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 familiar 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 11In 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 physically
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:
setkey()
requires:computing the order vector for the column(s) provided, here,
origin, and
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.
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 argumentenables 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]
# i.V1 has same type (character) as x.origin. No coercion needed.
# on= matches existing index, using index
# Starting bmerge ...
# forder.c received 1 rows and 1 columns
# bmerge done in 0.001s elapsed (0.000s cpu)
# Constructing irows for '!byjoin || nqbyjoin' ... 0.000s elapsed (0.000s cpu)
# 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 21origin 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 21on 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.
j:= 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 24We 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.
Now, let’s check if 24 is replaced with 0
in the hour column.
hour, we had to
setkey() on it, which inevitably 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: 2 1014
# 3: 3 920
# 4: 4 1241
# 5: 5 853
# 6: 6 798key 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”origin
matches “LGA”, “JFK”, “EWR” and dest matches
“XNA”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 = NULL]
# 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 6First 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.
That is what auto indexing does. At the moment, it is only
implemented for binary operators == and %in%.
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 (unless one already exists), 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 MbWhen we use == or %in% on a single column
for the first time, a secondary index is created automatically, 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
# 0.249 0.006 0.254
head(ans)
# x y
# 1: 989 0.7757157
# 2: 989 0.6813302
# 3: 989 0.2815894
# 4: 989 0.4954259
# 5: 989 0.7885886
# 6: 989 0.5547504
## 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
# 0.001 0.000 0.000
system.time(dt[x %in% 1989:2012])
# user system elapsed
# 0.000 0.000 0.001Running the first time took 0.254 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 recent version we extended auto indexing to expressions involving
more than one column (combined with & operator). In the
future, we plan to extend binary search to work with more binary
operators like <, <=, >
and >=.
We will discuss fast subsets using keys and secondary indices to joins in the next vignette, “Joins and rolling joins”.