I am tinkering with redis and mysql to see how caching can improve performance. Accesing data from Cache is/should be faster than accessing it from database.
I calculated the time required for both the case in my program and found out that accesing from cache was much slower than accesing from the database . I was/am wondering what might be the cause(s).
Some points to consider:
I am using Azure Redis Cache.
The main application is on VM instance.
I hosted MYSQL server on another VM instance.
The table is very small with 200-300 records.
There is no error in the time calculation logic.
EDIT:
Load time for cache=about 1.2s
Load time for mysql= about 15ms
Turns out my application and MySQL server were in a same region while the redis cache was in a different region across the globe causing much higher latency.
But I would still want someone to explain why the fetch time for sql was much more smaller.
If the table of 200-300 rows is fully cached in MySQL's "buffer_pool", then it won't take much time to fetch all of them and send them back to the client. 15ms is reasonable (though it depends on too many things to be more specific).
If you are fetching 1 row, and you have an index (esp, the PRIMARY KEY) to locate that one row, I would expect it to be even faster than 15ms.
I'm summarizing a 40K-row table; it is taking under 2ms. But note: client and server are on the same machine. 15ms could represent the client and server being a few hundred miles apart.
How long does a simple SELECT 1 take? That will give you a clue of the latency, below which you cannot go without changing the physical location of machines.
QUESTION OUTLINE
Our AWS RDS instance starts slowing down after about 7-14 days, by a quite large factor (~400% load times for a specific set of queries). RDS monitoring shows no signs of resource shortage. (see below the question update for detailed problem description)
Question Update
So after more than one month of investigating and some developer support by AWS, I am not exactly closer to a solution.
Here are a couple of steps which I checked off the list, more or less without any further hint of the problem:
Index / Fragmentation (all tables have correct indexes/keys and have no fragmentation)
MySQL Stats Update (manually updating stats source)
Thread Concurrency (changing innodb_thread_concurrency to various different parameters)
Query Cache Hit Ratio doesn't show problems
EXPLAIN to see if any SELECTs are actually slow or not using indexes/keys
SLOW QUERY LOG (returns no results, because see paragraph below, it's a number of prepared SELECTs)
RDS and EC2 are within one VPC
For explanation, the used PlayFramework (2.3.8) has BoneCP and we are using eBeans to select our data. So basically I am running through a nested object and all those child objects, this produces a couple of hundred prepared SELECTs for the API call in question. This should basically also be fine for the used hardware, neither CPU nor RAM are extensively used by these operations.
I also included NewRelic for more insights on this issue and did some JVM profiling. Obviously, most of the time is consumed by NETTY/eBeans?
Is anyone able to make sense of this?
ORIGINAL QUESTION: Problem Outline
Our AWS RDS instance starts slowing down after about 7-14 days, by a quite large factor (~400% load times for a specific set of queries). RDS monitoring shows no signs of resource shortage.
Infrastructure
We run a PlayFramework backend for a mobile app on AWS EC2 instances, connected to AWS RDS MySQL instances, one PROD environment, one DEV environment. Usually the PROD EC2 instance is pointing to the PROD RDS instance, and the DEV EC2 points to the DEV RDS (hi from captain obvious!); however sometimes we also let the DEV EC2 point to the PROD DB for some testing purposes. The PlayFramework in use is working with BoneCP.
Detailed Problem Description
In a quite essential sync process, our app is making a certain API call many times a day per user. I discussed the backgrounds of the functionality in this SO question, where, thanks to comments, I could nail the problem down to be a MySQL issue of some kind.
In short, the API call is loading a set of data, the maximum is about 1MB of json data, which currently takes about 18s to load. When things are running perfectly fine, this takes about 4s to load.
Curious enough, what "solved" the problem last time was upgrading the RDS instance to another instance type (from db.m3.large to db.m4.large, which is just a very marginal step). Now, after about 2-3 weeks, the RDS instance is once again performing slow as before. Rebooting the RDS instance showed no effect. Also re-launching the EC2 instance shows no effect.
