Backtracking In Q/ word ladder

This is a variation on the word ladder puzzle. Here is the puzzle:

You are presented with a set of strings (assume they are unique). Each string can be of arbitrary length. Your goal is to find the length of the longest chain.
A chain can start from any string. Each following link in the chain must be exactly one letter shorter than the previous link, any character from any position from the previous link can be dropped, this new link must appear in the set of words given.

For example, if you are given the set  of strings (“a”,”ab”,”abc”,”abdc”, “babdc”,”dd”, “ded”)

“ded” -> “dd”
“babdc” -> “abdc” -> “abc” -> “ab” -> “a”

are valid chains. The longest chain’s length is 5.

First I will present python code that my friend wrote, it is very concise and has a kdb flavor (at least in my mind).


Python Code:

def word_list(array):
  # Storing the solutions for each word in a dictionary
  dict_array = {word:0 for word in array}

  # Sorting the word list by lengths
  array.sort(key = lambda s: len(s))

  # Finding solutions to each word by using the previous solutions
  for word in array:
    options = [0]
    for i in range(len(word)):
      word_minus_i = "".join([word[k] for k in range(len(word)) if k != i])
      #check if the word appears in list of words
      if word_minus_i in dict_array:
        # If it does add a link to that word
    dict_array[word] = max(options)
  return max(dict_array.values())+1

Now to a KDB version:

I had two versions of the code both rely on the same insight as the python code

The difference is in the post processing.

The key insight into this problem is that if you hash all the strings. Then if you create every possible one letter drop you can check if that string is in the legal set of strings, if it is you can record which longer string it maps to.

At that point you have a list of every connection between two strings. Since checking the hash takes constant time and generating every 1 letter missing string takes 1- number of letters in the string* number of unique strings. This processing takes linear time.

At that point, we have a graph, and at first I wanted to reuse the code from the previous post. I would generate all the connected components, the use those nodes to figure out how many different length strings there were. The connected component with the largest number of distinct length strings is going to be the longest chain and the number of distinct lengths is the total chain length. (this code is [3-6]x faster for small inputs (10,000, 100,000 words) than the python but is about the same speed once we get up to a set of 1 million words)

The second version, uses a quicker technique to just calculate the depth of the graph from every node.

Let’s start from the first version:
Let’s generate 10,000 strings (.Q.a is “abcdefghijklmnopqrstuvwxyz”)
We want strings of length between 1 and 20 (1+til 20)
We want ten thousand strings, so we take 10000 of the list 1 2 3 .. 20
We then sample the number from the alphabet (3?.Q.a) gives us a 3 letter string
We do this for each right (\:) number in the list
We only want the distinct words

q)words:distinct words:(10000#1+til 20)?\:.Q.a

Here we are sampling 10 of these words:


So we need a function that will generate the indexes of all the possible 1 letter drops of a word.
The most intuitive way I know to do this, is to create equal length boolean string with one 0 and rotate that 0 around the whole string. So for example in the 3 letter word case we have:
We then find the indexes where there is a 1 and that would gives all the 1 missing indexes.

/0b,(x-1)#1b creates a string of length x with 1 0 value
/rotate\: says to rotate this string for each right argument (which is just the range from 0 to x)
/where finds the indexes that are 1
allDrops:{where each (til x) rotate\: 0b,(x-1)#1b}

Lets do a little processing on the list of strings, and put them into a table:

/sort the words by count of the number of letters and only store the distinct words in hwords

q)hwords:distinct words iasc count each words

/create a table, with the distinct words, the number of letters in the word, and a symbolic version of the word for fast lookup, with a unique attribute so that KDB knows to hash this list

q)show t:([]wsym:`u#`$hwords;w:hwords;c:count each hwords);
wsym w c
s ,”s” 1
t ,”t” 1
j ,”j” 1
n ,”n” 1

/wsym contains a symbolic version of the word
/w is just the original word
/c is the length of the word

We then want to populate a dictionary with all the indexes, so we select the distinct counts of the words and run them through the allDrops function

q)drops:n!allDrops each n:exec distinct c from t /we select the distinct counts run the function and key the distinct counts to the result from the function.
1 | ,`long$()
2 | (,1;,0)
3 | (1 2;0 1;0 2)
4 | (1 2 3;0 1 2;0 1 3;0 2 3)
5 | (1 2 3 4;0 1 2 3;0 1 2 4;0 1 3 4;0 2 3 4)

We do this mostly because we know that we will need these indexes many times, but there are relatively few distinct lengths of words.

Now we calculate all the links:

/drops c will give us a list of lists of all the 1 letter missing possible indexes for each word.
/To show what this does let’s select by c so that we can see what it does for each c
select d:drops first c by c from t
c | d ..
–| ————————————————————————-..
1 | ,`long$() ..
2 | (,1;,0) ..
3 | (1 2;0 1;0 2) ..
4 | (1 2 3;0 1 2;0 1 3;0 2 3)

Because q will apply a particular list on indexes and preserve the shape, we just need to tell it, that each word should be projected onto all of these lists. This is done with the @’ this will pair off each list of lists with the words. Here is what that looks like , again grouping by c so that we can see what that looks like:

q)select by c from update d:(w)@’drops c from t
c | wsym w d ..
–| ————————————————————————-..
1 | x ,”x” ,”” ..
2 | cw “cw” (,”w”;,”c”) ..
3 | fvw “fvw” (“vw”;”fv”;”fw”) ..
4 | yoku “yoku” (“oku”;”yok”;”you”;”yku”)

We see that one letter words, become empty strings, two letter words become a list of words each 1 letter long. three letter strings become a list of 2 letter words.
Now we just need to find where these words are in our original table. If they are not there they will get the 1+last index, which is how q lets you know that a value is missing in a lookup. We will also cast all these strings to symbols so that we can do faster lookups (`$):

/col is the index of the original string and row are all the matches. We are modeling the graph using an adjacency matrix idea.
q)show sparseRes:update col:i, row:t[`wsym]?`$(w@’drops c) from t
wsym w c col row
h ,”h” 1 0 864593
r ,”r” 1 1 864593
f ,”f” 1 2 864593
w ,”w” 1 3 864593
/We see that no 1 letter string has any matches which is why all of the indexes are last in the table, this makes sense since one letter strings can’t match anything but the empty string and all of our strings have at least one character
/Again selecting by c so we can see a variety of lengths,
q)select by c from update col:i, row:t[`wsym]?`$(w@’drops c) from t
c | wsym w col row ..
–| ————————————————————————-..
1 | x ,”x” 25 ,864593 ..
2 | cw “cw” 701 3 11 ..
3 | fvw “fvw” 17269 541 275 357 ..
4 | yoku “yoku” 64705 864593 7279 2368 3524 ..
5 | vvbud “vvbud” 114598 864593 46501 864593

As we can see the row column is a list of matches and also contains false matches that go to the end of the table we would like to clean that up. We can do that using ungroup.
Ungroup will take a keyed table, and generate a row for each item in the list of records.
In our case it will create a col, row  record for each item in the row column.

q)show sparseRes:ungroup `col xkey sparseRes
col wsym w c row
0 h h 1 864593
1 r r 1 864593
2 f f 1 864593

Now we want to delete all of the fake links, so that will be wherever the row is the length of the original table:

q) show sparseRes:delete from sparseRes where row=count t
col wsym w c row
26 bj b 2 2
26 bj j 2 10
27 oj o 2 2
27 oj j 2 12

At this point we are done processing the data.  We have found all the connections between the words, all that is left is to find the connected components, which we know how to do from the previous post. I use a slightly different function that calculates the connected components on a sparse matrix. That is represented as a table with columns row and col.

neighbors: exec col from m where row in j; /now we are searching a table instead of a matrix
f:{n:exec col from y where row in .[_;x]; /new neighbors
x[0]:count x[1];x[1]:distinct x[1],n; /update the two pieces of x
x}[;m]; /project this function on the sparseMatrix
last f over (0; neighbors)};

points:til max m[`row]; /
while[count points;
i:first points;
connected,:enlist n:`s#n:distinct asc i,findConnectedSparse[i;m]; /add point in case it is island
points:points except n];

Given these two functions that work on sparse adjacency matrixes. The final step is:

q)max {count select distinct c from x} each t allComponentsSparse sparseRes

Let’s unpack this a bit:

sparseRes is our table of links.

allComponentsSparse will return all of the connected components in this graph.

`s#0 28 42 56 61 94 117 133 149 157 170 175 181 187 195 220 241 260 288 323 3..
`s#1 62 64 77 79 98 115 126 131 144 154 162 166 173 189 191 208 214 258 267 2..
`s#2 29 40 66 91 108 112 114 125 135 154 167 168 184 211 226 245 267 281 291 ..
`s#3 69 77 94 102 109 110 119 129 159 183 192 196 212 214 216 220 224 247 259..

apply the table t to this will give us a table that is indexed on the connected components


So for each connected component we are getting a table that has only those strings that match.
let’s look at the first one:

q)first t allComponentsSparse sparseRes
wsym w c
q ,”q” 1
aq “aq” 2
oq “oq” 2
qw “qw” 2
wq “wq” 2
jq “jq” 2

We can see immediately a problem, we only need one 2 letter word but we are getting every possible match. Since we are only interested in the length we can simply count the number of distinct c.

q)count select distinct c from first t allComponentsSparse sparseRes

So we see that the first connected component has 4 links in the chain.

Doing this for each connected component and then selecting the max ensures that we have found the length of the longest chain.

q)max {count select distinct c from x} each t allComponentsSparse sparseRes

That completes the first method.

Here is the whole function together

hwords:distinct words iasc count each words;
t:([]wsym:`u#`$hwords;w:hwords;c:count each hwords);
allDrops:{where each (til x) rotate\: 0b,(x-1)#1b};
drops:n!allDrops'[n:1+til max t[`c]];
sparseRes:ungroup `col xkey update col:i, row:t[`wsym]?`$(w@’drops c) from t;
sparseRes:delete from sparseRes where row=count t;
max {count select distinct c from x} each t allComponentsSparse sparseRes}

neighbors: exec col from m where row in j;
f:{n:exec col from y where row in .[_;x]; /new neighbors
x[0]:count x[1];x[1]:distinct x[1],n; /update the two pieces of x
x}[;m]; /project this function on the sparse matrix
last f over (0; neighbors)};

points:til max m[`row];
while[count points;
i:first points;
connected,:enlist n:`s#n:distinct asc i,findConnectedSparse[i;m]; /add point in case it is island
points:points except n];

Now for method 2, instead of relying on the previous algorithm to find all the connected components, we just want to keep track of how deep each chain is.

We can do this by creating a new table q which only has columns, row and col and is grouped by row. Since we created the original links by going from columns to rows, we know that the links travel up from shorter strings(row) to longer ones(col)

q)show q:select col by row from sparseRes
row| col ..
—| ————————————————————————..
0 | 28 42 56 61 94 117 133 149 157 170 175 181 187 195 220 241 260 288 323 3..
1 | 62 64 77 79 98 115 126 131 144 154 162 166 173 189 191 208 214 258 267 2..
2 | 29 40 66 91 108 112 114 125 135 154 167 168 168 184 211 226 245 267 281 ..
3 | 69 69 77 94 102 109 110 119 129 159 183 192 196 212 214 216 220 224 247 ..
4 | 38 48 60 74 86 92 95 115 123 131 132 135 145 184 195 197 213 238 246 249..
5 | 43 49 119 124 137 142 143 180 219 226 231 239 246 277 289 303 308 319 32..
6 | 28 31 72 73 88 100 104 126 141 145 161 175 190 198 235 240 248 253 289 3..

Now since this table is keyed on the row column, we can index into the table using a index table that has the row numbers and keep doing this until there are no more rows to return. The number of times we can index before we return nothing, is the length of the chain from that row.

First lets demonstrate indexing:

q)indexTable:([]row:0 1)

q)q ([]row:0 1)
28 42 56 61 94 117 133 149 157 170 175 181 187 195 220 241 260 288 323 342 36..
62 64 77 79 98 115 126 131 144 154 162 166 173 189 191 208 214 258 267 272 27..

We get the two rows that match from q.

If we flatten this list and rename this column row. We can reindex into q,

q)q select row:raze col from q ([]row:0 1)
452 549 634
391 391
587 786 803
395 493 759
524 629
704 834
613 823

We see the next level of neighbors, we can keep doing this. Again we can take advantage of over and just count how long before we return an empty table.

