I have a database that is going to experience the integer exhaustion problem that Basecamp famously faced back in November. I have several months to figure out what to do.
Is there a no-downtime-required, proactive solution to migrating this column type? If so what is it? If not, is it just a matter of eating the downtime and migrating the column when I can?
Is this article sufficient, assuming I have several days/weeks to perform the migration now before I'm forced to do it when I run out of ids?
Use logical replication.
With logical replication you can have different data types at primary and standby.
Copy the schema with pg_dump -s, change the data types on the copy and then start logical replication.
Once all data is copied over, switch the application to use the standby.
For zero down time, the application has to be able to reconnect and retry, but that's always a requirement in such a case.
You need PostgreSQL v10 or better for that, and your database
shouldn't modify the schema, as DDL is not replicated.
should not use sequence (SERIAL or IDENTITY), as the last used value would not be replicated
Another solution for pre-v10 databases where all transactions are short:
Add a bigint column to the table.
Create a BEFORE trigger that sets the new column whenever a row is added or updated.
Run a series of updates that set the new column from the old one where it IS NULL. Keep those batches short so you don't lock long and don't deadlock much. Make sure these transaction run with session_replication_role = replica so they don't trigger triggers.
Once all rows are updated, create a unique index CONCURRENTLY on the new column.
Add a unique constraint USING the index you just created. That will be fast.
Perform the switch:
BEGIN;
ALTER TABLE ... DROP oldcol;
ALTER TABLE ... ALTER newcol RENAME TO oldcol;
COMMIT;
That will be fast.
Your new column has no NOT NULL set. This cannot be done without a long invasive lock. But you can add a check constraint IS NOT NULL and create it NOT VALID. That is good enough, and you can later validate it without disruptions.
If there are foreign key constraints, things get a little more complicated. You have to drop these and create NOT VALID foreign keys to the new column.
Create a copy of the old table but with modified ID field. Next create a trigger on the old table that inserts new data to both tables. Finally copy data from the old table to the new one (it would be a good idea to distinguish pre-trigger data with post-trigger for example by id if it is sequential). Once you are done switch tables and delete the old one.
This obviously requires twice as much space (and time for copy) but will work without any downtime.
Related
Can I do row-specific update / delete operations in a DB2 table Via SQL, in a NON QUNIQUE Primary Key Context?
The Table is a PHYSICAL FILE on the NATIVE SYSTEM of the AS/400.
It was, like many other Files, created without the unique definition, which leads DB2 to the conclusion, that The Table, or PF has no qunique Key.
And that's my problem. I can't override the structure of the table to insert a unique ID ROW, because, I would have to recompile ALL my correlating Programs on the AS/400, which is a serious issue, much things would not work anymore, "perhaps". Of course, I can do that refactoring for one table, but our system has thousands of those native FILES, some well done with Unique Key, some without Unique definition...
Well, I work most of the time with db2 and sql on that old files. And all files which have a UNIQUE Key are no problem for me to do those important update / delete operations.
Is there some way to get an additional column to every select with a very unique row id, respective row number. And in addition, what is much more important, how can I update this RowNumber.
I did some research and meanwhile I assume, that there is no chance to do exact alterations or deletes, when there is no unique key present. What I would wish would be some additional ID-ROW which is always been sent with the table, which I can Refer to when I do my update / delete operations. Perhaps my thinking here has an fallacy as non Unique Key Tables are purposed to be edited in other ways.
Try the RRN function.
SELECT RRN(EMPLOYEE), LASTNAME
FROM EMPLOYEE
WHERE ...;
UPDATE EMPLOYEE
SET ...
WHERE RRN(EMPLOYEE) = ...;
I want to load many rows from a CSV file.
