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I have a project with a postgresql database. I'm handling migrations with Flyway. Now I have some initial data, that I want to add to the database when the application starts. It's a data that should always be there in the beginning. How could I handle this data initialization properly?
I've been thinking about using Flyways repeatable migrations. It is run always if the hash of the sql file changes. The problem is, that then I would need to construct it with sql insert statements. The problem there is, that what if the object already exists? Ideally, I would want that I could specify the data in the sql, and then the migration either inserts it to the table if it doesn't exist. But it should look for each field, not just by primary key. Because if I want to change something in one row, then I would want that to update to the database. Of course I could always drop the whole contents of the table, and then run the migration, but isn't that little cumbersome in the long run? Like always after little edit, I need to drop table and run the migration... I just wonder if there is some better way to handle the initial data?
You can specify the primary key value with INSERT or COPY by including the column like any other. With the former, you could add an ON CONFLICT DO UPDATE clause to make any possible changes. If you're using 9.4 or below, ON CONFLICT isn't available so you're stuck with DELETE and a plain INSERT or COPY, although knowing the primary keys means you don't have to delete the entire table.
I'm converting data from one schema to another. Each table in the source schema has a 'status' column (default NULL). When a record has been converted, I update the status column to 1. Afterwards, I can report on the # of records that are (not) converted.
While the conversion routines are still under development, I'd like to be able to quickly reset all values for status to NULL again.
An UPDATE statement on the tables is too slow (there are too many records). Does anyone know a fast alternative way to accomplish this?
The fastest way to reset a column would be to SET UNUSED the column, then add a column with the same name and datatype.
This will be the fastest way since both operations will not touch the actual table (only dictionary update).
As in Nivas' answer the actual ordering of the columns will be changed (the reset column will be the last column). If your code rely on the ordering of the columns (it should not!) you can create a view that will have the column in the right order (rename table, create view with the same name as old table, revoke grants from base table, add grants to view).
The SET UNUSED method will not reclaim the space used by the column (whereas dropping the column will free space in each block).
If the column is nullable (since default is NULL, I think this is the case), drop and add the column again?
While the conversion routines are still under development, I'd like to be able to quickly reset all values for status to NULL again.
If you are in development why do you need 70 million records? Why not develop against a subset of the data?
Have you tried using flashback table?
For example:
select current_scn from v$database;
-- 5607722
-- do a bunch of work
flashback table TABLE_NAME to scn 5607722;
What this does is ensure that the table you are working on is IDENTICAL each time you run your tests. Of course, you need to ensure you have sufficient UNDO to hold your changes.
hm. maybe add an index to the status column.
or alterately, add a new table with the primary key only in it. then insert to that table when the record is converted, and TRUNC that table to reset...
I like some of the other answers, but I just read in a tuning book that for several reasons it's often quicker to recreate the table than to do massive updates on the table. In this case, it seems ideal, since you would be writing the CREATE TABLE X AS SELECT with hopefully very few columns.
My web application parses data from an uploaded file and inserts it into a database table. Due to the nature of the input data (bank transaction data), duplicate data can exist from one upload to another. At the moment I'm using hideously inefficient code to check for the existence of duplicates by loading all rows within the date range from the DB into memory, and iterating over them and comparing each with the uploaded file data.
Needless to say, this can become very slow as the data set size increases.
So, I'm looking to replace this with a SQL query (against a MySQL database) which checks for the existence of duplicate data, e.g.
SELECT count(*) FROM transactions WHERE desc = ? AND dated_on = ? AND amount = ?
This works fine, but my real-world case is a little bit more complicated. The description of a transaction in the input data can sometimes contain erroneous punctuation (e.g. "BANK 12323 DESCRIPTION" can often be represented as "BANK.12323.DESCRIPTION") so our existing (in memory) matching logic performs a little cleaning on this description before we do a comparison.
Whilst this works in memory, my question is can this cleaning be done in a SQL statement so I can move this matching logic to the database, something like:
SELECT count(*) FROM transactions WHERE CLEAN_ME(desc) = ? AND dated_on = ? AND amount = ?
