I have one machine on which several applications are hosted. Applications work on separated data and don't interact - each application only needs access to its own data. I want to use PostgreSQL as RDBMS. Which one of the following is best and why?
One global Postgres sever, one global database, one schema per application.
One global Postgres server, one database per application.
One Postgres server per application.
Feel free to suggest additional architectures if you think they would be better than the ones above.
The questions you need to ask yourself: does any application ever need to access data from another application (in the same SQL statement). If you can can answer that with a clear NO, then you should at least go for separate databases. Cross-database queries aren't that straight-forward in Postgres, so if the different applications do need a lot of data from other applications, then solution 1 might be deployment layout to think about. If this would only concern very few tables, then using foreign data wrappers with different databases might still be a better solution.
Solution 2 and 3 are more or less the same from the perspective of each application. One thing to keep in mind when deciding between 2 and 3 is availability. Some configuration changes to Postgres require a restart of the service. Is an outage of all applications acceptable in that case, even though the change was only necessary for one?
But you can always start with option 2 and then move database to different servers later.
Another question to ask is if all applications always use the same (major) Postgres version With solution 2 you must make sure that all applications are compatible with a new Postgres version if one of them wants to upgrade e.g. because of new features that the application wants to use.
Solution 1 is stupid : a SQL schema is not a database. Use SQL schema for one application that have multiple "parts" like "Poduction", "sales", "marketing", "finances"...
While the final volume of the data won't be too heavy and the number of user won't be too much, use only one PG cluster to facilitate administration tasks
If the volume of data or the number of user increases, it will be time to separates your different databases on new distinct PG clusters....
I have created a db long ago using django. Now as we are migrating the application, so I need all the CREATE TABLE sql queries which django might have run to create the entire db for our service (which has around 70-80 tables and each table has avg 30-70 columns).
Both the servers old and new are using Postgres for databases.
But the technology stack is completely different (A 3rd party proprietary application which will host the service) instead of django.
If I start to write all the tables again from scratch, it will take at least a week or two.
Is there any way either from Postgres or from django which can generate the CREATE TABLE sql schema for an entire db keeping all the relationship as is?
Also, I have to do minor modification to that schema as per customer requirement.
p.s - pg_dump won't work as I need actual schema itself to get it reviewed from client.
Asking for your help.
We start docker image stand-alone Keycloak in openshift.
We add new users using REST. But everything works very slowly. For 240000 users the uploads takes 24 hours.
Has anyone come across this?
How do you add and update users?
Our server: CPU 4 4 GB.
Our version of keyclock 7.3.1.GA.
The connection is constantly breaking and we are forced to send user packages
Based on key cloak, the following actions were performed:
CREATE EXTENSION pg_trgm;
CREATE INDEX in_user_entity_001
ON user_entity USING GIST (lower (username) gist_trgm_ops);
Explanations:
The pg_trgm extension is included in the set of plugins supplied with PostgreSQL and is supported by PostgreSQL developers (https://www.postgresql.org/docs/11/pgtrgm.html).
The created index is matched to a specific long query.
How can I obtain the creation date or time of an IBM's DB2 database without connecting to the specified database first? Solutions like:
select min(create_time) from syscat.tables
and:
db2 list tables for schema SYSIBM
require me to connect to the database first, like:
db2 connect to dbname user userName using password
Is there another way of doing this through a DB2 command instead, so I wouldn't need to connect to the database?
Can db2look command be used for that?
Edit 01: Background Story
Since more than one person asked why do I need to do this and for what reasons, here is the background story.
I have a server with DB2 DBMS where many people and automated scripts are using it to create some databases for temporary tasks and tests. It's never meant to keep the data for long time. However for one reason or another (ex: developer not cleaning after himself or tests stopping forcefully before they can do the clean up) some databases never get dropped and they start to get accumulated till the hard disk is filled out eventually. So The idea of the app is to look up the age of the database and drop it, if it's older than 6 months (for example).
