I have 2 very large (billions of rows) splayed tables, Trades and StockPrices, on a remote server. I want to do an asof join
h:hopen `:RemoteServer:Port
h"aj[`Stock`Date`Time,
select from Trades where Date within 2014.04.01 2014.04.13,
StockPrices
]"
But I just get the error (I'm Studio for KDB+)
An error occurred during execution of the query.
The server sent the response:
splay
Studio Hint: Possibly this error refers to nyi op on splayed table
So what would be the correct way to do such a join?
Also, performance and efficiency is an issue with such a big table -- what should I be doing to ensure the query doesn't take hours and doesn't consume to much of the server's system resources?
You need to map the splayed StockPrices table into memory. This can be done by using a select query:
q)(`::6060)"aj[`sym`time;select from trade;quote]" / bad
'splay
q)(`::6060)"aj[`sym`time;select from trade;select from quote]" / good
sym time prx bid ask
-------------------------------------------
aea 01:01:16.347 637.7554 866.0131 328.1476
aea 01:59:14.108 819.5301 115.053 208.1114
aea 02:42:44.724 69.38325 641.8554 333.3092
This page may be useful for looking up errors from Kdb+: http://code.kx.com/q/ref/error-list/
Regarding optimising performance of aj see http://code.kx.com/q/ref/joins/#aj-aj0-asof-join
Also, if there isn't an overlap of data between days, it may be faster to run the query on a day by day basis, possibly in parallel.
If there is an overlap of data across days, combining the date & time columns into a single timestamp column would speed up the lookup.
Related
While using Power BI for a few months now, we (the user group) encountered an issue that is not really clear to us.
We use Power-BI with a remote SQL-Server data source, we access the data source through direct query.
Let's pretend we have 2 Tables as below-
Table name: Issue
Column:
ResolutionTime(Date/Time)
IssueID(Unique Numbers)
Table Name: WorkItem
Column:
start (Date/Time)
end (Date/Time)
IssueID (Unique Numbers, Foreign Key to "Issue" table)
Table WorkItem also contain a calculated column "WorkTime" which uses this DAX-expression as below-
WorkTime = WorkItem[end] - WorkItem[start]
The two tables are configured through Power-Bi having a two-way 1:n relationship that can be queried to collect all "WorkItem"(s) assigned to an "Issue" entry, using the "IssueID" as correlation column.
To be able to compute the aggregated "work-time" for each "WorkItem", we use a new/calculated table with the following DAX expression to aggregate the total amount of time invested for a single "Issue":
SumWork =
SUMMARIZE(
WorkItem, WorkItem[IssueID], "All work per item", SUM(WorkItem[WorkTime])
)
The above table computes the total invested work-time for a particular issue, grouping/summarizing results based on the "IssueID" foreign key. This new calculated table is also configured to have a relationship with the "Issue" table, this time a "1:1" relationship, using the IssueID as correlation column.
Now to compute the time that the issue was worked on + the time for Resolution should be summarized in a calculated column inside "Issue", but this does not work:
ResolutionAndWorkTime = Issue[ResolutionTime] + SumWork["All work per item"]
But the above DAX expression fails to compile, as it always reports that it returns "more than one result", thus not being a singular result. But that is suprising, as the two table ("Issue" and "SumWork" are related to each other with a "1:1" relationship).
Tables:
Issues
IssueID ResolutionTime ResolutionAndWorkTime
1 03:20:20 ???
2 01:20:20 ???
3 00:20:20 ???
WorkItem
IssueID start end WorkTime
1 1-2-2020 3:20:20 1-2-2020 3:25:20 00:05:00
1 2-2-2020 6:20:20 2-2-2020 7:20:20 01:00:00
3 1-3-2020 3:20:20 1-3-2020 3:29:20 00:09:00
Any ideas what to look for? Data-types? Table-definition? Table-relationships? We checked other Stackoverflow questions/answers, but no good ideas retrieved so far.
NOTE that a lot of join/merge features of Power BI are not available if direct-query is used and thus joining the tables is not really an option (we think).
You need this following code for your new Calculated column.
Visit HERE To know more about RELATED.
ResolutionAndWorkTime = Issues[ResolutionTime] + RELATED(SumWork[All work per item])
Based on input provided by "mkRabbani" (see other answer) we investigated why "RELATED" does not function as expected. The problem originates in the access to the database. As suspected earlier the function delivers the expected results once the database access is switched to "import" instead of "direct-query".
As a workaround we now joins the data inside the SQL server by using traditional database views. Of course this only works for scenarios where the database is under control of the data analytics team.
I have a query, that I did not write, that takes 2.5 minutes to run. I am trying to optimize it without being able to modify the underlying tables, i.e. no new indexes can be added.
During my optimization troubleshooting I commented out a filter and all of a sudden my query ran in .5 seconds. I have screwed with the formatting and placing of that filter and if it is there the query takes 2.5 minutes, without it .5 seconds. The biggest problem is that the filter is not on the table that is being table-scanned (With over 300k records), it is on a table with 300 records.