Where CLEAN_ME is a proc which strips the field of the erroneous data.
Obviously the cleanest (no pun intended!) solution would be to store the already cleaned data in the database (either in the same column, or in a separate column), but before I resort to that I thought I'd try and find out whether there's a cleverer way around this.
Thanks a lot
can this cleaning be done in a SQL statement
Yes, you can write a stored procedure to do it in the database layer:
mysql> CREATE FUNCTION clean_me (s VARCHAR(255))
-> RETURNS VARCHAR(255) DETERMINISTIC
-> RETURN REPLACE(s, '.', ' ');
mysql> SELECT clean_me('BANK.12323.DESCRIPTION');
BANK 12323 DESCRIPTION
This will perform very poorly across a large table though.
Obviously the cleanest (no pun intended!) solution would be to store the already cleaned data in the database (either in the same column, or in a separate column), but before I resort to that I thought I'd try and find out whether there's a cleverer way around this.
No, as far as databases are concerned the cleanest way is always the cleverest way (as long as performance isn't awful).
Do that, and add indexes to the columns you're doing bulk compares on, to improve performance. If it's actually intrinsic to the type of data that desc/dated-on/amount are always unique, then express that in the schema by making it a UNIQUE index constraint.
The easiest way to do that is to add a unique index on the appropriate columns and to use ON DUPLICATE KEY UPDATE. I would further recommend transforming the file into a csv and loading it into a temporary table to get the most out of mysql's builtin functions, which are surely faster than anything that you could write yourself - if you consider that you would have to pull the data into your own application, while mysql does everything in place.
The cleanest way is indeed to make sure only correct data is in the database.
In this example the "BANK.12323.DESCRIPTION" would be returned by:
SELECT count(*) FROM transactions
WHERE desc LIKE 'BANK%12323%DESCRIPTION' AND dated_on = ? AND amount = ?
But this might impose performance issues when you have a lot of data in the table.
Another way that you could do it is as follows:
Clean the description before inserting.
Create a primary key for the table that is a combination of the columns that uniquely identifier the entry. Sounds like that might be cleaned description, date and amount.
Use the either the 'replace' or 'on duplicate key' syntax, which ever is more appropriate. 'replace' actually replaces the existing row in the db with the updated one when an existing unique key confict occurs, e.g:
REPLACE INTO transactions (desc, dated_on, amount) values (?,?,?)
'on duplicate key' allows you to specify which columns to update on a duplicate key error:
INSERT INTO transaction (desc, dated_on, amount) values (?,?,?)
ON DUPLICATE KEY SET amount = amount
By using the multi-column primary key, you will gain a lot of performance since primary key lookups are usually quite fast.
If you prefer to keep your existing primary key, you could also create a unique unix on those three columns.
Whichever way you choose, I would recommend cleaning the description before going into the db, even if you also store the original description and just use the cleaned one for indexing.
OK, so practically every database based application has to deal with "non-active" records. Either, soft-deletions or marking something as "to be ignored". I'm curious as to whether there are any radical alternatives thoughts on an `active' column (or a status column).
For example, if I had a list of people
CREATE TABLE people (
id INTEGER PRIMARY KEY,
name VARCHAR(100),
active BOOLEAN,
...
);
That means to get a list of active people, you need to use
SELECT * FROM people WHERE active=True;
Does anyone suggest that non active records would be moved off to a separate table and where appropiate a UNION is done to join the two?
Curiosity striking...
EDIT: I should make clear, I'm coming at this from a purist perspective. I can see how data archiving might be necessary for large amounts of data, but that is not where I'm coming from. If you do a SELECT * FROM people it would make sense to me that those entries are in a sense "active"
Thanks
You partition the table on the active flag, so that active records are in one partition, and inactive records are in the other partition. Then you create an active view for each table which automatically has the active filter on it. The database query engine automatically restricts the query to the partition that has the active records in it, which is much faster than even using an index on that flag.
