Is there a way to generate some kind of in-order identifier for a table records?
Suppose that we have two threads doing queries:
Thread 1:
begin;
insert into table1(id, value) values (nextval('table1_seq'), 'hello');
commit;
Thread 2:
begin;
insert into table1(id, value) values (nextval('table1_seq'), 'world');
commit;
It's entirely possible (depending on timing) that an external observer would see the (2, 'world') record appear before the (1, 'hello').
That's fine, but I want a way to get all the records in the 'table1' that appeared since the last time the external observer checked it.
So, is there any way to get the records in the order they were inserted? Maybe OIDs can help?
No. Since there is no natural order of rows in a database table, all you have to work with is the values in your table.
Well, there are the Postgres specific system columns cmin and ctid you could abuse to some degree.
The tuple ID (ctid) contains the file block number and position in the block for the row. So this represents the current physical ordering on disk. Later additions will have a bigger ctid, normally. Your SELECT statement could look like this
SELECT *, ctid -- save ctid from last row in last_ctid
FROM tbl
WHERE ctid > last_ctid
ORDER BY ctid
ctid has the data type tid. Example: '(0,9)'::tid
However it is not stable as long-term identifier, since VACUUM or any concurrent UPDATE or some other operations can change the physical location of a tuple at any time. For the duration of a transaction it is stable, though. And if you are just inserting and nothing else, it should work locally for your purpose.
I would add a timestamp column with default now() in addition to the serial column ...
I would also let a column default populate your id column (a serial or IDENTITY column). That retrieves the number from the sequence at a later stage than explicitly fetching and then inserting it, thereby minimizing (but not eliminating) the window for a race condition - the chance that a lower id would be inserted at a later time. Detailed instructions:
Auto increment table column
What you want is to force transactions to commit (making their inserts visible) in the same order that they did the inserts. As far as other clients are concerned the inserts haven't happened until they're committed, since they might roll back and vanish.
This is true even if you don't wrap the inserts in an explicit begin / commit. Transaction commit, even if done implicitly, still doesn't necessarily run in the same order that the row its self was inserted. It's subject to operating system CPU scheduler ordering decisions, etc.
Even if PostgreSQL supported dirty reads this would still be true. Just because you start three inserts in a given order doesn't mean they'll finish in that order.
There is no easy or reliable way to do what you seem to want that will preserve concurrency. You'll need to do your inserts in order on a single worker - or use table locking as Tometzky suggests, which has basically the same effect since only one of your insert threads can be doing anything at any given time.
You can use advisory locking, but the effect is the same.
Using a timestamp won't help, since you don't know if for any two timestamps there's a row with a timestamp between the two that hasn't yet been committed.
You can't rely on an identity column where you read rows only up to the first "gap" because gaps are normal in system-generated columns due to rollbacks.
I think you should step back and look at why you have this requirement and, given this requirement, why you're using individual concurrent inserts.
Maybe you'll be better off doing small-block batched inserts from a single session?
If you mean that every query if it sees world row it has to also see hello row then you'd need to do:
begin;
lock table table1 in share update exclusive mode;
insert into table1(id, value) values (nextval('table1_seq'), 'hello');
commit;
This share update exclusive mode is the weakest lock mode which is self-exclusive — only one session can hold it at a time.
Be aware that this will not make this sequence gap-less — this is a different issue.
We found another solution with recent PostgreSQL servers, similar to #erwin's answer but with txid.
When inserting rows, instead of using a sequence, insert txid_current() as row id. This ID is monotonically increasing on each new transaction.
Then, when selecting rows from the table, add to the WHERE clause id < txid_snapshot_xmin(txid_current_snapshot()).
txid_snapshot_xmin(txid_current_snapshot()) corresponds to the transaction index of the oldest still-open transaction. Thus, if row 20 is committed before row 19, it will be filtered out because transaction 19 will still be open. When the transaction 19 is committed, both rows 19 and 20 will become visible.
When no transaction is opened, the snapshot xmin will be the transaction id of the currently running SELECT statement.
The returned transaction IDs are 64-bits, the higher 32 bits are an epoch and the lower 32 bits are the actual ID.
Here is the documentation of these functions: https://www.postgresql.org/docs/9.6/static/functions-info.html#FUNCTIONS-TXID-SNAPSHOT
Credits to tux3 for the idea.
Related
I would like to query data from a table, and if a row is locked, show it as a different color. Is this possible using postgresql's locking for update?
e.g.
select
*,
(select from pg_x -- link row somehow )
from table
thank you
There is no good way to do that. The row locks are stored in the row (system column xmax), but this attribute serves other purposes too, and the flags that determine if it is indeed a row lock or perhaps a rolled back update are not exposed via SQL.
