I have a user object represented in JPA which has specific sub-types. Eg, think of User and then a subclass Admin, and another subclass Power User.
Let's say I have 100k users. I have successfully implemented the second level cache using Ehcache in order to increase performance and have validated that it's working.
http://docs.jboss.org/hibernate/core/3.3/reference/en/html/performance.html#performance-cache
I know it does work (ie, you load the object from the cache rather than invoke an sql query) when you call the load method. I've verified this via logging at the hibernate level and also verifying that it's quicker.
However, I actually want to select a subset of all the users...for example, let's say I want to do a count of how many Power Users there are.
Furthermore, my users have an associated ZipCode object...the ZipCode objects are also second level cached...what I'd like to do is actually be able to ask queries like...how many Power Users do i have in New York state...
However, my question is...how do i write a query to do this that will hit the second level cache and not the database. Note that my second level cache is configured to be read/write...so as new users are added to the system they should automatically be added to the cache...also...note that I have investigated the Query cache briefly but I'm not sure it's applicable as this is for queries that are run multiple times...my problem is more a case of...the data should be in the second level cache anyway so what do I have to do so that the database doesn't get hit when I write my query.
cheers,
Brian
(...) the data should be in the second level cache anyway so what do I have to do so that the database doesn't get hit when I write my query.
If the entities returned by your query are cached, have a look at Query#iterate(). This will trigger a first query to retrieve a list of IDs and then subsequent queries for each ID... that would hit the L2 cache.
Related
Questions
Does the user option preload refer to caching on the client or on the server?
Are there any ways to make this occur asynchronously so that users don't take a large performance hit when first requesting data from a table?
More Info
In Dynamics Ax 2012, under File > User Options > Preload a user can select which tables are preloaded the first time they're accessed.
I've not found anything to say whether this behaviour relates to caching on the client or the AOS.
The fact it's a user setting implies that it's the client.
But it could be an AOS setting where users with this option take the initial hit of preloading the entire table, whilst those without would benefit from any caching caused by other users, but wouldn't trigger the load themselves.
If it's the latter we could improve performance by removing this option from all (human) users, leaving it enabled only on our batch user account, having scheduled jobs on each AOS to request a record from each table, thus triggering the preload without any user being negatively impacted.
Ref: http://dynamicbusinesssolutions.ru/axshared.en/html/9cd36702-2fa7-470c-a627-08
If a table is large or frequently changed it is not a candidate for entire table cache. This applies to ordinary users and batch users alike.
The EntireTable cache is located on the server, but the load is initiated by the user, the first user doing the select takes a performance hit.
To succesfully disable a table from preload, you can disable it using the Admin user, it will apply to all users. Or you can let all users disable it by themselves.
Personally I never change the user setup. If a table is large I change the table CacheLookup property as a customization.
See Set-based Caching:
When you set a table's CacheLookup property to EntireTable, all the
records in the table are placed in the cache after the first select.
This type of caching follows the rules of single record caching. This
means that the SELECT statement WHERE clause must include equality
tests on all fields of the unique index that is defined in the table's
PrimaryIndex property.
The EntireTable cache is located on the server
and is shared by all connections to the Application Object Server
(AOS). If a select is made on the client tier to a table that is
EntireTable cached, it first looks in its own cache and then searches
the server-side EntireTable cache.
An EntireTable cache is created for
each table for a given company. If you have two selects on the same
table for different companies the entire table is cached twice.
Note: Avoid using EntireTable caches for large tables because once
the cache size reaches 128 KB the cache is moved from memory to disk.
A disk search is much slower than an in-memory search.
I am working with node.js and mongodb.
I am going to have a database setup and use socket.io to have real-time updates that will have the db queried again as well or push the new update to the client.
I am trying to figure out what is the best way to filter the database?
Some more information in regards to what is being queried and what the real time updates are:
A document in the database will include information such as an address, city, time, number of packages, name, price.
Filters include city/price/name/time (meaning only to see addresses within the same city, or within the same time period)
Real-time info: includes adding a new document to the database which will essentially update the admin on the website with a notification of a new address added.