Here is an example of how to create a partitioned table in Oracle. Oracle doesn't have boolean column types, so I've modified your table structure for Oracle purposes.
CREATE TABLE people
(
id NUMBER(10),
name VARCHAR2(100),
active NUMBER(1)
)
PARTITION BY LIST(active)
(
PARTITION active_records VALUES (0)
PARTITION inactive_records VALUES (1)
);
If you wanted to you could put each partition in different tablespaces. You can also partition your indexes as well.
Incidentally, this seems a repeat of this question, as a newbie I need to ask, what's the procedure on dealing with unintended duplicates?
Edit: As requested in comments, provided an example for creating a partitioned table in Oracle
Well, to ensure that you only draw active records in most situations, you could create views that only contain the active records. That way it's much easier to not leave out the active part.
We use an enum('ACTIVE','INACTIVE','DELETED') in most tables so we actually have a 3-way flag. I find it works well for us in different situations. Your mileage may vary.
Moving inactive stuff is usually a stupid idea. It's a lot of overhead with lots of potential for bugs, everything becomes more complicated, like unarchiving the stuff etc. What do you do with related data? If you move all that, too, you have to modify every single query. If you don't move it, what advantage were you hoping to get?
That leads to the next point: WHY would you move it? A properly indexed table requires one additional lookup when the size doubles. Any performance improvement is bound to be negligible. And why would you even think about it until the distant future time when you actually have performance problems?
I think looking at it strictly as a piece of data then the way that is shown in the original post is proper. The active flag piece of data is directly dependent upon the primary key and should be in the table.
That table holds data on people, irrespective of the current status of their data.
The active flag is sort of ugly, but it is simple and works well.
You could move them to another table as you suggested. I'd suggest looking at the percentage of active / inactive records. If you have over 20 or 30 % inactive records, then you might consider moving them elsewhere. Otherwise, it's not a big deal.
Yes, we would. We currently have the "active='T/F'" column in many of our tables, mainly to show the 'latest' row. When a new row is inserted, the previous T row is marked F to keep it for audit purposes.
Now, we're moving to a 2-table approach, when a new row is inserted, the previous row is moved to an history table. This give us better performance for the majority of cases - looking at the current data.
The cost is slightly more than the old method, previously you had to update and insert, now you have to insert and update (ie instead of inserting a new T row, you modify the existing row with all the new data), so the cost is just that of passing in a whole row of data instead of passing in just the changes. That's hardly going to make any effect.
The performance benefit is that your main table's index is significantly smaller, and you can optimise your tablespaces better (they won't grow quite so much!)
Binary flags like this in your schema are a BAD idea. Consider the query
SELECT count(*) FROM users WHERE active=1
Looks simple enough. But what happens when you have a large number of users, so many that adding an index to this table would be required. Again, it looks straight forward
ALTER TABLE users ADD INDEX index_users_on_active (active)
EXCEPT!! This index is useless because the cardinality on this column is exactly two! Any database query optimiser will ignore this index because of it's low cardinality and do a table scan.
Before filling up your schema with helpful flags consider how you are going to access that data.
https://stackoverflow.com/questions/108503/mysql-advisable-number-of-rows
We use active flags quite often. If your database is going to be very large, I could see the value in migrating inactive values to a separate table, though.
You would then only require a union of the tables when someone wants to see all records, active or inactive.
In most cases a binary field indicating deletion is sufficient. Often there is a clean up mechanism that will remove those deleted records after a certain amount of time, so you may wish to start the schema with a deleted timestamp.
Moving off to a separate table and bringing them back up takes time. Depending on how many records go offline and how often you need to bring them back, it might or might not be a good idea.
If the mostly dont come back once they are buried, and are only used for summaries/reports/whatever, then it will make your main table smaller, queries simpler and probably faster.
We use both methods for dealing with inactive records. The method we use is dependent upon the situation. For records that are essentially lookup values, we use the Active bit field. This allows us to deactivate entries so they wont be used, but also allows us to maintain data integrity with relations.
