I have an API which is being used by around 200 websites right now. The number is expected to grow very soon. I need to store information of each visitor (IP address etc) on clients' websites. The number of daily visitors for each client ranges from 2000 to 50000. That means I am adding 400000 to 500000 rows everyday. For that right now I am making a different table for each client.
Now the problem is when I try to fetch data from all tables combined, it takes a lot of time. How should I handle this? How should I store the data?
Thanks!
I always try to keep tables to a minimum in my schemas. Perhaps you should make a client table with relevant client information and then have a visitor table with all the visitor information. Link the two with a foreign key.
Since all the tables are the same, I'd just keep the visitor information in one table, with a column to identify the client / website.
The question then is whether a large table like that will still perform... Obviously you need your indexing and so on, but here are a couple of ideas:
Partitioning: I know nothing about partitioning on Mysql (but have tried it on Postgresql). The idea is to design the physical data storage to suit your data retrieval / work needs. Might be an idea if your table gets huge.
"Live" and "archive" tables. I'm sure there's proper terminology for this. Again, depending on how you're analysing your data, you can keep today's / this week's / this month's / whatever you need's data in the "live" table where new records are added, then have housekeeping functions that move older records to a larger archive table. The idea would be to keep only the records you want to analysis frequently in the smaller live table, so query performance is fast.
Lastly, you might be pleasantly surprised by the performance of Mysql even on large tables. I've got a Postgresql table with several million records and performance is more than adequate without any playing around.
Do not store raw data in mysql. Put visitors data into queue (based on redis, rabbitmq etc) and store only aggregated data which is necessary for your business model.
Related
I'm developing a custom tracking tool for marketing campaigns. This tool is in the middle between the ads and the landing pages. It takes care of saving all data from the user, such as the info in the user-agent, the IP, the clicks on the landing page and the geocoding data of the IPs of the users (country, ISP, etc).
At the moment I have some design issues:
The traffic on these campaigns is very very high, so potentially I have millions of rows insert a day. This system can have more than one user, so I can't store all this data on a single table because would become a mess. Maybe I can split the data in more tables, one table per user, but I'm not sure about this solution.
The data saving process must be done as quickly as possible (some milliseconds), so I think that NodeJS is much better than PHP for doing this. Especially with regard to speed and server resources. I do not want the server to crash from lack of RAM.
I need to group these data for statistic purposes. For example, I have one row for every user that visit my landing page, but I need to group these data for showing the number of impressions on this specific landing page. So all these queries need to be executed as faster as possible with this large amount of rows.
I need to geocode the IP addresses, so i need accurate information like the Country, the ISP, the type of connection etc, but this can slow down the data saving process if I call an API service. And this must be done in real-time and can't be done later.
After the saving process, the system should do a redirect to the landing page. Time is important for not losing any possible lead.
Basically, I'm finding the best solutions for:
Efficiently manage a very large database
Saving data from the users in the shortest time possible (ms)
If possible, make geocode an ip in the shortest time possible, without blocking execution
Optimize the schema and the queries for generating statistics
Do you have any suggestion? Thanks in advance.
One table per user is a worse mess; don't do that.
Millions of rows a day -- dozens, maybe hundreds, per second? That probably requires some form of 'staging' -- collecting multiple rows, then batch-inserting them. Before discussing further, please elaborate on the data flow: Single vs. multiple clients. UI vs. batch processes. Tentative CREATE TABLE. Etc.
Statistical -- Plan on creating and incrementally maintaining "Summary tables".
Are you trying to map user IP addresses to Country? That is a separate question, and it has been answered.
"Must" "real-time" "milliseconds". Face it, you will have to make some trade-offs.
More info: Go to http://mysql.rjweb.org/ ; from there, see the three blogs on Data Warehouse Techniques.
How to store by day
InnoDB stores data in PRIMARY KEY order. So, to get all the rows for one day adjacent to each other, one must start the PK with the datetime. For huge databases, may improve certain queries significantly by allowing the query to scan the data sequentially, thereby minimizing disk I/O.