We will do this by creating a function that takes a dictionary and updates it, that way we can store both the intermediate results and the number of times we went down and all the nodes we visited.

the keys will be cur, which is the list of nodes we wish to visit next, depth, which is how deep we gone so far, and visited a list of all the nodes we saw. Then  we can write a function dive which will dive one level down into the table.

dive:{ $[count x[`cur]:select row:raze col from y x[`cur];
/index the table on the current neighbors we are exploring.
/We set cur to be the next level of neighbors
/if there are any new neighbors:
/we will add 1 to depth, and add the current new neighbors to visited and return x
[x[`depth]+:1;x[`visited],:exec row from x[`cur];
/otherwise we will return x

We then create a table of all the nodes we would like to visit with the initialized keys:

q)show n:update visited:count[n]#() from n:([]depth:0;cur:exec row from q);
depth cur visited
0 0
0 1
0 2
0 3
0 4

lets’ run dive on one the first of n:

q)first n
depth | 0
cur | 0
visited| ()
q)dive over first n
depth | 4
cur | +(,`row)!,()
visited| 28 42 56 61 94 117 133 149 157 170 175 181 187 195 220 241 260 288 3..

We see that we get back a dictionary of all the nodes visited as well as, the maximum depth achieved. If we do this for all n, we can select the max depth and we are done.

q)exec max depth from (dive/)each n}

Notice, we are storing all the visited nodes, so we can reconstruct what the path actually was, but we are not actually using it. So the entire second version looks like this:

hwords:distinct words iasc count each words;
t:([]wsym:`u#`$hwords;w:hwords;c:count each hwords);
allDrops:{where each (til x) rotate\: 0b,(x-1)#1b};
drops:n!allDrops'[n:1+til max t[`c]];
sparseRes:ungroup `col xkey update col:i, row:t[`wsym]?`$(w@’drops c) from t;
sparseRes:delete from sparseRes where row=count t;
q:select col by row from sparseRes;
dive:{ $[count x[`cur]:select row:raze col from y x[`cur];
[x[`depth]+:1;x[`visited],:exec row from x[`cur];:x]
n:update visited:count[n]#() from n:([]depth:0;cur:exec row from q);
exec max depth from (dive/)each n}

On my computer, I was able to find the length of the longest chain for a list of million words in under 15 seconds, using this second version and in a minute using the first version. If I was using less than one hundred thousand words, then  the two versions were about the same.

The python code took about a minute for a million words, (without using anything fancy, I’m sure it can be improved by replacing pieces with numpy components or using the latest version of python 3.7. where dictionary look ups are faster).

The real benefit here from KDB, is that reconstructing the path is super straightforward since we have the path indexes and can use them to index into the word table.



Graph Algo, Finding Friends

This is a rather standard interview question:

Suppose that you have points and the points are connected by edges. Imagine that the points are numbered, then we can represent whether there exists a connection between two points by writing all the pairs of points.

We can also represent this graph, as matrix, where 1 means that there exists a connection between i and j and 0 otherwise. We call this matrix the adjacency matrix since it tells us which points are connected.adjacencymatrix_1002

Given such a matrix, we want to find all the connected components. A connected component is either just one node if it has no connections or all the nodes that can be reached by traveling along the edges.

In the picture above, each of these graphs has only one connected component, but together there are three distinct connected components, since there is no way to reach from the first graph to the second by traveling along an edge.

First I will show my original solution in KDB. Then a slight improvement.
First note that the matrix must be symmetric, since if point 1 is connected to point 2, then point 2 is connected to point 1.

I wanted to write a function that would get all the points that were connected to a single point.

If I think of a particular row in this matrix, then all the 1s represent the neighbors of this point. If I then find all of their neighbors, and so on, I will have all the points that can possibly be reached.

First imagine I have a matrix, a:
To create it, I first create 100 zeros (100#0)
Then I pick six places to add connections, (6?100)
I replace the zeros with 1s.
I add this matrix to itself flipped, so that it is symmetric (a+flip a)
Divide by two so that any place that 1s overlapped are either 1 or .5, (.5*)
Then I cast it to an integer so that they are all 1s or 0s (`long$)

q)a:`long$.5*a+flip a:10 10#@[100#0;6?100;:;1]
0 0 0 1 0 0 0 0 0 0
0 0 0 0 0 1 1 0 0 0
0 0 0 0 0 0 0 0 1 0
1 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 1 0 0
0 1 0 0 0 0 0 0 0 0
0 1 0 0 0 0 0 0 0 0
0 0 0 0 1 0 0 0 0 0
0 0 1 0 0 0 0 0 0 1
0 0 0 0 0 0 0 0 1 0

Okay given a, I can find the neighbors of any of the 10 points.

0 0 0 0 0 1 1 0 0 0
q)where a[1]
5 6

So we get a list of points that are neighbors.

We need to create a function that will keep asking for neighbors and discard any duplicates.

We can find all the neighbors of the neighbors by selecting the neighbor rows of matrix  and then asking where for each row:

q)where each a 5 6

We get a list of neighbors.

We can then raze this list to flatten it:

q)raze where each a 5 6
1 1

Then we can ask for the distinct items:

q)distinct raze where each a 5 6

Now we would like to add this list to our original neighbors and check if we can find more neighbors:

q)distinct raze where each a 5 6 1
1 5 6

Running this again we get:

q)distinct raze where each 1 5 6
5 6 1

So there are no more neighbors to find, just the order changes and we will flip back and forth.

Anytime, we need to apply a function like this we can make use of the adverb in q called over. Over will apply the function until the results are the same for two successive iterations.

So my function to find all connected with comments looks like:

neighbors: where m[i]; /find the initial list of neighbors
f:{distinct raze x,where each y x}[;m]; /create the function where second argument     is fixed to be the adjacency matrix.
f over neighbors} /run the function over the initial neighbors and keep adding neighbors until there are no more to add.

/testing on a:
5 6 1

As expected this gives us all points connected to point 1, including point 1 itself.
To list all of the connected components we need to run this on each of the points:

q)findAllConnected[;a] each til count a
3 0
5 6 1
8 2 9
0 3
7 4
1 5 6
1 5 6
4 7
2 9 8
8 2 9


We get 10 rows specifying the connections. Now we need find the unique set.

The easiest way to do this is to sort each one and ask for the distinct lists:

q)distinct asc each findAllConnected[;a] each til count a
`s#0 3
`s#1 5 6
`s#2 8 9
`s#4 7

Which gives us 4 connected components.

We can look for the islands, by checking if there are any elements not on any of these lists. If so, they must unconnected to anything else.

Items not in the list are islands (only one node no connections)

q)connected:distinct asc each findAllConnected[;a] each til count a
q)islands:(til count a) except raze connected

q)count each connected /size of the components

We get an empty list, which means there are no islands to report.

Problem solved.

For those who have followed, you can see that we are actually doing a lot of duplicate work, since we keep asking for neighbors for points that we have already seen. To solve this we simply keep track of how many neighbors we have found so far and only query for the neighbors we have yet to see.

Instead of x being a list of neighbors, it is now a list with two items. The first is the number of neighbors found so far and the second is the list of neighbors.

We can use dot(.) application of drop(_) to remove the neighbors we have seen so far from the neighbors list. (.[_;x]):


neighbors: where m[i];

f:{n:raze where each y .[_;x]; /new neighbors

x[0]:count x[1];x[1]:distinct x[1],n; /update the two pieces of x

x}[;m]; /project this function on the matrix

last f over (0; neighbors)} /apply the function on the neighbors, with 0 currently visited

/running on a:

5 6 1

/large sparse matrix to test with

q)c:7h$.5*c+flip c:1000 1000#@[1000000#0;1000?1000000;:;1]

/Time both on my 2012 macbook
q)\t distinct asc each findAllConnected[;c] each til count c
q)\t distinct asc each findAllConnectedFast[;c] each til count c

A 10x improvement, the keen observer will notice that we don’t have to run the function on all the points in the first place.  We can go down the points and skip all the points that we have already seen. Here is what that looks like:


points:til count m;


while[count points;

i:first points;

connected,:enlist n:`s#n:distinct asc i,findAllConnectedFast[i;m]; /add point in case it is island

points:points except n];


q)allComponents a
`s#0 3
`s#1 5 6
`s#2 8 9
`s#4 7

q)\t allComponents c

This results in a 1000x increase speed up. Because we are only calling the function the minimum number of times.

Stable Sorting

Stable Sorting preserves the order of a column even after sorting on a second column.

KDB provides stable sorting by default. This is a great thing, and it allows you to easily implement grouped attributes on a table.

A table that looks like:

q)t:([]time:09:30:00+`time$til 9;sym:9#`AAPL`MSFT`IBM;px:100*9?1.0)
time sym px
09:30:00.000 AAPL 81.12026
09:30:00.001 MSFT 20.86614
09:30:00.002 IBM 99.07116
09:30:00.003 AAPL 57.94801
09:30:00.004 MSFT 90.29713
09:30:00.005 IBM 20.11578
09:30:00.006 AAPL 6.832366
09:30:00.007 MSFT 59.89167
09:30:00.008 IBM 4.881728

which is sorted by time and sort it by symbol so that all the symbols that are the same are together, while still preserving the time attribute.

q)`sym xasc t
time sym px
09:30:00.000 AAPL 81.12026
09:30:00.003 AAPL 57.94801
09:30:00.006 AAPL 6.832366
09:30:00.002 IBM 99.07116
09:30:00.005 IBM 20.11578
09:30:00.008 IBM 4.881728
09:30:00.001 MSFT 20.86614
09:30:00.004 MSFT 90.29713
09:30:00.007 MSFT 59.89167

While this seems pretty cool, I want to argue that this behavior is not only good, it is also simpler than the alternative.

In many languages, perl,java,python there is something called a comparator interface. Which usually asks the user to define a function

F(a,b) => if a comes before b;
if a comes after b:
if they are equal

The search then uses some algo to swap the items.

However, we can simplify this interface. We can call it swapporator, it only allows two options, swap, True or False. The items are presented in the order of the original list to the swap function. This will automatically be stable.




As of Join Performance a surprising result

The Kx wiki describes in detail how to structure as of join queries for best performance.

3. There is no need to select on quote, i.e. irrespective of the number of quote records, use:

aj[`sym`time;select .. from trade where ..;quote]

instead of

aj[`sym`time;select .. from trade where ..;
             select .. from quote where ..]

The reason for this is that since the quote table is partitioned on date and grouped on sym the as of join function simply scans the sectors of the disk in linear order and grabs the first matches using binary search.

Linear access to disk is very different from random access.  So when you perform a search linearly on partitioned historical database it runs pretty fast.

However, someone asked me if you were going to query over and over, the same data whether then it made sense to do some pre-filtering on the quote table before doing multiple ajs.

At the time, I said it shouldn’t be faster. I thought that the overhead of reading from disk afresh each time was going to be much smaller than the overhead of allocating a very large amount of room in memory.

So I tested it:

taj1:{[t;d] now:.z.T;
do[10;aj[`sym`time;select from t where date=d;select from quote where date=d]];        after:.z.T;

quotecache:select from quote where date=d, sym in exec sym from t;                                do[10;aj[`sym`time;select from t where date=d;quotecache]];

The timings, were not even close the taj2, which precaches data when run 10 times, for a trade table with only 1000 records on only 100 symbols took more than 5 minutes to run. While taj1 was taking only 5 seconds, that is approximately 2 aj per second.

When I scaled the number of symbols to 1000, the cached version didn’t comeback and I had to cancel the query after 25 minutes.  The non cached version took 10 seconds or 1 aj per second.

The intuition is correct, if you can prefilter, then not having to read in all the data twice should be faster. So I checked the meta of the cached table, it was loosing the p attribute while filtering. 

I then created version taj3:

quotecache:update `p#sym from select from quote where date=d, sym in exec sym  from t;
do[10;aj[`sym`time;select from t where date=d;quotecache]];

If you reapply the p attribute the first query for 1000 symbols costs you 3.6 seconds, but increasing the number of times you run the aj is almost free. So running 10 times only costs 4 seconds. Running 20 times the taj3 also took 4 seconds. So if you know you have a limited universe than reducing and pre-filtering is worth it if you will be running the queries again and again, JUST REMEMBER TO REAPPLY THE P ATTRIBUTE. Since the quote table is only filtered, the p attribute will be a really cheap operation, since order is preserved.

Q Idioms assemembered

This is more of an expanding list of Q idioms I have had to either assemble or remember or some combination.

