There are a lot of different tutorials across the internet about pagination with JDBC/iterating over huge result set.
So, basically there are a number of approaches I've found so far:
Vendor specific sql
Scrollable result set (?)
Holding plain result set in a memory and map the rows only when necessary (using fetchSize)
The result set fetch size, either set explicitly, or by default equal
to the statement fetch size that was passed to it, determines the
number of rows that are retrieved in any subsequent trips to the
database for that result set. This includes any trips that are still
required to complete the original query, as well as any refetching of
data into the result set. Data can be refetched, either explicitly or
implicitly, to update a scroll-sensitive or
scroll-insensitive/updatable result set.
Cursor (?)
Custom seek method paging implemented by jooq
Sorry for messing all these but I need someone to clear that out for me.
I have a simple task where service consumer asks for results with a pageNumber and pageSize. Looks like I have two options:
Use vendor specific sql
Hold the connection/statement/result set in the memory and rely on jdbc fetchSize
In the latter case I use rxJava-jdbc and if you look at producer implementation it holds the result set, then all you do is calling request(long n) and another n rows are processed. Of course everything is hidden under Observable suggar of rxJava. What I don't like about this approach is that you have to hold the resultSet between different service calls and have to clear that resultSet if client forgets to exhaust or close it. (Note: resultSet here is java ResultSet class, not the actual data)
So, what is recommended way of doing pagination? Is vendor specific sql considered slow compared to holding the connection?
I am using oracle, ScrollableResultSet is not recommended to be used with huge result sets as it caches the whole result set data on the client side. proof
Keeping resources open for an indefinite time is a bad thing in general. The database will, for example, create a cursor for you to obtain the fetched rows. That cursor and other resources will be kept open until you close the result set. The more queries you do in parallel the more resources will be occupied and at some point the database will reject further requests due to an exhausted resource pool (e.g. there is a limited number of cursors, that can be opened at a time).
Hibernate, for example, uses vendor specific SQL to fetch a "page" and I would do it just like that.
There are many approaches because there are many different use cases.
Do you actually expect users to fetch every page of the result set? Or are they more likely to fetch the first page or two and try something else if the data they're interested in isn't there. If you are Google, for example, you can be pretty confident that people will look at results from the first page, a small number will look at results from the second page, and a tiny fraction of results will come from the third page. It makes perfect sense in that case to use vendor-specific code to request a page of data and only run that for the next page when the user asks for it. If you expect the user to fetch the last page of the result, on the other hand, running a separate query for each page is going to be more expensive than running a single query and doing multiple fetches.
How long do users need to keep the queries open? How many concurrent users? If you're building an internal application that dozens of users will have access to and you expect users to keep cursors open for a few minutes, that might be reasonable. If you are trying to build an application that will have thousands of users that will be paging through a result over a span of hours, keeping resources allocated is a bad idea. If your users are really machines that are going to fetch data and process it in a loop as quickly as possible, a single ResultSet with multiple fetches makes far more sense.
How important is it that no row is missed/ every row is seen exactly once/ the results across pages are consistent? Multiple fetches from a single cursor guarantees that every row in the result is seen exactly once. Separate paginated queries might not-- new data could have been added or removed between queries being executed, your sort might not be fully deterministic, etc.
ScrollableResultSet caches result on client side - this requires memory resources. But for example PostgreSQL does it by default and nobody complains. Some databases simply use client's memory to hold the whole resultset. In most cases the database has to process much more data to re-evaluate the query.
Also you usually have much more clients, than database instances.
Also note that query re-execution - using rownum - as implemented by Hibernate does not guarantee correct(consistent) results. If data are modified between executions and default isolation level is used.
It really depends on use case. Changing Oracle's init parameter for max. connections and also for open cursors requires database restart.
So ScrollableResultSet and cursors can be used only when you can predict amount of (concurrent) users.
Related
I need help in understanding why the below code is taking 3 to 4 seconds.
UPDATE: Use case for my application is to get the activity feed of a person since last login. This feed could contain updates from friends or some new items outside of his network that he may find interesting. The Activity table stores all such activities and when a user logs in, I run a query on the GAE-DataStore to return above activities. My application supports infinite scrolling too, hence I need the cursor feature of GAE. At a given time, I get around 32 items but the activities table could have millions of rows (as it contains data from all the users).
Currently the Activity table is small and contains 25 records only and the below java code reads only 3 records from the same table.
Each record in the Activity table has 4 UUID fields.
I cannot imagine how the query would behave if the table contained millions of rows and result contained 100s of rows.
Is there something wrong with the below code I have below?
