I want to know whether it is useful to use ConcurrentHashMaps for user data. I have the user data saved in a mysql database and retrieve them when a user logs in (or someone edits the user). Every time when the user goes on another page, these user data will be refreshed. Should I use a map and save changes from my application there while having a database in background or should I directly download it from the db. I want to make the application as performant as possible.
What you are describing is a cache. Suppose the calls to the database cost a lot because there is a lot of info to load, or the query that is used to extract the data is complex and requires a lot of time. Here comes in play the cache data structure. It is basically an in memory storage, which is really faster w.r.t querying the database, because indeed, it is already loaded in memory.
The process of filling the cache takes the same time as querying the db for the data (generally more but in the same order). So it makes sense to use caches only if it brings benefit in time. There is a compromise though, speed vs freshness of data. Depending on your use-case you must find the right compromise between those two, and you shall afterwards find out if it is really convenient.
As you describe it, i.e user updates that needs to be saved and displayed, using a cache seems a bit an overkill IMO, unless you have lot of registered users, and so many of those are using the system simultaneously. If you decide to use it keep in mind of some concurrency issues that may rise. Concurrent hash maps saves you from many hazards but with performance compromise.
If the performance is the priority I think you should keep the logged users in memory.
That way, the read requests would be fast as you would not need to query the database. However, you would need to update the map if any of the logged users would be somehow edited.
A human cannot tell the difference between a 1ms delay and a 50ms delay. So it is overkill to optimize beyond "good enough".
MySQL already does a flavor of caching; your addition of another cache may actually slow down the response time.
Related
Give this
public void do(RequestObject request, Callback<RequestObject> callback);
Where Callback is called when the request is processed. One client has to set status of the request to the database. The client fetches some items passes them to the above method and the callback sets the status.
It was working ok for small number of items and slower IO. But now, the IO is speed up and the status is written to database vary frequently. This is causing my database (MySQL) to make so many disk read write calls. My disk usage goes through the roof.
I was thinking of aggregating the setting of status but power in not reliable, that is not a plausible solution. How should re'design this?
EDIT
When the process is started I insert a value and when there is an update, I fetch the item and update the item. #user2612030 Your question lead me to believe, using hibernate might be what is causing more reads than it is necessary.
I can upgrade my disk drive to SSD but that would only do so much. I want a solution that scales.
An SSD is a good starting point, more RAM to MySQL should also help. It can't get rid of the writes, but with enough RAM (and MySQL configured to use it!) there should be few physical reads. If you are using the default configuration, tune it. See for example https://www.percona.com/blog/2016/10/12/mysql-5-7-performance-tuning-immediately-after-installation/ or just search for MySQL memory configuration.
You could also add disks and spread the writes to multiple disks with multiple controllers. That should also help a bit.
It is hard to give good advice without knowing how you record status values. Inserts or updates? How many records are there? Data model? However, to really scale you need to shard the data somehow. That way one server can handle data in one range and another server data in another range and so on.
For write-heavy applications that is non-trivial to set up with MySQL unless you do the sharding in the application code. Most solutions with replication work best for read-mostly applications. You may want to look into a NoSQL database, for example MongoDB, that has been designed for distributing writes from the outset. MongoDB has other challenges (eventual consistency), but it can deliver scalable writes.
Which is the most efficient way to retrieve data for performing search operation.
Following is the requirement application needs search like feature for known variables(search keywords).
NB:: Currently application already have search keywords stored in keys that are stored in data cache in form of objects maintained at application level and are used for other purpose than performing search.
There are two possibilities now that are available to enable searching
(1) perform some pattern matching with java.util.regex.Pattern and then fetch the identified result rows from the cache or
(2) Ask the database to perform the match and retrieve matching rows?
Need to know which is more efficient.
Any inputs on it or data on simulators performed for similar operation would be appreciated ?
Option 1 is preferable because it does not involve network I/O.
Pattern matching and looking up in local cache will most likely take nanoseconds or a few milliseconds while sending a request to the database over the wire and waiting for the response will take a few dozen (or a few hundred) milliseconds. It's irrelevant that the database possibly implements the actual data look-up a bit faster than your own code.
This became too big to put into a comment:
To simply answer your question: Option 1 is preferable with what you describe, eg a local cache and a database accessible over network.
I'd like to emphasize "local" cache. If we're talking about a distributed cache you incur the network penalty and then the answer would be "we need more information". Factors to consider are the average size of a row, median network latency, read and write probability,... Answering this is a real pain.
When I face such a decision, I usually go through the following steps to decide what to use. The main metric here is simplicity, ie I'm looking for the most simple solution possible to save my time while still having a responsive site.
When starting, I try with no cache.
If that doesn't suffice and I still have one app server, I implement a local cache.