  1. Cross product of two lists is faster in table form

    q)show x:til 3

    0 1 2

    q)show y:2#7

    7 7

    q)x cross y

    0 7

    0 7

    1 7

    1 7

    2 7

    2 7

    q) ([] x) cross ([] y)

    x y

    0 7

    0 7

    1 7

    1 7

    2 7

    2 7

    \t til[1000] cross 1000#5


    \t flip value flip ([] til 1000) cross ([] x1:1000#5)


    /and if you are happy working with it in table form

    \t ([] til 1000) cross ([] x1:1000#5)


  2. Take last N observations for a column in a tableBoth of these can be used to create a list of indexes and the columns can be simply   projected on to the list of indexes

    /a) Intuitive way take last n from c

    q) N:10; C:til 100000;

    q) {[n;c]c{y-x}[til n] each til count c}[N;C] /timing 31 milli


    1 0

    2 1 0

    3 2 1 0

    4 3 2 1 0


    /b) Faster way using xprev and flip

    q) \t {[n;c] flip (1+til n) xprev \\: c}[10;c]  /timing 9 millesec

  3. Create a Polynomial Function from the coefficients

    poly:{[x] (‘)[wsum[x;];xexp/:[;til count x]]};

    f:poly 0 1 2 3;

    f til 5

    0 6 34 102 228

  4. Count Non Null entries

    All in K:




    Q translation:

    fastest:{count[x]-sum null x}

    slower:{sum not null x}

    slowest:{count first group null x}

  5. Camel case char separated symbols:

camelCase:{[r;c]`$ssr[;r;””] each @'[h;i;:;]upper h @’ i:1+ss'[;r] h:string x}
most common case
q)c:` sv/: `a`b cross `e`fff`ggk cross `r`f

6.  Progress style bars:

p:0;do[10; p+:1; system “sleep .1”;1 “\r “,p#”#”];-1 “”;

{[p]1 “\r “,(a#”#”),((75-a:7h$75*p%100)#” “),string[p],”%”;if[p=100;-1 “”;]}

7.  AutoCorr:

autocorr:{x%first x:x{(y#x)$neg[y]#x}/:c-til c:count x-:avg x}

8.  Index of distinct elements:

idistinct:{first each group x}

Tree Tables/Parent Vector representation of Dictionaries


Vector, the Journal of the British APL Association

TreeTables and the parent vector are magical things and they are a great way to flatten deeply nested structures. In particular, I will use this representation of a dictionary in the next post to allow you to import q libraries under a different namespace, kind of like what python allows with “import x as y”.

In this post I will focus on how we can easily rewrite a dictionary into a flat table with 4 columns. Let me motivate this a bit first. Take a deeply nested dictionary:

a| `b`c!(2;`d`e!(6;`f`g!7 8))
b| `c`d!4 5
c| `d`e!(6;`f`g!7 8)

In json that might look like this:


As we can see each key is a letter and each value is either another dictionary or a number. In q we can apply functions into these arbitrarily nested dictionaries provided all the values conform. So for example we can add 10+d

a| `b`c!(12;`d`e!(16;`f`g!17 18))
b| `c`d!14 15
c| `d`e!(16;`f`g!17 18)

However, once your dictionaries stop conforming things can get a bit hairy. As a simple example let’s add a simple dictionary f!f to the key f in d

a| `b`c!(2;`d`e!(6;`f`g!7 8))
b| `c`d!4 5
c| `d`e!(6;`f`g!7 8)
f| (,`f)!,`f

Suddenly 10+d throws a type error. Because we can’t add 10 to the non numeric dictionary. We can always right logic to avoid those cases, but it would be simpler to simply pull out all the conforming values add 10 to them and then replace them in the original structure. A tabular tree representation aids in this. As an example the same dictionary in tabular form: (I have omitted some results in the middle for ease of reading.) Which I will call treeTable:

l p  c  d
0 0  :: 1
1 0  `a 1
1 0  `b 1
1 0  `c 1
1 0  `f 1
2 1  `b 1
2 1  `c 1

4 15 8  0
5 21 7  0
5 22 8  0

The column c (child) contains every key and value, column d tells you if the row is leaf node or has a dictionary below it. Pulling out all the numeric children is as simple as:

numeric:6 7 8 9h
select c from treeTable where not d, (abs type each c) in numeric

This table can then  be modified and assuming we can convert it back into its dictionary form we could then  work with nested data easily.

How the Magic is done:

At this point you either believe this structure is useful or you believe it isn’t but you are still interested in understanding how this transformation happens.

Let’s look at the original treeTable and unpack the meaning of the columns.

The first column  represents the level of the dictionary that the key or value is located in.
The second column is the index of the parent row in this table. The root of the table is self parenting meaning that the 0th row is at the 0th level and it’s parent is 0 (keep this in mind it will be useful in a second). All elements will have a parent.
The third column is the child and it is the value at this level of nesting. It will either be a key if there are more levels below or it will just be the value at that level.
The fourth and final column indicates whether or not this row is a dictionary. That is, whether the child should be treated as a key or a value.

To convert a dictionary into a treeTable we use breadth first search, that is the purpose of the column. We first define a primitive treeTable with only one row the root.

l p  c  d
0 0  :: 1

All treeTables will have this row. If the thing we are trying to convert into a treeTable is not a dictionary but is instead SOMEKIND_OF_THING_THAT_IS_NOT_A_DICTIONARY. Then there will be only one more row in this table.

l p  c  d
0 0  :: 1

We will then know we are done because there are no more dictionaries to unpack at the last level.

If instead we get a dictionary. We will first record all the keys at that first level and return the table.

Our ability to find a value at a particular level is enabled through the use of the parent column in the treeTable. Suppose we have a simple dictionary:


We can record this as the following two columns:

p c index
0 :: 0 
0 `d 1 
0 `e 2 
2 `f 3 
2 `g 4 
1  6 5 
3  7 6 
4  8 7

The first row is the root it is self parenting. The next two rows are both top level keys. So their parent is the root. f and g both are under e so their parent is 2, but 6 is under d so it’s parent is 1. To make this easier to see, I have added the virtual index column which is always available.  Then the final two rows are under f and g respectively.

If we want the parent of particular row, and we have the parent column, which I will call p:0 0 0 2 2 1 3 4

We can index into that row to get the parent. p 6 -> 3 which is f

We can repeat this until we get to the root. p 3 -> 2; p 2-> 0 ; p 0 -> 0. To find the root in one step we can use KDB’s built-in converge function which will apply until two consecutive results are the same. This is why it was so convenient for the root to be self-parenting. So see the path to the root use scan instead of over.

p over 6 -> 0

p scan 6 -> 3 2 0. Now that we have a path, we just need to get the keys that correspond to that path, this is done by indexing the path against the child column.

c 3 2 0 -> f e ::

We now can get the unique path to any element.

The next step is using the path to index into any level of the dictionary. This is accomplished with a special object called getItems.

getItems is defined by combining the indexing at depth verb with a function that reverses the path list and checks if the path list happens to be only the root. In which case, we simply return the original item.

Using just those two ideas, we are able to construct the treeTable. The algorithm is to index one level at a time each time recording the if the level contains dictionaries or not. If it contains no dictionaries we are done and we will return the same table twice in row, which means that our function will converge. Using breadth first search we avoid any stackoverflow issues that could happen with a recursive solution, instead the function becomes tail recursive, meaning all the necessary ingredients to call the function again are returned as the output. That is why on the first call the function returns the first row of a treeTable. That way each call after simply indexes deeper into the original dictionary to return more levels of the treeTable.

The Code and An Example:

 getItems:('[;] over (.[d;];{$[x~(enlist[::]);x;1_reverse x]}));
 $[98h~type t;;t:([]l:(1#0);p:0;c:(::);d:1b)];
 lev:last t[`l];
 k:exec i from t where l=lev,d;
 $[count k;;:t];
 paths:t[`c] (t[`p]\')k;
 items:getItems each paths;
 id:where bd:99h=type each items;
 p:raze (count each key each items[id])#'k[id];
 c:raze key each items[id];
 id:where not bd;
 t upsert flip `l`p`c`d!(lvl;p;c;df)}[getItems];
 tTT over ()}

/an example of a nested structure 

b:`c`d!4 5
e:`f`g!7 8
a:`b`c!(2; c)
q)toTreeTable d
l p c d
0 0 :: 1
1 0 `a 1
1 0 `b 1
1 0 `c 1
2 1 `b 1
2 1 `c 1
2 2 `c 1
2 2 `d 1
2 3 `d 1
2 3 `e 1
3 5 `d 1
3 5 `e 1
3 9 `f 1
3 9 `g 1
3 4 2 0
3 6 4 0
3 7 5 0
3 8 6 0
4 11 `f 1
4 11 `g 1
4 10 6 0
4 12 7 0
4 13 8 0
5 18 7 0
5 19 8 0


And Back Again!

Now that we covered how to get a treeTable we can also understand how to go back to a dictionary.

We apply the opposite approach. The core function returns a dictionary. Each time we return a dictionary that is slightly deeper than the previous time. We put placeholder empty dictionaries until we build the final result. Since we know whether each row is a key or a value, we know whether the current item requires a placeholder.

 dS:exec {x!count[x]#enlist[()!()]}[c] by p from tt where l=lev, d;
 dS:dS,.[!; value exec p,c from tt where l=lev, not d];
 pR:tt[`c](-1_|:) each (tt[`p]\')[key dS];
 pC:tt[`c]key dS;
 paths:raze each {(1_x;enlist[y])}'[pR;pC];
 $[lev>1;.[;;:;]/[dSoFar;paths;value dS];first value dS]}[tt];
 tD/[()!();1+til last tt[`l]]}

Wow This is Even More General Than We Thought:

When I first built this, I tried to make sure that I covered simple dictionaries and values. So I was curious what would happen to keyed tables.  Keyed tables are special in that they are essentially dictionaries whose key and values are both dictionaries. Since a dictionary is a pair of lists and a list of dictionaries is a table. A keyed table is simply a dictionary whose key is a table and and whose value is a table. A trivial example to illustrate this point:

q)k:([]k:til 5)
q)v:([]v:10*til 5)
k| v 
-| --
0| 0 
1| 10
2| 20
3| 30
4| 40
/Indexing against the key table returns the value table
/but we can also apply select only certain rows using the k column
/in this case I reverse the key table and take the first 2 rows.
q)kv[2#reverse k]

Now what happens if we turn a key table into a treeTable:

l p c d
0 0 :: 1
1 0 (,`k)!,0 1
1 0 (,`k)!,1 1
1 0 (,`k)!,2 1
1 0 (,`k)!,3 1
1 0 (,`k)!,4 1
2 1 `v 1
2 2 `v 1
2 3 `v 1
2 4 `v 1
2 5 `v 1
3 6 0 0
3 7 10 0
3 8 20 0
3 9 30 0
3 10 40 0

It converts the key part of the table into key dictionaries that are the parents of the value dictionaries in the table. And we can turn it back:

q)toDictFromTreeTable toTreeTable kv
k| v 
-| --
0| 0 
1| 10
2| 20
3| 30
4| 40
q)kv ~toDictFromTreeTable toTreeTable kv

In other words, keyedTables are treated like dictionaries, this means that if you only want to look at values, you will only see values, simply by select from the treeTable where not d. The internal dictionaries inside a keyed table are broken apart into their component dictionaries and the values are stored independently.

Tables Get Treated as singletons.

Since tables are actually lists of dictionaries, and lists are treated as values. A table is also treated as a value and placed directly into the child column.

q)t:([] til 10)
q)toTreeTable t
l p c d
0 0 :: 1
1 0 +(,`x)!,0 1 2 3 4 5 6 7 8 9 0

The function from TreeTable correctly undoes the toTreeTable function
but the treeTable form is actually more nested than the original table.

q)toDictFromTreeTable toTreeTable t

We can fix this by expanding our parent vector to notate whether a current element is a dictionary, list or an atom. That way we would create a node that is the head of every list and then iterate through the indexes in the list. This is left as an exercise, or until I need this functionality.

Another Dependency Management Scheme

This post is based on an accumulator pattern I encountered when converting recursive functions to be tail recursive.

For a long time I have been looking for a way to cleanly handle errors that arise in functions. It’s well known among programmers that only about 20% of code does any real work, the rest is there to take care of  IO and error conditions. That’s one of the afterthoughts that Joe Morasco has in How to learn a new programming language.

It takes me a few hours to recall how to make the linked list
work and get something on the air. To allow someone else to play the
game unsupervised (which means doing some error handling) usually
takes me a few days of programming.

KDBQ lets you omit a lot of ceremony and boilerplate so you can get to work faster. Often a function that does something useful, ie real work, is a few lines. However, you still need to do the error handling somewhere and it’s best if we can keep our functions doing mostly work. If there are specific error conditions that need to be handled then by all means handle it, otherwise assume that the input you get is what you expect. (I know this sounds crazy to all those paranoid defensive programmers, but frankly you might like where this post goes.)

The only ceremony that I insist on is that every function you write takes a dictionary as the input. I find this to be pretty liberating in general as you can easily add functionality over time without breaking an API and it allows for the function to implement sensible default values while allowing customization.

