Assuming I have a table sets with a field filters, containing array of key - value mappings, table items to which select query must be applied to extract rows based on these filters, and associated table for M:M relations to link each set with each item. I am seeking for a method or mechanism to cancel select query if sets.filters were updated, otherwise M:M relation will be built invalid as based on yet not refreshed filters.
The concrete scenario when a problem takes place is:
Receive file with items data, to parse, and insert into items returning new relevant ids(primary keys here);
After insertion, select from relevant sets for filters;
Take items ids and select from items using filters;
Update M:M association table for all the items returned at step 3.
So, unfortunately between step 3 and 4 or even earlier, API call makes an update on one of the sets rows, changing its filters. As the result - M:M table is invalid, because one filter was changed(lets say the filters contained kind of weight <= 100 kilos expression, however after the mentioned update it has become weight <= 50 kilos, so if there are some new items with weight greater than 50, those items ids should not be in M:M table, obviously).
Is there some efficient way to cancel select query from items during transaction? Or maybe there is a strong query to use. My idea is to rollback changes post-factum, checking sets.modified_at column. But it seems as doing additional job by wasting disk and cpu time.
Related
The idea of the SaaS tool is to have dynamic tables with dynamic custom fields and values of different types, we were thinking to use "force.com/salesforce.com" example but is seems to be too complicated to maintain moving forward, also making some reports to create with a huge abstraction level, so we came up with simple idea but we have to be sure that this is kinda good approach.
This is the architecture we have today (in few steps).
Each tenant has it own separate database on the cluster (Postgres 12).
TABLE table, used to keep all of those tables as reference, this entity has ManyToOne relation to META table and OneToMany relation with DATA table.
META table is used for metadata configuration, has OneToMany relation with FIELDS (which has name of the fields as well as the type of field e.g. TEXT/INTEGER/BOOLEAN/DATETIME etc. and attribute value - as string, only as reference).
DATA table has ManyToOne relation to TABLES and 50 character varying columns with names like: attribute1...50 which are NULL-able.
Example flow today:
When user wants to open a TABLE DATA e.g. "CARS", we load the META table with all the FIELDS (to get fields for this query). User specified that he want to query against: Brand, Class, Year, Price columns.
We are checking by the logic, the reference for Brand, Class, Year and Price in META>FIELDS table, so we know that Brand = attribute2, Class = attribute 5, Year = attribute6 and Price = attribute7.
We parse his request into a query e.g.: SELECT [attr...2,5,6,7] FROM DATA and then show the results to user, if user decide to do some filters on it, based on this data e.g. Year > 2017 AND Class = 'A' we use CAST() functionality of SQL for example SELECT CAST(attribute6 AS int) AND attribute5 FROM DATA WHERE CAST(attribute6 AS int) > 2017 AND attribute5 = 'A';, so then we can actually support most principles of SQL.
However moving forward we are scared a bit:
Manage such a environment for more tenants while we are going to have more tables (e.g. 50 per customer, with roughly 1-5 mil per TABLE (5mil is maximum which we allow, for bigger data we have BigQuery) which is giving us 50-250 mil rows in single table DATA_X) which might affect performance of the queries, especially when we gave possibilities to manage simple WHERE statements (less,equal,null etc.) using some abstraction language e.g. GET CARS [BRAND,CLASS,PRICE...] FILTER [EQ(CLASS,A),MT(YEAR,2017)] developed to be similar to JQL (Jira Query Language).
Transactions lock, as we allow to batch upload CSV into the DATA_X so once they want to load e.g. 1GB of the data, it kinda locks the table for other systems to access the DATA table.
Keeping multiple NULL columns which can affect space a bit (for now we are not that scared as while TABLE creation, customer can decide how many columns he wants, so based on that we are assigning this TABLE to one of hardcoded entities DATA_5, DATA_10, DATA_15, DATA_20, DATA_30, DATA_50, where numbers corresponds to limitations of the attribute columns, and those entities are different, we also support migration option if they decide to switch from 5 to 10 attributes etc.
We are on super early stage, so we can/should make those before we scale, as we knew that this is most likely not the best approach, but we kept it to run the project for small customers which for now is working just fine.
We were thinking also about JSONB objects but that is not the option, as we want to keep it simple for getting the data.
What do you think about this solution (fyi DATA has PRIMARY key out of 2 tables - (ID,TABLEID) and built in column CreatedAt which is used form most of the queries, so there will be maximum 3 indexes)?
If it seem bad, what would you recommend as the alternative to this solution based on the details which I shared (basically schema-less RDBMS)?
IMHO, I anticipate issues when you wanted to join tables and also using cast etc.
