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.
Related
I wonder what is the difference between having one table with 6 millions row (aka with a huge DB) and 100k active users:
CREATE TABLE shoes (
id serial primary key,
color text,
is_left_one boolean,
stock int
);
With also 6 index like:
CREATE INDEX blue_left_shoes ON shoes(color,is_left_one) WHERE color=blue AND is_left_one=true;
Versus: 6 tables with 1 million rows:
CREATE TABLE blue_left_shoes(
id serial primary key,
stock int
);
The latter one seems more efficient because users don't have to ask for the condition since the table IS the condition, but perhaps creating the indexes mitigate this?
This table is used to query either left, right, "blue", "green" or "red" shoes and to check the number of remaining items, but it is a simplified example but you can think of Amazon (or any digital selling platform) tooltip "only 3 items left in stock" for the workload and the usecase. It is the users (100k active daily) who will make the query.
NB: The question is mostly for PostgreSQL but differences with other DB is still relevant and interesting.
In the latter case, where you use a table called blue_left_shoes
Your code needs to first work out which table to look at (as opposed to parameterising a value in the where clause)
As permutations and options increase, you need to increase the number of tables, and increase the logic in your app that works out which table to use
Anything that needs to use this database (i.e. a reporting tool or an API) now needs to re implement all of these rules
You are imposing logic at a high layer to improve performance.
If you were to partition and/or index your table appropriately, you get the same effect - SQL queries only look through the records that matter. The difference is that you don't need to implement this logic in higher layers
As long as you can get the indexing right, keeping this is one table is almost always the right thing to do.
Partitioning
Database partitioning is where you select one or more columns to decide how to "split up" your table. In your case you could choose (color, is_left_one).
Now your table is logically split and ordered in this way and when you search for blue,true it automatically knows which partition to look in. It doesn't look in any other partitions (this is called partition pruning)
Note that this occurs automatically from the search criteria. You don't need to manually work out a particular table to look at.
Partitioning doesn't require any extra storage (beyond various metadata that has to be saved)
You can't apply multiple partitions to a table. Only one
Indexing
Creating an index also provides performance improvements. However indexes take up space and can impact insert and update performance (as they need to be maintained). Practically speaking, the select trade off almost always far outweighs any insert/update negatives
You should always look at indexes before partitioning
Non selective indexes
In your particular case, there's an extra thing to consider: a boolean field is not "selective". I won't go into details but suffice to say you shouldn't create an index on this field alone, as it won't be used because it only halves the number of records you have to look through. You'd need to include some other fields in any index (i.e. colour) to make it useful
In general, you want to keep all "like" data in a single table, not split among multiples. There are good reasons for this:
Adding new combinations is easier.
Maintaining the tables is easier.
You an easily do queries "across" entities.
Overall, the database is more efficient, because it is more likely that pages will be filled.
And there are other reasons as well. In your case, you might have an argument for breaking the data into 6 separate tables. The gain here comes from not having the color and is_left_one in the data. That means that this data is not repeated 6 million times. And that could save many tens of megabytes of data storage.
I say the last a bit tongue-in-cheek (meaning I'm not that serious). Computers nowadays have so much member that 100 Mbytes is just not significant in general. However, if you have a severely memory limited environment (I'm thinking "watch" here, not even "smart phone") then it might be useful.
Otherwise, partitioning is a fine solution that pretty much meets your needs.
For this:
WHERE color=blue AND is_left_one=true
The optimal index is
INDEX(color, is_left_one) -- in either order
Having id first makes it useless for that WHERE.
It is generally bad to have multiple identical tables instead of one.
There are some similar questions on the topic, but they are not really helping me.
I want to implement a soft delete feature like on StackOverflow, where items are not really deleted, but just hidden. I am using a SQL database. Here are 3 options:
Add a is_deleted boolean field.
Advantages: Simple.
Disadvantages: No date record. Forces me to add a is_deleted = 0 in every query.
Add a deleted_date date field. This is set to NULL if it's not deleted.
Advantages: Has date.
Disadvantages: Still cluttering my queries.
For both of the above
It will also impact performance because there are all these useless rows. They still have to be maintained in indexes. Also an index on the deleted column won't help when fetching non-deleted (the majority) of the rows. Full table scan is needed.