Method 1: Query the db with the filters being searched?
Method 2: Query the db for all searches and then filter it on the client side (Javascript)?
Method 3: Query the db for all searches then store it in localStorage then query localStorage for what the filters are?
Trying to figure out what is the fastest way for the user to filter it?
Also, if it is different than what is the most cost effective way, then the most cost effective as well (which I am assuming is less db queries)...
It's hard to say because we don't see exact conditions of the filter, but in general:
Mongo can use only 1 index in a query condition. Thus whatever fields are covered by this index can be used in an efficient filtering. Otherwise it might do full table scan which is slow. If you are using an index then you are probably doing the most efficient query. (Mongo can still use another index for sorting though).
Sometimes you will be forced to do processing on client side because Mongo can't do what you want or it takes too many queries.
The least efficient option is to store results somewhere just because IO is slow. This would only benefit you if you use them as cache and do not recalculate.
Also consider overhead and latency of networking. If you have to send lots of data back to the client it will be slower. In general Mongo will do better job filtering stuff than you would do on the client.
According to you if you can filter by addresses within time period then you could have an index that cuts down lots of documents. You most likely need a compound index - multiple fields.
Laravel 4 has a query cache built into its query builder: just add ->remember(), according to the docs.
Can anybody tell me how I can apply this method to all queries in my application, without appending ->remember() to each and every database call in it? Some kind of after filter, I suppose.
You might be able to extend the query builder and simply overload the get() method to first call remember(), and then do the get() statement.
Practically, though, if you want to cache every single query, you might as well just do this at the database level. MySQL, for example, has a configuration option to automatically cache all queries for a certain amount of time. However, in an application that does a lot of inserts/updates/deletes, this will have poor performance since the cache is cleared for that table on every such call.
Using Laravel for every query would also mean getting outdated data if you do inserts/updates/deletes meanwhile, so you'd have to clear the cache every time you update.
Best practice would be to diligently decide if a query should be cached or not.
Let me start by describing the scenario. I have an MVC 3 application with SQL Server 2008. In one of the pages we display a list of Products that is returned from the database and is UNIQUE per logged in user.
The SQL query (actually a VIEW) used to return the list of products is VERY expensive.
It is based on very complex business requirements which cannot be changed at this stage.
The database schema cannot be changed or redesigned as it is used by other applications.
There are 50k products and 5k users (each user may have access to 1 up to 50k products).
In order to display the Products page for the logged in user we use:
SELECT TOP X * FROM [VIEW] WHERE UserID = #UserId -- where 'X' is the size of the page
The query above returns a maximum of 50 rows (maximum page size). The WHERE clause restricts the number of rows to a maximum of 50k (products that the user has access to).
The page is taking about 5 to 7 seconds to load and that is exactly the time the SQL query above takes to run in SQL.
Problem:
The user goes to the Products page and very likely uses paging, re-sorts the results, goes to the details page, etc and then goes back to the list. And every time it takes 5-7s to display the results.
That is unacceptable, but at the same time the business team has accepted that the first time the Products page is loaded it can take 5-7s. Therefore, we thought about CACHING.
We now have two options to choose from, the most "obvious" one, at least to me, is using .Net Caching (in memory / in proc). (Please note that Distributed Cache is not allowed at the moment for technical constraints with our provider / hosting partner).
But I'm not very comfortable with this. We could end up with lots of products in memory (when there are 50 or 100 users logged in simultaneously) which could cause other issues on the server, like .Net constantly removing cache items to free up space while our code inserts new items.
The SECOND option:
The main problem here is that it is very EXPENSIVE to generate the User x Product x Access view, so we thought we could create a flat table (or in other words a CACHE of all products x users in the database). This table would be exactly the result of the view.
However the results can change at any time if new products are added, user permissions are changed, etc. So we would need to constantly refresh the table (which could take a few seconds) and this started to get a little bit complex.