The files contain data like these "article_name,article_time,start_time,end_time"
There is a contraint on the table: for the same article name, i don't insert a new row if the new article_time falls in an existing range [start_time,end_time] for the same article.
ie: don't insert row y if exists [start_time_x,end_time_x] for which time_article_y inside range [start_time_x,end_time_x] , with article_name_y = article_name_x
I tried with psycopg by selecting the existing article names ad checking manually if there is an overlap --> too long
I tried again with psycopg, this time by setting a condition 'exclude using...' and tryig to insert with specifying "on conflict do nothing" (so that it does not fail) but still too long
I tried the same thing but this time trying to insert many values at each call of execute (psycopg): it got a little better (1M rows processed in almost 10minutes), but still not as fast as it needs to be for the amount of data I have (500M+)
I tried to parallelize by calling the same script many time, on different files but the timing didn't get any better, I guess because of the locks on the table each time we want to write something
Is there any way to create a lock only on rows containing the same article_name? (and not a lock on the whole table?)
Could you please help with any idea to make this parallellizable and/or more time efficient?
Lots of thanks folks
Your idea with the exclusion constraint and INSERT ... ON CONFLICT is good.
You could improve the speed as follows:
Do it all in a single transaction.
Like Vao Tsun suggested, maybe COPY the data into a staging table first and do it all with a single SQL statement.
Remove all indexes except the exclusion constraint from the table where you modify data and re-create them when you are done.
Speed up insertion by disabling autovacuum and raising max_wal_size (or checkpoint_segments on older PostgreSQL versions) while you load the data.
Docs for Redshift say:
ALTER TABLE locks the table for reads and writes until the operation completes.
My question is:
Say I have a table with 500 million rows and I want to add a column. This sounds like a heavy operation that could lock the table for a long time - yes? Or is it actually a quick operation since Redshift is a columnar db? Or it depends if column is nullable / has default value?
I find that adding (and dropping) columns is a very fast operation even on tables with many billions of rows, regardless of whether there is a default value or it's just NULL.
As you suggest, I believe this is a feature of the it being a columnar database so the rest of the table is undisturbed. It simply creates empty (or nearly empty) column blocks for the new column on each node.
I added an integer column with a default to a table of around 65M rows in Redshift recently and it took about a second to process. This was on a dw2.large (SSD type) single node cluster.
Just remember you can only add a column to the end (right) of the table, you have to use temporary tables etc if you want to insert a column somewhere in the middle.
Personally I have seen rebuilding the table works best.
I do it in following ways
Create a new table N_OLD_TABLE table
Define the datatype/compression encoding in the new table
Insert data into N_OLD(old_columns) select(old_columns) from old_table Rename OLD_Table to OLD_TABLE_BKP
Rename N_OLD_TABLE to OLD_TABLE
This is a much faster process. Doesn't block any table and you always have a backup of old table incase anything goes wrong
I need to migrate a DDL from Postgres to DB2, but I need that it works the same as in Postgres. There is a table that generates values from a sequence, but the values can also be explicitly given.
Postgres
create sequence hist_id_seq;
create table benchmarksql.history (
hist_id integer not null default nextval('hist_id_seq') primary key,
h_c_id integer,
h_c_d_id integer,
h_c_w_id integer,
h_d_id integer,
h_w_id integer,
h_date timestamp,
h_amount decimal(6,2),
h_data varchar(24)
);
(Look at the sequence call in the hist_id column to define the value of the primary key)
The business logic inserts into the table by explicitly providing an ID, and in other cases, it leaves the database to choose the number.
If I change this in DB2 to a GENERATED ALWAYS it will throw errors because there are some provided values. On the other side, if I create the table with GENERATED BY DEFAULT, DB2 will throw an error when trying to insert with the same value (SQL0803N), because the "internal sequence" does not take into account the already inserted values, and it does not retry with a next value.
And, I do not want to restart the sequence each time a provided ID was inserted.
This is the problem in BenchmarkSQL when trying to port it to DB2: https://sourceforge.net/projects/benchmarksql/ (File sqlTableCreates)
How can I implement the same database logic in DB2 as it does in Postgres (and apparently in Oracle)?
