Consider the following demo schema
trades:([]symbol:`$();ccy:`$();arrivalTime:`datetime$();tradeDate:`date$(); price:`float$();nominal:`float$());
marketPrices:([]sym:`$();dateTime:`datetime$();price:`float$());
usdRates:([]currency$();dateTime:`datetime$();fxRate:`float$());
I want to write a query that gets the price, translated into USD, at the soonest possible time after arrivalTime. My beginner way of doing this has been to create intermediate tables that do some filtering and translating column names to be consistent and then using aj and ajo to join them up.
In this case there would only be 2 intermediate tables. In my actual case there are necessarily 7 intermediate tables and records counts, while not large by KDB standards, are not small either.
What is considered best practice for queries like this? It seems to me that creating all these intermediate tables is resource hungry. An alternative to the intermediate tables is 2 have a very complicated looking single query. Would that actually help things? Or is this consumption of resources just the price to pay?
For joining to the next closest time after an event take a look at this question:
KDB reverse asof join (aj) ie on next quote instead of previous one
Assuming that's what your looking for then you should be able to perform your price calculation either before or after the join (depending on the size of your tables it may be faster to do it after). Ultimately I think you will need two (potentially modified as per above) aj's (rates to marketdata, marketdata to trades).
If that's not what you're looking for then I could give some more specifics although some sample data would be useful.
My thoughts:
The more verbose/readible your code, the better for you to debug later and any future readers/users of your code.
Unless absolutely necessary, I would try and avoid creating 7 copies of the same table. If you are dealing with large tables memory could quickly become a concern. Particularly if the processing takes a long time, you could be creating large memory spikes. I try to keep to updating 1-2 variables at different stages e.g.:
res: select from trades;
res:aj[`ccy`arrivalTime;
res;
select ccy:currency, arrivalTime:dateTime, fxRate from usdRates
]
res:update someFunc fxRate from res;
Sean beat me to it, but aj for a time after/ reverse aj is relatively straight forward by switching bin to binr in the k code. See the suggested answer.
I'm not sure why you need 7 intermediary tables unless you are possibly calculating cross rates? In this case I would typically join ccy1 and ccy2 with 2 ajs to the same table and take it from there.
Although it may be unavoidable in your case if you have no control over the source data, similar column names / greater consistency across schemas is generally better. e.g. sym vs symbol
Related
Is it possible to have a two level partition scheme in kdb+? For example, one level is based on trading dates and the other level is on stock symbols. Thanks!
If you use the standard partitioned-by-date-and-`p#attribute-on-sym approach then you do effectively have a two level partition. It is very efficient to query/filter by date
select from table where date=x
and also generally still very efficient to query/filter by sym
select from table where date within(x;y),sym=z
Particularly if you run secondary threads and have good disk performance.
So you should get good performance when querying a sym across all history, and also when querying all syms for a given date. This is also the simplest way to write daily data.
I don't believe there's any real way to have a true two-level partition without writing the data twice in two different ways.
If your queries span a large range of symbols or involve extensive queries on each one that would benefit from parallel computation, you can segment by first letter ranges: A-N, N-Z, or finer grained than that.
As long as you maintain`p# within and across segments (so that e.g. IBM only appears in one place), you can throw threads and disks at the problem.
If I have table A, and table B, and I have data that start off in table A but end up in table B, and I have a table C which has a foreign key that points to the primary key of A, but when the data get removed from A and ends up in table B, it should point to B instead (having the same id as A's data did). Will this cause confusion. Heres and example to show what I mean:
A (Pending results)
id =3
B( Completed Results)
null
C(user)
id = 1
results id = 3 (foreign key to both A and B)
After three minutes, the results have been posted.
A (Pending results)
null
B( Completed Results)
id = 3
C(user)
id = 1
results id = 3 (foreign key to both A and B)
Is there anything wrong with this implementation. Or would it be better to have A and B as one table? The table could grow very large which is what I am worried about. As separate tables, the reads to table A would be far greater than the reads to table B and table A would be much smaller, as it is just pending results. If A and B were combined into one table, then it would be both pending and a history of all completed results, so finding the ones which are pending would take much more time I assume. All of this is being done is postgresql if that makes a difference.
So I guess my question is: Is this implementation fine for a medium scale, or given the information I just said, should I combine table A and B (Even though B will grow infinitely large whereas A only contains present data and is significantly smaller).
