I'm writing an application that uses Hector to access a Cassandra database. I have some situations where I only need to query one column, and some where I need to query multiple columns at once. Writing one method that takes an array of column names and returns a list of columns using SliceQuery would be simplest in terms of code, but I'm wondering whether there's a significant drawback to using SliceQuery for one column compared to using ColumnQuery.
In short, are there enough (or any) performance benefits of using ColumnQuery over SliceQuery for one column to make it worth the extra code to deal with a one-column case separately?
By looking at Hector's code , the difference between using a ColumnQuery (ThriftColumnQuery.java) and a SliceQuery (ThriftSliceQuery.java) is the different thrift command being sent - "get" or "get_slice" (respectively).
I didn't find an exact documentation of how each of those operations are implemented by Cassandra's server, but I took a quick look in Cassandra's sources and after examining CassandraServer.java I got the impression that the "get" operation is there more for client's convenience than for better performance when querying a single column:
For a "get" request, a SliceByNamesReadCommand instance is created and executed.
For a "get_slice" request (assuming you're using Hector's setColumnNames method and not setRange), a SliceByNamesReadCommand instance is created for each of the wanted columns and then executed (the row is read only once though).
Bottom line, as far as I see it there's not much more than the (negligible) overhead of creating some collections meant for handling the multiple columns.
If you're still worried however, I believe it shouldn't be too difficult to handle the two cases differently when wrapping the use of Hector in your DAOs.
Hope I managed to help.
Related
I've been using SearchScope.fetchObjects() method till this time, and then it just occurred to me that fetchRows might be the better choice in some cases (when you don't need metadata like class names, object stores etc). Something tells me it might be faster, but I didn't found any arguments about what method to use in which case, and why.
Here is SearchScope documentation.
The difference in performance of fetchRows() and fetchObjects() is negligible in most cases. If you process significant volume of data and still are concerned about performance I suggest making a simple test.
The only reason for existence of fetchRows() is the possibility to query disparate object classes using JOIN.
I have a question about getting 'random' chunks of available content from a RESTful service, without duplicating what the client has already cached. How can I do this in a RESTful way?
I'm serving up a very large number of items (little articles with text and urls). Let's pretend it's:
/api/article/
My (software) clients want to get random chunks of what's available. There's too many to load them all onto the client. They do not have a natural order, so it's not a situation where they can just ask for the latest. Instead, there are around 6-10 attributes that the client may give to 'hint' what type of articles they'd like to see (e.g. popular, recent, trending...).
Over time the clients get more and more content, but at the server I have no idea what they have already, and because they're sent randomly, I can't just pass in the 'most recent' one they have.
I could conceivably send up the GUIDS of what's stored locally. The clients only store 50-100 locally. That's small enough to stuff into a POST variable, but not into the GET query string.
What's a clean way to design this?
Key points:
Data has no logical order
Clients must cache the content locally
Each item has a GUID
Want to avoid pulling down duplicates
You'll never be able to make this work satisfactorily if the data is truly kept in a random order (bear in mind the Dilbert RNG Effect); you need to fix the order for a particular client so that they can page through it properly. That's easy to do though; just make that particular ordering be a resource itself; at that point, you've got a natural (if possibly synthetic) ordering and can use normal paging techniques.
The main thing to watch out for is that you'll be creating a resource in response to a GET when you do the initial query: you probably should use a resource name that is a hash of the query parameters (including the client's identity if that matters) so that if someone does the same query twice in a row, they'll get the same resource (so preserving proper idempotency). You can always delete the resource after some timeout rather than requiring manual disposal…
I'm trying to introduce caching into an existing server application because the database is starting to become overloaded.
Like many server applications we have the concept of a data layer. This data layer has many different methods that return domain model objects. For example, we have an employee data access object with methods like:
findEmployeesForAccount(long accountId)
findEmployeesWorkingInDepartment(long accountId, long departmentId)
findEmployeesBySearch(long accountId, String search)
Each method queries the database and returns a list of Employee domain objects.
Obviously, we want to try and cache as much as possible to limit the number of queries hitting the database, but how would we go about doing that?
I see a couple possible solutions:
1) We create a cache for each method call. E.g. for findEmployeesForAccount we would add an entry with a key account-employees-accountId. For findEmployeesWorkingInDepartment we could add an entry with a key department-employees-accountId-departmentId and so on. The problem I see with this is when we add a new employee into the system, we need to ensure that we add it to every list where appropriate, which seems hard to maintain and bug-prone.
2) We create a more generic query for findEmployeesForAccount (with more joins and/or queries because more information will be required). For other methods, we use findEmployeesForAccount and remove entries from the list that don't fit the specified criteria.
I'm new to caching so I'm wondering what strategies people use to handle situations like this? Any advice and/or resources on this type of stuff would be greatly appreciated.
I've been struggling with the same question myself for a few weeks now... so consider this a half-answer at best. One bit of advice that has been working out well for me is to use the Decorator Pattern to implement the cache layer. For example, here is an article detailing this in C#:
http://stevesmithblog.com/blog/building-a-cachedrepository-via-strategy-pattern/
This allows you to literally "wrap" your existing data access methods without touching them. It also makes it very easy to swap out the cached version of your DAL for the direct access version at runtime quite easily (which can be useful for unit testing).
I'm still struggling to manage my cache keys, which seem to spiral out of control when there are numerous parameters involved. Inevitably, something ends up not being properly cleared from the cache and I have to resort to heavy-handed ClearAll() approaches that just wipe out everything. If you find a solution for cache key management, I would be interested, but I hope the decorator pattern layer approach is helpful.
