I put together a few lines to partition my kdb table, which contains string columns of course and thus must to be enumerated.
I wonder if this code is completely correct or if it can be simplified further. In particular, I have some doubt about the need to create a partitioned table schema given the memory table and the disk table will have exactly the same layout. Also, there might be a way to avoid creating the temporary tbl_mem and tbl_mem_enum tables:
...
tbl_mem: select ts,sym,msg_type from oms_mem lj sym_mem;
tbl_mem_enum: .Q.en[`$sym_path] tbl_mem;
delete tbl_mem from `.;
(`$db;``!((17;2;9);(17;2;9))) set ([]ts:`time$(); ticker:`symbol$(); msg_type:`symbol$());
(`$db) upsert (select ts,ticker:sym,msg_type from tbl_mem_enum)
delete tbl_mem_enum from `.;
PS: I know, I shouldn't use "_" to name variables, but then what do I use to separate words in a variable or function name? . is also a kdb function.
I think you mean that your table contains symbol columns - these are the columns that you need to enumerate (strings don't need enumeration). You can do the write and enumeration in a single step. Also if you are using the same compression algo/level on all columns then it may be easier to just use .z.zd:
.z.zd:17 2 9i;
(`$db) set .Q.en[`$sym_path] select ts, ticker:sym, msg_type from oms_mem lj sym_mem;
It's generally recommended to use camelCase instead of '_'. Some useful info here: http://www.timestored.com/kdb-guides/q-coding-standards
Related
In Postgresql (and Sybase ADS), I am making my own trigger-based multimaster replication across both platforms which must dynamically handle various composite keys and sometimes no PK on certain tables. To make it easiest, I am trying to auto generate the INSERT/UPDATE/DELETE where the user can choose which columns they want to copy over by listing column names in a cell separated by semicolon.
-"SELECT Address, city, us_state, zipcode FROM public.place;" would be a table that needs to replicate.
-The Metatable for Publication/Subscriptions would have a cell containing 'Address;city;us_state;zipcode'.
-I am using Insert/update/delete triggers to capture new row data and want to use the columns to dynamically make a statement like
"insert into place (Address,city,us_state,zipcode) VALUES (NEW.Address,NEW.city,NEW.us_state,NEW.zipcode);" which can be read and executed on the desination via script. I will do the same action for UPDATE and DELETE, using OLD prefix in the UPDATE and DELETE generated statements where needed.
I am not looking for someone to do a bunch of work, but to give an idea of any functions, logic and statements involved. Thank you for any ideas or advice.
You can split a String and create an Array using the regexp_split_to_array function.
Probably something like: regexp_split_to_array(metacolumn, ';')
More info about string functions: https://www.postgresql.org/docs/9.6/functions-string.html
I'm trying to populate a prices and quotes database using AquaQ's TorQ. For this purpose I use the .loader.loadallfiles function. The difference being that prices is daily data and quotes is more intraday e.g. FX rates.
I do the loading as follows:
/- check the location of database directory
hdbdir:hsym `$getenv[`KDBHDB]
/hdbdir:#[value;`hdbdir;`:hdb]
rawdatadir:hsym `$getenv[`KDBRAWDATA]
target:hdbdir;
rawdatadir:hsym `$("" sv (getenv[`KDBRAWDATA]; "prices"));
.loader.loadallfiles[`headers`types`separator`tablename`dbdir`partitioncol`partitiontype`dataprocessfunc!(`date`sym`open`close`low`high`volume;"DSFFFFF";enlist ",";`prices;target;`date;`year;{[p;t] `date`sym`open`close`low`high`volume xcols update volume:"i"$volume from t}); rawdatadir];
rawdatadir:hsym `$("" sv (getenv[`KDBRAWDATA]; "quotes"));
.loader.loadallfiles[`headers`types`separator`tablename`dbdir`partitioncol`partitiontype`dataprocessfunc!(`date`sym`bid`ask;"ZSFF";enlist ",";`quotes;target;`date;`year;{[p;t] `date`sym`bid`ask`mid xcols update mid:(bid+ask)%2.0 from t}); rawdatadir];
and this works fine. However when loading the database I get errors attemping to select from either table. The reason is that for some partitions there aren't any prices or or there aren't any quotes data. e.g. attempting to:
quotes::`date`sym xkey select from quotes;
fails with an error saying the the partition for year e.g. hdb/2000/ doesn't exist which is true, there are only prices for year 2000 and no quotes
As I see there are two possible solutions but neither I know how to implement:
Tell .loader.loadallfiles to create empty schema for quotes and prices in partitions for which there isn't any data.
While loading the database, gracefully handle the case where there is no data for a given partition i.e. select from ... where ignore empty partitions
Try using .Q.chk[`:hdb]
Where `:hdb is the filepath of your HDB
This fills in missing tables, which will then allow you to preform queries.
Alternatively you can use .Q.bv, where the wiki states:
If your table exists in the latest partition (so there is a prototype
for the schema), then you could use .Q.bv[] to create empty tables
on the fly at run-time without having to create those empties on disk.
To give you a bit of background. I have a process which does this large complex calculation which takes a while to complete. It runs on a timer. After some investigation I realise that what is causing the slowness isn't the actual calculation but the internal q function, union.