When I need to scale out by adding more app servers (behind a load-balancer), I try with no caches again (relying on the DB cache)
Only if that hits a performance limit, I implement a distributed cache system by attaching redis or memcache instances as needed (probably keeping a small cache on the individual app servers).
I'm in the early stages of doing a web project which will require working with arrays containing around 500 elements of custom object type. Objects will likely contain between 10 and 40 fields (based on user input), mostly booleans, strings and floats. I'm gonna use PHP for this project, but I'm also interested to know how to treat this problem in Java.
I know that "premature optimization is the root of all evil", but I think I need to decide now, how do I handle those arrays. Do I keep them in the Session object or do I store them in the database (mySQL) and keep just a minimum amount of keys in the session. Keeping data in the session would make application work faster, but when visitor numbers start growing I risk using up too much memory. On the other hand reading and writing from and into database all the time will degrade performance.
I'd like to know where the line is between those two approaches. How do I decide when it's too much data to keep inside session?
When I face a problem like this I try to estimate the size of per user data that I want to keep fast.
If your case, suppose for example to have 500 elements with 40 fields each of which sizing 50 bytes (making an average among texts, numbers, dates, etc.). So we have to keep in memory about 1MB per user for this storage, so you will have about 1GB every 1000 users only for this cache.
Depending on your server resource availability you can find bottlenecks: 1000 users consume CPU, memory, DB, disks accesses; so are in this scenario 1GB the problem? If yes keep them in DB if no keep them in memory.
Another option is to use an in-memory DB or a distributed cache solution that does it all for you, at some cost:
architectural complexity
eventually licence costs
I would be surprised if you had that amount of unique data for each user. Ideally, some of this data would be shared across users, and you could have some kind of application-level cache that stores the most recently used entries, and transparently fetches them from the database if they're missing.
This kind of design is relatively straightforward to implement in Java, but somewhat more involved (and possibly less efficient) with PHP since it doesn't have built-in support for application state.
Part of my project requires that we maintain stats of our customers products. More or less we want to show our customers how often their products has been viewed on the site
Therefore we want to create some form of Product Impressions Counter. I do not just mean a counter when we land on the specific product page, but when the product appears in search results and in our product directory lists.
I was thinking that after calling the DB I would extract the specific product ids and pass them to a service that will then inserted then into the stats tables. Or another is using some form of singleton buffer writer which writes to the DB after it reaches a certains size?
Has anyone ever encountered this in there projects and have any ideas that they would like to share?
And / or does anyone know of any framework or tools that could aid this development?
Any input would be really appreciated.
As long as you don't have performance problems, do not over-engineer your design. On the other hand, depending on how big the site is, it seem that you are going to have performance problems due to huge amount of writes.
I think real time updates will have a huge performance impact. Also it is very likely that you will update the same data multiple times in short period of time. Another thing is that, although interesting, storing this statistics is not mission-cricital and it shouldn't affect normal system work. Final thought: inconsistencies and minor inaccuracy is IMHO acceptable in this use case.
Taking all this into account I would temporarily hold the statistics in memory and flush them periodically as you've suggested. This has the additional benefit of merging events for the same product - if between two flushed some product was visited 10 times, you will only perform one update, not 10.
Technically, you can use properly synchronized singleton with background thread (a lot of handcrafting) or some intelligent cache with write-behind technology.
Modern database provide caching support. Most of the ORM frameworks cache retrieved data too. Why this duplication is necessary?
Because to get the data from the database's cache, you still have to:
Generate the SQL from the ORM's "native" query format
Do a network round-trip to the database server
Parse the SQL
Fetch the data from the cache
Serialise the data to the database's over-the-wire format
Deserialize the data into the database client library's format
Convert the database client librarie's format into language-level objects (i.e. a collection of whatevers)
By caching at the application level, you don't have to do any of that. Typically, it's a simple lookup of an in-memory hashtable. Sometimes (if caching with memcache) there's still a network round-trip, but all of the other stuff no longer happens.
Here are a couple of reasons why you may want this:
An application caches just what it needs so you should get a better cache hit ratio
Accessing a local cache will probably be a couple of orders of magnitude faster than accessing the database due to network latency - even with a fast network
Scaling read-write transactions using a strongly consistent cache
Scaling read-only transactions can be done fairly easily by adding more Replica nodes.
However, that does not work for the Primary node since that can be only scaled vertically:
And that's where a cache comes into play. For read-write database transactions that need to be executed on the Primary node, the cache can help you reduce the query load by directing it to a strongly consistent cache, like the Hibernate second-level cache:
Using a distributed cache
Storing an application-level cache in the memory of the application is problematic for several reasons.
First, the application memory is limited, so the volume of data that can be cached is limited as well.