What I eventually settled on was something that would allow me to create dependent functions by ordering them.

I think of them as “getters.” Each function “gets” a value using a dictionary of configurations.

A list of getters can be easily seen on a page and read. If you know what each getter is doing then you can keep reading, otherwise you can go look at the definition.

I then would create a getter dictionary, each key in the getter dictionary will eventually be assigned a value. The value is either the value that was already in the input or the value of applying the current getter to the dictionary.

To make this a bit clearer, suppose that I want to create a function that gives my pet animal a name.

I have the following dependencies:

What pet animal am I getting? Turtle, Elephant, Dog, Snake

getAnimalType:{[args] first 1?`Turtle`Elephant`Dog`Snake}

Is the animal a boy or a girl? (obviously turtles and elephants are more likely to be girls)

   d:`Turtle`Elephant`Dog`Snake!0.7 0.7 0.5 0.5;
    $[first d[args[`AnimalType]]>1?1.0;`girl;`boy]}

Choose a name based on those two criteria.

getName:{[args] $[`boy~args[`BoyorGirl];`Kunee;`Kuni]} 
/(we like the name IPA transcription kʊniː)

So here is the basic idea: We combine these three separate verbs into a larger verb:

  :setIfMissing /[args;key getters; value getters]}

We can read this function by looking at what keys are being set in the getters dictionary. The keys tell you what this function plans on using or setting; the values tell you how the function will set them if the key is missing in the dictionary you pass in.

So for example if you don’t know what you want you can simply pass an empty dictionary to getPetAnimalName which will give you back something like:

AnimalType| Turtle
BoyorGirl | girl
Name | Kuni

Now suppose you are set on a particular pet animal you can pass a dictionary that has the animal preset.

AnimalType| Elephant
BoyorGirl | girl
Name | Kuni

So far we have simply seen that this construct of setIfMissing allows us to push in default arguments and have default verbs that use our preset arguments. Now we can also show that we also get basic error handling for free.

Suppose that we break the function that chooses the animal type. We would expect that the function will not return boy or a girl if we pass nothing, but should work as usual if we do pass it an animal type.

/break function getAnimalType
q)getAnimalType:{[args] break;first 1?`Turtle`Elephant`Dog`Snake}
q)getPetAnimalName[()!()] /we get back as much as was possible 
Name | Kuni
q)getPetAnimalName[presetAnimalDict] /works as before
AnimalType| Elephant
BoyorGirl | girl
Name | Kuni

Mechanics of how the verb setIfMissing operates:

The premise of this verb, as its name suggests, is that it sets the key in the dictionary that was passed in if that key was missing. If that key exists, it does nothing. To set a key we call the function that was passed in the dictionary we have so far. This allows functions to depend on values from previous functions or from arguments that are passed in. If a function throws an error, it catches the error and tries to set the next key that is given to it.

I have created essentially two different versions of this verb. They follow two different philosophies.

Simple Version: with primitive error handling and logging

The first version is the most simple and just logs what keys it is setting and what keys it failed to set. Here it is, pretty short:

log:{neg[2] (string .z.Z)," : ",x,"\t",y;}
setIfMissing:{[a;k;f]  log["INFO";"setting key: ",string k];
  .[{[a;k;f]$[ k in key a;a; @[a;k;:;f[a]]]};
     {[a;k;e] log["ERROR";
   "Fail to set key: ",(string k)," Error: ",e]];a}[a;k]]};

Here I have fully elucidated every step:

/first we create a logging utility
 neg[2] /this is the handle to standard error 
 (string .z.Z) /we convert the time into a string
 ," : ",x,"\t",y; / and append the type of error to the message
/so for example 
/.log["INFO";"HELLO WORLD"] 
/prints 2017.09.17T09:06:14.309 : INFO HELLO WORLD

/now we define setIfMissing
setIfMissing:{[a;k;f] /a is the dictionary,k is the key, f is the function
 .log["INFO";"setting key: ",string k]; /log that we will be setting k
 .[ /protected execution will trap any errors
 {[a;k;f] /a,k,f are the same as the beginning
 $[ k in key a; /check if k is in the dictionary
 a; /if it is return a
 @[ /general apply, allow you to modify contents of a container
 a; /a is our container or dictionary in this case
 k; /k is the key or index in the container we will modify
 :; /ammend (set) is the function we will use 
 f[a] /is the value we will set, which is the verb f applied to a
 / this is what allows f to depend on any set items in the dictionary
 ] /this ends general apply 
 ] /this ends the conditional of whether k was in a
 }; / this ends the first function in the protected execution
 (a;k;f); /These are the arguments to the protected execution
 {[a;k;e] /a is still the dictionary, k is the key and e is the error
 .log["ERROR";"Fail to set key: ",(string k)," Error: ",e]; /log error
 a} /return the dictionary, and ends the trap function
 / this allows us to chain functions even if there is an error
 [a;k] /pass in the value of a and k, 
 /otherwise the error handler only sees the error
 ] /ends the protected execution
 }; /ends setIfMissing

Fancier version with introspection and profiling:

The second version of setIfMissing is likely to please those defensive programmers that I mentioned earlier.  I will not go through and elucidate this entire function. Instead I will point out the salient differences in this significantly more involved version of setIfMissing.

The first difference is that there no longer is text logging. Instead the dictionary that you pass in gets updated with all of the information about what happened. There are two tables that get added simErrors and simProfiling. So for example here is what our examples from before look like:

            | ::
simErrors   | (+(,`failed)!,`AnimalType`BoyorGirl`Name)!+(,`reason)!,("break"..
simProfiling| (+(,`simKey)!,,`initialized)!+(,`time)!,,2017.09.17T09:53:24.471
AnimalType | `Elephant
 | ::
simErrors | (+(,`failed)!,())!+(,`reason)!,()
simProfiling | (+(,`simKey)!,`initialized`AnimalType`BoyorGirl`Name`WelcomeM..
BoyorGirl | `girl
Name | `Kuni

We can look at those tables

failed    | reason 
----------| ------------
AnimalType| "break" 
BoyorGirl | "AnimalType"
Name      | "BoyorGirl" 
simKey     | time 
-----------| -----------------------
initialized| 2017.09.17T09:55:59.166
failed| reason
------| ------
simKey     | time 
-----------| -----------------------
initialized| 2017.09.17T10:09:04.062
AnimalType | 2017.09.17T10:09:04.062
BoyorGirl  | 2017.09.17T10:09:04.062
Name       | 2017.09.17T10:09:04.062

As we can see, we now get all of the logging information from the object itself. This is very powerful because it allows us to do queries against this data later to either improve performance by using the profiling table or to see where the errors are coming from.

The second feature takes advantage of the simErrors table. Here is where we become super defensive,  instead of naively trying to run every function against every key, we can preemptively fail if we know a function depends on an earlier key that failed. This is the reason, we now no longer return a name if boyOrGirl failed. Since getName explicitly depends on boyOrGirl being set, so we can premtively bail and not even bother running the query.  Therefore, you can see that reason given will either be the error message from running the function or the keys that were required. To demonstrate this a bit further, let’s add another function to our getPetAnimalName a welcome message:

  "Welcome Home, ", string args[`AnimalType]," ", args[`Name]}
 :setIfMissing /[args;key getters; value getters]}

Now if we run getPetAnimalName:

failed        | reason 
--------------| -----------------
AnimalType    | "break" 
BoyorGirl     | "AnimalType" 
Name          | "BoyorGirl" 
WelcomeMessage| "AnimalType,Name"

As we can see all keys that were required were mentioned as the reason for the failure. In particular WelcomeMessage failed because both AnimalType and Name were missing. We can do this by inspecting the function before calling it, if any of the keys in the failed column of simErrors table appear in the function, we report them and bail.

Finally, the last feature of setIfMissing is that we can pass the dictionary by name or by value. So far in all the examples we have passed the dictionary by value and gotten a new value back with some keys set. However, if we call setIfMissing with a name of a dictionary it will return the name and modify the dictionary in place.

AnimalType| Elephant
AnimalType    | `Elephant
              | ::
simErrors     | (+(,`failed)!,())!+(,`reason)!,()
simProfiling  | (+(,`simKey)!,`initialized`AnimalType`BoyorGirl`Name`WelcomeM..
BoyorGirl     | `girl
Name          | `Kuni
WelcomeMessage| "Welcome Home, Elephant Kuni!"

Finally here is the code for the introspecting and more defensive setIfMissing:

 if[not `simErrors in key a[];
 $[neg[11h]~type a;
 a set a[],simT;
 $[ k in key a;
 ({[k;d]d _ k}[k];{[k;d]d upsert (k;.z.Z)}[k])]; 
 [$[any c:@[0!a[`simErrors];`failed] in `$1_'s where(s:-4!last value f) like "`*";
 'raze "," sv string @[0!a[`simErrors];`failed] where c;
 ({[k;d]d _ k}[k];{[k;d]d upsert (k;.z.Z)}[k];{y;x}[res])]]
 {[a;k;e]  @[a;`simErrors;upsert;(k;e)]}[a;k]]};

/#### Short and Sweet Documentation:
How to use:
/Create a list of getter function that take a dictionary as inputs

/Create a meta function that composes these getters
 :kwargs:setIfMissing/[kwargs;key getters;value getters]}

/Apply the meta function to either a dictionary or a name of a dictionary
sigil | ::
simErrors | (+(,`failed)!,())!+(,`reason)!,()
simProfiling| (+(,`simKey)!,`started`A`B`C)!+(,`time)!,2017.09.14T13:44:24.05..
A | 5
B | 42
C | 47
sigil | ::
simErrors | (+(,`failed)!,())!+(,`reason)!,()
simProfiling| (+(,`simKey)!,`started`A`B`C)!+(,`time)!,2017.09.14T13:45:02.09..
A | 5
B | 42
C | 47
sigil | ::
A | 10
B | 14
simErrors | (+(,`failed)!,())!+(,`reason)!,()
simProfiling| (+(,`simKey)!,`started`A`B`C)!+(,`time)!,2017.09.14T13:47:59.24..
C | 24

/We can look at profiling information
simKey | time 
-------| -----------------------
started| 2017.09.14T13:45:02.099
A | 2017.09.14T13:45:02.099
B | 2017.09.14T13:45:02.099
C | 2017.09.14T13:45:02.099

/We get automatic error handling and prememptive failiure
/let's add an error into getA
sigil | ::
simErrors | (+(,`failed)!,`A`C)!+(,`reason)!,("broken";,"A")
simProfiling| (+(,`simKey)!,`started`B)!+(,`time)!,2017.09.14T13:51:06.419 20..
B | 42
failed| reason 
------| --------
A | "broken"
C | ,"A" 
/If the value is set the meta function will still work.
sigil | ::
A | 10
B | 14
simErrors | (+(,`failed)!,())!+(,`reason)!,()
simProfiling| (+(,`simKey)!,`started`A`B`C)!+(,`time)!,2017.09.14T13:53:46.03..
C | 24

 The sim prefix is used in the dictionary


Mechanics of the As-Of-Join Primitive

Recently, I had the necessity to perform an operation that didn’t exist in KDB, but whose scent reminded me of a primitive provided in KDB – As Of Join (aj). I will sketch the problem that motivated me. Then I will describe what the As Of Join operation does and how it is implemented in KDB. Finally, I will describe the new verb I created that solves the original problem.

