i've written a maintenance script for our database and would like to run that script on whichever tables most need vacuuming/reindexing during our down time each day. is there any way to determine that within postgres?
i would classify tables needing attention like this:
tables that need vacuuming
tables that need reindexing (we find this makes a huge difference to performance)
i see something roughly promising here
It sounds like you are trying to re-invent auto-vacuum. Any reason you can't just enable that and let it's do it's job?
For the actual information you want, look at pg_stat_all_tables and pg_stat_all_indexes.
For a good example of how to use the data in it, look at the source for auto-vacuum. It doesn't query the views directly, but it uses that information.
I think you really should consider auto-vacuum.
However, if i did understood right your needs, that's what i'll do:
For every table (how many tables do you have?) define the criterias;
For example, talbe 'foo' need to be reindex every X new records and vacuum every X update, delete or insert
Write out your own application to do that.
Every day it check the tables status, save it in a log (to compare the rows difference over the time), and then reindex/vacuum the tables whose match yours criterias.
Sounds a little hacking, but i think is a good way to do an custom-autovacuum-with-custom-'triggers'-criteria
How about adding the same trigger function that runs after any CRUD action to all the tables.
The function will receives table name, checks the status of the table, and then run vacuum or reindex on that table.
Should be a "simple" pl/sql trigger, but then those are never simple...
Also, if your DB machine is strong enough, and your downtime long enough, just run a script every night to reindex it all, and vacuum it all... that way even if your criteria was not met at test time (night) but the was close to it (few records less than your criteria), it will not pose an issue the next day when it does reach the criteria...
Related
Let's say I have a database with lots of tables, but there's one big table that's being updated regularly. At any given point in time, this table contains billions of rows, and let's say that the table is updated so regularly that we can expect a 100% refresh of the table by the end of each quarter. So the volume of data being moved around is in the order tens of billions. Because this table is changing so constantly, I want to implement a PITR, but only for this one table. I have two options:
Hack PostgreSQL's in-house PITR to apply only for one table.
Build it myself by creating a base backup, set up continuous archiving, and using a python script to execute the log of SQL statements up to a point in time (or use PostgreSQL's EXECUTE statement to loop through the archive). The big con with this is that it won't have the timeline functionality.
My problem is, I don't know if option 1 is even possible, and I don't know if option 2 even makes sense (looping through billions of rows sounds like it defeats the purpose of PITR, which is speed and convenience.) What other options do I have?
I have a large table that I need to re-sort periodically. I am partly basing this on a suggestion I was given to stay away from using cluster keys since I am inserting data ordered differently (by time) from how I need it clustered (by ID), and that can cause re-clustering to get a little out of control.
Since I am writing to the table on a hourly I am wary of causing problems with these two processes conflicting: If I CTAS to a newly sorted temp table and then swap the table name it seems like I am opening the door to have a write on the source table not make it to the temp table.
I figure I can trigger a flag when I am re-sorting that causes the ETL to pause writing, but that seems a bit hacky and maybe fragile.
I was considering leveraging locking and transactions, but this doesn't seem to be the right use case for this since I don't think I'd be locking the table I am copying from while I write to a new table. Any advice on how to approach this?
I've asked some clarifying questions in the comments regarding the clustering that you are avoiding, but in regards to your sort, have you considered creating a nice 4XL warehouse and leveraging the INSERT OVERWRITE option back into itself? It'd look something like:
INSERT OVERWRITE INTO table SELECT * FROM table ORDER BY id;
Assuming that your table isn't hundreds of TB in size, this will complete rather quickly (inside an hour, I would guess), and any inserts into the table during that period will queue up and wait for it to finish.
There are some reasons to avoid the automatic reclustering, but they're basically all the same reasons why you shouldn't set up a job to re-cluster frequently. You're making the database do all the same work, but without the built in management of it.
If your table is big enough that you are seeing performance issues with the clustering by time, and you know that the ID column is the main way that this table is filtered (in JOINs and WHERE clauses) then this is probably a good candidate for automatic clustering.
So I would recommend at least testing out a cluster key on the ID and then monitoring/comparing performance.
To give a brief answer to the question about resorting without conflicts as written:
I might recommend using a time column to re-sort records older than a given time (probably in a separate table). While it's sorting, you may get some new records. But you will be able to use that time column to marry up those new records with the, now sorted, older records.
You might consider revoking INSERT, UPDATE, DELETE privileges on the original table within the same script or procedure that performs the CTAS creating the newly sorted copy of the table. After a successful swap you can re-enable the privileges for the roles that are used to perform updates.
in mysql i have 3 tables. one is 500,000, another 300,000, and finally around 5,000
they each get maybe 50-500 additional rows daily
should i run analyze and optimize table on them? if so then how often?
optimize table rebuilds the table for InnoDB so it could take a wicked long time to run. It's used for reclaiming space and recreating indexes. I'd say run that rarely if at all.
optimize table doc
analyze should be redone whenever the overall distribution of the indexed data changes significantly. So if you're inserting the same type of stuff at the same rate over time - no need to run analyze often - do it maybe once a month. But if things change drastically - such that you get way more of one type of data than another or something else unusual - then run it afterwards.
I run it for example after loading a new table with data and perhaps a good idea would be to run it against all like once a week if you have no clue.
