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.
Related
I've been reading up on this (via Google search) for a while now and still not a getting a clear answer so finally decided to post.
Am trying to get a clear idea on what is a good process for setting up automated ETL process. Let's take the following problem:
Data:
Product Code, Month, Year, Sales, Flag
15 million rows for data, where you had 5000 products. Given this data, calculate whether the cumulative sales for a particular product exceeds X. And set flag = 1 if at that point in time the threshold was exceeded.
How would people approach this task? My approach was to attempt it using SQL Server but that was painfully slow at times. In particular there was step in the transformation that would have required me to write a Stored Proc that created an index on a temp table on the fly on order to speed up .. all of which seemed like a lot of bother.
Should I have coded it in Java or Python? Should I have used Alteryx or Lavastorm? Is this something I should ideally be doing using Hadoop?
I'm thinking about building a web-based data logging and visualization service. The basic idea is that at some timed interval something (e.g. a sensor) reports a value (e.g. temperature) to the server. The server records this value into a database. There would be a web-based UI that allows me to view this data on a time-based graph. Ideally this graph would have various resolutions (last 30 seconds, last week, last year, etc). In a super ideal world, I would be able to zoom into the data for any point in time.
The problem is that the sensors are going to generate enormous amounts of data. For example, a sensor that reports a value every 5 seconds will generate about 18k values a day. I'm imagining a system that has thousands of sensors. Over time, this becomes lots of data.
The naive solution is to throw this data into a relational database and retrieve it in the various ways I want, but that won't scale.
The simple solution is to reduce the amount of data by performing periodic roll-ups of the data. New data might go into a table that has data points every 5 seconds. Every hour, some system pumps this data into another table that has data points every minute and the original data is deleted. This repeats for a few levels. The downside to this is that the further back in time you go, the less detailed the data is. That's probably fine. I would imagine that I would need enormous amounts of hardware to support full resolution of data over all time as compared to a system with this sort of rollup.
Is there a better way to do this? Is there an existing solution? I have to imagine this is a fairly common problem.
You probably want a fixed sized database like RRDTool: http://oss.oetiker.ch/rrdtool/
Also Graphite is built on top of a similar datastore implementation: http://graphite.wikidot.com/
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
Short version of the question:
If you have a table with a large number of small rows and you want to retrieve a single record from this table via an index probably consisting of two columns is this likely to be something that wil be low cost and fast or high cost and slow
Longer version of question and background:
I am a consultant working with a software development company and I have an argument with them about the performance implications of a piece of functionality that I want to add to the application they are building (and I am designing).
At the moment, we write out a log record every time somebody retrieves a client record. I want to put the name and time of the last person prevously to access that record onto the client page each time that record is retrieved.
They are saying that the performance implications of this will be high but based on my reasonable but not expert knowledge of how B trees work, this doesn't seem right even if the table is very large.
If you create an index on the GUID of the client record and the date/time of access (descending), then you ought to be able to retrieve the required record via an index scan which would just need to find the first entry for that GUID and then stop? And that with a b-tree index, most of the index would be cached so the number of physical disc accesses needed would be very small and the query time therefore significantly less than 1s.
Or have I got this completely wrong
You will have problems with GUID index fragmentation but because your rows do not increase in size (as you said in the comments) you will not have page-splitting problems. The random insert issue is fixable by doing reorganizing and rebuilding.
Besides that, there is nothing wrong with your approach. If the table is larger than RAM you will likely have a single disk IO per access (the intermediate index levels will be cached). If your data fits in RAM you will pay about 0.2 to 0.5ms per query. If your data is on a magnetic disk a seek will likely require 8-12ms. On an SSD you are back to 0.2ms to 0.5ms (maybe 0.05ms more).
Why don't you just create some test data (by selecting a cross product from sys.object of 1M rows) and measure it. It takes little time and you will find out for sure.
should be low cost and fast since the columns are indexed and that would be O(n) I think
You say last person to access? You mean that for every read you will have a write?
