Mysql behaves in a special (presumably more performant) manner when a table has no variable-width columns
Does postgres have similar behavior? Does adding a single variable-width column to a table make any major difference?
The general advice given in the postgres docs (read the tip) is that variable length fields often perform better because it results in less data so the table fits in less disk blocks and takes up less space in cache memory. Modern CPU's are so much faster then the memory and disks that the overhead of variable length field is worth the reduction in IO.
Notice that postgresql stores NULL values in a bitmap at the beginning of the row and omits the field if the value is NULL. So any nullable column has basically a variable width. The way postgresql stores it data (Database page layout) suggests that retrieving the last column would be slower then the first column. But this will probably only have a noticable impact if you have many columns and the data was mostly in cache to start with. Otherwise the disk io will be the dominant factor.
From what I know, no it doesn't
Check this link out for general talk about datatypes my conclusion from this read is whatever special behavior mysql exhibits, postgresql doesn't which to me is good. http://www.depesz.com/index.php/2010/03/02/charx-vs-varcharx-vs-varchar-vs-text/
presumably more performant
I would never ever believe any "performance myth" unless I test it with my own set of data and with a workload that is typical for my application.
If you need to know if your workload is fast enough on DBMS X with your data, don't look at anything else than the numbers you obtain from a realistic benchmark in your environment with hardware that matches production.
Any other approach can be replaced by staring at good crystal ball
Related
From time to time our Oracle response times decrease significally for a minute or two, without having extra load.
we were able to identify an insert statement, which produces a lot of buffer busy waits.
From the ADDM report, we got the following hint:
Consider partitioning the INDEX "IDX1" with object
ID 4711 in a manner that will evenly distribute concurrent DML across
multiple partitions.
To be honest: I am not sure what that means. I don't know what a partitioned index is. I only can Image that it means to create a Partition with a local index.
Can you help me out here?
There is a very high frequency of reading and writing to that table. no updates or deletes are used.
Thanks,
E.
I am not sure what that means.
Oracle is telling you that there is a lot of concurrent ("at the same time") activity on a very small part of your index. This happens a lot.
Consider an index column TAB1_PK on table TAB1 whose values are inserted from a sequence TAB1_S. Suppose you have 5 database sessions all inserting into TAB1 at the same time.
Because TAB1_PK is indexed, and because the sequence is generating values in numeric order, what happens is that all those sessions have to read and update the same blocks of the index at the same time.
This can cause a lot of contention -- way more than you would expect, due to the way indexes work with multi-version read consistency. I mean, in some rare situations (depending on how the transaction logic is written and the number of concurrent sessions), it can really be crippling.
The (really) old way to avoid this problem was to use a reverse key index. That way, the sequential column values did not all go to the same index blocks.
However, that is a two-edged sword. On the one hand, you get less contention because you're inserting all over the index (good). On the other hand, your rows are going all over the index, meaning you cannot cache them all. You've just turned a big logical I/O problem into a physical I/O problem!
Nowadays, we have a better solution -- a GLOBAL HASH PARTITION on the index.
With a GHP, you can specify the number of hash buckets and use that to trade-off between how much contention you need to handle vs how compact you want the index updates (for better buffer caching). The more index hash partitions you use, the better your concurrency but the worse your index block buffer caching will be.
I find a number (of global hash partitions) around 16 is pretty good.
According to Postgres pg_stat_statements documentation:
The module requires additional shared memory proportional to
pg_stat_statements.max. Note that this memory is consumed whenever the
module is loaded, even if pg_stat_statements.track is set to none.
and also:
The representative query texts are kept in an external disk file, and
do not consume shared memory. Therefore, even very lengthy query texts
can be stored successfully. However, if many long query texts are
accumulated, the external file might grow unmanageably large.
From these it is unclear what the actual memory cost of a high pg_stat_statements.max would be - say at 100k or 500k (default is 5k). Is it safe to set the levels that high, would could be the negative repercussions of such high levels? Would aggregating statistics into an external database via logstash/fluentd be a preferred approach above certain sizes?
