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.

## Data {#data}

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

## Introduction

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.

## 1. Secondary indices

### a) What are secondary indices?

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).

### b) Set and get secondary indices

#### – How can we set the column `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.

#### – How can we get all the secondary indices set so far in `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.

### c) Why do we need secondary indices?

#### – Reordering a data.table can be expensive and not always ideal

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.

#### – There can be only one `key` at the most

Now 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!

#### – Secondary indices can be reused

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.

#### – The new `on` argument allows for cleaner syntax and automatic creation and reuse of secondary indices

As 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.

## 2. Fast subsetting using `on` argument and secondary indices

### a) Fast subsets in `i`

#### – Subset all rows where the origin airport matches “JFK” using `on`

    flights["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

#### – How can I subset based on `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.

### b) Select in `j`

All 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.

#### – Return `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

### c) Chaining

#### – On the result obtained above, use chaining to order the column in decreasing order.

    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

### d) Compute or do in `j`

#### – Find the maximum arrival delay correspondong to `origin = "LGA"` and `dest = "TPA"`.

    flights[.("LGA", "TPA"), max(arr_delay), on = c("origin", "dest")]
    # [1] 486

### e) sub-assign by reference using `:=` in `j`

We 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

- This is particularly a huge advantage of secondary indices. Previously, just to update a few rows of `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.

### f) Aggregation using `by`

#### – Get the maximum departure delay for each `month` corresponding to `origin = "JFK"`. Order the result by `month`

    ans <- 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

- We would have had to set the `key` back to `origin, dest` again, if we did not use `on` which internally builds secondary indices on the fly.

### g) The mult argument

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.

#### – Subset only the first matching row where `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

#### – Subset only the last matching row where `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

### h) The nomatch argument

We can choose if queries that do not match should return `NA` or be skipped altogether using the `nomatch` argument.

#### – From the previous example, subset all rows only if there's a match

    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

- There are no flights connecting “JFK” and “XNA”. Therefore, that row is skipped in the result.

## 3. Auto indexing

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
    #   0.204   0.004   0.209
    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
    #   0.000   0.000   0.001
    system.time(dt[x %in% 1989:2012])
    #    user  system elapsed
    #       0       0       0

- Running the first time took 0.209 seconds where as the second time took 0.001 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”.