Sounds like you've already found that this does not work. I couldn't follow your example properly because "A", "B", and "C" never work for me. I suspect those kinds of formulaic labels are better than specifics for other people. You just can't win ;-) In any case, it sounds like you're facing a practical concern about table size, and are being tempted to use a design that splits a natural table into two parts. (Hot and old.) As you found, that doesn't really work with the keys in a system. The relational model (etc., etc.) doesn't have a concept for "this thing is a child of this or that." So, you're swimming up stream there. Regardless, this kind of setup is very commonplace in the wild, so much so that it's got a name. Well, several names. "Polymorphic Association" from Bill Karwin's SQL Anti-Patterns is common. That's a good book, and short, by the way. Similarly, "promiscuous association" is a term you'll see. Or sometimes you'll see the table itself listed as a "jump table", or a "hub", etc.
I suspect there's a reason this non-relational pattern is so widely used: It makes sense to humans. An area where the relational model is always a tight pinch is when you have things which are kinds of things. Like, people who are staff or student. So many fields in common, several that are distinct to their specific type. One table? Two? Three? Table inheritance in Postgres might help...at least it's trying to. Anyway, polymorphic relations are problematic in an RDBMS because they're not able to be modeled or constrained automatically. You need custom code to figure out that this record is a child of that table...or the other table. You can't bake that into the relations. If you're interested in various solutions to this design problem, Karwin's chapter is quite good, easy to read, and full of alternative designs. If you don't feel like tracking down the book but are a bit interested, check out this article from a few years ago:
https://hashrocket.com/blog/posts/modeling-polymorphic-associations-in-a-relational-database
Chances are, your interest right now is more day-to-day. Is sounds like you've got a processing pipeline with a few active records and an ever-increasing collection of older records. You don't mention your Postgres version, but you might have less to worry about than you imagine. First up, you could consider partitioning the table. A partitioned table has a single logical table that you talk to in your queries with a collection of smaller physical tables under the hood. You can get at the partitions directly, but you don't need to. You just talk to my_big_table and Postgres figures out where to look. So, you could split the data on week, month, etc. so that no one bucket every gets too big for you. In this case, the individual partitions have their own indexes too. So, you'll end up with smaller tables and smaller indexes under the hood. For this, you're best off using PG 11, or maybe PG 10. Partitioning is a big topic, and the Postgres feature set isn't a perfect match for every situation...you have to work within its limits. I'll leave it at that now as it's likely not what you need first.
Simpler than partitioning is an awesome Postgres feature you may not know about, partial indexes. This isn't unique to Postgres (SQL Server calls the same sort of feature a "filtered" index), but I don't think MySQL has it. Okay, the idea is really simple: Build an index that only includes rows that match a condition. Here's an example:
CREATE INDEX tasks_pending
ON tasks (status)
WHERE status = 'Pending'
If you're table has 100M records, a full B-tree has to catalog all 100M rows. You need that for a uniqueness check on a primary key...but it's big and expensive. Now imagine your 100M records have only 1,000 rows where status = pending. You've got an index with just those 1,000 rows. Tiny, fast, perfect. The beauty part here is that the partial index doesn't necessarily get bigger as your historical data set grows. And, shout out to historical data sets, they're very nice to have when you need to get aggregates, etc. in a simple search. If you split things into multiple tables, you'll need to write longer queries with UNION. (That wouldn't be the case with partitions where the physical division is masked by the logical partition master table.)
HTH
We have a system where we do some aggregations in Redshift based on some conditions. We aggregate this data with complex joins which usually takes about 10-15 minutes to complete. We then show this aggregated data on Tableau to generate our reports.
Lately, we are getting many changes regarding adding a new dimension ( which usually requires join with a new table) or get data on some more specific filter. To entertain these requests we have to change our queries everytime for each of our subprocesses.
I went through OLAP a little bit. I just want to know if it would be better in our use case or is there any better way to design our system to entertain such adhoc requests which does not require developer to change things everytime.
Thanks for the suggestions in advance.
It would work, rather it should work. Efficiency is the key here. There are few things which you need to strictly monitor to make sure your system (Redshift + Tableau) remains up and running.
Prefer Extract over Live Connection (in Tableau)
Live connection would query the system everytime someone changes the filter or refreshes the report. Since you said the dataset is large and queries are complex, prefer creating an extract. This'll make sure data is available upfront whenever someone access your dashboard .Do not forget to schedule the extract refresh, other wise the data will be stale forever.
