I have a table with 28 million rows that I want to update. It has around 60 columns and a ID column (primary key) with an index created on it. I created four new columns and I want to populate them with the data from four columns from other table which also has an ID column with an index created on it. Both tables have the same amount of rows and just the primary key and the index on the IDENTI column. The query has been running for 15 hours and since it is high priority work, we are starting to get nervous about it and we don't have so much time to experiment with queries. We have never update a table so big (7 GB), so we are not sure if this amount of time is normal.
This is the query:
UPDATE consolidated
SET IDEDUP2=uni.IDEDUP2
USE21=uni.USE21
USE22=uni.USE22
PESOXX2=uni.PESOXX2
FROM uni_group uni, consolidated con
WHERE con.IDENTI=uni.IDENTI
How can I make it faster? Is it possible? If not, is there a way to check how much longer it is going to take (without killing the process)?
Just as additional information, we have ran before much more complex queries for 3 million row tables (postgis) and It has taken it about 15 hours as well.
You should not repeat the target table in the FROM clause. Your statement creates a cartesian join of the consolidated table with itself, which is not what you want.
You should use the following:
UPDATE consolidated con
SET IDEDUP2=uni.IDEDUP2
USE21=uni.USE21
USE22=uni.USE22
PESOXX2=uni.PESOXX2
FROM uni_group uni
WHERE con.IDENTI = uni.IDENTI
Related
I have a table in Postgres database which has a lot of records (30,00,000+).
I want to delete all records which are older than an year but I see that there is no timestamp or date column in this table.
How can I delete the old records in this case? (first I want to get the count of records which are older than 1 year)
Also, will deleting huge number of records in a single SQL query cause performance issues while the deletion is in progress?
As #laurenze_albe has said, there is no way to get the age of a row of data. Probably, you'll end up deleting based on some kind of id field. If you have the time, you could monitor how many records are added during a week, and then make a guess for the number of records in a year.
If you get max(id) from today and then max(id) a week from now, you could subtract the difference and multiply by 52.
A row in a PostgreSQL table has no age unless you explicitly store it with the data, so there is no way to do that. You have to use a condition based on the data.
Deleting many data can take a long time, even if all foreign keys are indexed. The king's way to speedy mass deletions is table partitioning.
If I have large amounts of data in a table defined like
CREATE TABLE sensor_values ( ts TIMESTAMPTZ(35, 6) NOT NULL,
value FLOAT8(17, 17) DEFAULT 'NaN' :: REAL NOT NULL,
sensor_id INT4(10) NOT NULL, );
Data comes in every minute for thousands of points. Quite often though I need to extract and work with daily values over years (On a web frontend). To aid this I would like a sensor_values_days table that only has the daily sums for each point and then I can use this for faster queries over longer timespans.
I don't want a trigger for every write to the db as I am afraid that would slow down the already bottle neck of writes to the db.
Is there a way to trigger only after so many rows have been inserted ?
Or perhaps an index and maintains a index of a sum of entries over days ? I don't think that is possible.
What would be the best way to do this. It would not have to be very up to date. Losing the last few hours or a day would not be an issue.
Thanks
What would be the best way to do this.
Install clickhouse and use AggregatingMergeTree table type.
With postgres:
Create per-period aggregate table. You can have several with different granularity, like hours, days, and months.
Have a cron or scheduled task run at the end of each period plus a few minutes. First, select the latest timestamp in the per-period table, so you know at which period to start. Then, aggregate all rows in the main table for periods that came after the last available one. This process will also work if the per-period table is empty, or if it missed the last update then it will catch up.
In order to do only inserts and no updates, you have to run it at the end of each period, to make sure it got all the data. You can also store the first and last timestamp of the rows that were aggregated, so later if you check the table you see it did use all the data from the period.
After aggregation, the "hour" table should be 60x smaller than the "minute" table, that should help!
Then, repeat the same process for the "day" and "month" table.