Similarly, we though we could implement some sort of Cache Provider and, upon request from a user, we would run the original SQL query and select the products from the view (5-7s, acceptable only once) and save that result in a flat table called ProductUserAccessCache in SQL. Next request, we would get the values from this cached-table (as we could easily identify the results were cached for that particular user) with a fast query without calculations in SQL.
Any time a product was added or a permission changed, we would truncate the cached-table and upon a new request the table would be repopulated for the requested user.
It doesn't seem too complex to me, but what we are doing here basically is creating a NEW cache "provider".
Does any one have any experience with this kind of issue?
Would it be better to use .Net Caching (in proc)?
Any suggestions?
We were facing a similar issue some time ago, and we were thinking of using EF caching in order to avoid the delay on retrieving the information. Our problem was a 1 - 2 secs. delay. Here is some info that might help on how to cache a table extending EF. One of the drawbacks of caching is how fresh you need the information to be, so you set your cache expiration accordingly. Depending on that expiration, users might need to wait to get the fresh info more than they would like to, but if your users can accept that they migth be seing outdated info in order to avoid the delay, then the tradeoff would worth it.
In our scenario, we decided to better have the fresh info than quick, but as I said before, our waiting period wasn't that long.
Hope it helps
Question: How can I process (read in) batches of records 1000 at a time and ensure that only the current batch of 1000 records is in memory? Assume my primary key is called 'ID' and my table is called Customer.
Background: This is not for user pagination, it is for compiling statistics about my table. I have limited memory available, therefore I want to read my records in batches of 1000 records at a time. I am only reading in records, they will not be modified. I have read that StatelessSession is good for this kind of thing and I've heard about people using ScrollableResults.
What I have tried: Currently I am working on a custom made solution where I implemented Iterable and basically did the pagination by using setFirstResult and setMaxResults. This seems to be very slow for me but it allows me to get 1000 records at a time. I would like to know how I can do this more efficiently, perhaps with something like ScrollableResults. I'm not yet sure why my current method is so slow; I'm ordering by ID but ID is the primary key so the table should already be indexed that way.
As you might be able to tell, I keep reading bits and pieces about how to do this. If anyone can provide me a complete way to do this it would be greatly appreciated. I do know that you have to set FORWARD_ONLY on ScrollableResults and that calling evict(entity) will take an entity out of memory (unless you're doing second level caching, which I do not yet know how to check if I am or not). However I don't see any methods in the JavaDoc to read in say, 1000 records at a time. I want a balance between my lack of available memory and my slow network performance, so sending records over the network one at a time really isn't an option here. I am using Criteria API where possible. Thanks for any detailed replies.
May useing of ROWNUM feature of oracle will hepl you.
Lets say we need to fetch 1000 rows(pagesize) of table CUSTOMERS and we need to fetch second page(pageNumber)
Creating and Calling some query like this may be the answer
select * from
(select rownum row_number,customers.* from Customer
where rownum <= pagesize*pageNumber order by ID)
where row_number >= (pagesize -1)*pageNumber
Load entities as read-only.
For HQL
Query.setReadOnly( true );
For Criteria
Criteria.setReadOnly( true );
http://docs.jboss.org/hibernate/orm/3.6/reference/en-US/html/readonly.html#readonly-api-querycriteria
Stateless session quite different with State-Session.
Operations performed using a stateless session never cascade to associated instances. Collections are ignored by a stateless session
http://docs.jboss.org/hibernate/orm/3.3/reference/en/html/batch.html#batch-statelesssession
Use flash() and clear() to clean up session cache.
session.flush();
session.clear();
Question about Hibernate session.flush()
ScrollableResults should works that you expect.
Do not forget that each item that you loaded takes memory space unless you evict or clear and need to check it really works well.
ScrollableResults in Mysql J/Connecotr works fake, it loads entire rows, but I think oracle connector works fine.
Using Hibernate's ScrollableResults to slowly read 90 million records
If you find alternatives, you may consider to use this way
1. Select PrimaryKey of every rows that you will process
2. Chopping them into PK chunk
3. iterate -
select rows by PK chunk (using in-query)
process them what you want