Query fast with literal but slow with variable - sql

I am using Typeorm for SQL Server in my application. When I pass the native query like connection.query(select * from user where id = 1), the performance is really good and it less that 0.5 seconds.
If we use the findone or QueryBuilder method, it is taking around 5 seconds to get a response.
On further debugging, we found that passing value directly to query like this,
return getConnection("vehicle").createQueryBuilder()
.select("vehicle")
.from(Vehicle, "vehicle")
.where("vehicle.id='" + id + "'").getOne();
is faster than
return getConnection("vehicle").createQueryBuilder()
.select("vehicle")
.from(Vehicle, "vehicle")
.where("vehicle.id =:id", {id:id}).getOne();
Is there any optimization we can do to fix the issue with parameterized query?

I don't know Typeorm but it seems to me clear the difference. In one case you query the database for the whole table and filter it locally and in the other you send the filter to the database and it filters the data before it sends it back to the client.
Depending on the size of the table this has a big impact. Consider picking one record from 10 million. Just the time to transfer the data to the local machine is 10 million times slower.

Related

SQL Query performance - UI responsiveness concerns

I am running a query on localhost, I am extremely unfamiliar with SQL. I am using a golang library to generate the query statement. This is for an enterprise app so I don't have time to evaluate and code all possible performance cases. I'd prefer good performance for the largest possible queries:
upto 6 query parameters eg. BETWEEN 'created' AND 'abandoned', BETWEEN X AND Y, IN (1,2,3.....25), IN ('A', 'B', 'C'....'Z')
JOINs/subqueries between a 2-5 tables
returning between 50K-5M records (LAT and LNG)
Currently I am using JOIN to find the lat, lng for a record, and some query parameters. Should I join differently, (left, right)? Should the FROM table be the record or the relation? Subqueries?
Is this query performance reasonable from a UI perspective? This is on localhost (docker) on a fairly low performance laptop, under WSL (16GB RAM / 6 core CPU [2.2GHz]).
-- [2547.438ms] [rows:874731]
SELECT "Longitude","Latitude"
FROM Wells
JOIN Well_Reports ON Well_Reports.Well_ID = Wells.Well_ID
JOIN Lithologies on Lithologies.Well_Report_ID = Well_Reports.Well_Report_ID
where Lithologies.Colour IN
(
'NULL',
'Gray','White','Dark','Black','Dark Gray','Dark Brown','Dark Red','Dark Blue',
'Dark Green','Dark Yellow','Bluish Green','Brownish Gray','Brownish Green','Brownish Yellow',
'Light','Light Gray','Light Red','Greenish Gray','Light Yellow','Light Green','Light Blue',
'Light Brown','Blue Gray','Greenish Yellow','Greenish Gray'
);
The UI is a heatmap. I haven't really hit performance issues returning 1-million rows.
Angular is the framework. Im breaking the HTTP response into 10K record chunks
My initial impression was 3+ seconds is too long for a UI to start populating data. I was already breaking the response to the UI into chunks, that portion was efficient and async. It never occurred to me to simply break the SQL requests into smaller chunks with LIMIT and OFFSET, so the server can start responding with data immediately (<200ms) even if it takes +5s to completely finish loading.
I'll write an answer to this effect.
Thanks and best regards,
schmorrison
A few things.
where someColumn in (null, ...)
This will not return rows where the value of someColumn is null because, x in (a, b, c ...) translates to x = a or x = b or x = c, and null is not equal to null.
You need a construct like this instead
where someColumn is null or someColumn in (...)
Second, you mentioned you're returning between 50k and 5M rows to the UI. I question the sanity of this... how is the UI rendering 5 million sets of coordinates for the user to see/use? I suppose there could be some extreme edge cases where this is really what you need to do, but it just seems really unlikely.
Third, if your concern is UI responsiveness, the proper way to handle that is to make asynchronous requests. I don't know anything about golang, but I did find this page with a quick google search. Study up on that kind of technique.
Finally, if you really do need to work with data sets this big, the critical point will be to identify the common search criteria and talk to your DBA about appropriate indexes. We can't provide much help in this regard without a lot more schema information, but if you have a specific query that is taking a long time with a particular set of parameters, you can come back and create a question for that query, along with providing the query plan, and we can help you out.
As pointed out by The Impaler,
"If you want good performance for dynamic queries that return 5
million rows, then you'll need to learn the intricacies of database
engines, not only SQL. A high level knowledge unfortunately won't cut
it."
and I expected as much.
I was simply querying the SQL DB for the whole set at once, this is wrong and I know that.
The changes I made generates the following statements (I know the specifics have changed):
-- [145.955ms] [rows:1]
SELECT count(*)
FROM "tblWellLogs"
WHERE (
DateWellCompleted BETWEEN
'1800-01-01 06:58:00.000' AND '2021-09-12 06:00:00.000'
)
AND "FinalStatusOfWellL" NOT IN
(
5,6,7,8,9,16,27,36
)
AND WaterUseL IN
(
1,29,3,8,26,4,6
)
AND (
wyRate BETWEEN
0 AND 3321
)
AND (
TotalOrFinishedDepth BETWEEN
0 AND 248
);
Calculated total(69977) chunk (17494) currentpage(3)
-- [117.195ms] [rows:17494]
SELECT "Easting","Northing"
FROM "tblWellLogs"
WHERE (
DateWellCompleted BETWEEN
'1800-01-01 06:58:00.000' AND '2021-09-12 06:00:00.000'
)
AND "FinalStatusOfWellL" NOT IN
(
5,6,7,8,9,16,27,36
)
AND WaterUseL IN
(
1,29,3,8,26,4,6
)
AND (
wyRate BETWEEN
0 AND 3321
)
AND (
TotalOrFinishedDepth BETWEEN
0 AND 248
)
ORDER BY (SELECT NULL)
OFFSET 34988 ROWS
FETCH NEXT 17494 ROWS ONLY;
Chunk count: 17494
JSON size: 385135
Request time: 278.6612ms
The response to the UI is as so:
GET /data/wells/xxxx/?page=3&completed.start=1800-01-01T06:58:36.000Z&completed.end=2021-09-12T06:00:00.000Z&status=supply,research,geothermal,other,unknown&use=domestic,commercial,industrial,municipal,irrigation,agriculture&depth.start=0&depth.end=248&rate.start=0&rate.end=3321&page=3 HTTP/1.1 200 385135
{
"data":[[0.0, 0.0],......],
"count": 87473,
"total": 874731,
"pages": 11,
"page": 1
}
In this case, the DBs are essentially immutable (I may get an updated dataset once per year). I figure I can predefine a set of chunk size variations for each DB based on query result size rather than just DB size. I am also retrieving the next page before the client requests it. I am caching requests at the browser and client layers. I only perform the Count(*) request if the client doesn't provide it, and it isn't in the cache.
I did find that running the request concurrently simply over-burdened my CPU, all the requests returned almost simultaneously but took +5s each rather than ~1second each.