If you want up-to-date stats, you can UNION ALL the results of the "per day" table (for example) to the results of the live table, but only pull the current day out of the live table, since all the previous days's worth of data have been summarized into the "per day" table. Hopefully, the current day's data will be cached in RAM.
It would not have to be very up to date. Losing the last few hours or a day would not be an issue.
Also if you want to partition your huge table, make sure you do it before its size becomes unmanageable...
Materialized Views and a Cron every 5 minutes can help you:
https://wiki.postgresql.org/wiki/Incremental_View_Maintenance
In PG14, we will have INCREMENTAL MATERIALIZED VIEW, but for the moment is in devel.
I'm looking at a postgres system with tables containing 10 or 100's of millions of rows, and being fed at a rate of a few rows per second.
I need to do some processing on the rows of these tables, so I plan to run some simple select queries: select * with a where clause based on a range (each row contains a timestamp, that's what I'll work with for ranges). It may be a "closed range", with a start and an end I know are contained in the table, and I know no new data will fall into the range, or an open range : ie one of the range boundary might not be "in the table yet" and rows being fed in the table might thus fall in that range.
Since the response will itself contains millions of rows, and the processing per row can take some time (10s of ms) I'm fully aware I'll use a cursor and fetch, say, a few 1000 rows at a time. My question is:
If I run an "open range" query: will I only get the result as it was when I started the query, or will new rows being inserted in the table that fall in the range while I run my fetch show up ?
(I tend to think that no I won't see new rows, but I'd like a confirmation...)
updated
It should not happen under any isolation level:
https://www.postgresql.org/docs/current/static/transaction-iso.html
but Postgres insures it only in Serializable isolation
Well, I think when you make a query, that means you create a new transaction and it will not receive/update data from any other transaction until it commit.
So, basically "you only get the result as it was when you started the query"
I have a table say 'T' in kdb which has rows over 6 billion. When I tried to execute query like this
select from T where i < 10
it throws wsfull expection. Is there any way I can execute queries like this in table having large amount of data.
10#T
The expression as you wrote it first makes a bitmap containing all of the elements where i (rownumber) < 10, which is as tall as one of your columns. It then does where (which just contains til 10) and then gets them from each row. You can save the last step with:
T[til 10]
but 10#T is shorter.
Assuming you have a partitioned table here, it is normally beneficial to have the partitioning column (date, int etc.) as the first item in the where clause of your query - otherwise as mentioned previously you are reading a six billion item list into memory, which will result in a 'wsfull signal for any machine with less than the requisite amount of RAM.
Bear in mind that row index starts at 0 for each partition, and is not reflective of position in the overall table. The query that you gave as an example in your question would return the first ten rows of each partition of table T in your database.
In order to do this without reaching your memory limit, you can try running the following (if your database is date-partitioned):
raze{10#select from T where date=x}each date
Hi all I've got an interesting task to update a single column in a table that has roughly 2 million rows. I've tried doing this using MVC Entity Framework, however I'm encountering "Out of memory exceptions" and I'm just wondering if there's another way.
The interesting part is that its not just a simple update. The procedure needs to read the TelephoneNumber column already in the table and this could be 014812001 for example. Then it needs to calculate a score for this number based on the number of occurrences greater than 1. So for example using the above number this would score a 6 as we have 3 x 1's and 3 x 0's giving a total of 6.
Once this score has been calculated this number needs to be inserted into the a column in the current row be processed, so in our case the row with the TelephoneNumber = 014812001.
Is this possible using TSQL or is it better to carry on with my Entity Framework approach?
For such a bulk update, I would always recommend doing this on the server itself - there's really no point in dragging down 2 million rows, updating a single column, and then pushing those back to the server again.....
I think based on your description, it should be fairly simple to create a little T-SQL user defined function that would calculate this score. Once you have that, you can issue a single T-SQL statement:
UPDATE dbo.YourTable
SET Score = dbo.fnCalculateScore(TelephoneNumber)
WHERE .... (whatever condition you might have) .....
That should be faster by several orders of magnitude than with your Entity Framework approach....