Primal Scenario:

We have the following tables produced by two systems in the same company. A table of user interactions and another table from an operational system recording transactions. To make this a bit more concrete, our operational system manages inventory in a particular warehouse and so has the following columns: Date,ItemID,Quantity,TimeStamp. Here are 10 sample rows and code to generate it:

For the sake of making things a bit narrower, 
I am truncating a GUID with the following helper functions
q)trunID:{`$8#string x}
q)genID:{trunID each x?0Ng}
q)sysTable:([] Date:20170805+til 10;
      ItemId:genID 10;
q)show sysTable
Date     ItemId   Qty timestamp
20170805 72de67b9 2   14:27:23
20170806 02e583e3 6   23:43:36
20170807 cffa7aa8 4   07:42:14
20170808 327b47a8 1   26:28:04
20170809 d1b9ccd1 0   11:48:17
20170810 6cb019b3 2   04:59:38
20170811 bb466f80 9   17:54:00
20170812 70112cc0 6   00:54:33
20170813 c9d56ab5 0   22:19:31
20170814 96b75d97 8   10:20:35

The user interactions table keeps track of user related events. It has the following columns: UserId, Date, EventType, ItemId, Qty, timestamp. Here are 10 sample rows and code to generate it:

q)userTable:([]UserId:genID 10;
           Date:20170805+til 10;
           ItemId:genID 10;
q)show userTable

UserId   Date     EventType ItemId   Qty timestamp
88c5bbf5 20170805 buy       b19c6770 6   25:51:08 
42eeccad 20170806 buy       f12c9963 6   01:31:20 
c5510eba 20170807 buy       9ee93847 1   22:36:58 
5f344195 20170808 buy       7bfa7efa 8   20:50:09 
9a4f68c6 20170809 buy       24e2f268 5   16:27:03
26ba3a3e 20170810 buy       94922173 4   17:13:45 
c046e464 20170811 buy       4dec3017 9   13:16:27 
0e09baa4 20170812 buy       f5f7b90e 2   24:29:14 
a9f44e57 20170813 buy       0cb8391a 7   07:38:13 
1e1f2a49 20170814 buy       2df03fb0 0   23:10:12 
We will remove all rows that don’t have EvenType=`buy, so we can ignore this column from now on, but I left it in as an example of the kind of data cleaning you might perform. Also for the rest of this tutorial, I create a universe of itemIDs and userIds that I sample from this creates a more realistic simulation where itemIds will overlap in the two tables.  itemids:genID 1000; userids:genID 100000

We want to perform some analytics to figure out which warehouses we should use for different products based on user geography. To do this we need to join the inventory data to the user data. Unfortunately, there is nothing that links these two tables together, these systems are independent even though they share the same real world events.  You work with what you’ve got. Since these events are connected, if we see a transaction in the user table that has the same date, same ItemId and same Qty we can be pretty sure that it is the same event. This can be performed with a simple left-join like so:

/This join takes less than half a second on 1 million rows 
/in both tables
q)\t userTable lj `ItemId`Date`Qty xkey sysTable

However, our company actually has many, many users so that just using Date and ItemId and Qty is going to get many possible matches of which KDB will always choose the first. This is exacerbated by the fact that most times Qty is 1. So we need to incorporate the timestamp column to get better matching results.  We would like to match two rows if Date ItemId and Qty match and the timestamps are sufficiently close.  In particular, we would like to take the first match from the userTable where everything else being equal the timestamps are within 5 minutes of each other (assume that if they are outside this window, some other system must be responsible for that row).

The As Of Join Primitive for TimeSeries analysis

The idea behind As Of Join is that you can look at the state of universe in another table as of a certain time. The most common example involves joining a trades table to a quote table. I won’t belabour that example. Instead, I’ll translate this into our fictional internet store company.  I want to know what the inventory was as of a particular time when a product was ordered. I have an inventory table:

       (i#til 10)+(2017.08.05D00:00:00.000+i?1000000000000000))
/let's sort it in asc order of the DateTimeStamp
q)invTable:`DateTimeStamp xasc invTable
q)show invTable
ItemId   Qty DateTimeStamp                
30c86bca 375 2017.08.05D00:00:00.075460419
73d8673e 772 2017.08.05D00:00:00.114482831
2e6d75b2 166 2017.08.05D00:00:00.330689365
6c55cdcf 860 2017.08.05D00:00:00.336812816
a3715f66 364 2017.08.05D00:00:00.457884749
9abd4b9f 396 2017.08.05D00:00:00.726431612
89e2e90e 37  2017.08.05D00:00:00.795442611
c7891977 999 2017.08.05D00:00:00.817328697

Given  an ItemId and DateTimeStamp we would like to know how much of that Item we had as of that Time.

In other words, we want the Qty for that ItemId where the time is as close as possible to the given time but no greater.

To make the problem a bit more interesting, we can imagine that we have a table of all the products and we want all the inventory as of 2017.08.07D00.00.00. We can do this pretty easily:

/First generate a table with all of the products and that timestamp;
q)show queryTable
q)show queryTable
ItemId   DateTimeStamp                
d44f9458 2017.08.07D00:00:00.000000000
aa25ba3a 2017.08.07D00:00:00.000000000
beec200c 2017.08.07D00:00:00.000000000
6e9b637f 2017.08.07D00:00:00.000000000
70c3756c 2017.08.07D00:00:00.000000000
a8f4636b 2017.08.07D00:00:00.000000000
befc6ca7 2017.08.07D00:00:00.000000000

 Then we put the columns we want to join 
 in order from least frequent to most frequent
 with the time column last. 
 Then the two tables, query table first
ItemId   DateTimeStamp                 Qty
d44f9458 2017.08.07D00:00:00.000000000 143
aa25ba3a 2017.08.07D00:00:00.000000000 774
beec200c 2017.08.07D00:00:00.000000000 925
6e9b637f 2017.08.07D00:00:00.000000000 711
70c3756c 2017.08.07D00:00:00.000000000 447
a8f4636b 2017.08.07D00:00:00.000000000 190
If we want to see when the time is that is matching 
we can use it's sister command
ItemId DateTimeStamp Qty
d44f9458 2017.08.06D03:43:09.477791637 143
aa25ba3a 2017.08.06D03:45:38.244558871 774
beec200c 2017.08.06D08:46:36.299622582 925
6e9b637f 2017.08.06D19:46:02.531682106 711
70c3756c 2017.08.06D07:43:15.845127668 447
a8f4636b 2017.08.06D03:44:03.353382316 190

We can verify that all the times are before 
 midnight on the 7th of august

Let’s now look at the internal mechanics of how this actually works.

First KDB finds all the candidate matches based on all the column till the last one. Then the aj verb makes use of the binary search function (bin) in KDB. Bin takes two arguments of the same type, the first is a sorted list in ascending order, the second is either a list or an item. For each second argument it returns the index that is equal or less in the first argument. A simple example will illustrate this:

q)0 1 2 3 4 5 6 7 bin 4
q)0 1 2 3.5 4 5 6 7 bin 3.0
q)0 1 2 3.5 4 5 6 7 bin 0.1 0.2 1.9 3.6
0 0 1 3

So assuming the candidate matches are sorted, picking out the latest as of the query time becomes as simple as performing binary search on each row we are trying to match against the candidate matches, which is exactly how KDB does it.

Another way of thinking about “bin” is that it returns the last element from a set of candidate matches, where match means being before the current element.

With this view in mind, it should be clear that we can’t use As Of Join to solve the Primal Scenario. Since as-of-join essentially gives you the last row that is before the time you are asking, whereas we are looking for the first row.  If we consider shifting the timestamps so that all the candidates within the tolerance are before the matching row’s timestamp we would still get the last match instead of the first one.

Luckily, there is another function called binr that returns the first index that is equal or greater to the query. Which leads us to:

q)0 1 2 3 4 5 6 7 binr 4
q)0 1 2 3.5 4 5 6 7 binr 3.0
q)0 1 2 3.5 4 5 6 7 binr 0.1 0.2 1.9 3.6
1 1 2 4

Since binr returns the first index that is equal or greater, we can think of it as returning the first match from a set of candidates where matches are determined as being greater or equal to the element being matched. This bin right function, so named because it returns the binary search from the right, is now enough to go and solve the primal scenario.

A New Verb AJR as of join right

Let’s take a quick look how aj is implemented:

k){.Q.ft[{d:x_z;$[&/j:-1<i:(x#z)bin x#y;y,'d i;+.[+.Q.ff[y]d;(!+d;j);:;.+d i j:&j]]}[x,();;0!z]]y}

Sure enough there is the bin verb right in the middle of the verb.

So we can create another verb called ajr and define it like this:

k){.Q.ft[{d:x_z;$[&/j:-1<i:(x#z)binr x#y;y,'d i;+.[+.Q.ff[y]d;(!+d;j);:;.+d i j:&j]]}[x,();;0!z]]y}

A quick note, the easiest way is to simply go into k mode by typing one backslash. But in a script it’s easier to just to write:

k)ajr:{.Q.ft[{d:x_z;$[&/j:-1<i:(x#z)binr x#y;
     y,'d i;+.[+.Q.ff[y]d;(!+d;j);:;.+d i j:&j]]}[x,();;0!z]]y}


"k" "ajr:{.Q.ft[{d:x_z;$[&/j:-1<i:(x#z)binr x#y;
     y,'d i;+.[+.Q.ff[y]d;(!+d;j);:;.+d i j:&j]]}[x,();;0!z]]y}"

However, you define this verb, you can now answer the question, what was the inventory like immediately after 2017.08.07D00:00:00.000000000 at the first recorded point.

ItemId   DateTimeStamp                 Qty
d44f9458 2017.08.07D00:00:00.000000000 38 
aa25ba3a 2017.08.07D00:00:00.000000000 5 
beec200c 2017.08.07D00:00:00.000000000 723
6e9b637f 2017.08.07D00:00:00.000000000 783
70c3756c 2017.08.07D00:00:00.000000000 56 
a8f4636b 2017.08.07D00:00:00.000000000 112
befc6ca7 2017.08.07D00:00:00.000000000 871
c8bc95e4 2017.08.07D00:00:00.000000000 586

/Similarly we can define the ajr0 verb as follows:
/ and that way we can see what time it matched
q)k)ajr0:{.Q.ft[{d: z;$[&/j:-1<i:(x#z)binr x#y;
    y,'d i;+.[+.Q.ff[y]d;(!+d;j);:;.+d i j:&j]]}[x,();;0!z]]y}
ItemId   DateTimeStamp                 Qty
d44f9458 2017.08.12D15:17:13.710407838 38 
aa25ba3a 2017.08.11D22:32:05.314057246 5 
beec200c 2017.08.13D03:55:10.402670877 723
6e9b637f 2017.08.11D19:32:36.058830770 783
70c3756c 2017.08.12D18:53:29.659736235 56 
a8f4636b 2017.08.15D10:47:32.423256268 112
befc6ca7 2017.08.09D03:48:48.974271199 871
c8bc95e4 2017.08.10D12:42:14.533837072 586


So returning back to our primal scenario, all we need to do is add the tolerance to the userTable timestamp. Then we can use this verb to join on all the previous columns and and take the first match within the tolerance.

first I will create another column 
that is 5 minutes offset into the future to match on
q)update timestamp:timestamp+00:05,
  timestampOriginal:timestamp from `userTable
q)show userTable
UserId   Date   EventType ItemId Qty timestamp timestampOriginal
1faaa20e 20170805 buy 992098f0 7 19:58:02 19:53:02 
a419e88a 20170806 buy 327d41d0 8 15:12:55 15:07:55 
79cdd3d7 20170807 buy bc8ff04f 0 00:38:48 00:33:48 
4155154c 20170808 buy b7318a04 3 04:21:50 04:16:50 
a22652c8 20170809 buy 9e35c5a0 7 09:01:45 08:56:45 
277f1b93 20170810 buy 8cc964a4 5 07:56:52 07:51:52 
a7966916 20170811 buy c2c5bfaf 9 22:59:05 22:54:05 

Then we will perform the new AS OF JOIN RIGHT
Date ItemId Qty timestamp UserId EventType timestampOriginal
20170805 2fc6d170 5 00:50:21 a5732847 buy 07:45:49 
20170805 78bc9724 5 00:54:57 3f039eb6 buy 04:12:17 
20170805 3135e6a8 8 00:57:40 705b75d5 buy 05:03:23 
20170805 4a4b9e36 9 01:42:09 6987be4e buy 02:00:52 
20170805 a6b5e1f3 5 01:59:56 3850deb3 buy 05:53:19 
20170805 d9d0a16c 9 02:12:31 620bda64 buy 10:39:09 
20170805 7c2652a7 3 02:30:49 7af80798 buy 04:19:24 
20170805 c90c6086 5 02:42:07 d4256817 buy 04:55:00 
20170805 be19e79f 6 02:43:57 5c0d3d58 buy 09:38:14 
20170805 264e5489 9 03:36:12 82b8912f buy 04:20:57 
20170805 0ba0bfd2 0 04:01:53 4f996c1d buy 16:11:22 
20170805 cb3e189d 0 04:07:03 675cd3b7 buy 08:43:38 
20170805 f2c41bca 6 04:36:03 855525d7 buy 09:33:50 
20170805 8eec99e2 2 04:36:26 15cab5af buy 05:07:27 
20170805 34894f41 9 04:42:40 ba2ca22d buy 06:11:16 

/It will match to the first at or after the timestamp,
 since we pushed the timestamp up 5 minutes it will be from within 
the tolerance to possibly the last index if no matches.
The issue is that we can now be outside the tolerance 
on the other side, to prevent this we can simply filter
We filter where the matches exist and 
timestamps are within the tolerance
q)select from joined where not null UserId,00:05>timestampOriginal-timestamp
Date ItemId Qty timestamp UserId EventType timestampOriginal
20170805 ac2dbea6 1 07:21:21 99303d09 buy 07:20:27 
20170805 b842007c 2 11:05:40 5171565d buy 11:04:20 
20170805 2d948578 7 19:10:14 75344162 buy 19:13:26 
20170805 c061f673 5 23:20:43 40cf305a buy 23:20:55 
20170805 9396fa95 2 24:25:48 d7482537 buy 24:22:43 
20170806 a73e1906 0 04:45:55 5c7ebb13 buy 04:43:15 
20170806 49ad69e9 6 08:16:19 c6df7f37 buy 08:18:55 
20170806 931226c9 3 20:39:59 30789ba9 buy 20:34:59 
20170807 39e53bb2 9 03:43:39 ccc71cae buy 03:47:14 
20170807 40f3f6de 8 05:56:22 fc967f51 buy 05:59:30 

That’s it.

here is all the code to simulate this:

trunID:{`$8#string x};
genID:{trunID each x?0Ng};
itemids:genID 1000;
userids:genID 100000;
sysTable:([] Date:i#20170805+til 100;
sysTable:`Date`timestamp xasc sysTable;
 Date:i#20170805+til 100;

k)ajr0:{.Q.ft[{d: z;$[&/j:-1<i:(x#z)binr x#y;y,'d i;+.[+.Q.ff[y]d;(!+d;j);:;.+d i j:&j]]}[x,();;0!z]]y};
k)ajr:{.Q.ft[{d:x_z;$[&/j:-1<i:(x#z)binr x#y;y,'d i;+.[+.Q.ff[y]d;(!+d;j);:;.+d i j:&j]]}[x,();;0!z]]y};
update timestamp:timestamp+00:05,
 timestampOriginal:timestamp from `userTable;
userTable:`Date`timestamp xasc userTable;

show select from joined where not null UserId,00:05>timestampOriginal-timestamp;

A Short Interlude on KDB Load balancing

As you might know KDB is inherently single threaded. In other words, it can do at most one thing at a time in sequence.

