White paper

# The application of foreign keys and linked columns in kdb+

Tables in a database define a relationship between different types of data, whether that relationship is static, dynamic (i.e. fluctuating as part of a time series) or a mixture of both. In general, it is regularly the case that database queries will require data from multiple tables for enrichment and aggregation purposes and so a key aspect of database design is developing ways in which data from several tables is mapped together quickly and efficiently. Although kdb+ contains a very rich set of functions for joining tables in real time, if permanent and well-defined relationships between different tables can be established in advance then data-retrieval latency and related memory usage may be significantly reduced.

This white paper will discuss foreign keys and linked columns in a kdb+ context, two ways whereby table structure and organisation can be optimized to successfully retrieve and store data in large-scale time-series databases.

Tests performed using kdb+ 3.0 (2013.04.05).

## Foreign keys

### Simple foreign keys

The concept behind a foreign key is analogous to that of an enumerated list. While enumerating a list involves separating it into its distinct elements and their associated indexes within the list, here we take an arbitrary table column and enumerate it across a keyed column, which may be either in the same table as itself or a different table in the same database. Behind the scenes, a pointer to the associated key column replaces the enumerated column’s values essentially creating a parent-child relationship if they are in the same table or a data link if they are in different tables.

In the case of a single key enumeration, creating a foreign key is very straightforward and is specified either within the initial table definition or on the fly through an update statement:

//Keyed table that will be used for enumeration; values in the keyed
//column must completely encapsulate the values in the column being enumerated
financials:(
[sym:ABC]
earningsPerShare:1.2 2.3 1.5;
bookValPerShare:2.1 2.5 3.2 )

//Schema of trade table prior to enumeration
trade:([]time:time$();sym:$();price:float$()) //Use the ‘$’ operator to create an enumerated list within the trade
//table by casting to the keyed table ‘financials’
update sym:financials$sym from trade  //Alternatively the foreign key may be defined at the outset q)trade:([]time:time$();sym:financials$();price:float$())
q)trade insert (.z.T;A;20.3)
,0


We can see that the sym column in the trade table is indeed an enumerated list with respect to the keyed table:

q)exec sym from trade
financials$,A  Technical note The first enumeration defined in a database will have type 20h, with each additional enumeration incrementing this value by 1 up to a maximum of 76h. In effect, 57 is the maximum number of enumerations/foreign keys that may exist in a single database, else the session will throw an elim error. When we inserted a row into the trade table above, kdb+ performed a lookup on the keyed table to see what row of the table this entry will map to (in this case row 0) and rather than placing the sym into the table directly, a pointer to this key value was inserted instead. Given that this entry is a reference and not a value, any alterations to the referenced key column will directly influence the trade table and indeed any other tables referencing this key. It is therefore vitally important that great care be taken when managing data that is being referenced elsewhere since modifying, rearranging or deleting this data will have unwanted knock-on effects: q)delete from financials where sym=A financials //trade table still has a reference to the sym entry in row 0 which is now 'B' q)trade time sym price ---------------------- 09:06:24.849 B 20.3  On the other hand – as is the case with a regular enumerated list – if an enumeration attempt is made where the referencing column value does not exist then the lookup fails and a cast error is returned: q)trade insert (.z.T;D;12.1) 'cast  Inserting the relevant mapping data into the keyed table will fix this problem: q)financials insert (D;1.3;4.0) ,2 q)trade insert (.z.T;D;12.1) ,1  The above example demonstrates a key benefit of using foreign keys, it ensures that trade data will always have relevant referential data available for queries and lookups, thus identifying missing or corrupt data and improving data integrity. Another benefit is that since we have created a link from the sym column in the trade table to various rows in the financials table, we can use this mapping to reference other rows as well using dot notation, just as if the referenced table columns were in the original: //Display the Price-Earnings and Price-Book ratios by sym q)select priceEarningsRatio:last price%sym.earningsPerShare,priceBookRatio:last price%sym.bookValPerShare by sym from trade sym| priceEarningsRatio priceBookRatio ---| --------------------------------- B | 8.826087 8.12 D | 9.307692 3.025  Similar to the case of kdb+ column attributes only a single foreign key can be referenced by a column at any one time; establishing a second foreign key will automatically delete the link to the first. Links to more than one table using a single column may be created by linking table 1 to table 2, table 2 to table 3 and so forth: //Create a new table holding exchange information q)exchange:([id:101 102 103 104];ex:LSENDQNYSEAMEX) q)update exchangeID:exchange$101 101 102 from financials
financials