First off all I know:
Premature optimization is the root of all evil
But I think wrong autocomplete can really blow up your site.
I would to know if there are any libraries out there which can do autocomplete efficiently(serverside) which preferable can fit into RAM(for best performance). So no browserside javascript autocomplete(yui/jquery/dojo). I think there are enough topic about this on stackoverflow. But I could not find a good thread about this on stackoverflow (maybe did not look good enough).
For example autocomplete names:
names:[alfred, miathe, .., ..]
What I can think off:
simple SQL like for example: SELECT name FROM users WHERE name LIKE al%.
I think this implementation will blow up with a lot of simultaneously users or large data set, but maybe I am wrong so numbers(which could be handled) would be cool.
Using something like solr terms like for example: http://localhost:8983/solr/terms?terms.fl=name&terms.sort=index&terms.prefix=al&wt=json&omitHeader=true.
I don't know the performance of this so users with big sites please tell me.
Maybe something like in memory redis trie which I also haven't tested performance on.
I also read in this thread about how to implement this in java (lucene and some library created by shilad)
What I would like to hear is implementation used by sites and numbers of how well it can handle load preferable with:
Link to implementation or code.
numbers to which you know it can scale.
It would be nice if it could be accesed by http or sockets.
Many thanks,
Alfred
Optimising for Auto-complete
Unfortunately, the resolution of this issue will depend heavily on the data you are hoping to query.
LIKE queries will not put too much strain on your database, as long as you spend time using 'EXPLAIN' or the profiler to show you how the query optimiser plans to perform your query.
Some basics to keep in mind:
Indexes: Ensure that you have indexes setup. (Yes, in many cases LIKE does use the indexes. There is an excellent article on the topic at myitforum. SQL Performance - Indexes and the LIKE clause ).
Joins: Ensure your JOINs are in place and are optimized by the query planner. SQL Server Profiler can help with this. Look out for full index or full table scans
Auto-complete sub-sets
Auto-complete queries are a special case, in that they usually works as ever decreasing sub sets.
'name' LIKE 'a%' (may return 10000 records)
'name' LIKE 'al%' (may return 500 records)
'name' LIKE 'ala%' (may return 75 records)
'name' LIKE 'alan%' (may return 20 records)
If you return the entire resultset for query 1 then there is no need to hit the database again for the following result sets as they are a sub set of your original query.
Depending on your data, this may open a further opportunity for optimisation.
I will no comply with your requirements and obviously the numbers of scale will depend on hardware, size of the DB, architecture of the app, and several other items. You must test it yourself.
But I will tell you the method I've used with success:
Use a simple SQL like for example: SELECT name FROM users WHERE name LIKE al%. but use TOP 100 to limit the number of results.
Cache the results and maintain a list of terms that are cached
When a new request comes in, first check in the list if you have the term (or part of the term cached).
Keep in mind that your cached results are limited, some you may need to do a SQL query if the term remains valid at the end of the result (I mean valid if the latest result match with the term.
Hope it helps.
Using SQL versus Solr's terms component is really not a comparison. At their core they solve the problem the same way by making an index and then making simple calls to it.
What i would want to know is "what you are trying to auto complete".
Ultimately, the easiest and most surefire way to scale a system is to make a simple solution and then just scale the system by replicating data. Trying to cache calls or predict results just make things complicated, and don't get to the root of the problem (ie you can only take them so far, like if each request missed the cache).
Perhaps a little more info about how your data is structured and how you want to see it extracted would be helpful.
I am adding some indexes to my DevExpress TdxMemDataset to improve performance. The TdxMemIndex has SortOptions which include the option for soCaseInsensitive. My data is usually a GUID string, so it is not case sensitive. I am wondering if I am better off just forcing all the data to the same case or if the soCaseInsensitive flag and using the loCaseInsensitive flag with the call to Locate has only a minor performance penalty (roughly equal to converting the case of my string every time I need to use the index).
At this point I am leaving the CaseInsentive off and just converting case.
IMHO, The best is to assure the data quality at Post time. Reasonings:
You (usually) know the nature of the data. So, eg. you can use UpperCase (knowing that GUIDs are all in ASCII range) instead of much slower AnsiUpperCase which a general component like TdxMemDataSet is forced to use.
You enter the data only once. Searching/Sorting/Filtering which all implies the internal upercassing engine of TdxMemDataSet it's a repeated action. Also, there are other chained actions which will trigger this engine whithout realizing. (Eg. a TcxGrid which is Sorted by default having GridMode:=True (I assume that you use the DevEx. components) and having a class acting like a broker passing the sort message to the underlying dataset.
Usually the data entry is done in steps, one or few records in a batch. The only notable exception is data aquisition applications. But in both cases above the user's usability culture allows way greater response times for you to play with. (IOW how much would add an UpperCase call to a record post which lasts 0.005 ms?) OTOH, users are very demanding with the speed of data retreival operations (searching, sorting, filtering etc.). Keep the data retreival as fast as you can.
Having the data in the database ready to expose reduces the risk of processing errors when you'll write (if you'll write) other modules (you need to remember to AnsiUpperCase the data in any module in any language you'll write). Also here a classical example is when you'll use other external tools to access the data (for ex. db managers to execute an SQL SELCT over the data).
hth.
Maybe the DevExpress forums (or ever a support email, if you have access to it) would be a better place to seek an authoritative answer on that performance question.
Anyway, is better to guarantee that data is on the format you want - for the reasons plainth already explained - the moment you save it. So, in that specific, make sure the GUID is written in upper(or lower, its a matter of taste)case. If it is SQL Server or another database server that have an guid datatype, make sure the SELECT make the work - if applicable and possible, even the sort.