I have a database containing records collected every 0.1 seconds, and I need to time-average the data from a given day to once every 20 minutes. So I need to return a day's worth of data averaged to every 20 minutes which is 24*3 values.
Currently I do a separate AVG call to the database for each 20-minute period within the day, which is 24*3 calls. My connection to the database seems a little slow (it is remote) and it takes ~5 minutes to do all the averages. Would it be faster to do a single query in which I access the entire day's worth of data then average it to every 20 minutes? If it helps to answer the question, I have to do some arithmetic to the data before averaging, namely multiplying several table columns.
You can calculate the number of minutes since midnight like:
datepart(hh,datecolumn)*60 + datepart(mi,datecolumn)
If you divide that by 20, you get the number of the 20 minute interval. For example, 00:10 would fall in interval 0, 00:30 in interval 1, and 15:30 in interval 46, and so on. With this formula, you can group on 20 minute intervals like:
select
(datepart(hh,datecolumn)*60 + datepart(mi,datecolumn)) / 20 as IntervalNr
, avg(value)
from YourTable
group by (datepart(hh,datecolumn)*60 + datepart(mi,datecolumn)) / 20
You can do math inside the avg call, like:
avg(col1 * col2 - col3 / col4)
In general reducing the number of queries is a good idea. Aggregate and do whatever arithmetic/filtering/grouping you can in the query (i.e. in the database), and then do 'iterative' computations on the server side (e.g. in PHP).
To be sure whether it would be faster or not, it should be measured.
However it should be faster, as you have a slow connection to the database, and this way the number of roundtrips has a bigger impact on the total time of execution.
How about a stored procedure on your database? If your database engine doesn't support one, how about having a script or something doing the math and populating a separate 'averages' table on your database server. Then you only have to read the averages from the remote client once a day only.
Computation in one single query would be slightly faster. Think of the overhead on multiple requests like setting up the connection, parsing the query or loading the stored procedure, etc.
But also make sure that you've accurate indicies which may result in a hugh performance increase. Some operations on hugh databases may last from minutes to hours.
If you are sending a lot of data, and the connection is the bottleneck, how and when you group and send the data doesn't matter. There is no good way to send 100MB every 10 minutes over a 56k modem. Figure out the size of your data and bandwidth and be sure you can even send it.
That said:
First be certain the network is the bottleneck. If so, try to work with a smaller data set if possible, and test different scenarios. In general, 1 large record set will use less bandwidth than 2 recordsets that are half the size.
If possible add columns to your table and compute and store the column product and interval index (see Andomar's post) every time you post data to the database.
Related
I have tried with below SQL query.
SELECT
sql_id,
child_number,
sql_fulltext,
elapsed_time,
executions,
round(elapsed_time_avg) elapsed_time_avg
FROM
(
SELECT
command_type,
sql_id,
child_number,
sql_fulltext,
elapsed_time,
cpu_time,
disk_reads,
executions,
( elapsed_time / executions ) elapsed_time_avg
FROM
v$sql
WHERE
executions > 0
order by elapsed_time_avg desc
)
where rownum <=10;
I expect all the time top 10 expensive query from the database. my query fetched but after some time change the SQL_id (results change) with a same SQL query.
Your approach is correct. (Wowever, I suggest sorting by ELAPSED_TIME instead of an average, since it's the total run time that matters most. A million fast queries can be worst than one slow query.) But you just have to keep in mind that queries will disappear from V$SQL as they age out of the shared pool. And it's hard to predict exactly how long something will stay in the shared pool.
You might want to look at the active session history, in V$ACTIVE_SESSION_HISTORY, which usually stores many hours worth of data. And then look at DBA_HIST_ACTIVE_SESS_HISTORY, which stores 8 days of data by default. You'll have to adjust your queries, since those two views don't store sums, they store a row for each wait. You'll need to count the number of rows per SQL_ID to find the estimated wait time. (V$ACTIVE_SESSION_HISTORY samples once per second, DBA_HIST_ACTIVE_SESS_HISTORY samples once every 10 seconds.)
One of the most important thing to realize about tuning SQL is that you're not looking for perfection. You don't want to trace every single statement, or you'll go crazy. If you sample the system every X seconds, and a statement doesn't show up, then you almost certainly don't care about that statement. It's fine if slow statements disappear from the top N list.
Problem: I am developing a reporting engine that displays data about how many bees a farm detected (Bees is just an example here)
I have 100 devices that each minute count how many bees were detected on the farm. Here is how the DB looks like:
So there can be hundreds of thousands of rows in a given week.
The farmer wants a report that shows for a given day how many bees came each hour. I developed two ways to do this:
The server takes all 100,000 rows for that day from the DB and filters it down. The server uses a large amount of memory to do this and I feel this is a brute force solution
I have a Stored Procedure that returns a temporarily created table, with every hour the amount of bees collected for each device totaled. The server takes this table and doesn't need to process 100,000 rows.
