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.
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.
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!
Are there any conditions under which records created in a table using a typical auto-increment field would be available for read out of sequence?
For instance, could a record with value 10 ever appear in the result of a select query when the record with value 9 is not yet visible to a select query?
The purpose for my question is… I want to know if it is reliable to use the maximum value retrieved from one query as the lower bound to identify previously unretrieved values in a later query, or could that potentially miss a row?
If that kind of race condition is possible under some circumstances, then are any of the isolation levels that can be used for the select queries that are immune to that problem?
Yes, and good on you for thinking about it.
You can trivially demonstrate this with three concurrent psql sessions, given some table
CREATE TABLE x (
seq serial primary key,
n integer not null
);
then
SESSION 1 SESSION 2 SESSION 3
BEGIN;
BEGIN;
INSERT INTO x(n) VALUES(1)
INSERT INTO x(n) VALUES (2);
COMMIT;
SELECT * FROM x;
COMMIT;
SELECT * FROM x;
It is not safe to assume that for any generated value n, all generated values n-1 have been used by already-committed or already-aborted xacts. They might be in progress and commit after you see n.
I don't think isolation levels really help you here. There's no mutual dependency for SERIALIZABLE to detect.
This is partly why logical decoding was added, so you can get a consistent stream in commit order.
I currently have tables that are partitioned out by year & month for our sales transactions. For example, we have sales tables that would look something like this:
factdailysales_201501
factdailysales_201502
factdailysales_201503 etc ...
Generally, I've always performed dynamic SQL to capture a Start Date, End Date, find out what partitions those are, and then loop through each of those partitions ... but its starting to become such a hassle and I've learned that this is probably not the best way to do it in terms of just maintenance, trouble shooting, and performance.
I decided to build a view that would UNION ALL of my sales partitions together. However, I don't want selecting from the view to have to scan all of the partitions on execution, it would take away the whole purpose of partitioning tables out. Because of this, I added check constraints on date to each of my sales tables. This way when I selected from the view, it would know which tables to access from instead of scanning every table.
Here are the following examples below:
SELECT SUM([retail])
FROM Sales_Orig
WHERE [Date] >= '2015-03-01'
This query has the execution plan of only pulling from the partitions that I need.
My problem that i'm facing right now is that most of the time when my team will be writing stored procedures, they would more than likely write their queries where a date variable is passed into the where statement.
DECLARE #SD DATE = '2015-03-01'
SELECT SUM([retail])
FROM Sales_Orig
WHERE [Date] >= #SD
However, when a variable is being passed in, the execution plan now scans ALL of the partitions in the view, causing the performance to take wayyy longer than when I hard coded in the date
I suppose I could do dynamic SQL again and insert the date string into the SELECT statement, but it would bring me back to the beginning of trying to get rid of dynamic SQL in the first place for this simple sales query.
So my question is, am I setting this up wrong? Am I on the right track? It seems that the view can't take in a variable for the check constraint and ends up scanning every table. Is there another approach anyone would recommend? Maybe my original solution of just looping through partitions via dynamic SQL is the best way to do it?
** EDIT **
http://sqlsunday.com/2014/08/31/partitioned-views/
This article is actually where I initially saw the idea! It seems when using that exact same solution, I'm still experiencing the same struggle!
Thanks!!
Okay this might work. It's a table-valued function that only access tables according to your #start and #end parameters so only accessing your "partitions" that it needs. I figured you could take this concept and write some dynamic SQL to create all the if statements.
Now of course new tables are added every day so how does that tie in. Well I think the best way would be is that every day you alter the function adding the next sales table. That way querying it is simple. And you could use the same dynamic sql you used to create the function to alter it which should be relatively simple.
Note: I added default values that are the min and max of the data type DATE. That way you could query something like everything from 20140101 and onward or vice versa.
Your tables
SELECT CAST('20150101' AS DATE) datesVal INTO factDailySales_20150101;
SELECT CAST('20150102' AS DATE) datesVal INTO factDailySales_20150102;
SELECT CAST('20150103' AS DATE) datesVal INTO factDailySales_20150103;
The Function
CREATE FUNCTION ufn_factTotalSales (#Start DATE = '17530101', #End DATE = '99991231')
RETURNS #factTotalSales TABLE
(
datesVal DATE
)
AS
BEGIN
IF(CAST('20150101' AS DATE) BETWEEN #Start AND #End)
BEGIN
INSERT INTO #factTotalSales
SELECT datesVal
FROM factDailySales_20150101
END
IF(CAST('20150102' AS DATE) BETWEEN #Start AND #End)
BEGIN
INSERT INTO #factTotalSales
SELECT datesVal
FROM factDailySales_20150102
END
IF(CAST('20150103' AS DATE) BETWEEN #Start AND #End)
BEGIN
INSERT INTO #factTotalSales
SELECT datesVal
FROM factDailySales_20150103
END
RETURN;
END
GO
All tables
SELECT *
FROM ufn_factTotalSales(default,default)
All tables greater than or equal to 20150102
SELECT *
FROM ufn_factTotalSales('20150102',default)
**All tables less than or equal to 20150102
SELECT *
FROM ufn_factTotalSales(default,'20150102')
All tables between specific range
SELECT *
FROM ufn_factTotalSales('20150101','20150102')
Is this the ideal solution? No. The ideal would be to combine all tables into one and having good indexes. I know you said that wouldn't work because of the way other code has been written. Hear me out. Now perhaps this is off the wall, lets say you do combine the tables but obviously there are old scripts looking for specific daily sales tables. Maybe you could create views with the dailySales names that access the factTotalSales. OR You could create synonyms for the factTotalSales that would correspond to each factDailySales.
Maybe you could look into that. It wouldn't be easy, but I think letting SQL Server optimize your queries the way it was designed is a better way of doing it instead of forcing it with dynamic SQL.
Just my two cents. Hope this helps. At the very least, I hope it gave you some ideas.
5 years later: option(recompile).
The planner needs to have access to the constants to eliminate the table entirely from the query plan. With a variable, without a forced recompile, a generic plan is used. (Related: parameter sniffing.)
While this means the query plan is larger as it has to include all tables, it does not mean that all tables are actually scanned: look at the IO stats, as table scan elimination occurs even if such shows in the query plan.
The 'Number Of Executions' in the query plan will be 0 when the tables are not scanned: unfortunately, these branches are still reported as a non-zero percentage cost "Table Scan" node in the query plan & UI, which will appear high proportionally if the query is trivially fast. The displayed percentage cost of these extra "Table Scan" nodes approaches zero as the amount of data returned from the actually used base tables increases.
This same optimization/elimination occurs when the view is not a Partitioned View (eg. base tables are missing partition column in PK), yet the underlying tables have a suitable Check Constraint on the filtered column. It also occurs when the view selects a constant value to establish the partition that is not otherwise stored in the table. With a constant in the query or recompiled plan the tables will be eliminated entirely. With a variable the tables will still not actually be scanned and thus eliminated logically during query execution.
The use of a proper Partitioned View is only really beneficial to allow a direct Insert & Update, with the major caveat that it requires the partition column to be in each table's PK and disallows the use of an identity column (making a Partitioned View largely useless IMOHO). SQL Server handles the optimizations very similarly for other quasi-Partitioned View cases.
(This is on SQL Server 2014; earlier versions might not have optimized the different patterns as efficiently.)
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.