There are only unpleasant alternatives:
Use the pageinspect contrib module to examine those flags. That would be a second scan of the table, and such a query doesn't respect MVCC visibility.
Run a second query:
SELECT * FROM atable
FOR UPDATE SKIP LOCKED;
That would lock all rows in the table and be very bad for concurrency.
Besides, that information would be pretty useless for the user. In a well-written application, row locks are only held for split seconds, so the information would be outdated by the time it reaches the user.
INSERT INTO A
SELECT * FROM B WHERE timestamp > (SELECT max(timestamp) FROM A);
or, written differently:
WITH selection AS
(SELECT * FROM B WHERE timestamp > (SELECT max(timestamp) FROM A))
INSERT INTO A SELECT * FROM selection;
If these queries run multiple times simultaneously, is it possible that I will end up with duplicated rows in A?
How does Postgres process these queries? Is it one or multiple?
If it is multiple queries (find max(timestamp)[1], select[2] then insert[3]) I can imagine this will cause duplicated rows.
If that is correct, would wrapping it in BEGIN/END (a transaction) help?
Yes, that might result in duplicate values.
A single statement sees a consistent view of the data in all tables as of the point in time when the statement started.
Wrapping that single statement into a transaction won't change that (a single statement is always executed as an atomic statement regardless of the number of sub-query involved).
The statement will never see uncommitted data from other transactions (which is the root cause why you can wind up with duplicate values).
The only safe way to avoid duplicate values, is to create a unique constraint (or index) on that column. In that case the INSERT would result in an error if such a value already exists.
If you want to avoid the error, use insert ... on conflict
This depends on the isolation level set in your database.
This is from the postgres documentation
By default, this is set to Repeatable read, which means that each query will get the output based on when the transaction first attempted to read the data. If 2 queries read before any one writes, then you will get duplicate data in these tables.
If you want to avoid having duplicate entries, you have a few options.
Try using the isolation level Serializable
Apply a unique index on a field of A in table B. Timestamp is not a great contender as you might legitimately have 2 rows with the same timestamp. Probably id of the table A is a good option.
Take a lock at the application level before performing such a query.
I need to transfer a large amount of data (several million rows) from one table to another. So far I’ve tried doing this….
INSERT INTO TABLE_A (field1, field2)
SELECT field1, field2 FROM TABLE_A_20180807_BCK;
This worked (eventually) for a table with about 10 million rows in it (took 24 hours). The problem is that I have several other tables that need the same process applied and they’re all a lot larger (the biggest is 20 million rows). I have attempted a similar load with a table holding 12 million rows and it failed to complete in 48 hours so I had to cancel it.
Other issues that probably affect performance are 1) TABLE_A has a field based on an auto-generated sequence, 2) TABLE_A has an AFTER INSERT trigger on it that parses each new record and adds a second record to TABLE_B
A number of other threads have suggested doing a pg_dump of TABLE_A_20180807_BCK and then load the data back into TABLE_A. I’m not sure a pg_dump would actually work for me because I’m only interested in couple of fields from TABLE_A, not the whole lot.
Instead I was wondering about the following….
Export to a CSV file…..
COPY TABLE_A_20180807_BCK (field1,field2) to 'd:\tmp\dump\table_a.dump' DELIMITER ',' CSV;
Import back into the desired table….
COPY TABLE_A(field1,field2) FROM 'd:\tmp\dump\table_a.dump' DELIMITER ',' CSV
Is the export/import method likely to be any quicker – I’d like some guidance on this before I start on another job that may take days to run, and may not even work any better! The obvious answer of "just try it and see" isn't really an option, I can't afford more downtime!
(this is follow-on question from this, if any background details are required)
Update....
I don't think there is any significant problems with the trigger. Under normal circumstances records are INPUTed into TABLE_A at a rate of about 1000/sec (including trigger time). I think the issue is likely to be size of the transaction, under normal circumstances records are inserted into in blocks of 100 records per INSERT, the statement shown above attempts to add 10 million records in a single transaction, my guess is that this is the problem, but I've no way of knowing if it really is, or if there's a suitable work around (or if the export/import method I've proposed would be any quicker)
Maybe I should have emphasized this earlier, every insert into TABLE_A fires a trigger that adds record to TABLE_B. It's the data that's in TABLE_B that's the final objective, so disabling the trigger isn't an option! This whole problem came about because I accidentally disabled the trigger for a few days, and the preferred solution to the question 'how to run a trigger on existing rows' seemed to be 'remove the rows and add them back again' - see the original post (link above) for details.