You're operating under a misconception: that sources external to the db get to dictate its internal keys. Ideally/conceptually, autogenerated ids will never need to be seen outside of the db, as conceptually there should be unique natural keys for export or reporting. Still, there are times when applications will need to manage some ids, often when setting up related entities (eg, JPA seems to want to work this way).
However, if you add an id value that you generated from a different source, the db won't be able to manage it. How could it? It's not efficient - for one thing, attempting to do so would do one of the following
Be unsafe in the face of multiple clients (attempt to add duplicate keys)
Serialize access to the table (for a potentially slow query, too)
(This usually shows up when people attempt something like: SELECT MAX(id) + 1, which would require locking the entire table for thread safety, likely including statements that don't even touch that column. If you try to find any "first-unused" id - trying to fill gaps - this gets more complicated and problematic)
Neither is ideal, so it's best to not have the problem in the first place. This is usually done by having id columns be autogenerated, but (as pointed out earlier) there are situations where we may need to know what the id will be before we insert the row into the table. Fortunately, there's a standard SQL object for this, SEQUENCE. This provides a db-managed, thread-safe, fast way to get ids. It appears that in PostgreSQL you can use sequences in the DEFAULT clause for a column, but DB2 doesn't allow it. If you don't want to specify an id every time (it should be autogenerated some of the time), you'll need another way; this is the perfect time to use a BEFORE INSERT trigger;
CREATE TRIGGER Add_Generated_Id NO CASCADE BEFORE INSERT ON benchmarksql.history
NEW AS Incoming_Entity
FOR EACH ROW
WHEN Incoming_Entity.id IS NULL
SET id = NEXTVAL FOR hist_id_seq
(something like this - not tested. You didn't specify where in the project this would belong)
So, if you then add a row with something like:
INSERT INTO benchmarksql.history (hist_id, h_data) VALUES(null, 'a')
or
INSERT INTO benchmarksql.history (h_data) VALUES('a')
an id will be generated and attached automatically. Note that ALL ids added to the table must come from the given sequence (as #mustaccio pointed out, this appears to be true even in PostgreSQL), or any UNIQUE CONSTRAINT on the column will start throwing duplicate-key errors. So any time your application needs an id before inserting a row in the table, you'll need some form of
SELECT NEXT VALUE FOR hist_id_seq
FROM sysibm.sysdummy1
... and that's it, pretty much. This is completely thread and concurrency safe, will not maintain/require long-term locks, nor require serialized access to the table.
I have about 10 tables with over 2 million records and one with 30 million. I would like to efficiently remove older data from each of these tables.
My general algorithm is:
create a temp table for each large table and populate it with newer data
truncate the original tables
copy tmp data back to original tables using: "insert into originaltable (select * from tmp_table)"
However, the last step of copying the data back is taking longer than I'd like. I thought about deleting the original tables and making the temp tables "permanent", but I lose constraint/foreign key info.
If I delete from the tables directly, it takes much longer. Given that I need to preserve all foreign keys and constraints, are there any faster ways of removing the older data?
Thanks.
The fastest process is likely to be exactly as you've outlined:
Copy new data into a temporary table
Drop indexes and foreign keys
Drop the old table
Copy the temporary table back to the old table name
Rebuild indexes and foreign keys.
The Postgres manual has some suggestions on perfomance, too, that may or may not apply. Frankly, however, it is significantly quicker to drop a table than to drop millions of rows (since each delete is performed tuple by tuple) and it is significantly quicker to insert millions of rows into a table with no constraints or indexes (as each constraint must be checked and each index must be updated for each record insert; by removing all constraints, you limit this to a single build of the index and a single verification for the constraint).
The "standard" solution for these problems typically involves partitioning your tables on the appropriate key, such that when you need to delete old data, you can simply drop a whole partition -- certainly the fastest deletion that you will ever get.
However, partitioning in PostgreSQL isn't as easy as some other databases -- you need to relocate data manually using triggers, and there are caveats (e.g. no global primary keys)
See the PostgreSQL manual on Partitioning