I also checked if the indices of the affected mySQL tables are set properly, which is the case. The API call itself is not eager-loading any BLOB fields or similar, I double-checked this. The CPU-usage of the RDS instances is below 1% most of the time, when I stress tested it with 100 simultaneous API calls, it went to ~5%, so this is not the bottleneck. Memory is fine too, so I guess the RDS instance doesn't start swapping which could slow down the whole process.
Giving hard evidence, a (smaller) public API call on the DEV environment currently takes 2.30s load, on the PROD environment it takes 4.86s. Which is interesting, because the DEV environment has both in EC2 and RDS a much smaller instance type. So basically the turtle wins the race here. (If you are interested in this API call I am happy to share it with you via PN, but I don't really want to post links to API calls, even if they are basically public.)
Conclusion
Concluding, it feels (I wittingly say 'feels') like the DB is clogged after x days of usage / after a certain amount of API calls. Not sure if this a RDS-specific issue, once I 'largely' reset the DB instance by changing the instance type, things run fast and smooth. But re-creating my DB instance from a snapshot every 2 weeks is not an option, especially if I don't understand why this is happening.
Do you have any ideas what further steps I could take to investigate this matter?
(Too long for just a comment) I know you have checked a lot of things, but I would like to look at them with a different set of eyes...
Please provide
SHOW VARIABLES; (probably need post.it or something, due to size)
SHOW GLOBAL STATUS;
how much RAM? Sounds like 7.5G
The query. -- Unclear what query/queries you are using
SHOW CREATE TABLE for the table(s) in the query -- indexes, datatypes, etc
(Some of the above may help with "clogging over time" question.)
Meanwhile, here are some guesses/questions/etc...
Some other customer sharing the hardware is busy.
It could be a network problem?
Shrink long_query_time to 1 so you can catch slow queries.
When are backups done on your instance?
4s-18s to load a megabyte -- what percentage of that is SQL statements?
Do you "batch" the inserts? Is it a single transaction? Are lengthy queries going on at the same time?
What, if any, MySQL tunables did you change from the AWS defaults?
6GB buffer_pool on a 7.5GB partition? That sounds dangerously tight. Can you see if there was any swapping?
Any PARTITIONing involved? (Of course the CREATE will answer that.)
There is one very important bit of information missing from your description: The total allocated space for the database. I/O for RDS is around 3x the allocated space, so for a 100GB allocation, you should get around 300 IOPS. That allocated space also includes logs.
Since you don't really know what's going on, the first step should be to turn on detailed monitoring, which will give you more idea of what is happening on the instance.
Until you have additional stats gathered during a slowdown, you can try increasing the allocated space, which will increase the IOPS available.
Also, check the events for the db - are logs getting purged on a regular basis? That might indicate that there's not enough space.
Finally, you can try going with PIOPS (provisioned IOPS) if you have an idea of what the application needs, though at this point it sounds like that would be a guess.
maybe your burst credit balance is (slowly) being depleted? finally, you end up with baseline performance, which may appear "too slow".
this would also explain why the upgrade to another instance type did help, as you then start with a full burst balance again.
i would suggest to increase the size of the volume, even if you don't need the extra space, as the baseline performance grows linearly with volume size.
I'm not sure if caching would be the correct term for this but my objective is to build a website that will be displaying data from my database.
My problem: There is a high probability of a lot of traffic and all data is contained in the database.
My hypothesized solution: Would it be faster if I created a separate program (in java for example) to connect to the database every couple of seconds and update the html files (where the data is displayed) with the new data? (this would also increase security as users will never be connecting to the database) or should I just have each user create a connection to MySQL (using php) and get the data?
If you've had any experiences in a similar situation please share, and I'm sorry if I didn't word the title correctly, this is a pretty specific question and I'm not even sure if I explained myself clearly.
Here are some thoughts for you to think about.
First, I do not recommend you create files but trust MySQL. However, work on configuring your environment to support your traffic/application.
You should understand your data a little more (How much is the data in your tables change? What kind of queries are you running against the data. Are your queries optimized?)
Make sure your tables are optimized and indexed correctly. Make sure all your query run fast (nothing causing a long row locks.)