The "Actual Execution Plan" of both the 0:0:0.5 vs 0:2:30 are identical down to the exact percentage costs of all steps:
Execution Plan
The only difference is that on the table-scanned table the "Actual Number of Rows" on the 2.5 min query shows 3.7 million rows. The table only has 300k rows. Where the .5 sec query shows Actual Number of Rows as 2,063. The filter is actually being placed on the FS_EDIPartner table that only has 300 rows.
With the filter I get the correct 51 records, but it takes 2.5 minutes to return. Without the filter I get duplication, so I get 2,796 rows, and only take half a second to return.
I cannot figure out why adding the filter to a table with 300 rows and a correct index is causing the Table scan of a different table to have such a significant difference in actual number of rows. I am even doing the "Table scan" table as a sub-query to filter its records down from 300k to 17k prior to doing the join. Here is the actual query in its current state, sorry the tables don't make a lot of sense, I could not reproduce this behavior in test data.
SELECT dbo.FS_ARInvoiceHeader.CustomerID
, dbo.FS_EDIPartner.PartnerID
, dbo.FS_ARInvoiceHeader.InvoiceNumber
, dbo.FS_ARInvoiceHeader.InvoiceDate
, dbo.FS_ARInvoiceHeader.InvoiceType
, dbo.FS_ARInvoiceHeader.CONumber
, dbo.FS_EDIPartner.InternalTransactionSetCode
, docs.DocumentName
, dbo.FS_ARInvoiceHeader.InvoiceStatus
FROM dbo.FS_ARInvoiceHeader
INNER JOIN dbo.FS_EDIPartner ON dbo.FS_ARInvoiceHeader.CustomerID = dbo.FS_EDIPartner.CustomerID
LEFT JOIN (Select DocumentName
FROM GentranDatabase.dbo.ZNW_Documents
WHERE DATEADD(SECOND,TimeCreated,'1970-1-1') > '2016-06-01'
AND TransactionSetID = '810') docs on dbo.FS_ARInvoiceHeader.InvoiceNumber = docs.DocumentName COLLATE Latin1_General_BIN
WHERE docs.DocumentName IS NULL
AND dbo.FS_ARInvoiceHeader.InvoiceType = 'I'
AND dbo.FS_ARInvoiceHeader.InvoiceStatus <> 'Y'
--AND (dbo.FS_EDIPartner.InternalTransactionSetCode = '810')
AND (NOT (dbo.FS_ARInvoiceHeader.CONumber LIKE 'CB%'))
AND (NOT (dbo.FS_ARInvoiceHeader.CONumber LIKE 'DM%'))
AND InvoiceDate > '2016-06-01'
The Commented out line in the Where statement is the culprit, uncommenting it causes the 2.5 minute run.
It could be that the table statistics may have gotten out of whack. These include the number of records tables have which is used to choose the best query plan. Try running this and running the query again:
EXEC sp_updatestats
Using #jeremy's comment as a guideline to point out the Actual Number of Rows was not my problem, but instead the number of executions, I figured out that the Hash Match was .5 seconds, the Nested loop was 2.5 minutes. Trying to force the Hash Match using Left HASH Join was inconsistent depending on what the other filters were set to, changing dates took it from .5 seconds, to 30 secs sometimes. So forcing the Hash (Which is highly discouraged anyway) wasn't a good solution. Finally I resorted to moving the poor performing view to a Stored Procedure and splitting out both of the tables that were related to the poor performance into Table Variables, then joining those table variables. This resulted in the most consistently good performance of getting the results. On average the SP returns in less than 1 second, which is far better than the 2.5 minutes it started at.
#Jeremy gets the credit, but since his wasn't an answer, I thought I would document what was actually done in case someone else stumbles across this later.
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.
In looking at the network in/out metrics for our AWS/EC2 instance, I would like to find the sources of the high network traffic occurrences.
I have installed up Log Parser Studio and run a few queries - primarily looking for responses that took a while:
SELECT TOP 10000 * FROM '[LOGFILEPATH]' WHERE time-taken > 1000
I am also targeting time spans that cover when the network in/out spikes have occurred:
SELECT TOP 20000 * FROM '[LOGFILEPATH]'
WHERE [date] BETWEEN TIMESTAMP('2013-10-20 02:44:00', 'yyyy-MM-dd hh:mm:ss')
AND TIMESTAMP('2013-10-20 02:46:00', 'yyyy-MM-dd hh:mm:ss')
One issue is that the log files are 2-7 gigs (targeting single files per query). In trying Log Parser Lizard, it crashed with an out of memory exception on large files (boo).
What are some other queries, and methodologies I should follow to identify the source of the high network traffic, which would hopefully help me figure out how to plug the hole?
Thanks.