1.
from what I have read, it hashes the query and keeps it in DB, saving the text to FS. So next concern is more expected then overloaded shared memory:
if many long query texts are accumulated, the external file might grow
unmanageably large
the hash of text is so much smaller then text, that I think you should not worry about extension memory consumption comparing long queries. Especially knowing that extension uses Query Analyser (which will work for EVERY query ANYWAY):
the queryid hash value is computed on the post-parse-analysis
representation of the queries
Setting pg_stat_statements.max 10 times bigger should take 10 times more shared memory I believe. The grows should be linear. It does not say so in documentation, but logically should be so.
There is no answer if it is safe or not to set setting to distinct value, because there is no data on other configuration values and HW you have. But as growth should be linear, consider this answer: "if you set it to 5K, and query runtime has grown almost nothing, then setting it to 50K will prolong it almost nothing times ten". BTW, my question - who is gong to dig 50000 slow statements? :)
2.
This extension already makes a pre-aggregation for "dis-valued" statement. You can select it straight on DB, so moving data to other db and selecting it there will only give you the benefit of unloading the original DB and loading another. In other words you save 50MB for a query on original, but spend same on another. Does it make sense? For me - yes. This is what I do myself. But I also save execution plans for statement (which is not a part of pg_stat_statements extension). I believe it depends on what you have and what you have. Definitely there is no need for that just because of a number of queries. Again unless you have so big file that extension can
As a recovery method if that happens, pg_stat_statements may choose to
discard the query texts, whereupon all existing entries in the
pg_stat_statements view will show null query fields
I am working on a application. It is in its initial stage so the number of records in table is not large, but later on it will have around 1 million records in the same table.
I want to know what points I should consider while writing select query which will fetch a huge amount of data from table so it does not slow down performance.
First rule:
Don't fetch huge amounts of data back to the application.
Unless you are going to display every single one of the items in the huge amount of data, do not fetch it. Communication between the DBMS and the application is (relatively) slow, so avoid it when possible. It isn't so slow that you shouldn't use the DBMS or anything like that, but if you can reduce the amount of data flowing between DBMS and application, the overall performance will usually improve.
Often, one easy way to do this is to list only those columns you actually need in the application, rather than using 'SELECT *' to retrieve all columns when you'll only use 4 of the 24 that exist.
Second rule:
Try to ensure that the DBMS does not have to look at huge amounts of data.
To the extent possible, minimize the work that the DBMS has to do. It is busy, and typically it is busy on behalf of many people at any given time. If you can reduce the amount of work that the DBMS has to do to process your query, everyone will be happier.
Consider things like ensuring you have appropriate indexes on the table - not too few, not too many. Designed judiciously, indexes can greatly improve the performance of many queries. Always remember, though, that each index has to be maintained, so inserts, deletes and updates are slower when there are more indexes to manage on a given table.
(I should mention: none of this advice is specific to Oracle - you can apply it to any DBMS.)
To get good performance with a database there is a lot of things you need to have in mind. At first, it is the design, and here you should primary think about normalization and denormalization (split up tables but still not as much as performance heavy joins are required).
There are often a big bunch of tuning when it comes to performance. However, 80% of the performance is determined from the SQL-code. Below are some links that might help you.
http://www.smart-soft.co.uk/Oracle/oracle-performance-tuning-part7.htm
http://www.orafaq.com/wiki/Oracle_database_Performance_Tuning_FAQ
A few points to remember:
Fetch only the columns you need to use on the client side.
Ensure you set up the correct indexes that are going to help you find records. These can be done later, but it is better to plan for them if you can.
Ensure you have properly accounted for column widths and data sizes. Don't use an INT when a TINYINT will hold all possible values. A row with 100 TINYINT fields will fetch faster than a row with 100 INT fields, and you'll also be able to fetch more rows per read.
Depending on how clean you need the data to be, it may be permissable to do a "dirty read", where the database fetches data while an update is in progress. This can speed things up significantly in some cases, though it means the data you get might not be the absolute latest.