If you already have id AUTO_INCREMENT (and if you continue to need it), then do this:
PRIMARY KEY(datetime, id), -- to get clustering, and be UNIQUE
INDEX(id) -- to keep AUTO_INCREMENT happy
If you have a year's worth of data, and the data won't fit in RAM, then this technique is very effective for small time ranges. But if your time range is bigger than the cache, you will be at the mercy of I/O speed.
Maintaining summary tables with changing data
This may be possible; I need to better understand the data and the changes.
You cannot scan a million rows in sub-second time, regardless of caching, tuning, and other optimizations. You can do the desired data with a Summary table much faster.
Shrink the data
Don't use BIGINT (8 bytes) if INT (4 bytes) will suffice; don't use INT if MEDIUMINT (3 bytes) will do. Etc.
Use UNSIGNED where appropriate.
Normalize repeated strings.
Smaller data will make it more cacheable, hence run faster when you do have to hit the disk.
I have a program that creates logs and these logs are used to calculate balances, trends, etc for each individual client. Currently, I store everything in separate MYSQL tables. I link all the logs to a specific client by joining the two tables. When I access a client, it pulls all the logs from the log_table and generates a report. The report varies depending on what filters are in place, mostly date and category specific.
My concern is the performance of my program as we accumulate more logs and clients. My intuition tells me to store the log information in the user_table in the form of a serialized array so only one query is used for the entire session. I can then take that log array and filter it using PHP where as before, it was filtered in a MYSQL query (using multiple methods, such as BETWEEN for dates and other comparisons).
My question is, do you think performance would be improved if I used serialized arrays to store the logs as opposed to using a MYSQL table to store each individual log? We are estimating about 500-1000 logs per client, with around 50000 clients (and growing).
It sounds like you don't understand what makes databases powerful. It's not about "storing data", it's about "storing data in a way that can be indexed, optimized, and filtered". You don't store serialized arrays, because the database can't do anything with that. All it sees is a single string without any structure that it can meaningfully work with. Using it that way voids the entire reason to even use a database.
Instead, figure out the schema for your array data, and then insert your data properly, with one field per dedicated table column so that you can actually use the database as a database, allowing it to optimize its storage, retrieval, and database algebra (selecting, joining and filtering).
Is serialized arrays in a db faster than native PHP? No, of course not. You've forced the database to act as a flat file with the extra dbms overhead.
Is using the database properly faster than native PHP? Usually, yes, by a lot.
Plus, and this part is important, it means that your database can live "anywhere", including on a faster machine next to your webserver, so that your database can return results in 0.1s, rather than PHP jacking 100% cpu to filter your data and preventing users of your website from getting page results because you blocked all the threads. In fact, for that very reason it makes absolutely no sense to keep this task in PHP, even if you're bad at implementing your schema and queries, forget to cache results and do subsequent searches inside of those cached results, forget to index the tables on columns for extremely fast retrieval, etc, etc.
PHP is not for doing all the heavy lifting. It should ask other things for the data it needs, and act as the glue between "a request comes in", "response base data is obtained" and "response is sent back to the client". It should start up, make the calls, generate the result, and die as fast as it can again.
It really depends on how you need to use the data. You might want to look into storing with mongo if you don't need to search that data. If you do, leave it in individual rows and create your indexes in a way that makes them look up fast.
If you have 10 billion rows, and need to look up 100 of them to do a calculation, it should still be fast if you have your indexes done right.
Now if you have 10 billion rows and you want to do a sum on 10,000 of them, it would probably be more efficient to save that total somewhere. Whenever a new row is added, removed or updated that would affect that total, you can change that total as well. Consider a bank, where all items in the ledger are stored in a table, but the balance is stored on the user account and is not calculated based on all the transactions every time the user wants to check his balance.
Let's pretend with me here:
PHP/MySQL web-application. Assume a single server and a single MySQL DB.
I have 1,000 bosses. Every boss has 10 workers under them. These 10 workers (times 1k, totaling 10,000 workers) each have at least 5 database entries (call them work orders for this purpose) in the WebApplication every work day. That's 50k entries a day in this work orders table.
