2 -37 UA JFK SFO 344 2586 18
# 81481: 2014 10 31 0 -33 UA JFK LAX 320 2475 17
# 81482: 2014 10 31 -6 -38 UA JFK SFO 343 2586 9
# 81483: 2014 10 31 -6 -38 UA JFK LAX 323 2475 11
## alternatively
# flights[.("JFK"), on = "origin"] (or)
# flights[list("JFK"), on = "origin"]
This statement performs a fast binary search based subset as well, by computing the index on the fly. However, note that it doesn't save the index as an attribute automatically. This may change in the future.
If we had already created a secondary index, using setindex(), then on would reuse it instead of (re)computing it. We can see that by using verbose = TRUE:
setindex(flights, origin)
flights["JFK", on = "origin", verbose = TRUE][1:5]
# on= matches existing index, using index
# Starting bmerge ...done in 0 secs
# year month day dep_delay arr_delay carrier origin dest air_time distance hour
# 1: 2014 1 1 14 13 AA JFK LAX 359 2475 9
# 2: 2014 1 1 -3 13 AA JFK LAX 363 2475 11
# 3: 2014 1 1 2 9 AA JFK LAX 351 2475 19
# 4: 2014 1 1 2 1 AA JFK LAX 350 2475 13
# 5: 2014 1 1 -2 -18 AA JFK LAX 338 2475 21
origin and dest columns?For example, if we want to subset "JFK", "LAX" combination, then:
flights[.("JFK", "LAX"), on = c("origin", "dest")][1:5]
# year month day dep_delay arr_delay carrier origin dest air_time distance hour
# 1: 2014 1 1 14 13 AA JFK LAX 359 2475 9
# 2: 2014 1 1 -3 13 AA JFK LAX 363 2475 11
# 3: 2014 1 1 2 9 AA JFK LAX 351 2475 19
# 4: 2014 1 1 2 1 AA JFK LAX 350 2475 13
# 5: 2014 1 1 -2 -18 AA JFK LAX 338 2475 21
on argument accepts a character vector of column names corresponding to the order provided to i-argument.
Since the time to compute the secondary index is quite small, we don't have to use setindex(), unless, once again, the task involves repeated subsetting on the same column.
jAll the operations we will discuss below are no different to the ones we already saw in the Keys and fast binary search based subset vignette. Except we'll be using the on argument instead of setting keys.
arr_delay column alone as a data.table corresponding to origin = "LGA" and dest = "TPA"flights[.("LGA", "TPA"), .(arr_delay), on = c("origin", "dest")]
# arr_delay
# 1: 1
# 2: 14
# 3: -17
# 4: -4
# 5: -12
# ---
# 1848: 39
# 1849: -24
# 1850: -12
# 1851: 21
# 1852: -11
flights[.("LGA", "TPA"), .(arr_delay), on = c("origin", "dest")][order(-arr_delay)]
# arr_delay
# 1: 486
# 2: 380
# 3: 351
# 4: 318
# 5: 300
# ---
# 1848: -40
# 1849: -43
# 1850: -46
# 1851: -48
# 1852: -49
jorigin = "LGA" and dest = "TPA".flights[.("LGA", "TPA"), max(arr_delay), on = c("origin", "dest")]
# [1] 486
:= in jWe have seen this example already in the Reference semantics and Keys and fast binary search based subset vignette. Let's take a look at all the hours available in the flights data.table:
# get all 'hours' in flights
flights[, sort(unique(hour))]
# [1] 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
We see that there are totally 25 unique values in the data. Both 0 and 24 hours seem to be present. Let's go ahead and replace 24 with 0, but this time using on instead of setting keys.
flights[.(24L), hour := 0L, on = "hour"]
Now, let's check if 24 is replaced with 0 in the hour column.
flights[, sort(unique(hour))]
# [1] 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
hour, we had to setkey() on it, which inevitablly reorders the entire data.table. With on, the order is preserved, and the operation is much faster! Looking at the code, the task we wanted to perform is also quite clear.bymonth corresponding to origin = "JFK". Order the result by monthans <- flights["JFK", max(dep_delay), keyby = month, on = "origin"]
head(ans)
# month V1
# 1: 1 881
# 2: 1 1014
# 3: 1 920
# 4: 1 1241
# 5: 1 853
# 6: 1 798
key back to origin, dest again, if we did not use on which internally builds secondary indices on the fly.The other arguments including mult work exactly the same way as we saw in the Keys and fast binary search based subset vignette. The default value for mult is “all”. We can choose, instead only the “first” or “last” matching rows should be returned.