My current attempt involves using the COPY command with a WHERE clause to split the contents of TABLE_A_20180807_BCK into a dozen small files and then re-load them one at a time. This may not give me any overall time saving, but although I can't afford 24 hours of continuous downtime, I can afford 6 hours of downtime for 4 nights.
Preparation (if you have access and can restart your server) set checkpoint_segments to 32 or perhaps more. This will reduce the frequency and number of checkpoints during this operation. You can undo it when you're finished. This step is not totally necessary but should speed up writes considerably.
edit postgresql.conf and set checkpoint_segments to 32 or maybe more
Step 1: drop/delete all indexes and triggers on table A.
EDIT: Step 1a
alter table_a set unlogged;
(repeat step 1 for each table you're inserting into)
Step 2. (unnecessary if you do one table at a time)
begin transaction;
Step 3.
INSERT INTO TABLE_A (field1, field2)
SELECT field1, field2 FROM TABLE_A_20180807_BCK;
(repeat step 3 for all tables being inserted into)
Step 4. (unnecessary if you do one table at a time)
commit;
Step 5 re-enable indexes and triggers on all tables.
Step 5a.
Alter table_a set logged;
I am solving an performance issue on PostgreSQL 9.6 dbo based system. Intro:
12yo system, similar to banking system, with most queried primary table called transactions.
CREATE TABLE jrn.transactions (
ID BIGSERIAL,
type_id VARCHAR(200),
account_id INT NOT NULL,
date_issued DATE,
date_accounted DATE,
amount NUMERIC,
..
)
In the table transactions we store all transactions within a bank account. Field type_id determines the type of a transaction. Servers also as C# EntityFramework Discriminator column. Values are like:
card_payment, cash_withdrawl, cash_in, ...
14 types of transaction are known.
In generally, there are 4 types of queries (no. 3 and .4 are by far most frequent):
select single transaction like: SELECT * FROM jrn.transactions WHERE id = 3748734
select single transaction with JOIN to other transaction like: SELECT * FROM jrn.transactions AS m INNER JOIN jrn.transactions AS r ON m.refund_id = r.id WHERE m.id = 3748734
select 0-100, 100-200, .. transactions of given type like: SELECT * FROM jrn.transactions WHERE account_id = 43784 AND type_id = 'card_payment' LIMIT 100
several aggregate queries, like: SELECT SUM(amount), MIN(date_issued), MAX(date_issued) FROM jrn.transactions WHERE account_id = 3748734 AND date_issued >= '2017-01-01'
In last few month we had unexpected row count growth, now 120M.
We are thinking of table partitioning, following to PostgreSQL doc: https://www.postgresql.org/docs/10/static/ddl-partitioning.html
Options:
partition table by type_id into 14 partitions
add column year and partition table by year (or year_month) into 12 (or 144) partitions.
I am now restoring data into out test environment, I am going to test both options.
What do you consider the most appropriate partitioning rule for such situation? Any other options?
Thanks for any feedback / advice etc.
Partitioning won't be very helpful with these queries, since they won't perform a sequential scan, unless you forgot an index.
The only good reason I see for partitioning would be if you want to delete old rows efficiently; then partitioning by date would be best.
Based on your queries, you should have these indexes (apart from the primary key index):
CREATE INDEX ON jrn.transactions (account_id, date_issued);
CREATE INDEX ON jrn.transactions (refund_id);
The following index might be a good idea if you can sacrifice some insert performance to make the third query as fast as possible (you might want to test):
CREATE INDEX ON jrn.transactions (account_id, type_id);
What you have here is almost a perfect case for column-based storage as you may get it using a SAP HANA Database. However, as you explicitly have asked for a Postgres answer and I doubt that a HANA database will be within the budget limit, we will have to stick with Postgres.
Your two queries no. 3 and 4 go quite into different directions, so there won't be "the single answer" to your problem - you will always have to balance somehow between these two use cases. Yet, I would try to use two different techniques to approach each of them individually.
From my perspective, the biggest problem is the query no. 4, which creates quite a high load on your postgres server just because it is summing up values. Moreover, you are just summing up values over and over again, which most likely won't change often (or even at all), as you have said that UPDATEs nearly do not happen at all. I furthermore assume two more things:
transactions is INSERT-only, i.e. DELETE statements almost never happen (besides perhaps in cases of some exceptional administrative intervention).
The values of column date_issued when INSERTing typically are somewhere "close to today" - so you usually won't INSERT stuff way in the past.
