I activated the wicket DebugBar in order to trace my session size. When I navigate on the web site, the indicated session size is stable at about 25k.
In the same time, the pagemap serialiazed on the disk continuously grows from about 25k for each page view.
What does that means? From what I understood, the pagemap on disk keeps all the pages. But why the session stays always at about 25k.
What is the impact on a big website. If I have 1000 parallel web sessions, the web server will need 25Mo to hold them and the disk 250Mo (10 pages * 25k * 1000)?
I will make some load test to check.
The debug bar value is telling you the size of your session in memory. As you browse to another page, the old page is serialized to the session store. This provides, among other things, back button support without killing your memory footprint.
So, to answer your first question, the size on disk grows because it is holding historical data while your session stays about the same because it is holding active data.
To answer your second question, its been some time since I have looked at it, but I believe the disk session store is capped at 10MB or so. Furthermore, you can change the behavior of the session store to meet your needs, but that's a whole different discussion.
See this Wiki page which describes the storage mechanisms in Wicket 1.5. It is a bit different than 1.4 but there is no such document for 1.4
Update: the Wiki page has been moved to the guide: https://ci.apache.org/projects/wicket/guide/7.x/guide/internals.html#pagestoring
Related
I have one e-commerce website in Magento 2.2.2 and it keeps on going down almost every day. Whenever it goes down, users get site took too long too respond and it never loads. To get web site working again I have to restart the server and then it works.
Total space on the server is 50GB. Out of which the whole website is around 18GB (11GB Media files and then vendor files etc.). Here are things that i cannot figure out why:
a.) The server shows that 33GB has been used although it should show only 18GB has been used. I have checked everywhere and I can't find what is consuming additional 15GB of space. Complete HTML folder is only 18GB.
b.) When I checked log files: it shows the following:
WARNING: Memory size allocated for the temporary table is more than 20% of innodb_buffer_pool_size. Please update innodb_buffer_pool_size or decrease batch size value (which decreases memory usages for the temporary table). Current batch size: 100000; Allocated memory size: 280000000 bytes; InnoDB buffer pool size: 1073741824 bytes.
I have already set innodb_buffer_pool_size to 2GB. But still, this problem keeps coming.
The server is an Amazon EC2 server and Magento is in production mode. Can allocating 100GB instead of 50GB will solve the problem?
Increased innodb buffer pool size to 10GB and logs do not show error anymore but server still goes down every day. Since RAM is only 4GB on our server, can that be the main cause? Because everyone is suggesting at least 8GB RAM?
Try the things below.
Magento2 has big log files and caching system. There may be increase your files in var folder.
But still you have to check whether your site belongs to more than 3000 products with high size images for products and you are storing all these in your server itself.
The suggestions what I can give, If your site have more products which I already mentioned better you have to use CDN for better performance. So the entire image will be process from the third party.
Next is You have to setup cloud flare to avoid the down time errors or customer side effect. You can make your index page to load while the server is down. And obviously you have to write script to restart the site automatically while its down.
In your server side check the memory size for php, you can better to give to 2G.
In Mysql side : Check the whether its making sleep query or not. If its making through your custom extension area ask your developer to optimize the code.
for eg : May be the code passing 'collection' for a single item fetch.
You can use the tool like nurelic
If everything is fine from your developer area please try to optimize the site with making memory limit mysql killing etc.. with your server side.
In the mean while magento is a big platform for e-commerce sector, so it has more area to cover by default. Better to avoid the unwanted modules from your active site, like disable the core modules which you are not using yet.
For an average site Use 16gb RAM,
A restart your mysql to make it effect ?
Also you need to set that buffer up to 20971520000, thats around 20GB.
Magento uses a lot of sessions and cache.
I create Java web app on IBM Bluemix. This application shares session object among instances via Session Cache Service.
I understand how to program my application with session cache. But I could not find any descriptions if the total amount of cached data exceeds cache space (e.g. for starter plan, I can use 1GB cache space.).
These are my questions.
Q1. Are there any trigger to remove cached data from cache space?
Q2. After exceeding cache space, what data will be removed? Is there any cache strategy such as Least Recently Used, Least Frequently Used and so on?
The Session Cache service on IBM Bluemix is based on WebSphere Extreme Scale. Hence a lot of background information is provided in the Knowledge Center of WebSphere Extreme Scale. The standard Liberty profile for the Session Cache uses a Least Recently Used (LRU) algorithm to manage the space. I haven't tried it yet, but the first linked document describes how to monitor the cache and obtain statistics.
What is a good tool for applying a layer of caching between a webserver and an application server.
Basic Requirements:
The application server needs a way to remove items from the cache and put items in the cache with an expiration date.
The webserver needs a way to pull items out of the cache in a very light-weight, fast manner without requiring thread allocation on the application server.
It does not neccessarily need to be a distributed cache (accessible from multiple machines), but it wouldn't hurt.
Strategies I have considered:
Static file caching. Request comes in, gets hashed, if a file exists we serve it, if not we route the request to the app server. Is high I/O a problem or file locking problems due to concurrency? Is it accurate that the file system is actually very fast due to kernel level caching in memory.