(I am using Objectify and app-engine cursors)
Filter filter = new FilterPredicate("creatorID", FilterOperator.EQUAL, userId);
Query<Activity> query = ofy().load().type(Activity.class).filter(filter);
query = query.startAt(Cursor.fromWebSafeString(previousCursorString));
QueryResultIterator<Activity> itr = query.iterator();
while (itr.hasNext())
{
Activity a = itr.next();
System.out.println (a);
}
I have gone through Google App Engine Application Extremely slow and verified that response time improves if I keep on refreshing my page (which calls the above code). However, the improvement is only ~30%
Compare this with any other database and the response time for such tiny data is in milliseconds, not even 100s of milliseconds.
Am I wrong in expecting a regular database kind of performance from the GAE DataStore?
I do not want to turn on memcache just yet as I want to improve this layer without caching first.
Not exactly sure what your query is supposed to do but it doesn't look like it requires a cursor query. In my humble opinion the only valid use case for cursor queries is a paginated query for data with a limited count of result rows. Since your query does not have a limit i don't see why you would want to use a cursor at all.
When you need millions of results you're probably doing ad-hoc analysis of data (as no human could ever interpret millions of raw data rows) you might be better off using BigQuery instead of the appengine datastore. I'm just guessing here, but for normal front end apps you rarely need millions of rows in a result but only a few (maybe hundreds at times) which you filter from the total available rows.
Another thing:
Are you sure that it is the query that takes long? It might as well be the wrapper around the query. Since you are using cursors you would have to recall the query until there are no more results. The handling of this could be costly.
Lastly:
Are you testing on appengine itself or on the local development server? The devserver can obviouily not simulate a cloud and thus could be slower (or faster) than the real thing at times. The devserver does not know about instance warmup times either when your query spawns new instances.
Speaking of cloud: The thing about cloud databases is not that they have the best performance for very little data but that they scale and perform consistently with a couple of hundreds and a couple of billions of rows.
Edit:
After performing a retrieval operation, the application can obtain a
cursor, which is an opaque base64-encoded string marking the index
position of the last result retrieved.
[...]
The cursor's position is defined as the location in the result list
after the last result returned. A cursor is not a relative position in
the list (it's not an offset); it's a marker to which the Datastore
can jump when starting an index scan for results. If the results for a
query change between uses of a cursor, the query notices only changes
that occur in results after the cursor. If a new result appears before
the cursor's position for the query, it will not be returned when the
results after the cursor are fetched.
(Datastore Queries)
These two statements make be believe that the query performance should be consistent with or without cursor queries.
Here are some more things you might want to check:
How do you register your entity classes with objectify?
What does your actual test code look like? I'd like to see how and where you measure.
Can you share a comparison between cursor query and query without cursors?
Improvement with multiple request could be the result of Objectifys integrated caching. You might want to disable caching for datastore performance tests
I work with a very large, enterprise application written in Java which queries an Oracle SQL database. We use JavaScript on the front end, and are always looking for ways to improve upon the performance of the application with increased use.
The issue we're having right now is that we are sending a query, via Java, that results in 39,000 records. This is putting a significant load on the server and causes the browser to hang. I should mention that the data is relatively static (only changes about once a year) and we could use an xml map or something similar (flat file) since we know the exact results that will be returned each time.
The query, however, is still taking 1.5 - 2 minutes to load, which is unacceptable. I wanted to see if there were any suggestions as to how this scenario can be optimized, especially if it can be done any quicker with JavaScript (or jQuery) and using AJAX for the db connection. Or, are we going about this problem all wrong?
You want to determine if the slowness is due to:
the query executing in the database
the network is slow returning 39k records
the javascript working with the 39k records after the ajax is complete
If you can run the query in sqlplus or toad, this will eliminate the web-tier and network all together. If this is slow, then tune the query by checking indexes.
If after adding the appropriate indexes, the query is still slow, then you could prebuild the query's results and store the results in a table or you could create a materialized view.
Once you have the query performing well from sqlplus, then add the network back into the equation. Run it from your web browser and see what overhead is being added.
If it is still slow, then you need to determine if the problem is the act of ajaxing the data or if the slowness occurs after the page does something with the data (ie. populating a data grid via javascript).
If the slowness is because the browser is waiting for the data, then you want to make sure it's only ever fetched once. You can do this by setting the cache headers in the ajax request to cache the result for 1 year. Or you can store the results in localstorage.
If the slowness is due to the browser working with the 39k rows (ie. moving the data into a data grid), then you have a few options.
find a better approach or library
use pagination
You may find performance issues from each of these areas. Most likely the query just needs to be tuned and by adding indexes or pre-querying the data and storing it will solve the problem.
Another thing to consider is if you really need 39k rows at one time. If you can, paginate at the db level so you're returning 100 rows per page.