Out of this, to prevent aggregating values over and over again unnecessarily, I would introduce yet another table: let's call it transactions_aggr, which is built up like this:
create table transactions_aggr (
account_id INT NOT NULL,
date_issued DATE,
sumamount NUMERIC,
primary key (account_id, date_issued)
)
which will give you a table of per-day preaggregated values.
To determine which values are already preaggregated, I would add another boolean-typed column to transactions, which indicates to me, which of the rows are contained in transactions_aggr and which are not (yet). The query no. 4 then would have to be changed in such a way that it reads only non-preaggregated rows from transactions, whilst the rest could come from transactions_aggr. To facilitate that you could define a view like this:
select account_id, date_issued, sum(amount) as sumamount from
(
select account_id, date_issued, sumamount as amount from transactions_aggr as aggr
union all
select account_id, date_issued, sum(amount) as amount from transactions as t where t.aggregated = false
)
group by account_id, date_issued
Needless to say that putting an index on transactions.aggregated (perhaps in conjunction with the account_id) could greatly help to improve the performance here.
Updating transactions_aggr can be done using multiple approaches:
You could use this as a one-time activity and only pre-aggregate the current set of ~120m rows once. This would at least reduce the load on your machine doing aggregations significantly. However, over time you will run into the same problem again. Then you may just re-execute the entire procedure, simply dropping transactions_aggr as a whole and re-create it from scratch (all the original data still is there in transactions).
You have a nice period somewhere during the week/month/in the night, where you have little or no queries are coming in. Then you can open a transaction, read all transactions WHERE aggregated = false and add them with UPDATEs to transactions_aggr. Keep in mind to then toggle aggregated to true (should be done in the same transaction). The tricky part of this, however, is that you must pay attention to what reading queries will "see" of this transaction: Depending on your requirements of accuracy during that timeframe of this "update job", you may have to consider switching the transaction isolation level to "READ_COMMITED" to prevent ghost reads.
On the matter of your query no. 3 you then could try to really go for the approach of partitioning based on type_id. However, I perceive your query as a little strange, as you are performing a LIMIT/OFFSET without ordering (e.g. there is no ORDER BY statement in place) having specified (NB: You are not saying that you would be using database cursors). This may lead to the effect that the implicit order, which is currently used, is changed, if you enable partitioning on the table. So be careful on side-effects which this may cause on your program.
And one more thing: Before really doing the partition split, I would first check on the data distribution concerning type_id by issuing
select type_id, count(*) from transactions group by type_id
Not that it turns out that, for example, 90% of your data is with card_payment - so that you will have a heavily uneven distribution amongst your partitions and the biggest performance hogging queries are those which would still go into this single "large partition".
Hope this helps a little - and good luck!
My application uses pessimistic locking. When a user opens the form for update a record, the application executes this query (table names are exemplary):
begin;
select *
from master m
natural join detail d
where m.master_id = 123456
for update nowait;
The query locks one master row and several (to several dozen) detail rows. Transaction is open until a user confirms or cancels updates.
I need to know what rows (at least master rows) are locked. I have excavated the documentation and postgres wiki without success.
Is it possible to list all locked rows?
PostgreSQL 9.5 added a new option to FOR UPDATE that provides a straightforward way to do this.
SELECT master_id
FROM master
WHERE master_id NOT IN (
SELECT master_id
FROM master
FOR UPDATE SKIP LOCKED);
This acquires locks on all the not-currently-locked rows, so think through whether that's a problem for you, especially if your table is large. If nothing else, you'll want to avoid doing this in an open transaction. If your table is huge you can apply additional WHERE conditions and step through it in chunks to avoid locking everything at once.
Is it possible? Probably yes, but it is the Greatest Mystery of Postgres. I think you would need to write your own extension for it (*).
However, there is an easy way to work around the problem. You can use very nice Postgres feature, advisory locks. Two arguments of the function pg_try_advisory_lock(key1 int, key2 int) you can interpret as: table oid (key1) and row id (key2). Then
select pg_try_advisory_lock(('master'::regclass)::integer, 123456)
locks row 123456 of table master, if it was not locked earlier. The function returns boolean.
After update the lock has to be freed:
select pg_advisory_unlock(('master'::regclass)::integer, 123456)
And the nicest thing, list of locked rows:
select classid::regclass, objid
from pg_locks
where locktype = 'advisory'
Advisory locks may be complementary to regular locks or you can use them independently. The second option is very temptive, as it can significantly simplify the code. But it should be applied with caution because you have to make sure that all updates (deletes) on the table in all applications are performed with this locking.
(*) Mr. Tatsuo Ishii did it (I did not know about it, have just found).