What is the purpose of setting a key in data.table

问题:

I am using data.table and there are many functions which require me to set a key (e.g. X[Y] ). As such, I wish to understand what a key does in order to properly set keys in my data tables.

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One source I read was ?setkey.

setkey() sorts a data.table and marks it as sorted. The sorted columns are the key. The key can be any columns in any order. The columns are sorted in ascending order always. The table is changed by reference. No copy is made at all, other than temporary working memory as large as one column.

My takeaway here is that a key would \"sort\" the data.table, resulting in a very similar effect to order(). However, it doesn\'t explain the purpose of having a key.

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The data.table FAQ 3.2 and 3.3 explains:

3.2 I don\'t have a key on a large table, but grouping is still really quick. Why is that?

data.table uses radix sorting. This is signicantly faster than other sort algorithms. Radix is specically for integers only, see ?base::sort.list(x,method=\"radix\"). This is also one reason why setkey() is quick. When no key is set, or we group in a different order from that of the key, we call it an ad hoc by.

3.3 Why is grouping by columns in the key faster than an ad hoc by?

Because each group is contiguous in RAM, thereby minimising page fetches, and memory can be copied in bulk (memcpy in C) rather than looping in C.

From here, I guess that setting a key somehow allows R to use \"radix sorting\" over other algorithms, and that\'s why it is faster.

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The 10 minute quick start guide also has a guide on keys.

1. Keys

Let\'s start by considering data.frame, specically rownames (or in English, row names). That is, the multiple names belonging to a single row. The multiple names belonging to the single row? That is not what we are used to in a data.frame. We know that each row has at most one name. A person has at least two names, a rst name and a second name. That is useful to organise a telephone directory, for example, which is sorted by surname, then rst name. However, each row in a data.frame can only have one name.

A key consists of one or more columns of rownames, which may be integer, factor, character or some other class, not simply character. Furthermore, the rows are sorted by the key. Therefore, a data.table can have at most one key, because it cannot be sorted in more than one way.

Uniqueness is not enforced, i.e., duplicate key values are allowed. Since the rows are sorted by the key, any duplicates in the key will appear consecutively

The telephone directory was helpful in understanding what a key is, but it seems that a key is no different when compared to having a factor column. Furthermore, it does not explain why is a key needed (especially to use certain functions) and how to choose the column to set as key. Also, it seems that in a data.table with time as a column, setting any other column as key would probably mess the time column too, which makes it even more confusing as I do not know if I am allowed set any other column as key. Can someone enlighten me please?

回答1:

Minor update: Please refer to the new HTML vignettes as well. This issue highlights the other vignettes that we plan to.

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I\'ve updated this answer again (Feb 2016) in light of the new on= feature that allows ad-hoc joins as well. See history for earlier (outdated) answers.

What exactly does setkey(DT, a, b) do?

It does two things:

1. reorders the rows of the data.table DT by the column(s) provided (a, b) by reference, always in increasing order.
2. marks those columns as key columns by setting an attribute called sorted to DT.

The reordering is both fast (due to data.table\'s internal radix sorting) and memory efficient (only one extra column of type double is allocated).

When is setkey() required?

For grouping operations, setkey() was never an absolute requirement. That is, we can perform a cold-by or adhoc-by.

## \"cold\" by
require(data.table)
DT <- data.table(x=rep(1:5, each=2), y=1:10)
DT[, mean(y), by=x] # no key is set, order of groups preserved in result

However, prior to v1.9.6, joins of the form x[i] required key to be set on x. With the new on= argument from v1.9.6+, this is not true anymore, and setting keys is therefore not an absolute requirement here as well.

## joins using < v1.9.6 
setkey(X, a) # absolutely required
setkey(Y, a) # not absolutely required as long as \'a\' is the first column
X[Y]

## joins using v1.9.6+
X[Y, on=\"a\"]
# or if the column names are x_a and y_a respectively
X[Y, on=c(\"x_a\" = \"y_a\")]

Note that on= argument can be explicitly specified even for keyed joins as well.

The only operation that requires key to be absolutely set is the foverlaps() function. But we are working on some more features which when done would remove this requirement.

• So what\'s the reason for implementing on= argument?

  There are quite a few reasons.

  1. It allows to clearly distinguish the operation as an operation involving two data.tables. Just doing X[Y] does not distinguish this as well, although it could be clear by naming the variables appropriately.

