I have a SQL query that return 92000 rows, and when i use the while with ResultSet.next(), it spend a lot of time.I found that the source of the problem is the condition of iteration ResultSet.next(). Have you an idea how can i ameliorate the performance and reduce spending time.
ResultSet.next() actually works with networking undeneath and communicates with the server to bring more data once you have iterated over previous rows.
So two tips:
Increase the prefech size in the results query
Create indexes in the database. This will increase Database Performance which will make
Have a look also at these two links that deal with your issue and fetch size:
http://www.precisejava.com/javaperf/j2ee/JDBC.htm#JDBC112
-http://www.eclipse.org/eclipselink/api/2.3/org/eclipse/persistence/config/QueryHints.html#JDBC_FETCH_SIZE
Increasing the fetch size means less times to take data from the database. Resultset is like a buffer that fetches X number of rows and refetches once you have iterated over them.
Finally, you could attempt to use threads while splitting your one query into 4-5 queries that are done in separate threads concurrently.
Related
I have been searching for an answer to this today, and it seems the best approach divides opinion somewhat.
I have 150,000 records that I need to retrieve from an Oracle database using JDBC. Is it better to retrieve the data using one select query and allowing the JDBC driver to take care of transferring the records from the database using Oracle cursor and default fetchSize - OR to split up the query into batches using LIMIT / OFFSET?
With the LIMIT / OFFSET option, I think the pros are that you can take control over the number of results you return in each chunk. The cons are that the query is executed multiple times, and you also need to run a COUNT(*) up front using the same query to calculate the number of iterations required.
The pros of retrieving all at once are that you rely on the JDBC driver to manage the retrieval of data from the database. The cons are that the setFetchSize() hint can sometimes be ignored meaning that we could end up with a huge resultSet containing all 150,000 records at once!!
Would be great to hear some real life experiences solving similar issues, and recommendations would be much appreciated.
The native way in Oracle JDBC is to use the prepareStatement for the query, executeQuery and fetch
in a loop the results with defined fetchSize
Yes, of course the details are Oracle Database and JDBC Driver Version dependent and in some case the required fetchSize
can be ignored. But the typical problem is that the required fetch size is reset to fetchSize = 1 and you effectively makes a round trip for each record. (not that you get all records at once).
Your alternative with LIMIT seems to be meaningfull on the first view. But if you investigate the implementation you will probably decide to not use it.
Say you will divide the result set in 15 chunks 10K each:
You open 15 queries, each of them on average with a half of the resource consumption as the original query (OFFSET select the data and skips them).
So the only think you will reach is that the processing will take aproximatly 7,5x more time.
Best Practice
Take your query, write a simple script with JDBC fetch, use 10046 trace to see the effective used fetch size.
Test with a range of fetch sizes and observe the perfomance; choose the optimal one.
my preference is to maintain a safe execution time with the ability to continue if interrupted. i prefer this approach because it is future proof and respects memory and execution time limits. remember you're not planning for today, you're planning for 6m down the road. what may be 150,000 today may be 1.5m in 6 months.
i use a length + 1 recipe to know if there is more to fetch, although the count query will enable you to do a progress bar in % if that is important.
when considering 150,000 record result set, this is a memory pressure question. this will depend on the average size of each row. if it is a row with three integers, that's small. if it is a row with a bunch of text elements to store user profile details then that's potentially very large. so be prudent with what fields you're pulling.
also need to ask - you may not need to pull all the records all the time. it may be useful to apply a sync pattern. to only pull records with an updated date newer than your last pull.
I have a design issue. I have a database with millions of records which I need to update.
We will use JDBC because we have to do some processing to calculate new fields values.
It is a one go, and I will not need it any more. So I was thinking about something simple. I wanted to create new tables and delete the old ones, but the DBA do not want to, because the need for storage would be huge.
I will have to process about 80 millions rows, and for each row to update 3 fields.
Would a simple jdbc approach, with a setFetchSize(1000) for example, would work?
I mean select a, b, c from mutable for update;
then the update ...
Would a JDBC program be able to support the workload?
I was also thinking about using SpringBatch or EasyBatch. But I am wondering if it is worth investigating time in this for just one go (and some very short timelines).
What is your experience with this?
I think you can do this in JDBC. I would suggest something like the following:
Create two or three threads. Each thread does the following
Create a connection.
Create a prepared statement that retrieves a disjoint subset of the rows
Set the fetch size to 100 or so. Definitely less than 1000.