Server issues aside, I see two main ways to handle the basic logic of the database here:
Each Boss has an ID. There is one table called workorders and it has a column named BossID to associate every work order with a boss. This leaves you with approximately 1 million entries a month in a single table, and to me that seems to add up fast.
Each Boss has it's own table that is created when that Boss signed up, i.e. work_bossID where bossID = the boss' unique ID. This leaves you with 1,000 tables, but these tables are much more manageable.
Is there a third option that I'm overlooking?
Which method would be the better-functioning method?
How big is too big for number of entries in a table (let's assume a small number of columns: less than 10)? (this can include: it's time to get a second server when...)
How big is too big for number of tables in a database? (this can include: it's time to get a second server when...)
I know that at some point we have to bring in talks of multiple servers, and databases linked together... but again, let's focus on a single server here with a singly MySQL DB.
If you use a single server, I don't think there is a problem with how big the table gets. It isn't just the number of records in a table, but how frequently it is accessed.
To manage large datasets, you can use multiple servers. In this case:
You can keep all workorders in a single table, and mirror them across different servers (so that you have slave servers)
You can shard the workorders table by boss (in this case you access the server depending on where the workorder belongs) - search for database sharding for more information
Which option you choose depends on how you will use your database.
Mirrors (master/slave)
Keeping all workorders in a single table is good for querying when you don't know which boss a workorder belongs to, eg. if you are searching by product type, but any boss can have orders in any product type.
However, you have to store a copy of everything on every mirror. In addition only one server (the master) can deal with update (or adding workorder) SQL requests. This is fine if most of your SQL queries are SELECT queries.
Sharding
The advantage of sharding is that you don't have to store a copy of the record on every mirror server.
However, if you are searching workorders by some attribute for any boss, you would have to query every server to check every shard.
How to choose
In summary, use a single table if you can have all sorts of queries, including browsing workorders by an attribute (other than which boss it belongs to), and you are likely to have more SELECT (read) queries than write queries.
Use shards if you can have write queries on the same order of magnitude as read queries, and/or you want to save memory, and queries searching by other attributes (not boss) are rare.
Keeping queries fast
Large databases are not really a big problem, if they are not overwhelmed by queries, because they can keep most of the database on hard disk, and only keep what was accessed recently in cache (on memory).
The other important thing to prevent any single query from running slowly is to make sure you add the right index for each query you might perform to avoid linear searches. This is to allow the database to binary search for the record(s) required.
If you need to maintain a count of records, whether of the whole table, or by attribute (category or boss), then keep counter caches.
When to get a new server
There isn't really a single number you can assign to determine when a new server is needed because there are too many variables. This decision can be made by looking at how fast queries are performing, and the CPU/memory usage of your server.
Scaling is often a case of experimentation as it's not always clear from the outset where the bottlenecks will be. Since you seem to have a pretty good idea of the kind of load the system will be under, one of the first things to do is capture this in a spreadsheet so you can work out some hypotheticals. This allows you do do a lot of quick "what if" scenarios and come up with a reasonable upper end for how far you have to scale with your first build.
For collecting large numbers of records there's some straight-forward rules:
Use the most efficient data type to represent what you're describing. Don't worry about using smaller integer types to shave off a few bytes, or shrinking varchars. What's important here is using integers for numbers, date fields for dates, and so on. Don't use a varchar for data that already has a proper type.
Don't over-index your table, add only what is strictly necessary. The larger the number of indexes you have, the slower your inserts will get as the table grows.
Purge data that's no longer necessary. Where practical delete it. Where it needs to be retained for an extended period of time, make alternate tables you can dump it into. For instance, you may be able to rotate out your main orders table every quarter or fiscal year to keep it running quickly. You can always adjust your queries to run against the other tables if required for reporting. Keep your working data set as small as practical.
Tune your MySQL server by benchmarking, tinkering, researching, and experimenting. There's no magic bullet here. There's many variables that may work for some people but might slow down your application. They're also highly dependent on OS, hardware, and the structure and size of your data. You can easily double or quadruple performance by allocating more memory to your database engine, for instance, either InnoDB or MyISAM.
Try using other MySQL forks if you think they might help significantly. There are a few that offer improved performance over the regular MySQL, Percona in particular.
