I'm rewriting some SQL code that generates a "bucket" column based on the current month (that happens to be stored as VARCHAR, ugh).
I noticed that the previous author decided to store a certain number of dates for comparison as variables, and then use a case statement to calculate the bucket.
See SQL Fiddle Here that demonstrates a slice of this (it happens about five times in three different ways similar to this).
Is there any reason I cannot simply get rid of most of the redundant variables and simplify it to a few lines of code (See This Fiddle)? Is the overhead of in-line function calls great enough to justify doing this, or does the query compiler cache the results of that portion anyways?
Related
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
I'm pretty new to PostgreSQL so I guess i'm missing some basic information, information that I didn't quite find while googling, guess I didn't really know the right keywords, hopefully here I'll get the missing information :)
I'm using PostgreSQL 11.4.
I've encountered many issues when I create a function that returns a query result as a table, and it executes it about 50 times slower then running the actual query, sometimes even more then that.
I understand that IMMUTABLE can be used when there is no table scans, just when I manipulate and return data based on the function parameters and STABLE when if the query with same parameters do a table scan and always returns the same results.
so the format of my function creation is this:
CREATE FUNCTION fnc_name(columns...)
RETURNS TABLE ( columns..) STABLE AS $func$
BEGIN
select ...
END $func$ LANGUAGE pgplsql;
I can't show the query here since it's work related, but still... there is something that I didn't quite understand about creating functions why is it so slow ? I need to fully understand this issue cause I need to create many more functions and it seems right now that I need to run the actual query to get proper performance instead of using functions and I still don't really have a clue as to why!
any information regarding this issue would be greatly appreciated.
All depends on usage of this function, and size of returned relation.
First I have to say - don't write these functions. It is known antipattern. I'll try to explain why. Use views instead.
Result of table functions written in higher PL languages like Perl, Python or PLpgSQL is materialized. When table is small (to work_mem) it is stored in memory. Bigger tables are stored in temp file. It can have significant overhead.
Function is a black box for optimizer - is not possible to push down predicates, there are not correct statistics, there is not possible to play with form of joins or order of joins. So some not trivial queries can be slower (little bit or significantly) due impossible optimizations.
There is a exception from these rules - simple SQL functions. SQL functions (functions with single SQL statement) can be inlined (when some prerequisites are true). Due inlining the body of function is merged to body of outer SQL query, and the result is same like you will write subquery directly. So result is not materialized and it is not a barrier for optimization.
There is a basic rule - use functions only when you cannot to calculate some data by SQL. Don't try to hide SQL or encapsulate SQL (elsewhere - for simplification some complex queries use views not functions). Same rules are valid for all SQL databases (Oracle, DB2, MSSQL). Postgres is not a exception.
This note is not against stored procedures (functions). It is great technology. But it requires specific style of programming. Wrapping queries into functions (when there is not any other) is bad.
why is an assign statement more efficient than not using assign?
co-workers say that:
assign
a=3
v=7
w=8.
is more efficient than:
a=3.
v=7.
w=8.
why?
You could always test it yourself and see... but, yes, it is slightly more efficient. Or it was the last time I tested it. The reason is that the compiler combines the statements and the resulting r-code is a bit smaller.
But efficiency is almost always a poor reason to do it. Saving a micro-second here and there pales next to avoiding disk IO or picking a more efficient algorithm. Good reasons:
Back in the dark ages there was a limit of 63k of r-code per program. Combining statements with ASSIGN was a way to reduce the size of r-code and stay under that limit (ok, that might not be a "good" reason). One additional way this helps is that you could also often avoid a DO ... END pair and further reduce r-code size.
When creating or updating a record the fields that are part of an index will be written back to the database as they are assigned (not at the end of the transaction) -- grouping all assignments into a single statement helps to avoid inconsistent dirty reads. Grouping the indexed fields into a single ASSIGN avoids writing the index entries multiple times. (This is probably the best reason to use ASSIGN.)
Readability -- you can argue that grouping consecutive assignments more clearly shows your intent and is thus more readable. (I like this reason but not everyone agrees.)
basically doing:
a=3.
v=7.
w=8.
is the same as:
assign a=3.
assign v=7.
assign w=8.
which is 3 separate statements so a little more overhead. Therefore less efficient.
Progress does assign as one statement whether there is 1 or more variables being assigned. If you do not say Assign then it is assumed so you will do 3 statements instead of 1. There is a 20% - 40% reduction in R Code and a 15% - 20% performance improvement when using one assign statement. Why this is can only be speculated on as I can not find any source with information on why this is. For database fields and especially key/index fields it makes perfect sense. For variables I can only assume it has to do with how progress manages its buffers and copies data to and from buffers.