Create an update statement
Execute the query
Iterate over the result set
For each row add batch to update the row
After fetch size rows execute the batch
Lets assume the fetch size is 100. The first execute will do a round trip which takes time. While that's happening go run another thread. When an execute returns processing the next 100 rows does not do a database round trip. The rows have already been fetched and the updates are being batched so this will not do a database round trip. After 100 rows execute the batch which will do a round trip and so will switch threads. Then it will fetch 100 more rows which will switch threads. I'm not sure whether two or three threads would be optimal but if I had to guess I'd try three.
But the above assumes the machine only has a single hardware thread which is not true. Most CPUs support 12 or more hardware threads so I would actually use 30 or so threads depending on what the hardware can support. Even with multiple CPUs you probably don't want more than 50 or so threads as that will start to introduce contention in the database.
The above assumes the external service is fast, much faster than the database. If not then processing each row is going to wait for the external service. In that case more threads. Since the updates will hit the database more slowly thread contention in the database is less of a concern.
One way to partition the query results into disjoint subsets is as follows:
SELECT c1, c2, etc, row
FROM (SELECT c1, c2, etc, ROWNUM FROM ...)
WHERE MOD(row, number_of_partitions) = ?
Then set the query param from 0 to number_of_partitions - 1, one for each thread. You have to do this as a subquery to get ROWNUM to work right.
Do not use updatable result sets. The performance will be abysmal, guaranteed.
Currently I have 20 million of records and I want to insert it to my table in Cassandra db. Each record will be around 1KB of size.
Currently what I'm doing is for each record, I make a PreparedStatement (com.datastax.driver.core) and execute it to transfer the data to the table (via com.datastax.driver.core.Sessions).
The whole process takes around 5 to 6 hours to finish. I have 03 nodes for cassandra (using HHDs). Up to my understanding, what I'm doing is serial inserting operation.
My question will be, is there anything I can do to speed up the whole inserting process?
You are probably using normal statements, wich are great for a few queries but definitly not for your use case, you need to use asynchronous queries to have a proper performance.
I used to load huge datas with the SSTableLoader but I had so much unconsistent datas and same queries returning different results, wich is why I won't recommend it.
I have a very large set of data (~3 million records) which needs to be merged with updates and new records on a daily schedule. I have a stored procedure that actually breaks up the record set into 1000 record chunks and uses the MERGE command with temp tables in an attempt to avoid locking the live table while the data is updating. The problem is that it doesn't exactly help. The table still "locks up" and our website that uses the data receives timeouts when attempting to access the data. I even tried splitting it up into 100 record chunks and even tried a WAITFOR DELAY '000:00:5' to see if it would help to pause between merging the chunks. It's still rather sluggish.
I'm looking for any suggestions, best practices, or examples on how to merge large sets of data without locking the tables.
Thanks
Change your front end to use NOLOCK or READ UNCOMMITTED when doing the selects.
You can't NOLOCK MERGE,INSERT, or UPDATE as the records must be locked in order to perform the update. However, you can NOLOCK the SELECTS.
Note that you should use this with caution. If dirty reads are okay, then go ahead. However, if the reads require the updated data then you need to go down a different path and figure out exactly why merging 3M records is causing an issue.
I'd be willing to bet that most of the time is spent reading data from the disk during the merge command and/or working around low memory situations. You might be better off simply stuffing more ram into your database server.
An ideal amount would be to have enough ram to pull the whole database into memory as needed. For example, if you have a 4GB database, then make sure you have 8GB of RAM.. in an x64 server of course.
I'm afraid that I've quite the opposite experience. We were performing updates and insertions where the source table had only a fraction of the number of rows as the target table, which was in the millions.
When we combined the source table records across the entire operational window and then performed the MERGE just once, we saw a 500% increase in performance. My explanation for this is that you are paying for the up front analysis of the MERGE command just once instead of over and over again in a tight loop.
Furthermore, I am certain that merging 1.6 million rows (source) into 7 million rows (target), as opposed to 400 rows into 7 million rows over 4000 distinct operations (in our case) leverages the capabilities of the SQL server engine much better. Again, a fair amount of the work is in the analysis of the two data sets and this is done only once.
Another question I have to ask is well is whether you are aware that the MERGE command performs much better with indexes on both the source and target tables? I would like to refer you to the following link:
http://msdn.microsoft.com/en-us/library/cc879317(v=SQL.100).aspx
From personal experience, the main problem with MERGE is that since it does page lock it precludes any concurrency in your INSERTs directed to a table. So if you go down this road it is fundamental that you batch all updates that will hit a table in a single writer.