Second, when traffic increases and we want to start new application nodes to handle the extra traffic, the new nodes would start with a cold cache, making the problem even worse as they incur a spike in database load until the cache is populated with data:
To address this issue, it's better to have the cache running as a distributed system, like Redis. This way, the amount of cached data is not limited by the memory size on a single node since sharding can be used to split the data among multiple nodes.
And, when a new application node is added by the auto-scaler, the new node will load data from the same distributed cache. Hence, there's no cold cache issue anymore.
Even if a database engine caches data, indexes, or query result sets, it still takes a round-trip to the database for your application to benefit from that cache.
An ORM framework runs in the same space as your application. So there's no round-trip. It's just a memory access, which is generally a lot faster.
The framework can also decide to keep data in cache as long as it needs it. The database may decide to expire cached data at unpredictable times, when other concurrent clients make requests that utilize the cache.
Your application-side ORM framework may also cache data in a form that the database can't return. E.g. in the form of a collection of java objects instead of a stream of raw data. If you rely on database caching, your ORM has to repeat that transformation into objects, which adds to overhead and decreases the benefit of the cache.
Also, the database's cache might not be as practical as one think. I copied this from http://highscalability.com/bunch-great-strategies-using-memcached-and-mysql-better-together -- it's MySQL specific, tho.
Given that MySQL has a cache, why is memcached needed at all?
The MySQL cache is associated with just one instance. This limits the cache to the maximum address of one server. If your system is larger than the memory for one server then using the MySQL cache won't work. And if the same object is read from another instance its not cached.
The query cache invalidates on writes. You build up all that cache and it goes away when someone writes to it. Your cache may not be much of a cache at all depending on usage patterns.
The query cache is row based. Memcached can cache any type of data you want and it isn't limited to caching database rows. Memcached can cache complex complex objects that are directly usable without a join.
The performance considerations related to the network roundtrips have correctly been pointed out.
To that, it must be added that caching data anywhere else than in the dbms (NOT "database"), creates a problem of potentially obsoleted data that is still being presented as being "up to date".
Giving in to the temptations of performance improvement goes at the expense of losing the guarantee (watertight or at least close to that) of absolutely reliably and guaranteeably correct and consistent data.
Consider this every time accuracy and consistency is crucial.
A lot of good answers here. I'll add one other point: I know my access pattern, the database doesn't.
Depending on what I'm doing, I know that if the data ends up stale, that's not really a problem. The DB doesn't, and would have to reload the cache with the new data.
I know that I'll come back to a piece of data a few times over the next while, so it's important to keep around. The DB has to guess at what to keep in the cache, it's doesn't have the information I do. So if I fetch it from the DB over and over, it may not be in cache if the server is busy. I could get a cache miss. With my cache, I can be sure I get a hit. This is especially true on data that is non-trivial to get (i.e. a few joins, some group functions) as opposed to just a single row. Getting a row with the primary key of 7 is easy for the DB, but if it has to do some real work, the cost of the cache miss is much higher.
No doubt that modern databases are providing caching facility but when you are having more traffic on you site and that time you need to perform many database transaction then you will no get high performance.So to increase performance in this case hibernate cache will help you,
by optimizing the database applications. The cache actually stores the data already loaded from the database, so that the traffic between our application and the database will be reduced when the application want to access that data again.The access time and traffic will be reduced between the application and the database.
That said - caches can sometimes become a burden and actually slowdown the server. When you have high load the algorithm for what is cached and what is not might not fit right with the requests coming in...what you get is a cache that starts to operate like FIFO in overtime...this begins to make itself known when the table that sits behind the cache has significantly more records than are ever going to be cached in memory...
A good trade off would be to cluster the data for what you want to cache. Have a main server which pumps updates to the clusters, the time for when to send/pump the updates should be able to be tailored for each table depending on TTL (time to live) settings.
Your logic and data on the user node can then sit on the same server which opens up in memory databases or if it does have to fetch data then you could set it up to use a pipe instead of a network call...
This is something that takes some thought on how you want to use the data and when/if you cluster then you have to be aware of distributed transactions (transactions over more than one database)...but if the data being cached will be updated on its own without links into other db spaces then you can get away with this....
The problem with ORM caching is that if the database is updated independently through another application then the ORM cache can become out of date...Also it can get tricky if you do an update to a set...the update might update something that is in your cache and it needs to have some sort of algorithm to identify which records need to be removed/updated in memory (slowing down the update!?) - and then this algorithm becomes incredibly tricky and bug prone!
If using ORM caching then keep to a simple rule...cache simple objects that hardly ever change (user/role details for example) and that are small in size and are hit many times in a request...if its outside of this then I suggest clustering the data for performance.