This return (24 * 100) rows. However it takes much longer than I expected to do this ~
What are some good candidate solutions for developing a solution that can consolidate and sum this data without taking 30 seconds just to sum a day of data (where I may need a months worth divided between days)?
If performance is your primary concern here, there's probably quite a bit you can do directly on the database. I would try indexing the table on time_collected_bees so it can filter down to 100K lines faster. I would guess that that's where your slowdown is happening, if the database is scanning the whole table to find the relevant entries.
If you're using SQL Server, you can try looking at your execution plan to see what's actually slowing things down.
Give database optimization more of a look before you architect something really complex and hard to maintain.
TL;DR
I have a table with about 2 million WRITEs over the month and 0 READs. Every 1st day of a month, I need to read all the rows written on the previous month and generate CSVs + statistics.
How to work with DynamoDB in this scenario? How to choose the READ throughput capacity?
Long description
I have an application that logs client requests. It has about 200 clients. The clients need to receive on every 1st day of a month a CSV with all the requests they've made. They also need to be billed, and for that we need to calculate some stats with the requests they've made, grouping by type of request.
So in the end of the month, a client receives a report like:
I've already come to two solutions, but I'm not still convinced on any of them.
1st solution: ok, every last day of the month I increase the READ throughput capacity and then I run a map reduce job. When the job is done, I decrease the capacity back to the original value.
Cons: not fully automated, risk of the DynamoDB capacity not being available when the job starts.
2nd solution: I can break the generation of CSVs + statistics to small jobs in a daily or hourly routine. I could store partial CSVs on S3 and on every 1st day of a month I could join those files and generate a new one. The statistics would be much easier to generate, just some calculations derived from the daily/hourly statistics.
Cons: I feel like I'm turning something simple into something complex.
Do you have a better solution? If not, what solution would you choose? Why?
Having been in a similar place myself before, I used, and now recommend to you, to process the raw data:
as often as you reasonably can (start with daily)
to a format as close as possible to the desired report output
with as much calculation/CPU intensive work done as possible
leaving as little to do at report time as possible.
This approach is entirely scaleable - the incremental frequency can be:
reduced to as small a window as needed
parallelised if required
It also, makes possible re-running past months reports on demand, as the report generation time should be quite small.
In my example, I shipped denormalized, pre-processed (financial calculations) data every hour to a data warehouse, then reporting just involved a very basic (and fast) SQL query.
This had the additional benefit of spreading the load on the production database server to lots of small bites, instead of bringing it to its knees once a week at invoice time (30000 invoiced produced every week).
I would use the service kinesis to produce a daily and almost real time billing.
for this purpose I would create a special DynamoDB table just for the calculated data.
(other option is to run it on flat files)
then I would add a process which will send events to kinesis service just after you update the regular DynamoDB table.
thus when you reach the end of the month you can just execute whatever post billing calculations you have and create your CSV files from the already calculated table.
I hope that helps.
Take a look at Dynamic DynamoDB. It will increase/decrease the throughput when you need it without any manual intervention. The good news is you will not need to change the way the export job is done.
Currently I have a project (written in Java) that reads sensor output from a micro controller and writes it across several Postgres tables every second using Hibernate. In total I write about 130 columns worth of data every second. Once the data is written it will stay static forever.This system seems to perform fine under the current conditions.
My question is regarding the best way to query and average this data in the future. There are several approaches I think would be viable but am looking for input as to which one would scale and perform best.
Being that we gather and write data every second we end up generating more than 2.5 million rows per month. We currently plot this data via a JDBC select statement writing to a JChart2D (i.e. SELECT pressure, temperature, speed FROM data WHERE time_stamp BETWEEN startTime AND endTime). The user must be careful to not specify too long of a time period (startTimem and endTime delta < 1 day) or else they will have to wait several minutes (or longer) for the query to run.
The future goal would be to have a user interface similar to the Google visualization API that powers Google Finance. With regards to time scaling, i.e. the longer the time period the "smoother" (or more averaged) the data becomes.
Options I have considered are as follows:
Option A: Use the SQL avg function to return the averaged data points to the user. I think this option would get expensive if the user asks to see the data for say half a year. I imagine the interface in this scenario would scale the amount of rows to average based on the user request. I.E. if the user asks for a month of data the interface will request an avg of every 86400 rows which would return ~30 data points whereas if the user asks for a day of data the interface will request an avg of every 2880 rows which will also return 30 data points but of more granularity.
Option B: Use SQL to return all of the rows in a time interval and use the Java interface to average out the data. I have briefly tested this for kicks and I know it is expensive because I'm returning 86400 rows/day of interval time requested. I don't think this is a viable option unless there's something I'm not considering when performing the SQL select.
Option C: Since all this data is static once it is written, I have considered using the Java program (with Hibernate) to also write tables of averages along with the data it is currently writing. In this option, I have several java classes that "accumulate" data then average it and write it to a table at a specified interval (5 seconds, 30 seconds, 1 minute, 1 hour, 6 hours and so on). The future user interface plotting program would take the interval of time specified by the user and determine which table of averages to query. This option seems like it would create a lot of redundancy and take a lot more storage space but (in my mind) would yield the best performance?