Using a key-value DB like mongodb, or redis. This would store the finished HTML/JSON fragments in db. The webserver would be equipped to read from the DB and route to the app server if needed. The app server would be equipped to insert/remove from the DB.
A memory cache like memcached or Varnish (don't know much about Varnish). My only concern with memcached is that I'm going to want to cache 3 - 10 gigabytes of data at any given time, which is more than I can safely allocate in memory. Does memcached have a method to spill to the filesystem?
Any thoughts on some techniques and pitfalls when trying this type of caching layer?
You can also use GigaSpaces XAP in memory data grid for caching and even hosting your web application. You can choose just the caching option or combine the power of two and gain single management of your environment along other things.
Unlike the key value pair approach you suggested, using GigaSpaces XAP you'll be able to have complex queries such as SQL, object based temples and much more. In your caching scenario you should check out more specifically the local cache related features.
Local Cache
Web Container
Disclaimer, I am a developer in GigaSpaces.
Eitan
Just to answer this from the POV of using Coherence (http://coherence.oracle.com/):
1. The application server needs a way to remove items from the cache and put items in the cache with an expiration date.
// remove one item from cache
cache.remove(key);
// remove multiple items from cache
cache.keySet().removeAll(keylist);
2. The webserver needs a way to pull items out of the cache in a very light-weight, fast manner without requiring thread allocation on the application server.
// access one item from cache
Object value = cache.get(key);
// access multiple items from cache
Map mapKV = cache.getAll(keylist);
3. It does not neccessarily need to be a distributed cache (accessible from multiple machines), but it wouldn't hurt.
Elastic. Just add nodes. Auto-discovery. Auto-load-balancing. No data loss. No interruption. Every time you add a node, you get more data capacity and more throughput.
Automatic high availability (HA). Kill a process, no data loss. Kill a server, no data loss.
A memory cache like memcached or Varnish (don't know much about Varnish). My only concern with memcached is that I'm going to want to cache 3 - 10 gigabytes of data at any given time, which is more than I can safely allocate in memory. Does memcached have a method to spill to the filesystem?
Use both RAM and flash. Transparently. Easily handle 10s or even 100s of gigabytes per Coherence node (e.g. up to a TB or more per physical server).
For the sake of full disclosure, I work at Oracle. The opinions and views expressed in this post are my own, and do not necessarily reflect the opinions or views of my employer.
I am working on a page cache replacement policy, I read many existing algorithms most of them are prefer to retain modified pages in cache. I don't really understand the reason behind it. Is it due to eviction cost or modified pages have higher chance of being used again?
Out of many different policies LRU(least recently used) policy provides good result with hardware support.
Is it due to eviction cost or modified pages have higher chance of being used again?
Yes
So according to locality of reference the recently modified page has more chances of being referenced again.
One more reason of retaining modified page in cache is that every page replacement of modified page (which has higher chances of being referenced again)requires two transfers. Firstly it is written into disk and secondly requested page comes in main memory. This very costly. But in case of non modified page (which has low chances of being referenced) only one transfer takes place i.e. requested page comes in memory.
So we run a downline report. That gathers everyone in the downline of the person who is logged in. Some people of clients run this with no problem as it returns less than 100 records.
Some people of clients however returns 4,000 - 6,000 rows which comes out to be about 8 MB worth of information. I actually had to up my buffer limit on my development machine to handle the large request.
What are some of the best ways to store this large piece of data and help prevent it from being run multiple times consecutively?
Can it be stored in a cookie?
Session is out of the question as this would eat up way to much memory on the server.
I'm open to pretty much anything at this point, trying to better streamline the old process into a much quicker efficient one.
Right now what is done, is it loads the entire recordset, it loops through the recordset building out the data into return_value cells.
Would this be better to turn into a jquery/ajax call?
The only main requirements are:
classic asp
jquery/javascript
T-SQL
Why not change the report to be paged? Phase 1: run the entire query, but the page only displays the right set of rows based on selected page. Now your response buffer problem is fixed. Phase 2: move the paging into the query using Row_Number(), now your database usage problem is fixed. Phase 3: offer the user an option of "display to screen" (using above) or "export to csv" where you can most likely export all the data, since csv is nice and compact.
Using a cookie seems unwise, given the responses to the question What is the maximum size of a web browser's cookie's key?.
I would suggest using ASP to create a file on the Web server and writing the data to that file. When the user requests the report, you can then determine if "enough time" has passed for it to be worth running the report again, or if the cached version is sufficient. User's login details could presumably be used for naming the file, or the Session.SessionID, or you could store something new in the user's session. Advantage of using their login would be that your cache of the report can exist longer than a user's session.
Taking Brian's Answer further, query page count, which would be records returned / items per page rounded up. Then join the results of every page query on client side. Pages start at a offset provided through the query. Now you have the full amount on the client without overflowing your buffer. And it can be tailored to an interface and user option (display x per page).