//Compound dot notation
time         sym ex
--------------------
09:06:24.849 B   LSE
09:07:44.282 D   NDQ


### Compound foreign keys

A foreign key link across two or more columns is possible in kdb+. In this case to allow the usage of dot notation an extra column is appended to the referencing table storing the index link of the table being referenced:

q)t1:([sym:ABC;ex:NYSENYSENDQ];sharesInIssue:3?1000)
q)t2:([]time:2?.z.T;sym:AB;exchange:NYSENYSE;price:2?10.)

//Append columns together using Each
q)update t1fkey:t1$(t2[sym],'t2[exchange]) from t2 q)t2 time sym exchange price t1fkey ----------------------------------------- 02:31:39.330 A NYSE 7.043314 0 04:25:17.604 B NYSE 9.441671 1 q)select sym, marketCap:price*t1fkey.sharesInIssue from t2 sym marketCap ------------- A 401.0 B 880.5  All future inserts into t2 must enumerate across t1 as below to avoid an error: q)t2 insert (.z.T;C;NDQ;4.05;t1$CNDQ) ,2
q)t2
time         sym exchange price    t1fkey
-----------------------------------------
02:31:39.330 A   NYSE     7.043314 0
04:25:17.604 B   NYSE     9.441671 1
20:08:25.689 C   NDQ      4.05     2


Alternatively, a complex foreign key may be initialized along with the table itself. The following notation is required:

q) t2:([]time:time$();sym:$();exchange:$();price:float$(); t1fkey:t1$()) q)t2 insert (.z.T;C;NDQ;4.05;t1$CNDQ)
,0


Note that since the enumerated column stores the row index lookup value and not the actual value. The column type is converted to an integer and not a symbol list. Once again all inserts must enumerate the foreign key values:

q)meta t2
c     | t f  a
------| ------
time  | t
t1fkey| i t1
price | f
q)t2
time         sym exchange price t1fkey
--------------------------------------
09:17:22.771 C   NDQ      4.05  2


### Removing foreign keys

To remove a foreign key from a table for simple foreign keys the keyword value is used:

q)update sym:value sym from trade


If a table has a large number of foreign keys then the following function may be used which looks up the index of each column containing a foreign key and applies the value function to each one:

removeKeys:{[x]
v[i]:value each (v:value flip x)i:where not null(0!meta x)f;
flip (cols x)!v }

q)meta removeKeys t2
c     | t f a
------| -----
time  | t
t1fkey| i
price | f


Calling the value function on a complex foreign key column will remove the table mapping but will leave the previously enumerated column intact as a list of integers.

### Contrasting foreign keys and joins

We start with a fresh trade table and another table, exInfo, which maps each symbol to its traded exchange:

trade:([]time:time$();sym:$();price:float$();size:int$());
exInfo:([sym:$()]exID:int$();exSym:$();location:$())

//One million row entries
n:10000000

//Start and end time
st:08:00:00.000
et:17:00:00.000

//100 random syms of length 3
syms:-100?3

//Exchange information
exdata:(
syms;
count[syms]#101 102 103 104;
count[syms]#LSENDQHKSETSE;
count[syms]#GBUSHKJP )
insert[exInfo;exdata]

tdata:(asc st+n?et-st;n?syms;n?100f;n?1000)