If you query large tables often and aggressively, it may make sense to de-normalize some of your data to reduce the number of expensive joins that have to be done. For instance, on a message board you might include the user's name in every message even though that seems like a waste of data, but it makes displaying large lists of messages very, very fast.
With all that in mind, the best thing to do is design your schema, build your tables, and then exercise them. Simulate loading in 6-12 months of data and see how well it performs once really loaded down. You'll find all kinds of issues if you use EXPLAIN on your slower queries. It's even better to do this on a development system that's slower than your production database server so you won't have any surprises when you deploy.
The golden rule of scaling is only optimize what's actually a problem and avoid tuning things just because it seems like a good idea. It's very easy to over-engineer a solution that will later do the opposite of what you intend or prove to be extremely difficult to un-do.
MySQL can handle millions if not billions of rows without too much trouble if you're careful to experiment and prove it works in some capacity before rolling it out.
i had database size problem as well in one of my networks so big that it use to slow the server down when i run query on that table..
in my opinion divide your database into dates decide what table size would be too big for you - let say 1 million entries then calculate how long it will take you to get to that amount. and then have a script every that period of time to either create a new table with the date and move all current data over or just back that table up and empty it.
like putting out dated material in archives.
if you chose the first option you'll be able to access that date easily by referring to that table.
Hope that idea helps
Just create a workers table, bosses table, a relationships table for the two, and then all of your other tables. With a relationship structure like this, it's very dynamic. Because, if it ever got large enough you could create another relationship table between the work orders to the bosses or to the workers.
You might want to look into bigints, but I doubt you'll need that. I know it that the relationships table will get massive, but thats good db design.
Of course bigint is for mySQL, which can go up to -9223372036854775808 to 9223372036854775807 normal. 0 to 18446744073709551615 UNSIGNED*
How to build a proper structure for an analytics service? Currently i have 1 table that stores data about every user that visits the page with my client's ID so later my clients will be able to see the statistics for a specific date.
I've thought a bit today and I'm wondering: Let's say i have 1,000 users and everyone has around 1,000 impressions on their sites daily, means i get 1,000,000 (1M) new records every day to a single table. How will it work after 2 months or so (when the table reaches 60 Million records)?
I just think that after some time it will have so much records that the PHP queries to pull out the data will be really heavy, slow and take a lot of resources, is it true? and how to prevent that?
A friend of mine working on something similar and he is gonna make a new table for every client, is this the correct way to go with?
Thanks!
Problem you are facing is I/O bound system. 1 million records a day is roughly 12 write queries per second. That's achievable, but pulling the data out while writing at the same time will make your system to be bound at the HDD level.
What you need to do is configure your database to support the I/O volume you'll be doing, such as - use appropriate database engine (InnoDB and not MyISAM), make sure you have fast enough HDD subsystem (RAID, not regular drives since they can and will fail at some point), design your database optimally, inspect queries with EXPLAIN to see where you might have gone wrong with them, maybe even use a different storage engine - personally, I'd use TokuDB if I were you.
And also, I sincerely hope you'd be doing your querying, sorting, filtering on the database side and not on PHP side.
Consider this Link to the Google Analytics Platform Components Overview page and pay special attention to the way the data is written to the database, simply based on the architecture of the entire system.
Instead of writing everything to your database right away, you could write everything to a log file, then process the log later (perhaps at a time when the traffic isn't so high). At the end of the day, you'll still need to make all of those writes to your database, but if you batch them together and do them when that kind of load is more tolerable, your system will scale a lot better.
You could normalize impressions the data like this;
Client Table
{
ID
Name
}
Pages Table
{
ID
Page_Name
}
PagesClientsVisits Table
{
ID
Client_ID
Page_ID
Visits
}
and just increment visits on the final table on each new impression. Then the maximum number of records in there becomes (No. of clients * No. of pages)
Having a table with 60 million records can be ok. That is what a database is for. But you should be careful about how many fields you have in the table. Also what datatype (=>size) each field has.
You create some kind of reports on the data. Think about what data you really need for those reports. For example you might need only the numbers of visits per user on every page. A simple count would do the trick.