Option D: Suggestions from the more experienced community?
Option A won't tend to scale very well once you have large quantities of data to pass over; Option B will probably tend to start relatively slow compared to A and scale even more poorly. Option C is a technique generally referred to as "materialized views", and you might want to implement this one way or another for best performance and scalability. While PostgreSQL doesn't yet support declarative materialized views (but I'm working on that this year, personally), there are ways to get there through triggers and/or scheduled jobs.
To keep the inserts fast, you probably don't want to try to maintain any views off of triggers on the primary table. What you might want to do is to periodically summarize detail into summary tables from crontab jobs (or similar). You might also want to create views to show summary data by using the summary tables which have been created, combined with detail table where the summary table doesn't exist.
The materialized view approach would probably work better for you if you partition your raw data by date range. That's probably a really good idea anyway.
http://www.postgresql.org/docs/current/static/ddl-partitioning.html
I need some inspiration for a solution...
We are running an online game with around 80.000 active users - we are hoping to expand this and are therefore setting a target of achieving up to 1-500.000 users.
The game includes a highscore for all the users, which is based on a large set of data. This data needs to be processed in code to calculate the values for each user.
After the values are calculated we need to rank the users, and write the data to a highscore table.
My problem is that in order to generate a highscore for 500.000 users we need to load data from the database in the order of 25-30.000.000 rows totalling around 1.5-2gb of raw data. Also, in order to rank the values we need to have the total set of values.
Also we need to generate the highscore as often as possible - preferably every 30 minutes.
Now we could just use brute force - load the 30 mio records every 30 minutes, calculate the values and rank them, and write them in to the database, but I'm worried about the strain this will cause on the database, the application server and the network - and if it's even possible.
I'm thinking the solution to this might be to break up the problem some how, but I can't see how. So I'm seeking for some inspiration on possible alternative solutions based on this information:
We need a complete highscore of all ~500.000 teams - we can't (won't unless absolutely necessary) shard it.
I'm assuming that there is no way to rank users without having a list of all users values.
Calculating the value for each team has to be done in code - we can't do it in SQL alone.
Our current method loads each user's data individually (3 calls to the database) to calculate the value - it takes around 20 minutes to load data and generate the highscore 25.000 users which is too slow if this should scale to 500.000.
I'm assuming that hardware size will not an issue (within reasonable limits)
We are already using memcached to store and retrieve cached data
Any suggestions, links to good articles about similar issues are welcome.
Interesting problem. In my experience, batch processes should only be used as a last resort. You are usually better off having your software calculate values as it inserts/updates the database with the new data. For your scenario, this would mean that it should run the score calculation code every time it inserts or updates any of the data that goes into calculating the team's score. Store the calculated value in the DB with the team's record. Put an index on the calculated value field. You can then ask the database to sort on that field and it will be relatively fast. Even with millions of records, it should be able to return the top n records in O(n) time or better. I don't think you'll even need a high scores table at all, since the query will be fast enough (unless you have some other need for the high scores table other than as a cache). This solution also gives you real-time results.
Assuming that most of your 2GB of data is not changing that frequently you can calculate and cache (in db or elsewhere) the totals each day and then just add the difference based on new records provided since the last calculation.
In postgresql you could cluster the table on the column that represents when the record was inserted and create an index on that column. You can then make calculations on recent data without having to scan the entire table.
First and formost:
The computation has to take place somewhere.
User experience impact should be as low as possible.
One possible solution is:
Replicate (mirror) the database in real time.
Pull the data from the mirrored DB.
Do the analysis on the mirror or on a third, dedicated, machine.
Push the results to the main database.
Results are still going to take a while, but at least performance won't be impacted as much.
How about saving those scores in a database, and then simply query the database for the top scores (so that the computation is done on the server side, not on the client side.. and thus there is no need to move the millions of records).
It sounds pretty straight forward... unless I'm missing your point... let me know.
Calculate and store the score of each active team on a rolling basis. Once you've stored the score, you should be able to do the sorting/ordering/retrieval in the SQL. Why is this not an option?
It might prove fruitless, but I'd at least take a gander at the way sorting is done on a lower level and see if you can't manage to get some inspiration from it. You might be able to grab more manageable amounts of data for processing at a time.
Have you run tests to see whether or not your concerns with the data size are valid? On a mid-range server throwing around 2GB isn't too difficult if the software is optimized for it.
Seems to me this is clearly a job for chacheing, because you should be able to keep the half-million score records semi-local, if not in RAM. Every time you update data in the big DB, make the corresponding adjustment to the local score record.
Sorting the local score records should be trivial. (They are nearly in order to begin with.)
If you only need to know the top 100-or-so scores, then the sorting is even easier. All you have to do is scan the list and insertion-sort each element into a 100-element list. If the element is lower than the first element, which it is 99.98% of the time, you don't have to do anything.
Then run a big update from the whole DB once every day or so, just to eliminate any creeping inconsistencies.