Simple select statements from the database take much longer using a left join and require more memory since the table mappings must be built up from scratch and the entire lookup table must be expanded to match the length of the source table prior to the output columns being specified:

q)\ts select time,sym,exSym from trade lj exInfo
68 469762928

q)update sym:exInfo$sym from trade q)\ts select time,sym,sym.exSym from trade 35 268436016  The difference above is even more evident in higher dimensions: q)//Remove the existing foreign key from the trade table and add the q)//exchange ID for joining across two columns q)update sym:value sym from trade q)update exID:exInfo[;exID] each sym from trade q)//Re-key exInfo to key on exchange ID as well as sym q)exInfo:symexID xkey 0!exInfo q)//Left join on two columns, takes almost twenty times longer q)\ts select time,sym,exSym from trade lj exInfo 1324 402654032 q)//Now create a complex foreign key q)update exfKey:exInfo$(trade[sym],'trade[exID]) from trade

q)//Same results as above in simple case
36 268436016


In the previous section we saw how foreign keys are established by enumerating across a key column; in kdb+ it is also possible to avoid the necessity of using key columns/enumerations and link two or more columns together, allowing all tables involved to be readily splayed to disk if desired. In general links may be applied to two or more tables whether the tables are in memory, splayed on disk or even in different kdb+ databases. We will consider all three scenarios here.

Taking the table of financial data as before, and a table of equity position data, with the financials table remaining unkeyed, we can create a mapping similar to that in the case of complex foreign keys, that is by creating an index of integers that are used as a lookup. We use the Enumeration operator ! to establish the connection once we have mapped each row in the referencing table equityPositions to the corresponding row number in the referenced table financials:

q)equityPositions:([]sym:5#ABCDE;size:5?10000;mtm:5?2.)
q)//Look up where the entries in the symbol column correspond to the
q)//rows in the now unkeyed ‘financials’ table, then store the
q)financials:0!financials
q)update finLink:financials!financials.sym?sym from equityPositions


Much as before, the finLink column in the equityPositions table is identified as a foreign key to the financials table within the table metadata (even though it is not strictly a foreign key as before) and select, exec, update, and delete statements incorporating dot notation may again be used.

Appending additional rows to the equityPositions table must maintain the link to the financials table by providing the finLink column with the row index in the financials table that will be mapped to in each case. In contrast to a foreign-key mapping, no enumeration is present and there are therefore no restrictions on what row numbers are inserted:

q)equityPositions insert (A;200;2.;financials!0)
,5
q)equityPositions
---------------------------
A   6927 1.266082   0
B   3700 1.150539   1
C   5588 0.01802349 2
D   5607 0.2896114  3
E   1666 1.541226   3
A   200  2          0

q)//Insert a sym not in the financials table, link to column 0
q)equityPositions insert (S;200;2.;financials!0)
,6

q)//Insert the same sym again, link to an index not in the financials table
q)equityPositions insert (S;200;2.;financials!6)
,7

q)equityPositions
------------------------------
A      6927 1.266082   0
B      3700 1.150539   1
C      5588 0.01802349 2
D      5607 0.2896114  3
E      1666 1.541226   3
A      200  2          0
S      200  2          0
S      200  2          6

q)//Unmapped data results in missing entries
sym earningsPerShare
--------------------
A
B   2.3
C   1.5
D   1.3
E
A   2.3
S   2.3
S


### Simple linked columns on disk

It is possible to create linked columns on tables that have already been splayed to disk.

//Create two tables and splay to disk
companyInfo:([]
sym:abcd;
exchange:NYSENDQNYSETSE;
sector:4?("Banking";"Retail";"Food Producers");
MarketCap:30000000+4?1000000 )

q):db/companyInfo/ set .Q.en[:db] companyInfo
:db/companyInfo/

q)t:([]sym:abcab; ex:NYSENDQLSENYSENDQ; price:5?100.)
q):db/t/ set .Q.en[:db] t
:db/t/

q)//Create a new column in ‘t’ linking to ‘companyInfo’ via the sym column
q):db/t/cLink set companyInfo!(companyInfosym)?(tsym)

//Update the .d file on disk so that it picks up the new column
q).[:db/t/.d;();,;cLink]
:db/t/.d
q)get :db/t/.d
symexpricecLink

q)//Load the table to update the changes in memory
q)\l db/t

//Sample query
sym sector    MarketCap
-----------------------
a   "Retail"  30886470
b   "Banking" 30230906
c   "Retail"  30352036
a   "Retail"  30886470