Is there a way to check if a query hit a cached result or not in BigQuery?

We are performance tuning, both in terms of cost and speed, some of our queries and the results we get are a little bit weird. First, we had one query that did an overwrite on an existing table, we stopped that one after 4 hours. Running the same query to an entirely new table and it only took 5 minutes. I wonder if the 5 minute query maybe used a cached result from the first run, is that possible to check? Is it possible to force BigQuery to not use cache?
If you run query in UI - expand Options and make sure Use Cached Result properly set
Also, in UI, you can check Job Details to see if cached result was used
If you run your query programmatically - you should use respective attributes - configuration.query.useQueryCache and statistics.query.cacheHit

how to handle query execution time (performance issue ) in oracle

I have situation need to execute patch script for million row of data.The current query execution time is not meet the expectation for few rows (18000) which is take around 4 hours( testing data before deploy for live ).
The patch script actually select million row of data in loop and update according to the specification , im just wonder how long it could take for million row of data since it take around 4 hour for just 18000 rows.
to overcome this problem im decided to create temp table hold the entire select statement data and proceed with the patch process using the temp table where the process could be bit faster compare select and update.
is there any other ways i can use to handle this situation ? Any suggestion and ways to solve this.
(Due to company policy im unable to post the PL/SQl script here )
seems there is no one can answer my question here i post my answer.
In oracle there is Parallel Execution which is allows spreading the processing of a single SQL statement across multiple threads.
So by using this method i solved my long running query (4 hours ) to 6 minz ..
For more information :
https://docs.oracle.com/cd/E11882_01/server.112/e25523/parallel002.htm
http://www.oracle.com/technetwork/articles/database-performance/geist-parallel-execution-1-1872400.html