Another option is to create a separate table to hold deleted items:
Advantages: Improved performance when querying non-deleted rows. No need to add conditions to my queries on non-deleted rows. Easier on index maintenance.
Disadvantages: Complexity: Requires data migration for both deletion and undeletion. Need for new tables. Referential integrity is harder to handle.
Is there a better option?
I personally would base my answer off of how often you anticipate your users wanting to access that deleted data or "restore" that deleted data.
If it's often, then I would go with a "Date_Deleted" field and put a calculated "IsDeleted" in my poco in the code.
If it's never (or almost never) then a history table or deleted table is good for the benefits you explained.
I personally almost never use deleted tables (and opt for isDeleted or date_deleted) because of the potential risk to referencial integrity. You have A -> B and you remove the record from B database... You now have to manage referencial integrity because of your design choice.
If the key is numeric, I handle a "soft-delete" by negating the key. (Of course, won't work for identity keys). You don't need to change your code at all, and can easily restore the record by multiplying by -1.
Just another approach to give some thought to... If the key is alphanumeric, you can do something similar by prepending a unique "marker" characters. Since deleted records will all begin with this marker, then will end up off by themselves in the index.
In my opinion, the best way forward, when thinking about scaling and eventual table/database sizes is your third option - a separate table for deleted items. Such a table can eventually be moved to a different database to support scaling.
I believe you have listed the three most common options. As you have seen, each has advantages and disadvantages. Personally, I like taking the longer view on things.
I think your analysis of the options is good but you missed a few relevant points which I list below. Almost all implementations that I have seen use some sort of deleted or versioning field on the row as you suggest in your first two options.
Using one table with deleted flag:
If your indexes all contain the deleted flag field first and your query's mostly contain a where isdeleted=false type structure then it DOES solve you performance problems and the indexes very efficiently exclude the deleted rows. Similar logic could be used for the deleted date option.
Using two Tables
In general you need to make massive changes to reports because some reports may refer to deleted data (like old sales figures might refer to a deleted sales category). One can overcome this by creating a view which is a union of the two tables to read from and only write to the active records table.
Let's suppose we create a field called dead to mark deleted rows. We can create a index where field dead is false.
In this way, we only search non-deleted rows using the hint use index.
Is it reasonable for an application to create database tables dynamically as a means of partitioning?
For example, say I have a large table "widgets" with a "userID" column identifying the owner of each row. If this table tended to grow extremely large, would it make sense to instead have the application create a new table called "widgets_{username}" for each new user? Assume that the application will only ever have to query for widgets belonging to a single user at a time (i.e. no need to try and join any of these user widget tables together).
Doing this would break up the one large table into more easily-managed chunks, but this doesn't seem like an elegant solution. In my mind, the database schema should be defined when the application is written, and any runtime data is stored as rows, not as additional tables.
As a more general question, is modifying the database schema at runtime ever ok?
Edit: This question is mostly hypothetical; I had a pretty good feeling that creating tables at runtime didn't make sense. That being said, we do have a table with millions of rows in our application. SELECTs perform fine, but things like deleting all rows owned by a particular user can take a while. Basically I'm looking for some solid reasoning why just dynamically creating a table for each user doesn't make sense for when I'm asked.
NO, NO, NO!! Now repeat after me, I will not do this because it will create many headaches and problems in the future! Databases are made to handle large amounts of information. they use indexes to quickly find what you are after. think phone book how effective is the index? would it be better to have a different book for each last name?
This will not give you anything performance wise. Keep a single table, but be sure to index on UserID and you'll be able to get the data fast. however if you split the table up, it becomes impossible/really really hard to get any info that spans multiple users, like search all users for a certain widget, count of all widgets of a certain type, etc. you need to have every query be built dynamically.
If deleting rows is slow, look into that. How many rows at one time are we talking about 10, 1000, 100000? What is your clustered index on this table? Could you use a "soft delete", where you have a status column that you UPDATE to "D" to mark the row as deleted. Can you delete the rows at a later time, with less database activity. is the delete slow because it is being blocked by other activity. look into those before you break up the table.