This is a good thing. The benefits include:

  • Ordered and inherently sequential transactions that see a consistent view of the world.
  • Fewer Cache misses since data is processed to completion instead of being rescheduled.
  • Programs are easier to reason about.

This post isn’t really about how awesome being single threaded is. There are plenty of articles about that featuring Node.js and javascript. Linked here. Also plenty of stack overflow answers about the subject and an old paper about KDB.


Synchronous: ADJECTIVE

  • Existing or occurring at the same time.

Asynchronous: ADJECTIVE

  • Not existing or occurring at the same time.
  • Computing Telecommunications
    Controlling the timing of operations by the use of pulses sent when the previous operation is completed rather than at regular intervals.

In general, in this single threaded world, you simply design components that do perform some function and they go out to other services that perform other functions. If you do everything over async IPC (Inter-Process-Communication). Then you are golden because all of your q processes are responding to the events as soon as they happen. In a simple example, if you have two q processes. One is responsible for holding realtime data and it’s derived/calculated values. And the other is responsible for calculating those derived values. The updater can keep updating its table and send async events to the model calculator to recalculate. When the model recalculates it can send an async request for the updater to include some values in it’s derived model.

However, perfect systems like this don’t get built. Instead, we have many q processes using synchronous IPC. There is a reason for this –though not a good one. It is significantly easier to reason about a synchronous request. In your mind you simply model all the requests one after another and then you do something with all of the requested values.

Unfortunately, if a request takes a long time to process you can force a service to spend all its time honoring your request. Continuing the example above, suppose someone didn’t understand the initial plan and decides to calculate all the values on the realtime service, that would cause the realtime service to spend more of it’s time processing and less of it’s time updating. We can of course turn off the ability to query a realtime service like this, but that is a bit like throwing the baby out with the bath water. Synchronous requests especially for a client are much easier to understand.

So let’s start with a quick analogy that will help explain what we want to do. Synchronous messages are like phone calls.

You call me, I answer the phone you ask a question, I answer.

Asynchronous messages are like text messages.

You text me. Sometime later I read the message. I might respond.

Ideally, what we would like is to hire a secretary who intercepts incoming calls answers the phone takes a message sends it as a text and keeps the caller on the line until the text is answered at which point the secretary can respond to the caller. Since this job is pretty easy we expect that we can have one secretary serving many callers and keeping them all on the line until their query has been resolved. Unfortunately, as of q version 3.5 we still cannot do this (though it has been promised in version 3.6).

So instead we will simulate this with another mode in KDBQ called multithreaded input mode. Multithreaded input mode allows you to answer queries concurrently (up to 1020) within one q process. However, it places many limitations on what kind of queries it can execute and you don’t want heavy calculations to actually execute concurrently for the reasons described earlier. So we settle for the next best thing. A single master forwards each query to a slave, the slave executes the query returns the result to the master and the master returns the result to the client. This allows us to scale a particular service without meddling with the client code. It turns out a bit of engineering is required to make this work and it does turn the Multithreaded input mode into a router, something e documentation says it is not built to do {YMMV}. ( I have tested it and it seems to work)


This works on linux for version 3.x and on version 3.0-3.4 on mac, it uses UNIX sockets and mac doesn’t support abstract sockets. I have implemented something similar using the fifo system as well, but it has it’s own drawbacks and won’t be described here. I’m sure something similar with files can be done in windows systems, but I haven’t spent the time working it out at this point.


You have one master process, this master is run with a negative port number, which will put it into multithreaded input mode. It also runs with a timer turned on so it will be updating and making sure it’s slaves are alive every so N milliseconds. I have set N a thousand so every 1 seconds (this is probably too often but it makes testing faster, since you can see the result of taking a slave offline within a second).

q master.q -p -5000 -t 1000

It runs with a master script called master.q

You also need to run some slave nodes that will listen on their own individual ports. For our example we will use 4001,4002,4003,4004.

q -p 4001

Now I will explain what is inside master.q

//the unix handles of the slave processes
h: {@[{hopen x};x;0Ni]} each handles;
han:(where han=0Ni)_han;
 we will define a function that updates all of
 open handles, so that none of them are stale. This will be executed
 in the main thread, which means that all of the slaves should 
 have returned by this point. So if they don't respond it means they
 are dead.
    alive:{@[{x (=;1b;1b)};x;0b]} each han;
    $[all[han] & count [han]=count[handles] ;;
      [dead:where not alive; hclose each han[key dead];
       inactive: handles where (key han) not in handles;
       h:{@[{hopen x};x;0Ni]} each (dead,inactive);
       hd:(where hd=0Ni)_hd:(dead,inactive)!h;
       `han upsert hd;]]};
Because each concurrent handle is unique we can assign a slave 
by binning the request handles over the number of active slaves.
In other words we are round robining over the slaves.
\{[m] (first value (.z.w mod count han)_han) m};;

The reason we have to do all this work, is that any command that changes the global state is illegal and therefore can’t be in, and though hopen isn’t illegal it pauses all of the concurrency for all the threads because it effectively changes global state.

As a consequence a serious limitation of this approach is that if a client opens a connection with the this multithreaded gateway while querying they can lock up all the other concurrent requests. In practice this is done by using the connection string query instead of hopen.

For example:

::5000 "2+2"

is equivalent to

h:hopen `::5000; h "2+2"

but the first case will lock up the multithreaded gateway and the second won’t.

Secondly this gateway does not support asynchronous requests with callbacks. That is you can call the async request but you won’t be able to make sure the host calls you back. There are probably some ugly workarounds, like including your host and port in the function.

This is a fully functional load balancer and allows clients to continue to write synchronous code without locking your servers. Technically each of these slaves can actually forward the requests onto other machines, since the slaves are not actually limited.

How the Mighty have Fallen, or How I learned to love GREP.

**Note This post was written over a two week period where I kept learning more about this problem. Many of the notes refer to later parts of the post, so that caveats can be explored. Also some parenthetical notes are almost incomprehensible without reading many of the linked articles on the page.  There is little I can do to summarize all of those articles, this post is already way too long.

This post is inspired by a friend of mine who was solving a particularly common “Big Data” problem. (I enjoyed the last two weeks immensely, and have a much greater appreciation for Ken Thompson, Andrew Gallant, Richard Stallman, and of course Arthur) 

[I use Big data loosely, it usually relates to a problem being too large for the machine that you have access too. A great essay can be found in Programming pearls Vol 1 Cracking the Oyster. Where a big data problem is one that uses more than a megabyte of memory. ]

Essentially, my friend had a data set that contained roughly 100 million rows and each row had a column that was a free text description. A human could easily read several records and identify which records belonged together in one category, but the sheer number of records prevented any such attempt to be made. On the other hand, a computer using only a small set of keywords would easily misclassify many of the records. It would be easier to create more categories than there were and then combine categories based on statistical analysis. My friend tried fuzzy matching using something I covered in an earlier post, however, the algorithm slows down drastically with larger datasets.

By the time he told me his problem he had already invented an on the fly hashing and categorization algorithm that I have still yet to fully grok (his algorithm might be faster but it is optimized for his specific problem).

Problem Statement: Assuming you have a set of rows with descriptions and a set of keyword/tokens, assign each row to one or more tokens.

In particular every token will have a set of rows that belong to it. Rows can be assigned to multiple tokens and tokens have many rows.   The total number of tokens was 1 million. The sample dataset would have 10 million rows [limited by the memory on one machine].

The first approach was to use pandas and use groupby methods. However, that was disastrously slow because Pandas strings are actually Python objects instead of being Numpy objects and are simply not optimized for speed.

This led me to move to pure Numpy. I started by constructing a toy problem. The toy problem would have a million rows. Each row would consist of only 5 randomly selected lowercase English letters separated by spaces. The goal would be to identify all the rows that contained each letter.

Here is that experiment:

In [1]:

import pandas as pd
import random
import string
import numpy as np
In [2]:
" ".join(random.sample(string.ascii_lowercase, 5))
'i k a r m'
In [3]:
df=pd.DataFrame({"d":[" ".join(random.sample(tokens, 5)) for i in range(I)], "x":range(I)})
In [4]:
d x
0 b c v l y 0
1 m l q u t 1
2 b f s i n 2
3 g p e k h 3
4 j k a b l 4
In [5]:
#convert the description column into a fixed size array. 
#being a bit conservative on the max size of this column is okay
In [6]:
#d is now just fixed strings
array([b'b c v l y', b'm l q u t', b'b f s i n', ..., b'p f k n h',
       b'y p t o i', b'e g o a i'], 
In [7]:
# We see 26 arrays with the indexes of the rows that have each char
list(map(lambda x: np.where(np.char.find(d, np.bytes_(x))>-1),tokens))
[(array([     4,     24,     39, ..., 999967, 999990, 999999]),),
 (array([     0,      2,      4, ..., 999990, 999992, 999993]),),
 (array([     0,      9,     11, ..., 999978, 999980, 999991]),),
 (array([    12,     15,     18, ..., 999979, 999983, 999985]),),
 (array([     3,     13,     14, ..., 999991, 999995, 999999]),),
 (array([     2,      7,     13, ..., 999980, 999989, 999997]),),
 (array([     3,      6,      7, ..., 999992, 999994, 999999]),),
 (array([     3,      6,     10, ..., 999987, 999993, 999997]),),
 (array([     2,      8,      9, ..., 999993, 999998, 999999]),),
 (array([     4,      5,     10, ..., 999990, 999993, 999994]),),
 (array([     3,      4,      5, ..., 999988, 999996, 999997]),),
 (array([     0,      1,      4, ..., 999984, 999987, 999996]),),
 (array([     1,     12,     15, ..., 999964, 999975, 999979]),),
 (array([     2,      9,     10, ..., 999985, 999989, 999997]),),
 (array([    16,     21,     25, ..., 999996, 999998, 999999]),),
 (array([     3,      6,     12, ..., 999992, 999997, 999998]),),
 (array([     1,     15,     20, ..., 999986, 999988, 999989]),),
 (array([     5,     13,     17, ..., 999974, 999984, 999989]),),
 (array([     2,     14,     18, ..., 999990, 999994, 999996]),),
 (array([     1,      7,     19, ..., 999985, 999994, 999998]),),
 (array([     1,      7,      8, ..., 999993, 999994, 999995]),),
 (array([     0,     10,     11, ..., 999974, 999983, 999988]),),
 (array([     6,      9,     18, ..., 999987, 999990, 999991]),),
 (array([    17,     25,     28, ..., 999988, 999992, 999995]),),
 (array([     0,      5,      7, ..., 999991, 999995, 999998]),),
 (array([     8,     17,     18, ..., 999976, 999995, 999996]),)]
In [8]:
list(map(lambda x: np.where(np.char.find(d, np.bytes_(x))>-1),tokens))
 1 loop, best of 3: 22.5 s per loop

The toy problem took 22.5 seconds on my core i7 desktop from 2015. All the benchmarks were done on the same machine to prevent skewing of results.