We use the "move to separation table" method where the data is no longer needed and the data is not part of a relation.
The situation really dictates the solution, methinks:
If the table contains users, then several "flag" fields could be used. One for Deleted, Disabled etc. Or if space is an issue, then a flag for disabled would suffice, and then actually deleting the row if they have been deleted.
It also depends on policies for storing data. If there are policies for keeping data archived, then a separate table would most likely be necessary after any great length of time.
No - this is a pretty common thing - couple of variations depending on specific requirements (but you already covered them):
1) If you expect to have a whole BUNCH of data - like multiple terabytes or more - not a bad idea to archive deleted records immediately - though you might use a combination approach of marking as deleted then copying to archive tables.
2) Of course the option to hard delete a record still exists - though us developers tend to be data pack-rats - I suggest that you should look at the business process and decide if there is now any need to even keep the data - if there is - do so... if there isn't - you should probably feel free just to throw the stuff away.....again, according to the specific business scenario.
From a 'purist perspective' the realtional model doesn't differentiate between a view and a table - both are relations. So that use of a view that uses the discriminator is perfectly meaningful and valid provided the entities are correctly named e.g. Person/ActivePerson.
Also, from a 'purist perspective' the table should be named person, not people as the name of the relation reflects a tuple, not the entire set.
Regarding indexing the boolean, why not:
ALTER TABLE users ADD INDEX index_users_on_active (id, active) ;
Would that not improve the search?
However I don't know how much of that answer depends on the platform.
This is an old question but for those search for low cardinality/selectivity indexes, I'd like to propose the following approach that avoids partitioning, secondary tables, etc.:
The trick is to use "dateInactivated" column that stores the timestamp of when the record is inactivated/deleted. As the name implies, the value is NULL while the record is active, but once inactivated, write in the system datetime. Thus, an index on that column ends up having high selectivity as the number of "deleted" records grows since each record will have a unique (not strictly speaking) value.
Then your query becomes:
SELECT * FROM people WHERE dateInactivated is NULL;
The index will pull in just the right set of rows that you care about.
Filtering data on a bit flag for big tables is not really good in terms of performance. In case when 'active' determinate virtual deletion you can create 'TableName_delted' table with the same structure and move deleted data there using delete trigger.
That solution will help with performance and simplifies data queries.
Working on a project at the moment and we have to implement soft deletion for the majority of users (user roles). We decided to add an is_deleted='0' field on each table in the database and set it to '1' if particular user roles hit a delete button on a specific record.
For future maintenance now, each SELECT query will need to ensure they do not include records where is_deleted='1'.
Is there a better solution for implementing soft deletion?
Update: I should also note that we have an Audit database that tracks changes (field, old value, new value, time, user, ip) to all tables/fields within the Application database.
I would lean towards a deleted_at column that contains the datetime of when the deletion took place. Then you get a little bit of free metadata about the deletion. For your SELECT just get rows WHERE deleted_at IS NULL
You could perform all of your queries against a view that contains the WHERE IS_DELETED='0' clause.
Having is_deleted column is a reasonably good approach.
If it is in Oracle, to further increase performance I'd recommend partitioning the table by creating a list partition on is_deleted column.
Then deleted and non-deleted rows will physically be in different partitions, though for you it'll be transparent.
As a result, if you type a query like
SELECT * FROM table_name WHERE is_deleted = 1
then Oracle will perform the 'partition pruning' and only look into the appropriate partition. Internally a partition is a different table, but it is transparent for you as a user: you'll be able to select across the entire table no matter if it is partitioned or not. But Oracle will be able to query ONLY the partition it needs. For example, let's assume you have 1000 rows with is_deleted = 0 and 100000 rows with is_deleted = 1, and you partition the table on is_deleted. Now if you include condition
WHERE ... AND IS_DELETED=0
then Oracle will ONLY scan the partition with 1000 rows. If the table weren't partitioned, it would have to scan 101000 rows (both partitions).
The best response, sadly, depends on what you're trying to accomplish with your soft deletions and the database you are implementing this within.