I am trying to union two simple tables, table A and table B. A is approximately 5m rows and B is 500. Both tables have only two columns. First column is a symbol. Table A is actually a compound primary key of a table. (Also, how do you copy directly from the console?)
n:5000000
big:([]n?`4;n?100)
small:([]500?`4;500?100)
\ts big union small
I tried keying both columns and upserting, join and then distinct, "big, small where not small in big" but nothing seems to work :(
Any help will be appreciated!
If you want to upsert the big table it has to be keyed and upsert operator should be used. For example
n:5000000
//big ids are unique numbers from 0 to 499999
//table is keyed with 1! operator
big:1!([]id:(neg n)?n;val:n?100)
//big ids are unique numbers. 250 from 0-4999999 and 250 from 500000-1000000 intervals
small:([]id:(-250?n),(n+-250?n);val:500?100)
If big is global variable it is efficient to upsert it as
`big upsert small
if big is local
big: big upsert small
As the result big will have 500250 elements, because there are 250 common keys (id column) in big and small tables
this may not be relevant, but just a quick thought. If your big table has a column which has type `sym and if this column does not really show up that much throughout your program, why not cast it to string or other value? if you are doing this update process every single day then as the data gets packed in your partitioned hdb, whenever the new data is added, kdb+ process has to reassign/rewrite its sym file and i believe this is the part that actually takes a lot of time, not the union calculation itself..
if above is true, i'd suggest either rewriting your schema for the table which minimises # of rehashing(not sure if this is the right term though!) on your symfile. or, as the above person mentioned, try to assign attribute to your table.. this may reduce the time too.
I am trying to figure out what would be the best way to go ahead and locate duplicates in a 5 column csv data. The real data has more than million rows in it.
Following is the content of mentioned 6 columns.
Name, address, city, post-code, phone number, machine number
Data does not have fixed length, data might in certain columns might be missing in certain instances.
I am thinking of using perl to first normalize all the short forms used in names, city and address. Fellow perl enthusiasts from stackoverflow have helped me a lot.
But there would still be a lot of data which would be difficult to match.
So I am wondering is it possible to match content based on "LIKELINESS / SIMILARITY" (eg. google similar to gugl) the likeliness would be required to overcome errors that creeped in while collecting data.
I have 2 tasks in hand w.r.t. the data.
Flag duplicate rows with certain identifier
Mention the percentage match between similar rows.
I would really appreciate if I could get suggestions as to what all possible methods could be employed and which would propbably be best because of their certain merits.
You could write a Perl program to do this, but it will be easier and faster to put it into a SQL database and use that.
Most SQL databases have a way to import CSV. For this answer, I suggest PostgreSQL because it has very powerful string functions which you will need to find your fuzzy duplicates. Create your table with an auto incremented ID column if your CSV data doesn't already have unique IDs.
Once the import is done, add indexes on the columns you want to check for duplicates.
CREATE INDEX name ON whatever (name);
You can do a self-join to look for duplicates in whatever way you like. Here's an example that finds duplicate names.
SELECT id
FROM whatever t1
JOIN whatever t2 ON t1.id < t2.id
WHERE t1.name = t2.name
PostgreSQL has powerful string functions including regexes to do the comparisons.
Indexes will have a hard time working on things like lower(t1.name). Depending on the sorts of duplicates you want to work with, you can add indexes for these transforms (this is a feature of PostgreSQL). For example, if you wanted to search case insensitively you can add an index on the lower-case name. (Thanks #asjo for pointing that out)
CREATE INDEX ON whatever ((lower(name)));
// This will be muuuuuch faster
SELECT id
FROM whatever t1
JOIN whatever t2 ON t1.id < t2.id
WHERE lower(t1.name) = lower(t2.name)
A "likeness" match can be achieved in several ways, a simple one would be to use the fuzzystrmatch functions like metaphone(). Same trick as before, add a column with the transformed row and index it.
Other simple things like data normalization are better done on the data itself before adding indexes and looking for duplicates. For example, trim out and squish extra whitespace.
UPDATE whatever SET name = trim(both from name);
UPDATE whatever SET name = regexp_replace(name, '[[:space:]]+', ' ');
Finally, you can use the Postgres Trigram module to add fuzzy indexing to your table (thanks again to #asjo).
I have a table that has around 40 columns. The only difference in the columns names is that the last 20 all start with "B" before the column name. This table is used for comparing. In other words, compare the data in the first 20 columns to the data in the last 20 columns.
I know this is very bad design, so how should this table be redesigned, so that there are only 20 columns, yet we can still compare the data?
EDIT: if it helps, we also use this data to find a matched cohort
Also note that performance is of main concern here. By duplicating the columns the getting of data is extremely fast.
Thanks!
Two possible architectures and a query tip.
1) Build your table with a "Type" column, and use that to flag "primary" vs. "alternate". In your case, "A" vs. "B" might be appropriate.
2) Build a vertical partition, two identical tables (for primary and alternate data), that share a common primary key. (If Id = 42 is in one table, it must be in the other--unless "alternate" data is optional, in which case don't populate the second table.) Also optionally, have a third table that tracks all possible primary keys, along with any data that is known to always be common to both tables.
Tip: Read up on SELECT...EXCEPT and SELECT...INTERSECT. They run disturbingly quickly, and are idea for comparing all columns and rows between two datasets for differences (except) and matches (intersect). You can use this fairly easily with either of the two structures, and it would work with your existing code as well (though it might be fussier to write the query).