I decided to abandon Numpy. 22 seconds for 26 tokens would not scale. At this rate finding the matches for a million tokens would take  (1 million / 26) * 22 seconds = 9.79344729 days. Multiplying by another 10 because my problem was only on 1 million rows would mean this problem was going to run for 90 days at least, you can’t even return most clothes after that much time.

So I turned to my favorite language optimized for big data. KDBQ.

Here is basically the same code as Numpy with comments inline:

.Q.a is just all the lowercase ascii letters

/create a function g that will generate the random string
g:{raze ” “,’5?.Q.a}
/create a table with a million rows using g and column d
/demonstrate the function on one letter “a” for the first 10 rows.
/This creates a boolean mask where 1 means that the row contains “a”
{first `boolean$ss[x;”a”]} each 10#t.d
/use where to save only the rows that have the letter
where {first `boolean$ss[x;”a”]} each 10#t.d
/combine this function and apply it for each letter returning 26 rows with the indexes
/for the first 10 rows
{where {first `boolean$ss[y;x]}[x] each 10#t.d} each .Q.a
2 4
1 3
1 5 9
3 7 8
5 8 9
1 7 9
5 8
6 9
4 5 6
0 2 3
0 2
/Time this function for a million rows
q)\t {where {first `boolean$ss[y;x]}[x] each t.d} each .Q.a
/Try to parallelize the problem with 6 slave threads using peach
q)\t {where {first `boolean$ss[y;x]}[x] each t.d} peach .Q.a
/Try to further parallelize the problem, no luck
q)\t {where {first `boolean$ss[y;x]}[x] peach t.d} peach .Q.a

KDB has done a lot better than Numpy on the same problem, BUT this problem will still take: 1.33547009 days * 10 because this was only a million rows. We get 10 days, which is more reasonable. But we can do better.

I decided to take advantage of the KDBQ enumerated string AKA symbol type.

This led to the following code and timings:

q)g:{raze `$’5?.Q.a}
q)first t[`d]
q)\t {where {x in y}[x] each t.d} each tokens
q)\t {where {x in y}[x] each t.d} peach tokens
q)\t {where {x in y}[x] peach t.d} peach tokens

Definitely an improvement, but nothing to write home about.

At this point, it was time to start looking to improve the algorithm. A quick StackOverflow search lead me to the name of the problem. MultiString/MultiPattern Search. There are two dominant algorithms: Aho-Corasick and Rabin-Karp. Aho-Corasick was developed by Aho of AWK fame and Margaret Corasick (not much is written on the internet about Margaret Corasick, except that she was clearly one of the leading female pioneers in computer science). The Algorithm is linear in the number of inputs and the number of tokens and the number of matches. The Rabin-Karp algorithm, in true Rabin style, is technically not linear but average case linear assuming a good hashing function is chosen and it involves using prime numbers so that you can roll through the string without looping back, more here.

I was going to start implementing one of these algorithms, but as I was searching for a good implementation, I found that the reference implementation was to be found in GNU Grep. So out of curiosity, I tried running my toy problem using grep.

I used KDBQ to write out my table to t.txt and deleted the first line so that I just had the rows of strings on each new line.  I then saved the another file called tokens.txt where each new line was an ascii letter. I then constructed the necessary grep to get the line numbers where a token appears.

grep -n token filename

999918: x b w a b
999923: a v u p u
999924: y v i a p
999927: c k j a g
999930: a l n i p
999935: p a b e k
999944: f a a n t
999949: b u a h p
999951: n j x u a

This will give you the line numbers and the matches using cut command you can return only the line numbers.

Here is what that looks like:

$ grep -n “a” t.txt | cut -d: -f1


I then decided to go read the tokens.txt file line by line and grep for the line in the table of a million rows. Each result of line numbers is written to a file in the results folder with that token name. So if we want all the rows that match a token we simply look at the row numbers in that file.

time while read LINE; do grep -n $LINE t.txt| cut -d: -f1 > results/$LINE; done <tokens.txt

real 0m0.749s
user 0m0.852s
sys 0m0.044s

Less than a second and we haven’t even parallelized the operation. Adding & so that things run in the background and waiting for the process to finish we see the toy problem for what it is, A TOY.

$ time $(while read LINE; do grep -n $LINE t.txt| cut -d: -f1 > results/$LINE & done <tokens.txt; wait)

real 0m0.216s
user 0m1.424s
sys 0m0.112s

With parallelization the toy problem takes less than a fifth of a second. Estimating the run time for the original problem we see that it will now take: (1 million/26)*.216 seconds = 2.3 hours * 10 this is now in the less than a day category.

Curious and surprised that grep was reading off the disk faster than KDBQ was reading from memory, I decided to find out why grep was so FAST.

Summarizing one of the original authors of GNU grep:

  • Like KDBQ, GREP memory maps files
  • Like KDBQ grep will often use the Boyer-Moore search algorithm that basically allows quick skipping of bytes where no match is possible.
  • Grep avoids looking at the lines and only reconstructs line breaks if there is a match

Mike Hartel’s famous quote from that article is very enlightening.

The key to making programs fast is to make them do practically nothing. ;-)

So essentially KDBQ is slower than grep because it still thinks of strings in each row as independent rows and it isn’t designed solely for ripping through strings. The tool was originally the brainchild of Ken Thompson of K&R C fame. So is it surprising that after 40 years the tool that was optimized for string searching is still the king? Not really, but we thought Arthur could do better, we eagerly await K6 which promises to be an entire OS, which will probably include something like grep. (I later realized that I could use Q in a different way to achieve the same result much faster, keep reading… )

A quick aside, for those unfamiliar with grep. Grep is a Unix command utility, that allows searching files for strings that match on a line. Many options were added as Grep developed. Grep gave you a way to search for regular expressions. That is, for things that are similar to a certain string. The extended regular expression engine was originally Aho’s invention and was called EGrep. This merged into Grep as the argument -e.  FGrep gave you a way to search for exact matches much faster which became -f. Similarly, -n allows you to print line numbers and -o allows you to print only the matching pattern.

Anyway, knowing something about the algorithms that allow grep to run so quickly, I decided that it was time to try a more realistic example. Firstly, I thought that longer words might further advantage grep since it could skip more quickly through the file, secondly, I wondered how linux would handle opening many background processes at once.

So to construct the more realistic dataset I went and found a file that contained the 20,000 most common english words.

I read the files into KDBQ and created my tokens and my table:

q)tokens:read0 `:20k.txt
/now instead always taking 5 random tokens we are randomly taking between 1 and 10 /random words without replacement, so that we get a unique set for each row.
q)g:{” ” sv (neg first 1+1?10)?tokens}
q)\t {where {first `boolean$ss[y;x]}[x] each t.d} peach 10#tokens
q)\t {where {first `boolean$ss[y;x]}[x] peach t.d} peach 10#tokens
q)\t {where {first `boolean$ss[y;x]}[x] each t.d} peach 100#tokens
q)\t {where {first `boolean$ss[y;x]}[x] peach t.d} peach 100#tokens
q)\t {where {first `boolean$ss[y;x]}[x] peach t.d} peach 1000#tokens

What we see is that getting the list of indexes for 10 tokens takes about 2.5 seconds, with no significant difference from adding another peach. Getting the indexes for 100 tokens takes 12 seconds, which looks like an improvement, to about 1.2 seconds per 10 tokens. However, getting the indexes for 1000 tokens takes about 3 minutes for an average of about 2 seconds per 10 tokens. So KDBQ is definitely performing better on longer tokens, and tokens get longer deeper into the list so that is contributing to the speedup. However, we really are just using KDBQ to place the files on the filesystem for grep to pick them up.

/save only the top 100 tokens in a separate file
q)save `:tokens.txt

Now we can see how grep does on this much more realistic problem.

Running grep on 100 tokens; we get the following timing result:

time $(while read LINE; do grep -n ” $LINE ” t.txt| cut -d: -f1 > results/$LINE & done <tokens.txt; wait)

real 0m4.460s
user 0m6.684s
sys 0m0.884s

So for 100 words it took 4.46 seconds, so less than 5 seconds. I decided to run it for the whole set of 20,000 words:

Here are the timings experimenting with using Fgrep and using and not using background processes:

It took 5 minutes to run on 20k tokens for a million line table.

time $(while read LINE; do grep -n ” $LINE ” t.txt| cut -d: -f1 > results/$LINE & done <20k.txt; wait)

real 4m54.079s
user 17m13.756s
sys 2m9.064s

Not running in parallel is a bit faster in terms of total CPU time, but is almost 6 times slower in wall clock time, which means you will be waiting awhile.
time while read LINE; do grep -n ” $LINE ” t.txt| cut -d: -f1 > results/$LINE; done <20k.txt

real 23m41.263s
user 14m14.508s
sys 1m57.508s

Using Fgrep was marginally faster because it searches a literal string without any pattern matching support, if you need pattern matching then you can’t use this, but the performance improvement was not all that significant. (This should have been a clue that something was wrong, see later in this post)

time $(while read LINE; do fgrep -n ” $LINE ” t.txt| cut -d: -f1 > results/$LINE & done <20k.txt; wait)

real 4m29.731s
user 17m13.004s
sys 2m2.336s

Now multiplying through for the more realistic example we find that (1 million/20k)*5 minutes=4.1 hours, multiplying by 10 we get 40 hours. So it seems that our toy problem was underestimating the difficulty of the problem.

40 hours seemed like too much time to wait. So off I went in search of something better. Luckily, I came across ripgrep. Ripgrep aims be to be a better version of Grep written in Rust instead of C. [Rust is a language that aims to be a better version of C, so this is fitting. The name rip grep, I’m sure has something to do with R.I.P grep]. The post  on Ripgrep taught me a lot about the underlying implementations of different search tools. It made me realize that some of the information I was reading from older posts was outdated. In particular, grep no longer used memory maps (–mmap), even if you asked for them explicitly. So grep was fast, but not as fast as it could be. Also, the newest grep had somehow increased it’s speed on non-ASCII characters. So some of the traditional speedups like setting up LC_ALL=C, which forces grep to use ASCII characters doesn’t actually increase the speed. Which many of these StackOverFlow answers mention here, here, and here.

Here are some timings that explicitly contradict these results:

Searching for aardvark in a 10 million row file results in no speed improvement when switching between ASCII and UTF-8

$ time LC_ALL=en_US.UTF-8 grep -F aardvark -w -o -n t.txt > results/aardvark

real 0m0.339s
user 0m0.260s
sys 0m0.076s
$ time LC_ALL=C grep -F aardvark -w -o -n t.txt > results/aardvark

real 0m0.340s
user 0m0.276s
sys 0m0.064s

Also, many posts recommended using GNU parallel, but I found it did a poor job of actually distributing work onto all the cores of my CPU and that it was probably much better if you were going to send work to multiple machines. Instead simply using background processes was going to be fine.

Once I read the article about ripgrep, it was time to test its bold claim that it was going to be faster than all the other tools. My more realistic example with 20k words became really easy for ripgrep, once I figured out that the 20k tokens can be split into  smaller 200 line files with split.  Split is a tool written by Richard Stallman that splits the original files into files with the prefix specified. Here I use ‘S’.

$split -l200 20k.txt S

The small-token-files could be used to match against the large input and output a line number and a match on each line.

For example, in our toy problem, if we want to match on letters a-z. We can break it up into chunks of 2 letters. We will get 13 files.

file 1.–> corresponds to letters a,b

file 3. –> corresponds to letter e,f

So that for each token-file we would get a corresponding file with matches. Here is that command and timing:

$ time $(for i in S*; do rg -F -f $i -n -o -w t.txt>results/groupby$i & done; wait)

real 0m36.187s
user 3m23.104s
sys 1m15.308s

Now my more realistic problem seemed like a joke. It was taking less than a minute to go through a 10 million row file with 20 thousand tokens. I was starting to become very impressed with ripgrep. However, I decided that I first needed to try and find the older versions of grep that supported mmap to make the comparison fair.

After building from source no less than 8 versions of grep, I discovered some very curious things.

  • grep lost mmap support as of 2010, so any version after 2.6 essentially was not going to work, and my current ubuntu only came with 2.25, 25 is larger than 6.
  • Old versions of grep were in general faster on large input patterns than new versions, they built the Aho-Corasick tree faster.
  • Grep versions before 2.5 did not support printing only the match (-o) option, which was necessary for us to collect all the indexes back together at the end.
  • So Grep versions 2.5.x were the only ones to support both -o and –mmap options

I ended up settling on grep 2.5.1 for a very strange reason.  I was excited that grep 2.5.1 was going really fast on some test inputs, when I discovered that when matching on multiple patterns at once and printing the line number. I would get the following output:


It seemed that grep had decided that it would be easier to just print the line number once per many matches. So I went to the next version grep 2.5.4, which printed the matches as expected.


