I was trying to decrease the cost of query execution by creating an index on the rating column. The table has 2680 tuples
SELECT * from cup_matches WHERE rating*3 > 20
However when i used pgAdmin to view the query cost before and after indexing, it remained the same. I thought it would decrease as the processes of indexing should decrease the cost of data being taken from the hardisk, due to indexing (reducing I/O cost), to the memory. Can someone tell me why did it stay the same?
The cost did not diminish because you are doing a mutation operation within the where so it cannot use the index. removing the "*3" operation should do the trick.
SELECT * from cup_matches WHERE rating > 20
Should have the performance increase, because you are no longer mutating the rating value. When values are mutated you need to do a complete table scan in order to do comparisons.
because the index is on rating and not on rating*3. To use your current index, try
SELECT * from cup_matches WHERE rating > 20/3
Related
I have read from different articles saying cursor pagination query has time complexity O(1) or O(limit) where limit is the number of item limit in sql. Some example article source:
https://uxdesign.cc/why-facebook-says-cursor-pagination-is-the-greatest-d6b98d86b6c0 and
https://dev.to/jackmarchant/offset-and-cursor-pagination-explained-b89
But I canont find related references explaining why the time complexity is O(limit). Say I have a table consist of 3 columns
id, name, created_at, where id is primary key,
if I use created_at as the cursor (which is unique and sequential), can someone explain why the time complexity is O(limit)？
Is it related to data structure used to store created_at?
After some reading, I guess the time complexity is talking about after retrieving the intermediate records, the time complexity of getting the final required records.
For offset case, all records will be selected, then database will discard x records where x is the offset, finally select y records (where y = limit), so the time complexity is O(offset + limit).
For cursor case, records matched the cursor where condition will be selected, then select y records (where y = limit), so the time complexity is O(limit).
I have a tricky problem trying to find an efficient way of ordering a set of objects (~1000 rows) that contain a large (~5 million) number of indexed data points. In my case I need a query that allows me to order the table by a specific datapoint. Each datapoint is a 16-bit unsigned integer.
I am currently solving this problem by using an large array:
Object Table:
id serial NOT NULL,
category_id integer,
description text,
name character varying(255),
created_at timestamp without time zone NOT NULL,
updated_at timestamp without time zone NOT NULL,
data integer[],
GIST index:
CREATE INDEX object_rdtree_idx
ON object
USING gist
(data gist__intbig_ops)
This index is not currently being used when I do a select query, and I am not certain it would help anyway.
Each day the array field is updated with a new set of ~5 million values
I have a webserver that needs to list all objects ordered by the value of a particular data point:
Example Query:
SELECT name, data[3916863] as weight FROM object ORDER BY weight DESC
Currently, it takes about 2.5 Seconds to perform this query.
Question:
Is there a better approach? I am happy for the insertion side to be slow as it happens in the background, but I need the select query to be as fast as possible. In saying this, there is a limit to how long the insertion can take.
I have considered creating a lookup table where every value has it's own row - but I'm not sure how the insertion/lookup time would be affected by this approach and I suspect entering 1000+ records with ~5 million data points as individual rows would be too slow.
Currently inserting a row takes ~30 seconds which is acceptable for now.
Ultimately I am still on the hunt for a scalable solution to the base problem, but for now I need this solution to work, so this solution doesn't need to scale up any further.
Update:
I was wrong to dismiss having a giant table instead of an array, while insertion time massively increased, query time is reduced to just a few milliseconds.
I am now altering my generation algorithm to only save a datum if it non-zero and changed from previous update. This has reduced insertions to just a few hundred thousands values which only takes a few seconds.
New Table:
CREATE TABLE data
(
object_id integer,
data_index integer,
value integer,
)
CREATE INDEX index_data_on_data_index
ON data
USING btree
("data_index");
New Query:
SELECT name, coalesce(value,0) as weight FROM objects LEFT OUTER JOIN data on data.object_id = objects.id AND data_index = 7731363 ORDER BY weight DESC
Insertion Time: 15,000 records/second
Query Time: 17ms
First of all, do you really need a relational database for this? You do not seem to be relating some data to some other data. You might be much better off with a flat-file format.
Secondly, your index on data is useless for the query you showed. You are querying for a datum (a position in your array) while the index is built on the values in the array. Dropping the index will make the inserts considerably faster.
If you have to stay with PostgreSQL for other reasons (bigger data model, MVCC, security) then I suggest you change your data model and ALTER COLUMN data SET TYPE bytea STORAGE external. Since the data column is about 4 x 5 million = 20MB it will be stored out-of-line anyway, but if you explicitly set it, then you know exactly what you have.