ASSIGN will combine multiple statements into one. If a, v and w are fields in your db, that means it will do something like INSERT INTO (a,v,w)...
rather than
INSERT INTO (a)...
INSERT INTO (v)
etc.
I'm trying to precompute a user-defined function on a per row basis. The idea is I have JSON object as a text object in one of the fields, and I want to parse out some other 'fields' from it, which can be returned in queries just like any other true field. However, the overhead of parsing the JSON is significant. Is there any way to precompute this parsing function in a way that speeds up queries?
Please refrain from arguing that there shouldn't be JSON as text on the database in the first place; I am aware of the pros and cons.
First off, you may be interested in the upcoming JSON data type of PostgreSQL 9.2 (to be released soon, now).
As to your question, you are looking for a materialized view (or the simpler form: a redundant precomputed column in your table). "Materialized View" is just the established term, not a special object in a PostgreSQL database. Basically you create a redundant table with precomputed values, that you refresh at certain events or on a timely basis.
A search for the term will give you some answers.
In addition to a materialized view, keep in mind that PostgreSQL can also index functions' output so you can do something like:
CREATE INDEX my_foo_bar_udf_idx ON foo (bar(baz));
This works only if the UDF is marked as immutable meaning output only depends on arguments. This gives you an option to run your function against the query arguments and then scan the index instead of the table. It doesn't meet all use cases, but it does meet many of them and it can often save you the headaches of materializing views.
I'm having a scaling issue with an application that uses a PostgreSQL 9 backend. I have one table who's size is about 40 million records and growing and the conditional queries against it have slowed down dramatically.
To help figure out what's going wrong, I've taken a development snapshot of the database and dump the queries with the execution time into the log.
Now for the confusing part, and the gist of the question ....
The run times for my queries in the log are vastly different (an order of magnitude+) that what I get when I run the 'exact' same query in DbVisualizer to get the explain plan.
I say 'exact' but really the difference is, the application is using a prepared statement to which I bind values at runtime while the queries I run in DbVisualizer has those values in place already. The values themselves are exactly as I pulled them from the log.
Could the use of prepared statements make that big of a difference?
The answer is YES. Prepared statements cut both ways.
On the one hand, the query does not have to be re-planned for every execution, saving some overhead. This can make a difference or be hardly noticeable, depending on the complexity of the query.
On the other hand, with uneven data distribution, a one-size-fits-all query plan may be a bad choice. Called with particular values another query plan could be (much) better suited.
Running the query with parameter values in place can lead to a different query plan. More planning overhead, possibly a (much) better query plan.
Also consider unnamed prepared statements like #peufeu provided. Those re-plan the query considering parameters every time - and you still have safe parameter handling.
Similar considerations apply to queries inside PL/pgSQL functions, where queries can be treated as prepared statements internally - unless executed dynamically with EXECUTE. I quote the manual on Executing Dynamic Commands:
The important difference is that EXECUTE will re-plan the command on
each execution, generating a plan that is specific to the current
parameter values; whereas PL/pgSQL may otherwise create a generic plan
and cache it for re-use. In situations where the best plan depends
strongly on the parameter values, it can be helpful to use EXECUTE to
positively ensure that a generic plan is not selected.
Apart from that, general guidelines for performance optimization apply.
Erwin nails it, but let me add that the extended query protocol allows you to use more flavors of prepared statements. Besides avoiding re-parsing and re-planning, one big advantage of prepared statements is to send parameter values separately, which avoids escaping and parsing overhead, not to mention the opportunity for SQL injections and bugs if you don't use an API that handles parameters in a manner you can't forget to escape them.
http://www.postgresql.org/docs/9.1/static/protocol-flow.html
Query planning for named prepared-statement objects occurs when the
Parse message is processed. If a query will be repeatedly executed
with different parameters, it might be beneficial to send a single
Parse message containing a parameterized query, followed by multiple
Bind and Execute messages. This will avoid replanning the query on
each execution.
The unnamed prepared statement is likewise planned during Parse
processing if the Parse message defines no parameters. But if there
are parameters, query planning occurs every time Bind parameters are
supplied. This allows the planner to make use of the actual values of
the parameters provided by each Bind message, rather than use generic
estimates.
So, if your DB interface supports it, you can use unnamed prepared statements. It's a bit of a middle ground between a query and a usual prepared statement.
If you use PHP with PDO, please note that PDO's prepared statement implementation is rather useless for postgres, since it uses named prepared statements, but re-prepares every time you call prepare(), no plan caching takes place. So you get the worst of both : many roundtrips and plan without parameters. I've seen it be 1000x slower than pg_query() and pg_query_params() on specific queries where the postgres optimizer really needs to know the parameters to produce the optimal plan. pg_query uses raw queries, pg_query_params uses unnamed prepared statements. Usually one is faster than the other, that depends on the size of parameter data.