dest matches “BOS” and “DAY”flights[c("BOS", "DAY"), on = "dest", mult = "first"]
# year month day dep_delay arr_delay carrier origin dest air_time distance hour
# 1: 2014 1 1 3 1 AA JFK BOS 39 187 12
# 2: 2014 1 1 25 35 EV EWR DAY 102 533 17
origin matches “LGA”, “JFK”, “EWR” and dest matches “XNA”flights[.(c("LGA", "JFK", "EWR"), "XNA"), on = c("origin", "dest"), mult = "last"]
# year month day dep_delay arr_delay carrier origin dest air_time distance hour
# 1: 2014 10 31 -5 -11 MQ LGA XNA 165 1147 6
# 2: NA NA NA NA NA NA JFK XNA NA NA NA
# 3: 2014 10 31 -2 -25 EV EWR XNA 160 1131 6
We can choose if queries that do not match should return NA or be skipped altogether using the nomatch argument.
flights[.(c("LGA", "JFK", "EWR"), "XNA"), mult = "last", on = c("origin", "dest"), nomatch = 0L]
# year month day dep_delay arr_delay carrier origin dest air_time distance hour
# 1: 2014 10 31 -5 -11 MQ LGA XNA 165 1147 6
# 2: 2014 10 31 -2 -25 EV EWR XNA 160 1131 6
First we looked at how to fast subset using binary search using keys. Then we figured out that we could improve performance even further and have more cleaner syntax by using secondary indices. What could be better than that? The answer is to optimise native R syntax to use secondary indices internally so that we can have the same performance without having to use newer syntax.
That is what auto indexing does. At the moment, it is only implemented for binary operators == and %in%. And it only works with a single column at the moment as well. An index is automatically created and saved as an attribute. That is, unlike the on argument which computes the index on the fly each time, a secondary index is created here.
Let's start by creating a data.table big enough to highlight the advantage.
set.seed(1L)
dt = data.table(x = sample(1e5L, 1e7L, TRUE), y = runif(100L))
print(object.size(dt), units = "Mb")
# 114.4 Mb
When we use == or %in% on a single column for the first time, a secondary index is created automtically, and it is used to perform the subset.
## have a look at all the attribute names
names(attributes(dt))
# [1] "names" "row.names" "class" ".internal.selfref"
## run thefirst time
(t1 <- system.time(ans <- dt[x == 989L]))
# user system elapsed
# 0.208 0.004 0.212
head(ans)
# x y
# 1: 989 0.5372007
# 2: 989 0.5642786
# 3: 989 0.7151100
# 4: 989 0.3920405
# 5: 989 0.9547465
# 6: 989 0.2914710
## secondary index is created
names(attributes(dt))
# [1] "names" "row.names" "class" ".internal.selfref"
# [5] "index"
indices(dt)
# [1] "x"
The time to subset the first time is the time to create the index + the time to subset. Since creating a secondary index involves only creating the order vector, this combined operation is faster than vector scans in many cases. But the real advantage comes in successive subsets. They are extremely fast.
## successive subsets
(t2 <- system.time(dt[x == 989L]))
# user system elapsed
# 0 0 0
system.time(dt[x %in% 1989:2012])
# user system elapsed
# 0 0 0
Running the first time took 0.212 seconds where as the second time took 0.000 seconds.
Auto indexing can be disabled by setting the global argument options(datatable.auto.index = FALSE).
Disabling auto indexing still allows to use indices created explicitly with setindex or setindexv. You can disable indices fully by setting global argument options(datatable.use.index = FALSE).
In the future, we plan to extend auto indexing to expressions involving more than one column. Also we are working on extending binary search to work with more binary operators like <, <=, > and >=. Once done, it would be straightforward to extend it to these operators as well.
We will extend fast subsets using keys and secondary indices to joins in the next vignette, “Joins and rolling joins”.