Then create a custom function in C that fetches your data value "directly" using the PG_GETARG_BYTEA_P_SLICE() macro and that would look somewhat like this (I am not a very accomplished PG C programmer so forgive me any errors, but this should help you on your way):
// Function get_data_value() -- Get a 4-byte value from a bytea
// Arg 0: bytea* The data
// Arg 1: int32 The position of the element in the data, 1-based
PG_FUNCTION_INFO_V1(get_data_value);
Datum
get_data_value(PG_FUNCTION_ARGS)
{
int32 element = PG_GETARG_INT32_P(1) - 1; // second argument, make 0-based
bytea *data = PG_GETARG_BYTEA_P_SLICE(0, // first argument
element * sizeof(int32), // offset into data
sizeof(int32)); // get just the required 4 bytes
PG_RETURN_INT32_P((int32*)data);
}
The PG_GETARG_BYTEA_P_SLICE() macro retrieves only a slice of data from the disk and is therefore very efficient.
There are some samples of creating custom C functions in the docs.
Your query now becomes:
SELECT name, get_data_value(data, 3916863) AS weight FROM object ORDER BY weight DESC;
I am currently just pulling in all records 1min leading up to the timestamp (e.g. if the timestamp I'm interested in is 2014.04.14T09:30):
select from Prices where timestamp within 2014.04.14T09:29 2014.04.14T09:30, stock=`GOOG
However, this is clearly not very robust. Sometimes the previous record may be at 09:25am and then the query returns nothing. Sometimes the query may return hundreds of records if there have been a lot of price changes, even though all I need is the last record returned.
I know this can be done with an asof join, but want to avoid it for the time being as Prices is simply too big at present.
I am also interested in doing the same, but in finding the first record after a given timestamp.
Note also that Prices is a splayed table
Select last record before the given timestamp:
q)select from Price where stock=`GOOG,i=last i,timestamp<2014.04.14T09:30
Select first record after the given timestamp:
q)select from Price where stock=`GOOG,i=first i,timestamp>2014.04.14T09:30
Use asof or aj to get the performance kdb+ is known for. The bigger Prices is, the more reason for doing so.
I would question your logic for avoiding aj. aj and asof use the bin operator which is binary search and hence more performant than scanning the timestamp column.
Let's create your table and run the solution from the other answer:
Prices:([]stock:`g#1000000?`GOOG,9?`4;timestamp:asc 2014.04.14+1000000?0t;price:1000000?100f,size:1000000?100j)
q)\t do[1000;select from Prices where timestamp<2014.04.14T09:30,stock=`GOOG,i=last i]
10205
We can make this a lot better by reordering the constraints:
q)\t do[1000;select from Prices where stock=`GOOG,timestamp<2014.04.14T09:30,i=last i]
2030
But nothing will beat this:
q)\t do[1000;Prices asof `stock`timestamp!(`GOOG;2014.04.14D09:30)]
9
By the way, you are using datetime in your question, which is deprecated, so I've replaced it with timestamp. This has no impact on performance.
Few more things to remember while using aj:
in-memory prices - the table should be `g#sym and time sorted within sym
on-disk prices - `p#sym and time sorted within sym
Also in case of partitioned/splayed tables, using the where constraints (except the date in the date-partitioned table) can severely impact the performance.
from some reasons I need to insert an artificial(dummy) column into a mdx expression. (the reason is that i need to obtain a query with specific number of columns )
to ilustrate, this is my sample query:
SELECT {[Measures].[AFR],[Measures].[IB],[Measures].[IC All],[Measures].[IC_without_material],[Measures].[Nonconformance_PO],[Measures].[Nonconformance_GPT],[Measures].[PM_GPT_Weighted_Targets],[Measures].[PM_PO_Weighted_Targets], [Measures].[AVG_LC_Costs],[Measures].[AVG_MC_Costs]} ON COLUMNS,
([dim_ProductModel].[PLA].&[SME])
* ORDER( {([dim_ProductModel].[Warranty Group].children)} , ([Measures].[Nonconformance_GPT],[Dim_Date].[Date Full].&[2014-01-01]) ,desc)
* ([dim_ProductModel].[PLA Text].members - [dim_ProductModel].[PLA Text].[All])
* {[Dim_Date].[Date Full].&[2013-01-01]:[Dim_Date].[Date Full].&[2014-01-01]} ON ROWS
FROM [cub_dashboard_spares]
it is not very important, just some measures and crossjoined dimensions. Now I would need to add f.e. 2 extra columns, I don't care whether this would be a measure with null/0 values or another crossjoined dimension. Can I do this in some easy way without inserting any data into my cube?