No, that would be a bad idea. However some DBMSs (e.g. Oracle) allow a single table to be partitioned on values of a column, which would achieve the objective without creating new tables at run time. Having said that, it is not "the norm" to partition tables like this: it is only usually done in very large databases.
Using an index on userID should result nearly in the same performance.
In my opinion, changing the database schema at runtime is bad practice.
Consider, for example, security issues...
Is it reasonable for an application to create database tables
dynamically as a means of partitioning?
No. (smile)
This is not a question about using another tool. It is not a question about using different data structure. It is issue about WHY I see what I see -- please read to the end before answering. Thank you.
THE STORY
I have one single table which has one condition, the records are not deleted. Instead the record is marked as not active (there is field for that) and in such case all fields (except for identifiers and this isActive field) are considered irrelevant.
More on identifiers -- there are two fields:
id -- int, primary key, clustered
name -- unique, varchar, external index
How the update is done for example (I use C#/Linq/MSSQL2005): I fetch the record basing on name, then change required fields and commit the changes, so the update is executed (UPDATE uses id, not name).
However there is a problem with storage. So why not not break this table into dual structure -- "header" table (id, name, isActive) and data table (id, rest of the fields). In case of a problem with storage we can delete all records from data table for real (for isActive=false).
edit (by Shimmy): header+data are not retrieved by LINQ with join. Data records are loaded on demand (and this always occurs because of the code).
comment (by poster): AFAIR there is no join, so this is irrelevant. Data for headers were loaded manually. See below.
Performance -- (my) Theory
Now, what about performance? Which one will be faster? Let's say you have 10000 records in both tables (single, header, data) and you update them one by one (all 3 tables) -- fields isActive and some field from the "data" fields.
My calculation was/is:
mono table -- search using external index, then jumping into the structure, fetching all the data, update using primary key.
dual tables -- search using external index, jumping into the header table, fetching all the data, search using primary key on data table (no jumping here, it is clustered index), fetching all the data, update both tables using primary keys.
So, for me mono structure should be faster, because in dual case I have the same operations plus some extras.
The results
No matter what I do, update, select, insert, dual structure is either slightly better (speed) or up to 30% faster. And now I am all puzzled -- I would understand that I if were insert/update/select only header records, but in every case data records are used as well.
The question -- why/how dual structure can be faster?
I think this all boils down to how much data is being fetched, inserted, and updated.
SELECT case - in the dual-table configuration you're fetching less data. Database runtime is heavily dominated by I/O time, so having the "header" fields replicated on each row in the single-table configuration means you have to read that same data over and over again. In the dual-table configuration you only read the header data once.
INSERT case - similar to the above, but related to writing the data instead of reading it.
UPDATE case - your code updates the "isActive" field, which if I've read it correctly is part of the "header" fields. In the single-table configuration you're forcing many rows to be updated for each "isActive" change. In the dual-table configuration you're only updating a single header row for each "isActive" change.
I think this is a case of premature optimization. I get the feeling that you understood that according to data normalization rules the dual-table configuration was "better" - but because the single-table case seemed like it would provide better performance you wanted to go with that design. Thankfully you took the time to test what would happen and found that observed performance did not match your expectations. GOOD JOB! I wish more people would take the time to test things out like this. I think the lesson to learn here is that data normalization is a Good Thing.
Remember, the best time to optimize something is NEVER! The second-best time to optimize things is when you have an observed performance problem. The worst time to optimize is during analysis.
I hope this helps.
Assumption: Sql Server for database.
Sql Server tends to be higher in performance on narrow tables rather than wide. While this might not be true for something such as a mainframe.
This really points to normalization until you decide NOT to for performance reasons, and in this case the assumption that de-normalized tables would be more efficient is incorrect. Normalized structures can be better managed in the resources than de-normalized in this environment. I suspect (no citable basis of this) that the resource (hardware, multiprocessors, threading etc) makes the normalized structure faster because more stuff gets done at the same time.
Have you looked at the two query plans? That often gives it away.
As for speculation, the size of a row in a table affects how fast you can scan it. smaller rows means more rows fit into a data page. the brunt of a query is usually in the I/O time, so using two smaller tables greatly reduces the amount of data you have to sift through in the indexes.
Also, the locks are more granular -- the first update could write to table1, and then the second update could write to table1 while you're finishing up table2.
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