One function that may be of particular use to you is the QUANTIZE() function. This allows you to aggregate stats for a period of time thus allowing you to see spikes in a given time period. Here is one query I use that allows me to see when we get scanned:
SELECT QUANTIZE(TO_LOCALTIME(TO_TIMESTAMP(date, time)), 900) AS LocalTime,
COUNT(*) AS Hits,
SUM(sc-bytes) AS TotalBytesSent,
DIV(MUL(1.0, SUM(time-taken)), Hits) AS LoadTime,
SQRROOT(SUB(DIV(MUL(1.0, SUM(SQR(time-taken))), Hits), SQR(LoadTime))) AS StandardDeviation
INTO '[OUTFILEPATH]'
FROM '[LOGFILEPATH]'
WHERE '[WHERECLAUSE]'
GROUP BY LocalTime
ORDER BY LocalTime
I usually output this to a .csv file and then chart in Excel to visually see where a period of time is out of normal range. This particular query breaks things down to 15 min segments based on the 900 passed to QUANTIZE. The TotalBytesSent, LoadTime and StandardDeviation allow me to see other aberrations in downloaded content or response times.
Another thing to look at is the number of requests a particular client has made to your site. The following query can help identify scanning or DoS activity coming in:
SELECT
DISTINCT c-ip as ClientIP,
COUNT(*) as Hits,
PROPCOUNT(*) as Percentage
INTO '[OUTFILEPATH]'
FROM '[LOGFILEPATH]'
WHERE '[WHERECLAUSE]'
GROUP BY ClientIP
HAVING (Hits > 50)
ORDER BY Percentage DESC
Adjusting the HAVING clause will set the minimum number of requests an IP will need to make before it shows up. Based on the activity and the WHERE clause, 50 may be too low. The PROPCOUNT() function gives a percentage of the overall value of a particular field. In this case, it gives the what percent a particular IP of all the requests made to the site. Typically this will surface the IP addresses of search engines as well, but those are pretty easy to weed out.
I hope that gives you some ideas on what you can do.
I just tried the following query on YouTube:
http://www.youtube.com/results?search_query=test&search=tag&page=100
and received the error message:
Sorry, YouTube does not serve more than 1000 results for any query.
(You asked for results starting from 2000.)
I also tried Google search for "test", and although it said there were about 3.44 billion results, I was only able to get to page 82 (or about 820 results).
This leads me to wonder, does performance start to degrade with paginated searches after N records (specifically wondering about with ROW_NUMBER() in SQL Server or similar feature in other DB systems), or are YouTube/Google doing this for other reasons? Granted, it's pretty unlikely that most people would need to go past the first 1000 results for a query, but I would imagine the limitation is specifically put in place for some technical reason.
Then again Stack Overflow lets you page through 47k results: https://stackoverflow.com/questions/tagged/c?page=955&sort=newest&pagesize=50
Yes. High offsets are slow and inefficient.
The only way to find the records at an offset, is to compute all records that came before and then discard them.
(I dont know ROW_NUMBER(), but would be LIMIT in standard SQL. So
SELECT * FROM table LIMIT 1999,20
)
.. in the above example, the first 2000 records have to be fetched first, and then discarded. Generally it can't skip ahead, or use indexes to jump right to the correct location in the data, because normally there would be a 'WHERE' clause filtering the results.
It is possible to cache the results, which is probably what SO does. So it doesn't actually have to compute the large offsets each and every time. (Most of SO's searches are a 'small' set of known tags, so its quite feasible to cache. A arbitrary search query is will have much versions to catch, making it impractical)
(Alternatively it might be using some other implementation that does allow arbitrary offsets)
Other places taking about similar things
http://sphinxsearch.com/docs/current.html#conf-max-matches
Back of the envolope test:
mysql> select gridimage_id from gridimage_search where moderation_status = "geograph" order by imagetaken limit 100999,3;
...
3 rows in set (11.32 sec)
mysql> select gridimage_id from gridimage_search where moderation_status = "geograph" order by imagetaken limit 3;
...
3 rows in set (4.59 sec)
(Arbitrary query choosen so as not to use indexes very well, if indexes can be used the difference is less pronounced and harder to see. But in a production system running lots of queries, 1 or 2ms difference is huge)
Update: (to show a indexed query)
mysql> select gridimage_id from gridimage_search order by imagetaken limit 10;
...
10 rows in set (0.00 sec)
mysql> select gridimage_id from gridimage_search order by imagetaken limit 100000,10;
...
10 rows in set (1.70 sec)
It's a TOP clause designed to limit the amount of physical reads that the database has to perform, which limits the amount of time that the query takes. Imagine you have 82 billion links to stories about "Japan" in your database. What if someone queries "Japan"? Are all 82 billion results really going to be clicked? No. The user needs the top 1000 most relevant results. When the search is generic, like "test", there is no way to determine relevance. In this case, YouTube/Google has to limit the volume returned so other users aren't affected by generic searches. What's faster, returning 1,000 results or 82,000,000,000 results?