If your tables are not being updated very often, you should consider using MySQL cache as this will reduce your IO and increase the query speed. (BUT wait! If your table is being updated all the time this will kill your server performance big time)
Your query cache is set to "ON". Based on my experience this is always bad idea unless your data does not change on all your tables. When you have it set to "ON" MySQL will cache every query. Then as soon as they data in the table changes, MySQL will have to clear the cached query "it is going to work harder while clearing up cache which will give you bad performance." I like to keep it set to "ON DEMAND"
from there you can control which query should be cache and which should not using SQL_CACHE and SQL_NO_CACHE
Another thing you want to review is your server configuration and specs.
How much physical RAM does your server have?
What types of Hard Drives are you using? SSD is not at what speed do they rotate? perhaps 15k?
What OS are you running MySQL on?
How is the RAID setup on your hard drives? "RAID 10 or RAID 50" will help you out a lot here.
Your processor speed will make a big different.
If you are not using MySQL 5.6.20+ you should consider upgrading as MySQL have been improved to help you even more.
How much RAM does your server have? is your innodb_log_buffer_size set to 75% of your total physical RAM? Are you using innodb table?
You can also use MySQL replication to increase the read sources of the data. So you have multiple servers with the same data and you can point half of your traffic to read from server A and the other half from Server B. so the same work will be handled by multiple server.
Here is one argument for you to think about: Facebook uses MySQL and have millions of hits per seconds but they are up 100% of the time. True they have trillion dollar budget and their network is huge but the idea here is to trust MySQL to get the job done.
This seems to be a clear issue; but I was unable to find an explicit answer. Consider a simple mysql database with indexed ID; without any complicated process. Just reading a row with WHERE clause. Does it really need to be cached? Reducing mysql queries apparently satisfies every one. But I tested reading a text from a flat cache file and by mysql query in a for loop of 1 - 100,000 cycles. Reading from flat file was only 1-2 times faster (but needed double memory). The CPU usage (by rough estimate from top in SSH) was almost the same.
Now I do not see any reason for using flat file cache. Am I right? or the case is different in long term? What may make slow query in such a simple system? Is it still useful to reduce mysql queries?
P.S. I do not discuss internal QC or systems like memcached.
It is depending of how you see the problem.
There is a limit on number of mysql connection can be established at any one time.
Holding the mysql connection resources in a busy site could lead to max connection error.
Establish a connection to mysql via TCP is a resource eater (if your database is sitting in different server). In this case, access a local disk file will be much faster.
If your server is located outside the network, the cost of physical distance will be heavier.
If records are updated once daily, stored into cache is truly request once and reused for the day.
I have read every possible answer to this question and searched via Google in order to find the correct answer to the following question, but I am rather a novice and don't seem to get a clear understanding.
A lot I've read has to do with web servers, but I don't have a web server, but an intranet database.
I have a MySQL dsatabase in a Windows server at work.
I will have many users accessing this database constantly to perform simple queries and writting back to it new records.
The read/write will not be that heavy (chances are 50-100 users will do so exactly at the same time, even if 1000's could be connected).
The GUI will be either via Excel forms and/or Access.
What I need to know is the maximum number of active connections I can have at any given time to the database.
I know I can change the number on Mysql Admin however I really need to know what will really work...
I don't want to put 1000 users if the system will really handle 100 correctly (after that, although connected, the performance will be too slow, for example)
Any ideas or own experiences will be appreciated
This depends mainly on your server hardware (RAM, cpu, networking) and server load for other processes if not dedicated to the database. I think you won't have an absolute answer and the best way is testing.
I think something like 1000 should work ok, as long as you use 64 bit MySQL server. With 32 bit, too many connections may create virtual memory pressure - a connection has an own thread, and every thread needs a stack, so the stack memory will reduce possible size of the buffer pool and other buffers.
MySQL generally does not slow down if you have many idle connections, however special commands e.g "show processlist" or "kill", that enumerate every connection will be somewhat slower.
If idle connection stays idle for too long (idle time exceeds wait_timeout parameter), it is dropped by the server. If this is the case in your possible scenario, you might want to increase wait_timeout (its default value is 8 hours)