My question is very simple and in the title. Google and stack overflow are giving me nothing so I figured it was time to ask a question.
I am currently in the process of making an sql query for when users register to my site. I have ALWAYS only used prepared statements b/c the extra coding in callable statements, and the performance hit of regular statements are both turn offs. However this query is causing me to think of possible alternatives to my previous one size fits all (prepared statements) ways.
This query has a total of 4 round trips to the database. The steps are
Insert a user into the database, get back the generated key (their user id) within a result set.
Take the user id and insert a row into the album table. Get back a generated key (album id)
Take the album id and insert a row into the images table. Get back a generated key (image id)
Take the image id and update the user tables current default column with the image id
Aside: For anyone interested in the way I am getting the keys back after my inserts it is with Statement.RETURN_GENERATED_KEYS and you can read a great article about this here - IBM Article
So anyway I'd like to know if the use of 4 round trip (but cacheable) prepared statements is okay or if I should go with batched (but not cacheable) statements?
JDBC batch statements let you reduce the number of roundtrips under a condition that there is no data dependency among the rows that you are inserting or updating. Your scenario fails this condition, because the changes are dependent on each other's data: statements 2 through 4 must pick up an ID from the prior statement 1 through 3.
On the other hand, four round-trips is definitely suboptimal. That is why scenarios like yours call for stored procedures: you can put all this logic into a create_user_proc, and return the user ID back to the caller. All insertions from 1 to 4 would happen inside your SQL code, letting you manage ID dependencies in SQL. You would be able to call this stored procedure in a single roundtrip, which is definitely faster, especially if you process multiple user registrations per minute.
I would advice to write one Stored Proc doing all this four operation and passing the all the required params from application (to stored proc) at once and there in stored proc, you can get the generated keys for resultset
To increase performance and reduce database round trips, I agree with dasblinkenlight and ajduke - stored procedures will achieve this.
But, it this really a performance bottleneck in your application?
How often do users register on your site?
Compare this to how often information is read from these tables (once per page access?)
If information in these tables are being read thousands of times more than being written via new registrations, then it might not be worth going for the stored procedure approach.
Why you might not want to use stored procedures and stick to prepared statements:
not as portable as using prepared statements (a different syntax/language for each database, some simpler databases don't even support them)
will not work with ORM solutions such as JPA* - you mentioned using PreparedStatements directly so this probably does not apply to you, at least not now but it might limit you later on if you wanted to use ORM in the future
*JPA 2.1 might actually support stored procedures, but as of writing it has not yet been released.
If we use the Limit clause in a query which also has ORDER BY clause and execute the query in JDBC, will there be any effect in performance? (using MySQL database)
Example:
SELECT modelName from Cars ORDER BY manuDate DESC Limit 1
I read in one of the threads in this forum that, by default a set size is fetched at a time. How can I find the default fetch size?
I want only one record. Originally, I was using as follows:
SQL Query:
SELECT modelName from Cars ORDER BY manuDate DESC
In the JAVA code, I was extracting as follows:
if(resultSett.next()){
//do something here.
}
Definitely the LIMIT 1 will have a positive effect on the performance. Instead of the entire (well, depends on default fetch size) data set of mathes being returned from the DB server to the Java code, only one row will be returned. This saves a lot of network bandwidth and Java memory usage.
Always delegate as much as possible constraints like LIMIT, ORDER, WHERE, etc to the SQL language instead of doing it in the Java side. The DB will do it much better than your Java code can ever do (if the table is properly indexed, of course). You should try to write the SQL query as much as possibe that it returns exactly the information you need.
Only disadvantage of writing DB-specific SQL queries is that the SQL language is not entirely portable among different DB servers, which would require you to change the SQL queries everytime when you change of DB server. But it's in real world very rare anyway to switch to a completely different DB make. Externalizing SQL strings to XML or properties files should help a lot anyway.
There are two ways the LIMIT could speed things up:
by producing less data, which means less data gets sent over the wire and processed by the JDBC client
by potentially having MySQL itself look at fewer rows
The second one of those depends on how MySQL can produce the ordering. If you don't have an index on manuDate, MySQL will have to fetch all the rows from Cars, then order them, then give you the first one. But if there's an index on manuDate, MySQL can just look at the first entry in that index, fetch the appropriate row, and that's it. (If the index also contains modelName, MySQL doesn't even need to fetch the row after it looks at the index -- it's a covering index.)
With all that said, watch out! If manuDate isn't unique, the ordering is only partially deterministic (the order for all rows with the same manuDate is undefined), and your LIMIT 1 therefore doesn't have a single correct answer. For instance, if you switch storage engines, you might start getting different results.
I work on an application that is deployed on the web. Part of the app is search functions where the result is presented in a sorted list. The application targets users in several countries using different locales (= sorting rules). I need to find a solution for sorting correctly for all users.