And that write is going to change an indexed date time column?
Then I would be worried too.
Writing on each record read will cause you lots of extra disk writes. This will block reads and it might be bad to your caching too. You also need to update your index a lot, and since you change the indexed data your index will be very fragmented.
It depends.
A single retrieval will be low cost and fast
on a decent indexed table
running on decent hardware
over a decent network
On the other hand, it takes time nonetheless.
If we are talking about one retrieval per hour, don't sweat over it. If we are talking about thousands of retrievals per second (as opposed to currently none) it will start to add up to the point it would be noticable.
Some questions you need to adress
Is my hardware up to spec
Does adding two fields result in a page split (unlikely)
How many extra pages need to be read for your regular result sets
How many retrievals/sec will be made
How many inserts/sec (triggering an index update) will be made
After you've adressed these questions, you should be able to make the determination yourself. As far as my gut feelings go, I would be surprised you would notice the performance difference.
I'm creating an app that will have to put at max 32 GB of data into my database. I am using B-tree indexing because the reads will have range queries (like from 0 < time < 1hr).
At the beginning (database size = 0GB), I will get 60 and 70 writes per millisecond. After say 5GB, the three databases I've tested (H2, berkeley DB, Sybase SQL Anywhere) have REALLY slowed down to like under 5 writes per millisecond.
Questions:
Is this typical?
Would I still see this scalability issue if I REMOVED indexing?
What are the causes of this problem?
Notes:
Each record consists of a few ints
Yes; indexing improves fetch times at the cost of insert times. Your numbers sound reasonable - without knowing more.
You can benchmark it. You'll need to have a reasonable amount of data stored. Consider whether or not to index based upon the queries - heavy fetch and light insert? index everywhere a where clause might use it. Light fetch, heavy inserts? Probably avoid indexes. Mixed workload; benchmark it!
When benchmarking, you want as real or realistic data as possible, both in volume and on data domain (distribution of data, not just all "henry smith" but all manner of names, for example).
It is typical for indexes to sacrifice insert speed for access speed. You can find that out from a database table (and I've seen these in the wild) that indexes every single column. There's nothing inherently wrong with that if the number of updates is small compared to the number of queries.
However, given that:
1/ You seem to be concerned that your writes slow down to 5/ms (that's still 5000/second),
2/ You're only writing a few integers per record; and
3/ You're queries are only based on time queries,
you may want to consider bypassing a regular database and rolling your own sort-of-database (my thoughts are that you're collecting real-time data such as device readings).
If you're only ever writing sequentially-timed data, you can just use a flat file and periodically write the 'index' information separately (say at the start of every minute).
This will greatly speed up your writes but still allow a relatively efficient read process - worst case is you'll have to find the start of the relevant period and do a scan from there.
This of course depends on my assumption of your storage being correct:
1/ You're writing records sequentially based on time.
2/ You only need to query on time ranges.
Yes, indexes will generally slow inserts down, while significantly speeding up selects (queries).
Do keep in mind that not all inserts into a B-tree are equal. It's a tree; if all you do is insert into it, it has to keep growing. The data structure allows for some padding, but if you keep inserting into it numbers that are growing sequentially, it has to keep adding new pages and/or shuffle things around to stay balanced. Make sure that your tests are inserting numbers that are well distributed (assuming that's how they will come in real life), and see if you can do anything to tell the B-tree how many items to expect from the beginning.
Totally agree with #Richard-t - it is quite common in offline/batch scenarios to remove indexes completely before bulk updates to a corpus, only to reapply them when update is complete.
The type of indices applied also influence insertion performance - for example with SQL Server clustered index update I/O is used for data distribution as well as index update, where as nonclustered indexes are updated in seperate (and therefore more expensive) I/O operations.
As with any engineering project - best advice is to measure with real datasets (skews page distribution, tearing etc.)
I think somewhere in the BDB docs they mention that page size greatly affects this behavior in btree's. Assuming you arent doing much in the way of concurrency and you have fixed record sizes, you should try increasing your page size