I have a normalized database and need to produce web based reports frequently that involve joins across multiple tables. These queries are taking too long, so I'd like to keep the results computed so that I can load pages quickly. There are frequent updates to the tables I am summarising, and I need the summary to reflect all update so far.
All tables have autoincrement primary integer keys, and I almost always add new rows and can arrange to clear the computed results in they change.
I approached a similar problem where I needed a summary of a single table by arranging to iterate over each row in the table, and keep track of the iterator state and the highest primary keen (i.e. "highwater") seen. That's fine for a single table, but for multiple tables I'd end up keeping one highwater value per table, and that feels complicated. Alternatively I could denormalise down to one table (with fairly extensive application changes), which feels a step backwards and would probably change my database size from about 5GB to about 20GB.
(I'm using sqlite3 at the moment, but MySQL is also an option).
I see two approaches:
You move the data in a separate database, denormalized, putting some precalculation, to optimize it for quick access and reporting (sounds like a small datawarehouse). This implies you have to think of some jobs (scripts, separate application, etc.) that copies and transforms the data from the source to the destination. Depending on the way you want the copying to be done (full/incremental), the frequency of copying and the complexity of data model (both source and destination), it might take a while to implement and then to optimizie the process. It has the advantage that leaves your source database untouched.
You keep the current database, but you denormalize it. As you said, this might imply changing in the logic of the application (but you might find a way to minimize the impact on the logic using the database, you know the situation better than me :) ).
Can the reports be refreshed incrementally, or is it a full recalculation to rework the report? If it has to be a full recalculation then you basically just want to cache the result set until the next refresh is required. You can create some tables to contain the report output (and metadata table to define what report output versions are available), but most of the time this is overkill and you are better off just saving the query results off to a file or other cache store.
If it is an incremental refresh then you need the PK ranges to work with anyhow, so you would want something like your high water mark data (except you may want to store min/max pairs).
You can create triggers.
As soon as one of the calculated values changes, you can do one of the following:
Update the calculated field (Preferred)
Recalculate your summary table
Store a flag that a recalculation is necessary. The next time you need the calculated values check this flag first and do the recalculation if necessary
Example:
CREATE TRIGGER update_summary_table UPDATE OF order_value ON orders
BEGIN
UPDATE summary
SET total_order_value = total_order_value
- old.order_value
+ new.order_value
// OR: Do a complete recalculation
// OR: Store a flag
END;
More Information on SQLite triggers: http://www.sqlite.org/lang_createtrigger.html
In the end I arranged for a single program instance to make all database updates, and maintain the summaries in its heap, i.e. not in the database at all. This works very nicely in this case but would be inappropriate if I had multiple programs doing database updates.
You haven't said anything about your indexing strategy. I would look at that first - making sure that your indexes are covering.
Then I think the trigger option discussed is also a very good strategy.
Another possibility is the regular population of a data warehouse with a model suitable for high performance reporting (for instance, the Kimball model).
I am trying to select 100s of rows at a DB that contains 100000s of row and update those rows afters.
the problem is I don't want to go to DB twice for this purpose since update only marks those rows as "read".
is there any way I can do this in java using simple jdbc libraries? (hopefully without using stored procedures)
update: ok here is some clarification.
there are a few instance of same application running on different servers, they all need to select 100s of "UNREAD" rows sorted according to creation_date column, read blob data within it, write it to file and ftp that file to some server. (I know prehistoric but requirements are requirements)
The read and update part is for to ensure each instance getting diffent set of data. (in order, tricks like odds and evens wont work :/)
We select data for update. the data transfers through the wire (we wait and wait) and then we update them as "READ". then release lock for reading. this entire thing takes too long. By reading and updating at the same time, I would like to reduce lock time (from time we use select for update to actual update) so that using multiple instances would increase read rows per second.
Still have ideas?
It seems to me there might be more than one way to interpret the question here.
You are selecting the rows for the
sole purpose of updating them and
not reading them.
You are selecting the rows to show
to somebody, and marking them as
read either one at a time or all as a group.
You want to select the rows and mark
them as read at the time you select
them.
Let's take Option 1 first, as that seems to be the easiest. You don't need to select the rows in order to update them, just issue an update with a WHERE clause:
update table_x
set read = 'T'
where date > sysdate-1;
Looking at option 2, you want to mark them as read when a user has read them (or a down stream system has received it, or whatever). For this, you'll probably have to do another update. If you query for the primary key, in addition to the other columns you'll need in the first select, you will probably have an easier time of updating, as the DB won't have to do table or index scans to find the rows.
In JDBC (Java) there is a facility to do a batch update, where you execute a set of updates all at once. That's worked out well when I need to perform a lot of updates that are of the exact same form.
Option 3, where you want to select and update all in one shot. I don't find much use for this, personally, but that doesn't mean others don't. I suppose some kind of stored procedure would reduce the round trips. I'm not sure what db you are working with here and can't really offer specifics.
Going to the DB isn't so bad. If you aren't returning anything 'across the wire' then an update shouldn't do you too much damage and its only a few hundred thousand rows. What is your worry?
If you're doing a SELECT in JDBC and iterating over the ResultSet to UPDATE each row, you're doing it wrong. That's an (n+1) query problem that will never perform well.
Just do an UPDATE with a WHERE clause that determines which of those rows needs to be updated. It's a single network round trip that way.
Don't be too code-centric. Let the database do the job it was designed for.
Can't you just use the same connection without closing it?