We had followed the approach below that will be of help to you
We have a table called as Cars and also have a couple of tables like CarsMeta, CarsExtension columns. The underlying Cars table will have all the common fields for a ll tenant's. Also, we will have the CarsMeta table point out what are the types of columns that you can have for extending the Cars entity. In the CarsExtension table, you will have columns like StringCol1...5, IntCol1....5, LongCol1...10
In this way, you can easily filter for data also like,
If you have a filter on the base table, perform the search, if results are found, match the ids to the CarsExtension table to get the list of exentended rows for this entity
In case the filter is on the extended fields, do a search on the extension table and match with that of the base entity ids.
As we will have the extension table organized like below
id - UniqueId
entityid - uniqueid (points to the primary key of the entity)
StringCol1 - string,
...
IntCol1 - int,
...
In this case, it will be easy to do a join for entity and then get the data along with the extension fields.
In case you are having the table metadata and data being inferred from separate tables, it will be a difficult task to maintain this over long period of time and also huge volume of data.
HTH
How can I properly page by ordering on a column that could possibly have repeated values? I have a table called posts, which has a column that holds the number of likes of a certain post, called num_likes, and I want to order by num_likes DESC. But the image below shows a problem that I run into - the new row inserted between the two pages causes repeated data to be fetched.
This link here explains the problem, and gives the solution of keyset pagination, but from what I've seen, that only works if the column that the rows are being sorted on are distinct / unique. How would I do this if that is not the case?
You can easily make the sort key unique by adding the primary key to it.
You don't have to display the primary key to the user, just use it internally to tell “equal” rows apart.
For querying and indexing, you can make use of PostgreSQL's ability to compare like this: (num_likes, id) >= (4, 325698).
I'm developing an application with SQLAlchemy and PostgreSQL. Users of the system modify data in 8 or so tables. Consider this contrived example schema:
I want to add visible logging to the system to record what has changed, but not necessarily how it has changed. For example: "User A modified product Foo", "User A added user B" or "User C purchased product Bar". So basically I want to store:
Who made the change
A message describing the change
Enough information to reference the object that changed, e.g. the product_id and customer_id when an order is placed, so the user can click through to that entity
I want to show each user a list of recent and relevant changes when they log in to the application (a bit like the main timeline in Facebook etc). And I want to store subscriptions, so that users can subscribe to changes, e.g. "tell me when product X is modified", or "tell me when any products in store S are modified".
I have seen the audit trigger recipe, but I'm not sure it's what I want. That audit trigger might do a good job of recording changes, but how can I quickly filter it to show recent, relevant changes to the user? Options that I'm considering:
Have one column per ID type in the log and subscription tables, with an index on each column
Use full text search, combining the ID types as a tsvector
Use an hstore or json column for the IDs, and index the contents somehow
Store references as URIs (strings) without an index, and walk over the logs in reverse date order, using application logic to filter by URI
Any insights appreciated :)
Edit It seems what I'm talking about it an activity stream. The suggestion in this answer to filter by time first is sounding pretty good.
Since the objects all use uuid for the id field, I think I'll create the activity table like this:
Have a generic reference to the target object, with a uuid column with no foreign key, and an enum column specifying the type of object it refers to.
Have an array column that stores generic uuids (maybe as text[]) of the target object and its parents (e.g. parent categories, store and organisation), and search the array for marching subscriptions. That way a subscription for a parent category can match a child in one step (denormalised).
Put a btree index on the date column, and (maybe) a GIN index on the array UUID column.
I'll probably filter by time first to reduce the amount of searching required. Later, if needed, I'll look at using GIN to index the array column (this partially answers my question "Is there a trick for indexing an hstore in a flexible way?")
Update this is working well. The SQL to fetch a timeline looks something like this:
SELECT *
FROM (
SELECT DISTINCT ON (activity.created, activity.id)
*
FROM activity
LEFT OUTER JOIN unnest(activity.object_ref) WITH ORDINALITY AS act_ref
ON true
LEFT OUTER JOIN subscription
ON subscription.object_id = act_ref.act_ref
WHERE activity.created BETWEEN :lower_date AND :upper_date
AND subscription.user_id = :user_id
ORDER BY activity.created DESC,
activity.id,
act_ref.ordinality DESC
) AS sub
WHERE sub.subscribed = true;
Joining with unnest(...) WITH ORDINALITY, ordering by ordinality, and selecting distinct on the activity ID filters out activities that have been unsubscribed from at a deeper level. If you don't need to do that, then you could avoid the unnest and just use the array containment #> operator, and no subquery:
SELECT *
FROM activity
JOIN subscription ON activity.object_ref #> subscription.object_id
WHERE subscription.user_id = :user_id
AND activity.created BETWEEN :lower_date AND :upper_date
ORDER BY activity.created DESC;
You could also join with the other object tables to get the object titles - but instead, I decided to add a title column to the activity table. This is denormalised, but it doesn't require a complex join with many tables, and it tolerates objects being deleted (which might be the action that triggered the activity logging).
I'm currently working on a project involving keeping track of users and their actions with my database (PostgreSQL as the RDMS), and I have run into an issue when trying to perform COUNT(*) on occurrences of each user. What I want is to be able to, efficiently, count the number of times each user appears from every record, and also be able to achieve looking at counts on a particular date range.