What you also can do is generate the report every night and delete the raw data afterwards.
So, read and think about it.
I have a pretty large social network type site I have working on for about 2 years (high traffic and 100's of files) I have been experimenting for the last couple years with tweaking things for max performance for the traffic and I have learned a lot. Now I have a huge task, I am planning to completely re-code my social network so I am re-designing mysql DB's and everything.
Below is a photo I made up of a couple mysql tables that I have a question about. I currently have the login table which is used in the login process, once a user is logged into the site they very rarely need to hit the table again unless editing a email or password. I then have a user table which is basicly the users settings and profile data for the site. This is where I have questions, should it be better performance to split the user table into smaller tables? For example if you view the user table you will see several fields that I have marked as "setting_" should I just create a seperate setting table? I also have fields marked with "count" which could be total count of comments, photo's, friends, mail messages, etc. So should I create another table to store just the total count of things?
The reason I have them all on 1 table now is because I was thinking maybe it would be better if I could cut down on mysql queries, instead of hitting 3 tables to get information on every page load I could hit 1.
Sorry if this is confusing, and thanks for any tips.
alt text http://img2.pict.com/b0/57/63/2281110/0/800/dbtable.jpg
As long as you don't SELECT * FROM your tables, having 2 or 100 fields won't affect performance.
Just SELECT only the fields you're going to use and you'll be fine with your current structure.
should I just create a seperate setting table?
So should I create another table to store just the total count of things?
There is not a single correct answer for this, it depends on how your application is doing.
What you can do is to measure and extrapolate the results in a dev environment.
In one hand, using a separate table will save you some space and the code will be easier to modify.
In the other hand you may lose some performance ( and you already think ) by having to join information from different tables.
About the count I think it's fine to have it there, although it is always said that is better to calculate this kind of stuff, I don't think for this situation it hurt you at all.
But again, the only way to know what's better your you and your specific app, is to measuring, profiling and find out what's the benefit of doing so. Probably you would only gain 2% of improvement.
You'll need to compare performance testing results between the following:
Leaving it alone
Breaking it up into two tables
Using different queries to retrieve the login data and profile data (if you're not doing this already) with all the data in the same table
Also, you could implement some kind of caching strategy on the profile data if the usage data suggests this would be advantageous.
You should consider putting the counter-columns and frequently updated timestamps in its own table --- every time you bump them the entire row is written.
I wouldn't consider your user table terrible large in number of columns, just my opinion. I also wouldn't break that table into multiple tables unless you can find a case for removal of redundancy. Perhaps you have a lot of users who have the same settings, that would be a case for breaking the table out.
Should take into account the average size of a single row, in order to find out if the retrieval is expensive. Also, should try to use indexes as while looking for data...
The most important thing is to design properly, not just to split because "it looks large". Maybe the IP or IPs could go somewhere else... depends on the data saved there.
Also, as the socialnetworksite using this data also handles auth and autorization processes (guess so), the separation between login and user tables should offer a good performance, 'cause the data on login is "short enough", while the access to the profile could be done only once, inmediately after the successful login. Just do the right tricks to improve DB performance and it's done.
(Remember to visualize tables as entities, name them as an entity, not as a collection of them)
Two things you will want to consider when deciding whether or not you want to break up a single table into multiple tables is:
MySQL likes small, consistent datasets. If you can structure your tables so that they have fixed row lengths that will help performance at the potential cost of disk space. One thing that from what I can tell is common is taking fixed length data and putting it in its own table while the variable length data will go somewhere else.
Joins are in most cases less performant than not joining. If the data currently in your table will normally be accessed all at the same time then it may not be worth splitting it up as you will be slowing down both inserts and quite potentially reads. However, if there is some data in that table that does not get accessed as often then that would be a good candidate for moving out of the table for performance reasons.
I can't find a resource online to substantiate this next statement but I do recall in a MySQL Performance talk given by Jay Pipes that he said the MySQL optimizer has issues once you get more than 8 joins in a single query (MySQL 5.0.*). I am not sure how accurate that magic number is but regardless joins will usually take longer than queries out of a single table.