### Compound linked columns on disk

Only a small adjustment to the single-column case is required to link tables together based on multiple columns. We demonstrate this by continuing with the above tables:

q)//We need to reload companyInfo otherwise the link example below will
q)//not execute properly since the enumerated sym columns from the
q)//splayed table ‘t’ have type 20h rather than 11h
q)\l db/companyInfo

q)//Initiate the mapping by flipping the columns to lists and searching
q)//on each sym/exchange combination
q):db/t/cLink2 set companyInfo!(flip companyInfosymexchange)?flip tsymex
:db/t/cLink2

//Update the .d file and reload the table once again
q).[:db/t/.d;();,;cLink2]
q)\l db/t

//Sample query, double link means sym 'c' does not map this time
sym sector    MarketCap
-----------------------
a   "Banking" 30450974
b   "Retail"  30909716
c   ""
a   "Banking" 30450974
b   "Retail"  30909716


### Linking across multiple kdb+ databases

For practical purposes kdb+ only allows only one on-disk database to be memory-mapped to each process at any one time. Occasionally however it may be necessary to perform analytics on data in several databases simultaneously and although it is possible to aggregate data from many locations to one centralized location via IPC, if the datasets in question are very large and span multiple days and weeks then this becomes impractical. In Unix-based operating systems such as Linux and macOS an alternative is to use symbolic links in conjunction with kdb+ linked columns to allow us to retrieve and analyze vast amounts of data while keeping the level of RAM usage to an acceptable level.

The following section will outline how to construct a link from a trade table in one partitioned database to a quote table in a separate database existing on the same file network. The method may easily be generalized to link between an arbitrary number of tables across an arbitrary number of databases. There are three main steps:

1. Initially we create a partitioned database containing the tables we will be working with across multiple dates along with some utility functions for creating the database links.
2. We then map the entries in the trade table to those in the quote table for each date using a standard as-of join across time and sym.
3. Lastly, we append a column linking the trade table to the quote table based on this mapping and save this to disk.

We use slightly modified versions of the standard .Q.en and .Q.dpft functions to ensure that no sym file clashes occur across the two databases. We will place these functions, and the other utility functions we will define, into the linked column namespace .lc.

The following code defines the trade and quote tables and writes them to disk in databases db1 and db2 respectively:

//Table schemas, being with fresh trade and quote schemas
time:time$(); sym:$();
price:float$(); size:int$() )
quote:([]
time:time$(); sym:$();
bid:float$(); bsize:int$();
ask:float$(); asize:int$() )

//Number of entries in trade table
n:10000

//Start and end of day
st:08:00:00.000
et:17:00:00.000
syms:ABCD

tdata:(asc st+n?et-st;n?syms;n?100f;n?1000)

//Generate 10x number of quotes
n*:10
qdata:(asc st+n?et-st;n?syms;n?100f;n?1000;n?100f;n?1000)
insert[quote;qdata]

//Historical database builder function
buildHDB:{[dir;dt;t] .Q.dpft[hsym $dir;dt;sym;t];} //Partition the tables to disk buildHDB[“/root/db1”;;trade] each .z.D-til 3 buildHDB[“/root/db2”;;quote] each .z.D-til 3  The first utility function creates a symlink in a directory basePath to a table rTab that exists in a remote directory remotePath. The symlink name will be the same name as rTab. For our purposes • basePath is the path to each trade table • remotePath the path to each quote table • rTab is the quote table name All arguments are passed as strings. //Check if the symlink exists and use Unix command ‘ln –s’ to create //the symlink if not .lc.createSymLink:{[basePath;remotePath;rTab] remoteTablePath:remotePath,"/",rTab; baseTablePath:basePath,"/",rTab; if[not($rTab) in key hsym $basePath; system "ln -s ",remoteTablePath," ",baseTablePath]; }  In order to avoid sym file clashes when loading tables from different kdb+ databases into memory we must save the trade table using an alternative sym file name. The following are custom versions of .Q.en and .Q.dpft that take an additional argument for saving splayed tables to disk using a bespoke sym file name instead of the default sym name: //d is the database directory the table will be saved to //a is the name of the alternative sym file name used to avoid clashes //p is the database partition slice //f is the table partition field //t is the table name q).lc.dpft:{[d;a;p;f;t] if[not all .Q.qm each r:flip .lc.en[d;a]. t;'unmappable]; {[d;t;i;x] @[d;x;:;t[x]i]}[d:.Q.par[d;p;t];r;iasc r f] each key r; @[;f;p#]@[d;.d;:;f,(r:key r) except f]; } //The following function is called above when splaying the table .lc.en:{[d;a;x] if[not -11h=type a;'$"expected symbol parameter type for a"];
@[x; cs@where 11h=type each x cs:key flip x; ( sv (hsym d),a)?] }