So, the problem is how do we achieve counting the total number of times a user appears from the tables contents, and how do we count the total number on a date range.
What I've tried
As you might know, Postgres doesn't support COUNT(*) very well using indices, so we have to consider other ways to reduce the # of records it looks at in order to speed up the query. So my first approach is to create a table to keep track of the number of times a user has a log message associated with them, and on what day (similar to the idea behind a materialized view, but I dont want continually refresh the materialized view with my count query). Here is what I've come up with:
CREATE TABLE users_counts(user varchar(65536), counter int default 0, day date);
CREATE RULE inc_user_date_count
AS ON INSERT TO main_table
DO ALSO UPDATE users_counts SET counter = counter + 1
WHERE user = NEW.user AND day = DATE(NEW.date_);
What this does is every time a new record is inserted into my 'main_table', we update the current users_counts table to increment the records whose date is equal to the new records date, and the user names are the same.
NOTE: the date_ column in 'main_table' is a timestamp so I must cast the new records date_ to be a DATE type.
The problem is, what if the user column value doesn't already exist in my new table 'users_count' for the current day, then nothing is updated.
Here is my question:
How do I write the rule such that we check if a user exists for the current day, if so increment that counter, otherwise insert new row with user, day, and counter of 1;
I also would like to know if my approach makes sense to do, or is there any ideas I am missing that I just haven't thought about. As my database grows, it is increasingly inefficient to perform counting, so I want to avoid any performance bottlenecks.
EDIT 1: I was able to actually figure this out by creating a separate RULE but I'm not sure if this is correct:
CREATE RULE test_insert AS ON INSERT TO main_table
DO ALSO INSERT INTO users_counts(user, counter, day)
SELECT NEW.user, 1, DATE(NEW.date)
WHERE NOT EXISTS (SELECT user FROM users.log_messages WHERE user = NEW.user_);
Basically, an insert happens if the user doesn't already exist in my CACHED table called user_counts, and the first rule above updates the count.
What I'm unsure of is how do I know when which rule is called first, the update rule or insert.. And there must be a better way, how do I combine the two rules? Can this be done with a function?
It is true that postgresql is notoriously slow when it comes to count(*) queries. However if you do have a where clause that limits the number of entries the query will be much faster. If you are using postgresql 9.2 or newer this query will be just as fast as it's in mysql because of index only scans which was added in 9.2 but it's best to explain analyze your query to make sure.
Does my solution make sense?
Very much so provided that your explain analyze show that index only scans are not being used. Trigger based solutions like the one that you have adapted find wide usage. But as you have realized the problem with the initial state arises (whether to do an update or an insert).
which rule is called first
Multiple rules on the same table and same event type are applied in
alphabetical name order.
from http://www.postgresql.org/docs/9.1/static/sql-createrule.html
the same applies for triggers. If you want a particular rule to be executed first change it's name so that it comes up higher in the alphabetical order.
how do I combine the two rules?
One solution is to modify your rule to perform an upsert (Look right at the bottom of that page for a sample upsert ). The other is to populate the counter table with initial values. The trick is to create the trigger at the same time to avoid errors. This blog post explains it really well.
While the initial setup will be slow each individual insert will probably be faster. The two opposing factors being the slowness of a WHERE NOT EXISTS query vs the overhead of catching an exception.
Tip: A block containing an EXCEPTION clause is significantly more
expensive to enter and exit than a block without one. Therefore, don't
use EXCEPTION without need.
Source the postgresql documentation page linked above.
I retrieve an ordered list of items from a table of items in a Sqlite Database. How can I swap the id so the order of two items in the Sqlite database table?.
The id shouldn't determine position or ordering. It should be an immutable identifier.
If you need to represent order in a database you need to create another orderNumber column. A couple options are (1) either have values that span a range or (2) have a pointer to next (like a linked list).
For ranges: Spanning a range helps you avoid rewriting the orderNumber column for all items after the insert point. For example, in the range, insert first gets 1, insert 2nd gets max range, insert 3rd between first and second gets mid-range number - if you reposition you have to assign mid-points of the items it's between. One downside is if the list gets enough churn (minimized by a large span) you may have to rebalance the ranges. The pro of this solution is you can get the ordered list just by ordering by this column in the sql statement.
For linked list: If the database has a next column that points to the id that's after it in order, you need to update a couple rows to insert something. Upside is it's simple. Downside is you can't order in the sql statement - you're relying on the code getting the list to sort it.
One other variation is you could pull the ordered list data out of that table altogether. For example, you could have an ordered list table that has listid, itemid, orderedNumber. That allows you to have one or multiple logical ordered lists of the items in that table it references.
Some other references:
How to store ordered items which often change position in DB
Best way to save a ordered List to the Database while keeping the ordering
https://dba.stackexchange.com/questions/5683/how-to-design-a-database-for-storing-a-sorted-list