However,  it was an order of magnitude slower. So I ended up attaching a small AWK program that would add the line numbers for the lines that had them implicitly. Here is that AWK program:

awk -F: ‘NF==2{line=$1} NF==1{$0=line FS $0}1’

This AWK program had almost no overhead when I tested it so it was much better than relying on grep to print all the line numbers for each match.

Once I had the right version of grep, things really started moving. I discovered that grep could match many more tokens at once than ripgrep.

In fact, my more realistic example with 20 thousand tokens ran in the same amount of time whether or not I split the token files. In fact, the overhead of starting many processes was slowing down grep2.5.1.


This led me to go and create the most realistic example I could without generating nonsense words myself. (I might do this later, but I was feeling a bit lazy and I think this problem is close enough). I went here and got 355k words. I went and recreated my dataset. I had a file of tokens.txt which had 354950 words to be exact. I deleted about 40 words that started with a ‘(single quote) because ripgrep has a bug (bug was fixed within 14 hours of posting it, amazing!) that prevents it from properly printing out only matches that match exactly that word[-o -w]( a very low priority bug , but I figured it was worth filing and skipping those words). I created my 10 million row dataset which had a random number of words between 1 and 10 from the tokens file. I saved that into t.txt.

I then created two very similar scripts that allowed ripgrep and grep2.5.1 to finally compete.



export LC_ALL=C
for F in S*
~/Downloads/grep-2.5.1/src/grep –mmap -F -f $F -n -w -o $FILE|awk -F: ‘NF==2{line=$1} NF==1{$0=line FS $0}1’ >results/group$F &
if (( ++pid_count > 4 )); then
wait -n


for F in S*
/home/pinhus/Downloads/rg –mmap -F -f $F -n -w -o $FILE >results/group$F &
if (( ++pid_count > 4 )); then
wait -n

The two programs behave very similarly and produce identical output. So I will describe them together.

The program takes a filename argument $1 and creates a count of how many processes are running so far. For each pattern File S* it runs a literal match on each patternfile and prints the line numbers along with each match into a corresponding file inside the results folder called group{patternfile}. It runs this in the background and increments the number of running processes. It waits until there are less than 4 running processes, decrements the number of processes and adds another process for the next patternfile. When it is all done it will wait until all the pattern files are written out.

The main difference between them is that the grep version uses strictly ASCII characters whereas the ripgrep version uses UTF8 by default.

There is also a difference in how to optimally split the pattern file. After much experimentation, I found that ripgrep could comfortably handle up to about 400 lines at a time, whereas grep2.5.1 was optimal with 20k lines at a time.

Here are the respective split commands:

#grep split, assuming 355k lines token file
# -n will create N chunks and not break a lines if you add l/N
split -n l/20 tokens.txt S

#ripgrep split assuming 355k lines in token file
split -n l/888 tokens.txt S

As you can see there are only 20 chunks created for the grep version and there are 888 files for the ripgrep version.

However, the real kicker is that the grep version now runs faster, because we have taken care of the overhead of having to many process handles at once.

Here are the timings:

time bash t.txt

real 1m27.812s
user 2m55.840s
sys 0m2.100s

time bash t.txt

real 3m29.019s
user 25m19.516s
sys 1m55.148s

Long Live grep!

Multiplying to the original problem of 1 million tokens we now see that grep will take about 4.5 minutes. Let’s say 5 minutes to be safe. And ripgrep will now take about 10.5 minutes.

The reason that grep now beats ripgrep is that grep uses Aho-Corasick to do multiple matching, whereas ripgrep actually uses a clever version of regex. Instead of implementing Aho-Corasick, ripgrep joins the entire pattern file with | which makes the regex engine match any of the those patterns. Ripgrep then goes one step further and tries to optimize this by searching for a common literal among all those patterns so that it can skip using the regex engine which is much slower. If it finds a common literal string among the patterns it will submit it to it’s implementation of the teddy algorithm, which takes advantage of Intel’s more recent SIMD instructions (single instruction multiple data, AKA vectorized processing). The teddy algorithm submits the literal to be checked against 16 bytes at a time and if it finds a match it will go into the slower regex engine to verify. This implementation forces our pattern files to be smaller since a pattern-file that is too big will not have a common literal or will match too often. In fact, the pattern-files have a limit that will throw an error if they are too big because they overwhelm the regex engine. Because of this limitation, ripgrep has to search the entire file many more times than grep has to. Additionally, ripgrep will perform terribly if the original patternfile is not sorted. I suspected that ripgrep was benefitting from our sorted tokens file, and was able to find common literals, which was allowing it to be much faster than a random tokens file. To test this I used round robin split,

#ripgrep split assuming 355k lines in token file
# replace the l with r. which causes the lines to be round robined across 888 files.
split -n r/888 tokens.txt S

The difference was staggering, using a 100 thousand line input file:

#pattern file is sorted and broken into sorted chunks
time bash t100k.txt

real 0m8.205s
user 0m59.716s
sys 0m1.400s

#pattern file is deliberately round robined across chunks
time bash t100k.txt

real 1m11.450s
user 9m8.252s
sys 0m2.092s

The difference is between 8 seconds for a 100 thousand row file and 1.2 minutes. Almost an order of magnitude difference. Sort your pattern file before using ripgrep. Also because ripgrep uses a nonnaive version of Boyer-Moore it is able to skip many more bytes. (We eagerly await this optimization from Arthur, after all SIMDs are a natural part of vectorized languages.)

On the other hand, because grep uses Aho-Corasick it can handle much larger pattern files, the chunks merely facilitate creation of a smaller tree to traverse and you don’t need to sort your pattern files. This also allows grep to read the input file many fewer times. Once again, algorithms beat brute force  (kind of, see last note where I discover that many matches were missed because of a bug in 2.5.1, most of the points stand but I think you might be better off using ripgrep).

Now to collect the outputs and create the final result, which is a token and all of it’s associated row numbers, we go back to KDBQ.

All of our output files live in the results directory and look something like this:


So we need a function that gets all the files from a directory:

tree:{ $[ x ~ k:key x; x; 11h = type k; raze (.z.s ` sv x,) each k;()]}

This is one of the utilities provided in the KDB Cookbooks/tips. It will recursively go down a directory and give you all the files. We just need 1 level, but it’s fast enough, so there is no need to write our own.

We create a table that will have two columns, an index column and a token column.


We then create a small function that will read in each file split on the colon and parse the line and upsert it into the table.

{`t upsert flip 0:[(“IS”;”:”);x]}

0: will read a text file and takes arguments that specify what it expects see, as well as a delimiter. In this case a colon.
flip arranges the tuple to be aligned with the table
`t upsert will insert tuple.

By default 0: will read the two columns into rows, so we need to flip it.
q)(“IS”;”:”) 0: `:results/groupSaaa
72 165 567 698 811 838 1003 1136 12..
abiliment abience a1 4th aberuncator abbey abductors abdominohysterectomy 10..
q)flip (“IS”;”:”) 0: `:results/groupSaaa
72i `abiliment
165i `abience
567i `a1
698i `4th
811i `aberuncator
838i `abbey

Running this on each file in the results directory like so:

q)\t {`t upsert flip 0:[(“IS”;”:”);x]} each tree `:results
q)\t select index by token from t
q)select index by token from t
token| index ..
—–| ———————————————————————-..
1080 | 1283 127164 391580 443156 543775 561432 608795 862810 889780 1029079 1..
10th | 27747 57768 238851 293795 332990 487295 564854 732020 755630 834528 83..
1st | 37915 113165 175561 203936 213171 318590 382626 411531 473224 653353 6..
2 | 26642 67272 108161 122598 192496 211019 250924 281576 300950 344445 45..
2nd | 3275 50750 93226 99089 107105 208955 212762 234366 238134 403142 48216

Reading in all the output files and aggregating by token takes less than 5 seconds.

However, once I did this aggregation, I thought there must be a better way to take advantage of a KDB only solution.


If you knew that in the description, the only tokens you were interested were words and that the only types of word breaks were spaces then you could do following:

/read in the raw 10 million row file
q)read0 `:t.txt
“cellulipetal outname kymograms splendiferousness preneural raploch noncontin..
“epicure casern mythologization”
“shellfishes renniogen lactonic”
/define raw as this file
q)raw:read0 `:t.txt
/split the first row on spaces
q)” ” vs first raw
/cast each word to a symbol
q)`$” ” vs first raw
/apply this function to each row in raw and call it sraw
q)sraw:{`$” ” vs x} each raw
/this takes about 19 seconds
q)\t sraw:{`$” ” vs x} each raw
/sraw looks like this
/create an index column that tracks each row (add 1 so that it will agree with grep)
q)index:1+til count sraw
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29..
/join together each row number with every symbol in that row
q)flatrows:raze index {x,’raze y}’ sraw
q)\t flatrows:raze index {x,’raze y}’ sraw
/flatrows looks like this:
1 `cellulipetal
1 `outname
1 `kymograms
1 `splendiferousness
1 `preneural
1 `raploch
1 `noncontingency
/create a new table that has a rid and a token
q)t2:([]index:flatrows[;0]; token:flatrows[;1])
q)\t t2:([]index:flatrows[;0]; token:flatrows[;1])
/t2 looks like this:
index token
1 cellulipetal
1 outname
1 kymograms
1 splendiferousness
1 preneural
/aggregate and get all indexes for each token
/this takes less than 8 seconds
q)\t res2:select index by token from t2
/res2 looks like
token| index ..
—–| ———————————————————————-..
1080 | 1283 127164 391580 443156 543775 561432 608795 862810 889780 1029079 1..
10th | 27747 57768 238851 293795 332990 487295 564854 732020 755630 834528 83..
1st | 37915 113165 175561 203936 213171 318590 382626 411531 473224 653353 6..
2 | 26642 67272 108161 122598 192496 211019 250924 281576 300950 344445 45..
2nd | 3275 50750 93226 99089 107105 208955 212762 234366 238134 403142 48216
/total time without ever leaving kdb: 19+8+5+8=40 seconds!!

/If you are not interested in all the tokens you can easily subselect
/as an example asked for a 1000 random subsample of tokens from res2
/sampleTokens looks like this
/I can ask for res2 where the token is in sampleTokens
q)select from res2 where token in sampleTokens
token | index ..
————| —————————————————————..
abaciscus | 19644 81999 125546 145360 159922 213205 223650 227560 353332 41..
abactinally | 92704 265473 480112 499419 510784 539605 617031 668847 752500 8..
abarthrosis | 25638 61103 215367 281385 305352 385693 553059 585997 598293 66..
abir | 96166 214361 258595 259398 309117 387755 419601 720156 746810 8..
abstemiously| 4611 27043 61983 72530 140138 152631 227466 272363 278554 31059..
/this takes a millisecond for 1000 tokens
q)\t select from res2 where token in sampleTokens

So if you know that everything is in ASCII and you know that the words in the description are space delimited nothing beats KDBQ. Arthur you win. I did think to tokenize my inputs, but I didn’t think to wrap them with an index number, until I was collecting the results.

A disclaimer where I suggest using ripgrep until grep reintroduces mmap for large files. Old versions of grep have bugs that are not documented all that well.

On the other hand if you need UTF-8 use ripgrep and if you need regular expressions use ripgrep. The recent lack of support for –mmap in grep means that the chance of doing better with it requires you to build it yourself and deal with bugs.

(It turns out that grep2.5.1 has a bug where -w will not match word boundaries correctly and 2.5.4 fixes that bug but introduces a performance penalty by printing every line, for every match – a task much better suited to AWK. To work around this and still get the same performance, you either need to add spaces to your matches or turn off -w and deal with the overlap matches that will inevitably occur.) I ended up using the command below that mostly did away with the performance penalty, but was still slower than ripgrep.

On the other hand, the bug I found in ripgrep while writing this post was fixed within 14 hours of me reporting it. A serious benefit of a tool that is actively supported.

time cat tokens.txt | parallel –pipe –block 1000k –round-robin grep2.5.1 -Ff – –mmap -on t.txt |awk -F: ‘NF==2{line=$1} NF==1{$0=line FS $0}1’ >results/test

real 4m41.146s
user 15m35.820s
sys 0m8.096s

I didn’t get a chance to fully optimize the block size, but it’s within reach of ripgrep.

GNU Parallel was used:

O. Tange (2011): GNU Parallel – The Command-Line Power Tool,
;login: The USENIX Magazine, February 2011:42-47.