Give your DBA beer. And hugs.
Jason
I have an app using an Oracle 11g database. I have a fairly large table (~50k rows) which I query thus:
SELECT omg, ponies FROM table WHERE x = 4
Field x was not indexed, I discovered. This query happens a lot, but the thing is that the performance wasn't too bad. Adding an index on x did make the queries approximately twice as fast, which is far less than I expected. On, say, MySQL, it would've made the query ten times faster, at the very least. (Edit: I did test this on MySQL, and there saw a huge difference.)
I'm suspecting Oracle adds some kind of automatic index when it detects that I query a non-indexed field often. Am I correct? I can find nothing even implying this in the docs.
As has already been indicated, Oracle11g does NOT dynamically build indexes based on prior experience. It is certainly possible and indeed happens often that adding an index under the right conditions will produce the order of magnitude improvement you note.
But as has also already been noted, 50K (seemingly short?) rows is nothing to Oracle. The Oracle database in fact has a great deal of intelligence that allows it to scan data without indexes most efficiently. Every new release of the Oracle RDBMS gets better at moving large amounts of data. I would suggest to you that the reason Oracle was so close to its "best" timing even without the index as compared to MySQL is that Oracle is just a more intelligent database under the covers.
However, the Oracle RDBMS does have many features that touch upon the subject area you have opened. For example:
10g introduced a feature called AUTOMATIC SQL TUNING which is exposed via a gui known as the SQL TUNING ADVISOR. This feature is intended to analyze queries on its own, in depth and includes the ability to do WHAT-IF analysis of alternative query plans. This includes simulation of indexes which do not actually exist. However, this would not explain any performance differences you have seen because the feature needs to be turned on and it does not actually build any indexes, it only makes recommendations for the DBA to make indexes, among other things.
11g includes AUTOMATIC STATISTICS GATHERING which when enabled will automatically collect statistics on database objects as it deems necessary based on activity on those objects.
Thus the Oracle RDBMS is doing what you have suggested, dynamically altering its environment on its own based on its experience with your workload over time in order to improve performance. Creating indexes on the fly is just not one of the things is does yet. As an aside, this has been hinted to by Oracle in private sevearl times so I figure it is in the works for some future release.
Does Oracle 11g automatically index fields frequently used for full table scans?
No.
In regards the MySQL issue, what storage engine you use can make a difference.
"MyISAM relies on the operating system for caching reads and writes to the data rows while InnoDB does this within the engine itself"
Oracle will cache the table/data rows, so it won't need to hit the disk. depending on the OS and hardware, there's a chance that MySQL MyISAM had to physically read the data off the disk each time.
~50K rows, depending greatly on how big each row is, could conceivably be stored in under 1000 blocks, which could be quickly read into the buffer cache by a full table scan (FTS) in under 50 multi-block reads.
Adding appropriate index(es) will allow queries on the table to scale smoothly as the data volume and/or access frequency goes up.
"Adding an index on x did make the
queries approximately twice as fast,
which is far less than I expected. On,
say, MySQL, it would've made the query
ten times faster, at the very least."
How many distinct values of X are there? Are they clustered in one part of the table or spread evenly throughout it?
Indexes are not some voodoo device: they must obey the laws of physics.
edit
"Duplicates could appear, but as it
is, there are none."
If that column has neither a unique constraint nor a unique index the optimizer will choose an execution path on the basis that there could be duplicate values in that column. This is the value of declaring the data model as accuratley as possible: the provision of metadata to the optimizer. Keeping the statistics up to date is also very useful in this regard.
You should have a look at the estimated execution plan for your query, before and after the index has been created. (Also, make sure that the statistics are up-to-date on your table.) That will tell you what exactly is happening and why performance is what it is.
50k rows is not that big of a table, so I wouldn't be surprised if the performance was decent even without the index. Thus adding the index to equation can't really bring much improvement to query execution speed.
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.