In SQL Server, the best solution would be to use a deleted_on/deleted_at column with a type of SMALLDATETIME or DATETIME (depending on the necessary granularity) and to make that column nullable. In SQL Server, the row header data contains a NULL bitmask for each of the columns in the table so it's marginally faster to perform an IS NULL or IS NOT NULL than it is to check the value stored in a column.
If you have a large volume of data, you will want to look into partitioning your data, either through the database itself or through two separate tables (e.g. Products and ProductHistory) or through an indexed view.
I typically avoid flag fields like is_deleted, is_archive, etc because they only carry one piece of meaning. A nullable deleted_at, archived_at field provides an additional level of meaning to yourself and to whoever inherits your application. And I avoid bitmask fields like the plague since they require an understanding of how the bitmask was built in order to grasp any meaning.
if the table is large and performance is an issue, you can always move 'deleted' records to another table, which has additional info like time of deletion, who deleted the record, etc
that way you don't have to add another column to your primary table
That depends on what information you need and what workflows you want to support.
Do you want to be able to:
know what information was there (before it was deleted)?
know when it was deleted?
know who deleted it?
know in what capacity they were acting when they deleted it?
be able to un-delete the record?
be able to tell when it was un-deleted?
etc.
If the record was deleted and un-deleted four times, is it sufficient for you to know that it is currently in an un-deleted state, or do you want to be able to tell what happened in the interim (including any edits between successive deletions!)?
Careful of soft-deleted records causing uniqueness constraint violations.
If your DB has columns with unique constraints then be careful that the prior soft-deleted records don’t prevent you from recreating the record.
Think of the cycle:
create user (login=JOE)
soft-delete (set deleted column to non-null.)
(re) create user (login=JOE). ERROR. LOGIN=JOE is already taken
Second create results in a constraint violation because login=JOE is already in the soft-deleted row.
Some techniques:
1. Move the deleted record to a new table.
2. Make your uniqueness constraint across the login and deleted_at timestamp column
My own opinion is +1 for moving to new table. Its take lots of
discipline to maintain the *AND delete_at = NULL* across all your
queries (for all of your developers)
You will definitely have better performance if you move your deleted data to another table like Jim said, as well as having record of when it was deleted, why, and by whom.
Adding where deleted=0 to all your queries will slow them down significantly, and hinder the usage of any of indexes you may have on the table. Avoid having "flags" in your tables whenever possible.
you don't mention what product, but SQL Server 2008 and postgresql (and others i'm sure) allow you to create filtered indexes, so you could create a covering index where is_deleted=0, mitigating some of the negatives of this particular approach.
Something that I use on projects is a statusInd tinyint not null default 0 column
using statusInd as a bitmask allows me to perform data management (delete, archive, replicate, restore, etc.). Using this in views I can then do the data distribution, publishing, etc for the consuming applications. If performance is a concern regarding views, use small fact tables to support this information, dropping the fact, drops the relation and allows for scalled deletes.
Scales well and is data centric keeping the data footprint pretty small - key for 350gb+ dbs with realtime concerns. Using alternatives, tables, triggers has some overhead that depending on the need may or may not work for you.
SOX related Audits may require more than a field to help in your case, but this may help.
Enjoy
Use a view, function, or procedure that checks is_deleted = 0; i.e. don't select directly on the table in case the table needs to change later for other reasons.
And index the is_deleted column for larger tables.
Since you already have an audit trail, tracking the deletion date is redundant.
I prefer to keep a status column, so I can use it for several different configs, i.e. published, private, deleted, needsAproval...
Create an other schema and grant it all on your data schema.
Implment VPD on your new schema so that each and every query will have the predicate allowing selection of the non-deleted row only appended to it.
http://download.oracle.com/docs/cd/E11882_01/server.112/e16508/cmntopc.htm#CNCPT62345
#AdditionalCriteria("this.status <> 'deleted'")
put this on top of your #entity
http://wiki.eclipse.org/EclipseLink/Examples/JPA/SoftDelete