In sql I can just write Select 0 or select "dummy1", but here it is not possible neither in ON ROWS nor in ON COLUMNS part of the query.
Thank you very much for your help,
Regards,
Peter
ps: so far I could just insert some measure more times, but I am interested whether there is a possibility to insert really "dummy" column
Your query just has the measures dimension on columns. The easiest way to extend it by some columns would be to repeat the last measure as many times that you get the correct number of columns.
Another possibility, which may be more efficient in case the last measure is complex to calculate would be to use
WITH member Measures.dummy as NULL
SELECT {[Measures].[AFR],[Measures].[IB],[Measures].[IC All],[Measures].[IC_without_material],[Measures].[Nonconformance_PO],[Measures].[Nonconformance_GPT],[Measures].[PM_GPT_Weighted_Targets],[Measures].[PM_PO_Weighted_Targets], [Measures].[AVG_LC_Costs],[Measures].[AVG_MC_Costs],
Measures.dummy, Measures.dummy, Measures.dummy
}
ON COLUMNS,
([dim_ProductModel].[PLA].&[SME])
* ORDER( {([dim_ProductModel].[Warranty Group].children)} , ([Measures].[Nonconformance_GPT],[Dim_Date].[Date Full].&[2014-01-01]) ,desc)
* ([dim_ProductModel].[PLA Text].members - [dim_ProductModel].[PLA Text].[All])
* {[Dim_Date].[Date Full].&[2013-01-01]:[Dim_Date].[Date Full].&[2014-01-01]}
ON ROWS
FROM [cub_dashboard_spares]
i. e. adding a dummy measure that should not need much computation as many times as you need it to the end of the columns.
I have a table mytable with some columns including the column datekey (which is a date and has an index), a column contents which is a varbinary(max), and a column stringhash which is a varchar(100). The stringhash and the datekey together form the primary key of the table. Everything is running on my local machine.
Running
SELECT TOP 1 * FROM mytable where datekey='2012-12-05'
returns 0 rows and takes 0 seconds.
But if I add a datalength condition:
SELECT TOP 1 * FROM mytable where datekey='2012-12-05' and datalength(contents)=0
it runs for a very long time and does not return anything before I give up waiting.
My question:
Why? How do I find out why this takes such a long time?
Here is what I checked so far:
When I click "Display estimated execution plan" it also takes a very long time and does not return anything before I give up waiting.
If I do
SELECT TOP 1000 datalength(contents) FROM mytable order by datalength(contents) desc
it takes 7 seconds and returns a list 4228081, 4218689 etc.
exec sp_spaceused 'mytable'
returns
rows reserved data index_size unused
564019 50755752 KB 50705672 KB 42928 KB 7152 KB
So the table is quite large at 50 GB.
Running
SELECT TOP 1000 * FROM mytable
takes 26 seconds.
The sqlservr.exe process is around 6 GB which is the limit I have set for the database.
It takes a long time because your query needs DATALENGTH to be evaluated for every row and then the results sorted before it can return the 1st record.
If the DATALENGTH of the field (or whether it contains any value) is something you're likely to query repeatedly, I would suggest an additional indexed field (perhaps a persisted computed field) holding the result, and searching on that.
This old msdn blog post seems to agree with #MartW answer that datalength is evaluated for every row. But it's good to understand what is really meant by "evaluated" and what is the real root of the performance degradation.
As mentioned in the question, the size of every value in the column contents may be large. It means that every value bigger than ~8Kb is stored in special LOB-storage. So, taking into account the size of the other columns, it's clear that most of the space occupied by the table is taken by this LOB-storage, i.e. it's around 50Gb.
Even if the length of contents column for every row has been already evaluated, which is proved in post linked above, it's still stored in LOB. So engine still needs to read some parts of the LOB-storage to execute the query.
If LOB-storage isn't in RAM at the time of a query execution then we need to read it from a disk, which is of course much slower than from RAM. Also possibly the read of LOB-parts is rather randomized than linear which is even more slow as it tends to raise the whole number of memory-blocks needed to be read from a disk.
At the moment it probably won't be using the primary key because of the stringhash column included before the datekey column. Try adding an additional index that just contains the datekey column. Once that key is created if it's still slow you could also try a query hint such as:
SELECT TOP 1 * FROM mytable where datekey='2012-12-05' and datalength(contents)=0 WITH INDEX = IX_datekey
You could also create a seperate length column that's updated either in your application or in an insert / update trigger.