I currently sort with ORDER BY in my SQL query, so the sorting is done according to the locale (or LC_LOCATE) set for the database. These rules are incorrect for those users with a locale different than the one set for the database.
Also, to further complicate the issue, I use pagination in the application, so when I query the database I ask for rows 1 - 15, 16 - 30, etc. depending on the page I need. However, since the sorting is wrong, each page contains entries that are incorrectly sorted. In a worst case scenario, the entire result set for a given page could be out of order, depending on the locale/sorting rules of the current user.
If I were to sort in (server side) code, I need to retrieve all rows from the database and then sort. This results in a tremendous performance hit given the amount of data. Thus I would like to avoid this.
Does anyone have a strategy (or even technical solution) for attacking this problem that will result in correctly sorted lists without having to take the performance hit of loading all data?
Tech details: The database is PostgreSQL 8.3, the application an EJB3 app using EJB QL for data query, running on JBoss 4.5.
Are you willing to develop a small Postgres custom function module in C? (Probably only a few days for an experienced C coder.)
strxfrm() is the function that transforms the language-dependent text string based on the current LC_COLLATE setting (more or less the current language) into a transformed string that results in proper collation order in that language if sorted as a binary byte sequence (e.g. strcmp()).
If you implement this for Postgres, say it takes a string and a collation order, then you will be able to order by strxfrm(textfield, collation_order). I think you can then even create multiple functional indexes on your text column (say one per language) using that function to store the results of the strxfrm() so that the optimizer will use the index.
Alternatively, you could join the Postgres developers in implementing this in mainstream Postgres. Here are the wiki pages about this issues: Collation, ICU (which is also used by Java as far as I know).
Alternatively, as a less sophisticated solution if data input is only through Java, you could compute these strxfrm() values in Java (Java will probably have a different name for this concept) when you add the data to the database, and then let Postgres index and order by these precomputed values.
How tied are you to PostgreSQL? The documentation isn't promising:
The nature of some locale categories is that their value has to be fixed for the lifetime of a database cluster. That is, once initdb has run, you cannot change them anymore. LC_COLLATE and LC_CTYPE are those categories. They affect the sort order of indexes, so they must be kept fixed, or indexes on text columns will become corrupt. PostgreSQL enforces this by recording the values of LC_COLLATE and LC_CTYPE that are seen by initdb. The server automatically adopts those two values when it is started.
(Collation rules define how text is sorted.)
Google throws up patch under discussion:
PostgreSQL currently only supports one collation at a time, as fixed by the LC_COLLATE variable at the time the database cluster is initialised.
I'm not sure I'd want to manage this outside the database, though I'd be interested in reading about how it can be done. (Anyone wanting a good technical overview of the issues should check out Sorting Your Linguistic Data inside the Oracle Database on the Oracle globalization site.)
I don't know any way to switch the database order by order. Therefore, one has to consider other solutions.
If the number of results is really big (hundred thousands ?), I have no solutions, except showing only the number of results, and asking the user to make a more precise request. Otherwise, the server-side could do, depending on the precise conditions....
Especially, using a cache could improve things tremendously. The first request to the database (unlimited) would not be so much slower than for a query limited in number of results. And the subsequent requests would be much faster. Often, paging and re-sorting makes for several requests, so the cache would work well (even with a few minutes duration).
I use EhCache as a technical solution.
Sorting and paging go together, sorting then paging.
The raw results could be memorized in the cache.
To reduce the performance hit, some hints:
you can run the query once for result set size, and warn the user if there are too many results (ask either for confirming a slow query, or add some selection fields)
only request the columns you need, let go all other columns (usually some data is not shown immediately for all results, but displayed on mouse move for example ; this data can be requested lazyly, only as needed, therefore reducing the columns requested for all results)
if you have computed values, cache the smaller between the database columns and the computed values
if you have repeated values in multiple results, you can request that data/columns separately (so you retrieve from the database once, and cache them only once), retrieve only a key (typically, and id) in the main request.
You might want to checkout this packge: http://www.fi.muni.cz/~adelton/l10n/postgresql-nls-string/. It hasn't been updated in a long time, and may not work anymore, but it seems like a reasonable startingpoint if you want to build a function that can do this for you.
This module is broken for Postgres 8.4.3. I fixed it - you can download fixed version from http://www.itreport.eu/__cw_files/.01/.17/.ee7844ba6716aa36b19abbd582a31701/nls_string.c and you'll have to compile and install it by hands (as described at related README and INSTALL from original module) but anyway sorting is working incorrectly. I tried it on FreeBSD 8.0, LC_COLLATE is cs_CZ.UTF-8