For example: we had a table on which INSERT took a crazy 0.2 seconds per entry, most of this time seemingly being wasted on transaction latching, so we switched this over to using MERGE and some quick tests showed that it allowed us to insert 256 entries in 0.4 seconds or even 512 in 0.5 seconds, we tested this with load generators and all seemed to be fine, until it hit production and everything blocked to hell on the page locks, resulting in a much lower total throughput than with the individual INSERTs.
The solution was to not only batch the entries from a single producer in a MERGE operation, but also to batch the batch from producers going to individual DB in a single MERGE operation through an additional level of queue (previously also a single connection per DB, but using MARS to interleave all the producers call to the stored procedure doing the actual MERGE transaction), this way we were then able to handle many thousands of INSERTs per second without problem.
Having the NOLOCK hints on all of your front-end reads is an absolute must, always.
We have about 10K rows in a table. We want to have a form where we have a select drop down that contains distinct values of a given column in this table. We have an index on the column in question.
To increase performance I created a little cache table that contains the distinct values so we didn't need to do a select distinct field from table against 10K rows. Surprisingly it seems doing select * from cachetable (10 rows) is no faster than doing the select distinct against 10K rows. Why is this? Is the index doing all the work? At what number of rows in our main table will there be a performance improvement by querying the cache table?
For a DB, 10K rows is nothing. You're not seeing much difference because the actual calculation time is minimal, with most of it consumed by other, constant, overhead.
It's difficult to predict when you'd start noticing a difference, but it would probably be at around a million rows.
If you've already set up caching and it's not detrimental, you may as well leave it in.
10k rows is not much... start caring when you reach 500k ~ 1 million rows.
Indexes do a great job, specially if you just have 10 different values for that index.
This depends on numerous factors - the amount of memory your DB has, the size of the rows in the table, use of a parameterised query and so forth, but generally 10K is not a lot of rows and particularly if the table is well indexed then it's not going to cause any modern RDBMS any sweat at all.
As a rule of thumb I would generally only start paying close attention to performance issues on a table when it passes the 100K rows mark, and 500K doesn't usually cause much of a problem if indexed correctly and accessed by such. Performance usually tends to fall off catastrophically on large tables - you may be fine on 500K rows but crawling on 600K - but you have a long way to go before you are at all likely to hit such problems.
Is the index doing all the work?
You can tell how the query is being executed by viewing the execution plan.
For example, try this:
explain plan for select distinct field from table;
select * from table(dbms_xplan.display);
I notice that you didn't include an ORDER BY on that. If you do not include ORDER BY then the order of the result set may be random, particularly if oracle uses the HASH algorithm for making a distinct list. You ought to check that.
So I'd look at the execution plans for the original query that you think is using an index, and at the one based on the cache table. Maybe post them and we can comment on what's really going on.
Incidentaly, the cache table would usually be implemented as a materialised view, particularly if the master table is generally pretty static.
Serious premature optimization. Just let the database do its job, maybe with some tweaking to the configuration (especially if it's MySQL, which has several cache types and settings).
Your query in 10K rows most probably uses HASH SORT UNIQUE.
As 10K most probably fit into db_buffers and hash_area_size, all operations are performed in memory, and you won't note any difference.
But if the query will be used as a part of a more complex query, or will be swapped out by other data, you may need disk I/O to access the data, which will slow your query down.
Run your query in a loop in several sessions (as many sessions as there will be users connected), and see how it performs in that case.
For future plans and for scalability, you may want to look into an indexing service that uses pure memory or something faster than the TCP DB round-trip. A lot of people (including myself) use Lucene to achieve this by normalizing the data into flat files.
Lucene has a built-in Ram Drive directory indexer, which can build the index all in memory - removing the dependency on the file system, and greatly increasing speed.
Lately, I've architected systems that have a single Ram drive index wrapped by a Webservice. Then, I have my Ajax-like dropdowns query into that Webservice for high availability and high speed - no db layer, no file system, just pure memory and if remote tcp packet speed.
If you have an index on the column, then all the values are in the index and the dbms never has to look in the table. It just looks in the index which just has 10 entries. If this is mostly read only data, then cache it in memory. Caching helps scalability and a lot by relieving the database of work. A query that is quick on a database with no users, might perform poorly if a 30 queries are going on at the same time.