Write efficient queries
OLAP systems are expected to query a large dataset. Make sure you write efficient queries. It's always better to first get a small dataset and join them rather than bringing everything in the memory and then joining / using where clause to filter the result.
A query like (select foo from table1 where ... )a left join (select bar from table2 where) might be the key at times where you only take out small and relevant data and then join.
Do not query infinite data.
Since this is analytical and not transactional data, have an upper bound on the data that Tableau will refresh. Historical data has an importance, but not from the time of inception of your product. Analysing the data for the past 3, 6 or 9 months can be the key rather than querying the universal dataset.
Create aggregates and let Tableau query that table, not the raw tables
Suppose you're analysing user traits. Rather than querying a raw table that captures 100 records per user per day, design a table which has just one (or two) entries per user per day and introduce a column - count which'll tell you the number of times the event has been triggered. By doing this, you'll be querying sufficiently smaller dataset but will be logically equivalent to what you were doing earlier.
As mentioned by Mr Prashant Momaya,
"While dealing with extracts,your storage requires (size)^2 of space if your dashboard refers to a data of size - **size**"
Be very cautious with whatever design you implement and do not forget to consider the most important factor - scalability
This is a typical problem and we tackled it by writing SQL generators in Python. If the definition of the metric is the same (like count(*)) but you have varying dimensions and filters you can declare it as JSON and write a generator that will produce the SQL. Example with pageviews:
{
metric: "unique pageviews"
,definition: "count(distinct cookie_id)"
,source: "public.pageviews"
,tscol: "timestamp"
,dimensions: [
['day']
,['day','country']
}
can be relatively easy translated to 2 scripts - this:
drop table metrics_daily.pageviews;
create table metrics_daily.pageviews as
select
date_trunc('day',"timestamp") as date
,count(distinct cookie_id) as "unique_pageviews"
from public.pageviews
group by 1;
and this:
drop table metrics_daily.pageviews_by_country;
create table metrics_daily.pageviews_by_country as
select
date_trunc('day',"timestamp") as date
,country
,count(distinct cookie_id) as "unique_pageviews"
from public.pageviews
group by 1,2;
the amount of complexity of a generator required to produce such sql from such config is quite low but in increases exponentially as you need to add new joins etc. It's much better to keep your dimensions in the encoded form and just use a single wide table as aggregation source, or produce views for every join you might need and use them as sources.
I am using a standard splayed format for my trade data where i have directories for each date and each column as separate file in there.
I am reading from csv files and storing using the below code. I am using the trial version 32 bit on win 7, 64 bit.
readDat: {[x]
tmp: read data from csv file(x)
tmp: `sym`time`trdId xasc tmp;
/trd: update `g#sym from trd;
trade:: trd;
.Q.dpft[`:/kdb/ndb; dt; `sym; `trade];
.Q.gc[];
};
\t readDat each 50#dtlist
I have tried both using the `g#sym and without it. Data has typically 1.5MM rows per date. select time for this is from 0.5 to 1 second for a day
Is there a way to improve times for either of the below queries.
\t select from trade where date=x
\t select from trade where date=x, sym=y
I have read the docs on segmentation, partitioning etc. but not sure if anything would help here.
On second thoughts, will creating a table for each sym speed up things? I am trying that out but wanted to know if there are memory/space tradeoffs i should be aware of.
Have you done any profiling to see what the actual bottleneck is? If you find that the problem has to do with disk read speed (using something like iostat) you can either get a faster disk (SSD), more memory (for bigger disk cache), or use par.txt to shard your database across multiple disks such that the query happens on multiple disks and cores in parallel.
As you are using .Q.dpft, you are already partitioning your DB. If your use case is always to pass one date in your queries, then segmenting by date will not provide any performance improvements. You could possibly segment by symbol range (see here), although this is never something I've tried.
One basic way to improve performance would be to select a subset of the columns. Do you really need to read all of the fields when querying? Depending on the width of your table this can have a large impact as it now can ignore some files completely.
Another way to improve performance would be to apply `u# to the sym file. This will speed up your second query as the look up on the sym file will be faster. Although this really depends on the size of your universe. The benefit of this would be marginal in comparison to reducing the number of columns requested I would imagine.
As user1895961 mentioned, selecting only certain columns will be faster.
KDB splayed\partitioned tables are almost exactly just files on the filesystem, the smaller the files and the fewer you have to read, the faster it will be. The balance between the number of folders and the number of files is key. 1.5mln per partition is ok, but is on the large side. Perhaps you might want to partition by something else.