  2. It also allows to understand the columns on which the join/subset is being performed immediately by looking at that line of code (and not having to traceback to the corresponding setkey() line).

  3. In operations where columns are added or updated by reference, on= operations are much more performant as it doesn\'t need the entire data.table to be reordered just to add/update column(s). For example,

     ## compare 
     setkey(X, a, b) # why physically reorder X to just add/update a column?
     X[Y, col := i.val]
     
     ## to
     X[Y, col := i.val, on=c(\"a\", \"b\")]
     

     In the second case, we did not have to reorder. It\'s not computing the order that\'s time consuming, but physically reordering the data.table in RAM, and by avoiding it, we retain the original order, and it is also performant.

  4. Even otherwise, unless you\'re performing joins repetitively, there should be no noticeable performance difference between a keyed and ad-hoc joins.

This leads to the question, what advantage does keying a data.table have anymore?

• Is there an advantage to keying a data.table?

  Keying a data.table physically reorders it based on those column(s) in RAM. Computing the order is not usually the time consuming part, rather the reordering itself. However, once we\'ve the data sorted in RAM, the rows belonging to the same group are all contiguous in RAM, and is therefore very cache efficient. It\'s the sortedness that speeds up operations on keyed data.tables.

  It is therefore essential to figure out if the time spent on reordering the entire data.table is worth the time to do a cache-efficient join/aggregation. Usually, unless there are repetitive grouping / join operations being performed on the same keyed data.table, there should not be a noticeable difference.

In most cases therefore, there shouldn\'t be a need to set keys anymore. We recommend using on= wherever possible, unless setting key has a dramatic improvement in performance that you\'d like to exploit.

Question: What do you think would be the performance like in comparison to a keyed join, if you use setorder() to reorder the data.table and use on=? If you\'ve followed thus far, you should be able to figure it out :-).

回答2:

A key is basically an index into a dataset, which allows for very fast and efficient sort, filter, and join operations. These are probably the best reasons to use data tables instead of data frames (the syntax for using data tables is also much more user friendly, but that has nothing to do with keys).

If you don\'t understand indexes, consider this: a phone book is \"indexed\" by name. So if I want to look up someone\'s phone number, it\'s pretty straightforward. But suppose I want to search by phone number (e.g., look up who has a particular phone number)? Unless I can \"re-index\" the phone book by phone number, it will take a very long time.

Consider the following example: suppose I have a table, ZIP, of all the zip codes in the US (>33,000) along with associated information (city, state, population, median income, etc.). If I want to look up the information for a specific zip code, the search (filter) is about 1000 times faster if I setkey(ZIP,zipcode) first.

Another benefit has to do with joins. Suppose a have a list of people and their zip codes in a data table (call it \"PPL\"), and I want to append information from the ZIP table (e.g. city, state, and so on). The following code will do it:

setkey(ZIP,zipcode)
setkey(PPL,zipcode)
full.info <- PPL[ZIP, nomatch=F]

This is a \"join\" in the sense that I\'m joining the information from 2 tables based in a common field (zipcode). Joins like this on very large tables are extremely slow with data frames, and extremely fast with data tables. In a real-life example I had to do more than 20,000 joins like this on a full table of zip codes. With data tables the script took about 20 min. to run. I didn\'t even try it with data frames because it would have taken more than 2 weeks.

IMHO you should not just read but study the FAQ and Intro material. It\'s easier to grasp if you have an actual problem to apply this to.

[Response to @Frank\'s comment]

Re: sorting vs. indexing - Based on the answer to this question, it appears that setkey(...) does in fact rearrange the columns in the table (e.g., a physical sort), and does not create an index in the database sense. This has some practical implications: for one thing if you set the key in a table with setkey(...) and then change any of the values in the key column, data.table merely declares the table to be no longer sorted (by turning off the sorted attribute); it does not dynamically re-index to maintain the proper sort order (as would happen in a database). Also, \"removing the key\" using setky(DT,NULL) does not restore the table to it\'s original, unsorted order.

Re: filter vs. join - the practical difference is that filtering extracts a subset from a single dataset, whereas join combines data from two datasets based on a common field. There are many different kinds of join (inner, outer, left). The example above is an inner join (only records with keys common to both tables are returned), and this does have many similarities to filtering.