SQL - When data are transfered

i need to get a large amount of data from a remote database. the idea is do a sort of pagination, like this
1 Select a first block of datas
SELECT * FROM TABLE LIMIT 1,10000
2 Process that block
while(mysql_fetch_array()...){
//do something
}
3 Get next block
and so on.
Assuming 10000 is an allowable dimension for my system, let us suppose i have 30000 records to get: i perform 3 call to remote system.
But my question is: when executing a select, the resultset is transmitted and than stored in some local part with the result that fetch is local, or result set is stored in remote system and records coming one by one at any fetch? Because if the real scenario is the second i don't perform 3 call, but 30000 call, and is not what i want.
I hope I explained, thanks for help
bye
First, it's highly recommended to utilize MySQLi or PDO instead of the deprecated mysql_* functions
http://php.net/manual/en/mysqlinfo.api.choosing.php
By default with the mysql and mysqli extensions, the entire result set is loaded into PHP's memory when executing the query, but this can be changed to load results on demand as rows are retrieved if needed or desired.
mysql
mysql_query() buffers the entire result set in PHP's memory
mysql_unbuffered_query() only retrieves data from the database as rows are requested
mysqli
mysqli::query()
The $resultmode parameter determines behaviour.
The default value of MYSQLI_STORE_RESULT causes the entire result set to be transfered to PHP's memory, but using MYSQLI_USE_RESULT will cause the rows to be retrieved as requested.
PDO by default will load data as needed when using PDO::query() or PDO::prepare() to execute the query and retrieving results with PDO::fetch().
To retrieve all data from the result set into a PHP array, you can use PDO::fetchAll()
Prepared statements can also use the PDO::MYSQL_ATTR_USE_BUFFERED_QUERY constant, though PDO::fetchALL() is recommended.
It's probably best to stick with the default behaviour and benchmark any changes to determine if they actually have any positive results; the overhead of transferring results individually may be minor, and other factors may be more important in determining the optimal method.
You would be performing 3 calls, not 30.000. That's for sure.
Each 10.000 results batch is rendered on the server (by performing each of the 3 queries). Your while iterates through a set of data that has already been returned by MySQL (that's why you don't have 30.000 queries).
That is assuming you would have something like this:
$res = mysql_query(...);
while ($row = mysql_fetch_array($res)) {
//do something with $row
}
Anything you do inside the while loop by making use of $row has to do with already-fetched data from your initial query.
Hope this answers your question.
according to the documentation here all the data is fetched to the server, then you go through it.
from the page:
Returns an array of strings that corresponds to the fetched row, or FALSE if there are no more rows.
In addition it seams this is deprecated so you might want to use something else that is suggested there.

Need for long and dynamic select query/view sqlite

I have a need to generate a long select query of potentially thousands of where conditions like (table1.a = ? OR table1.a = ? OR ...) AND (table2.b = ? OR table2.b = ? ...) AND....
I initially started building a class to make this more bearable, but have since stopped to wonder if this will work well. This query is going to be hammering a table of potentially 10s of millions of rows joined with 2 more tables with thousands of rows.
A number of concerns are stemming from this:
1.) I wanted to use these statements to generate a temp view so I could easily transfer over existing code base, the point here is I want to filter data that I have down for analysis based on selected parameters in a GUI, so how poorly will a view do in this scenario?
2.) Can sqlite even parse a query with thousands of binds?
3.) Isn't there a framework that can make generating this query easier other than with string concatenation?
4.) Is the better solution to dump all of the WHERE variables into hash sets in memory and then just write a wrapper for my DB query object that gets next() until a query is encountered this satisfies all my conditions? My concern here is, the application generates graphs procedurally on scrolls, so waiting to draw while calling query.next() x 100,000 might cause an annoying delay? Ideally I don't want to have to wait on the next row that satisfies everything for more than 30ms at a time.
edit:
New issue, it came to my attention that sqlite3 is limited to 999 bind values(host parameters) at compile time.
So it seems as if the only way to accomplish what I had originally intended is to
1.) Generate the entire query via string concatenations(my biggest concern being, I don't know how slow parsing all the data inside sqlite3 will be)
or
2.) Do the blanket query method(select * from * where index > ? limit ?) and call next() until I hit what valid data in my compiled code(including updating index variable and re-querying repeatedly)
I did end up writing a wrapper around the QSqlQuery object that will walk a table using index > variable and limit to allow "walking" the table.
Consider dumping the joined results without filters (denormalized) into a flat file and index it with Fastbit, a bitmap index engine.