You may also want to normalise you data, splitting it into multiple tables and using linked columns to join it back again on the fly. Linked columns, if set up correctly, can be very powerful and can help avoid reading too much data back from disk if filtering is added in.
Also try converting your data to char instead of sym, i found big performance increases from doing so.
I have a solution that can be parallelized, but I don't (yet) have experience with hadoop/nosql, and I'm not sure which solution is best for my needs. In theory, if I had unlimited CPUs, my results should return back instantaneously. So, any help would be appreciated. Thanks!
Here's what I have:
1000s of datasets
dataset keys:
all datasets have the same keys
1 million keys (this may later be 10 or 20 million)
dataset columns:
each dataset has the same columns
10 to 20 columns
most columns are numerical values for which we need to aggregate on (avg, stddev, and use R to calculate statistics)
a few columns are "type_id" columns, since in a particular query we may
want to only include certain type_ids
web application
user can choose which datasets they are interested in (anywhere from 15 to 1000)
application needs to present: key, and aggregated results (avg, stddev) of each column
updates of data:
an entire dataset can be added, dropped, or replaced/updated
would be cool to be able to add columns. But, if required, can just replace the entire dataset.
never add rows/keys to a dataset - so don't need a system with lots of fast writes
infrastructure:
currently two machines with 24 cores each
eventually, want ability to also run this on amazon
I can't precompute my aggregated values, but since each key is independent, this should be easily scalable. Currently, I have this data in a postgres database, where each dataset is in its own partition.
partitions are nice, since can easily add/drop/replace partitions
database is nice for filtering based on type_id
databases aren't easy for writing parallel queries
databases are good for structured data, and my data is not structured
As a proof of concept I tried out hadoop:
created a tab separated file per dataset for a particular type_id
uploaded to hdfs
map: retrieved a value/column for each key
reduce: computed average and standard deviation
From my crude proof-of-concept, I can see this will scale nicely, but I can see hadoop/hdfs has latency I've read that that it's generally not used for real time querying (even though I'm ok with returning results back to users in 5 seconds).
Any suggestion on how I should approach this? I was thinking of trying HBase next to get a feel for that. Should I instead look at Hive? Cassandra? Voldemort?
thanks!
Hive or Pig don't seem like they would help you. Essentially each of them compiles down to one or more map/reduce jobs, so the response cannot be within 5 seconds
HBase may work, although your infrastructure is a bit small for optimal performance. I don't understand why you can't pre-compute summary statistics for each column. You should look up computing running averages so that you don't have to do heavy weight reduces.
check out http://en.wikipedia.org/wiki/Standard_deviation
stddev(X) = sqrt(E[X^2]- (E[X])^2)
this implies that you can get the stddev of AB by doing
sqrt(E[AB^2]-(E[AB])^2). E[AB^2] is (sum(A^2) + sum(B^2))/(|A|+|B|)
Since your data seems to be pretty much homogeneous, I would definitely take a look at Google BigQuery - You can ingest and analyze the data without a MapReduce step (on your part), and the RESTful API will help you create a web application based on your queries. In fact, depending on how you want to design your application, you could create a fairly 'real time' application.
It is serious problem without immidiate good solution in the open source space. In commercial space MPP databases like greenplum/netezza should do.
Ideally you would need google's Dremel (engine behind BigQuery). We are developing open source clone, but it will take some time...
Regardless of the engine used I think solution should include holding the whole dataset in memory - it should give an idea what size of cluster you need.
If I understand you correctly and you only need to aggregate on single columns at a time
You can store your data differently for better results
in HBase that would look something like
table per data column in today's setup and another single table for the filtering fields (type_ids)
row for each key in today's setup - you may want to think how to incorporate your filter fields into the key for efficient filtering - otherwise you'd have to do a two phase read (
column for each table in today's setup (i.e. few thousands of columns)
HBase doesn't mind if you add new columns and is sparse in the sense that it doesn't store data for columns that don't exist.
When you read a row you'd get all the relevant value which you can do avg. etc. quite easily
You might want to use a plain old database for this. It doesn't sound like you have a transactional system. As a result you can probably use just one or two large tables. SQL has problems when you need to join over large data. But since your data set doesn't sound like you need to join, you should be fine. You can have the indexes setup to find the data set and the either do in SQL or in app math.