The next utility function maps the entries in the trade table to those in the quote table using an as-of join, constructs a link between the two tables and saves to disk using the modified .lc.dpft function defined above. The as-of join columns (usually time and sym) are passed in as a symbol list whereas the file path and table names are passed in as strings.

.lc.joinSaveTables:{[ajCols;basePath;dt;baseTable;remoteTable]
//Cast table names to syms for convenience
remoteTable:$remoteTable; baseTable:$baseTable;

//Force-load the asof join columns from remote table into memory
remoteFileHandle: sv (hsym $basePath),($string dt),remoteTable;
remoteTable set select sym,time from (get remoteFileHandle);

//Re-apply attributes of original table to the in-memory copy
![remoteTable;();0b;a[c]!{(#;enlist x;y)} .'
flip value a:exec a,c from meta get remoteFileHandle where c in ajCols];

//Join tables and set the link column to be the point at which the
//tables map together
baseTable set aj[
ajCols;
value baseTable;
?[value remoteTable; (); 0b; (ajCols!ajCols),(enlist id)!enlist i] ];
(exec i from select i from value remoteTable)?id from baseTable;

// Splay base table to disk but use different, independent sym file tsym
.lc.dpft[hsym \$basePath;tsym;dt;sym;baseTable]; }


Now that we have all the prerequisite utility functions defined, the following master function creates a symlink from the trade table directory to the quote table for each partition slice in question:

.lc.createPart:{[basePath;baseTable;remotePath; remoteTable;ajCols;dt]
raze remotePath,"/",string dt;remoteTable];
.lc.joinSaveTables[ajCols;basePath;dt;baseTable;remoteTable]; }


If we create the partitioned databases and run something like the following, a link will be created across all database partitions. For simplicity, the absolute directory paths are passed to the function as arguments and hence used when defining the symlink. The example above may accommodate relative directory paths with a little manipulation.

q).lc.createPart["/root/db1";"trade";"/root/db2";"quote";symtime;] each .z.D-til 3


Loading the database from memory, we achieve a trade table with an embedded link to the remote quote table.

q)\l db1
q)tables[]
quotetrade

c    | t f     a
-----| ---------
date | d
sym  | s       p
time | t
price| f
size | i
id   | j


Aggregations may be carried out using a single table; queries will be very efficient especially if repeated due to caching:

// First run
3030 1117552

// Second run
9 1116656

q)res
----------| --------------------------
A   08:00 | 50.87719 49.76909 48.63532
A   08:10 | 48.9346  49.99889 52.01281
A   08:20 | 47.48985 49.68657 53.01129
A   08:30 | 50.03407 51.82779 47.96814
`

## Conclusion

This white paper introduced how foreign keys and linked columns may be established and applied in kdb+ databases. As we observed, the benefits of using foreign keys are numerous; firstly, establishing a permanent link between tables is considerably more efficient than building up a relationship in real time through the use of a join, particularly if queries will be repeated regularly. Furthermore, enumerating columns ensures data integrity and helps identify missing or corrupt referential data. Database normalization is much easier to achieve allowing greater data consistency, reduction of redundant data and more flexible database design.

A drawback to using foreign keys is that keyed tables cannot be splayed to disk. This is circumvented using linked columns that can establish permanent mappings between tables whether they are both in memory or on disk. Although not featuring the benefits of enumeration, linked columns are useful for establishing mappings between tables in large-scale historical databases, allowing users to either map data within partition slices in a single database or map each table in a particular partition slice in one database to the corresponding partition slice in another.

## Author

Kevin Smyth has worked as a consultant for some of the world's leading financial institutions. Based in London, Kevin has implemented data capture and high-frequency data analysis projects across a large number of mainstream and alternative asset classes.

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