We have a SaaS application where each tenant has its own database in Postgres. How would I apply a patch to all the databses? For example if I want to add a table or add a column to a table, I have to either write a program that loops through all databases and execute a SQL against them or using pgadmin, go through them one by one.
Is there smarter and/or faster way?
Any help is greatly appreciated.
Yes, there's a smarter way.
Don't create a new database for each tenant. If everything is in one database then you only need to alter one database.
Pick one database, alter each table to have the column TENANT and add this to the primary key. Then insert into this database every record for all tenants and drop the other databases (obviously considerably more work than this as your application will need to be changed).
The differences with your approach are extensively discussed elsewhere:
What problems will I get creating a database per customer?
What are the advantages of using a single database for EACH client?
Multiple schemas versus enormous tables
Practicality of multiple databases per client vs one database
Multi-tenancy - single database vs multiple database
If you don't put everything in one database then I'm afraid you have to alter them all individually, and doing it programatically would be simplest.
At a higher level, all multi-tenant applications follow one of three approaches:
One tenant's data lives in one database,
One tenant's data lives in one schema, or
Add a tenant_id / account_id column to your tables (shared schema).
I usually find that developers use the following criteria when they evaluate these different approaches.
Isolation: Since you can put each tenant into its own database in one hand, and have tenants share the same table on the other, this becomes the most apparent dimension. If you provide your users raw SQL access or you're in a regulated industry such as healthcare, you may need strict guarantees from your database. That said, PostgreSQL 9.5 comes with row level security policies that makes this less of a concern for most applications.
Extensibility: If your tenants are sharing the same schema (approach #3), and your tenants have fields that varies between them, then you need to think about how to merge these fields.
This article on multi-tenant databases has a great summary of different approaches. For example, you can add a dozen columns, call them C1, C2, and so forth, and have your application infer the actual data in this column based on the tenant_id. PostgresQL 9.4 comes with JSONB support and natively allows you to use semi-structured fields to express variations between different tenants' data.
Scaling: Another criteria is how easily your database would scale-out. If you create a tenant per database or schema (#1 or #2 above), your application can make use of existing Ruby Gems or [Django packages][1] to simplify app integration. That said, you'll need to manually manage your tenants' data and the machines they live on. Similarly, you'll need to build your own sharding logic to propagate foreign key constraints and ALTER TABLE commands.
With approach #3, you can use existing open source scaling solutions, such as Citus. For example, this blog post describes how to easily shard a multi-tenant app with Postgres.
it's time for me to give back to the community :) So after 4 years, our multi-tenant platform is in production and I would like to share the following observations/experiences with all of you.
We used a database per each tenant. This has given us extreme flexibility as the size of the databases in the backups are not huge and hence we can easily import them into our staging environment for customers issues.
We use Liquibase for database development and upgrades. This has been a tremendous help to us, allowing us to package the entire build into a simple war file. All changes are easily versioned and managed very efficiently. There is a bit of learning curve here an there but nothing substantial. 2-5 days can significantly save you time.
Given that we use Spring/JPA/Hibernate, we use a technique called Dynamic Data Source Routing. So when a user logs-in, we find the related datasource with a lookup and connect them to the session to the right database. That's also when the Liquibase scripts get applied for updates.
This is, for now, I will come back with more later on.
Well, there are problems with one database for all tenants in our case for sure.
The backup file gets huge and becomes almost not practical hard to manage
For troubleshooting, we need to restore customer's data in our dev env, we just use that customer's backup file and usually the file is not as big as if we were to use one database for all customers.
Again, Liquibase has been key in allowing to manage updates across all the tenants seamlessly and without any issues. Without Liquibase, I can see lots of complications with this approach. So Liquibase, Liquibase and more Liquibase.
I also suspect that we would need a more powerful hardware to manage a huge database with large joins across millions of records vs much lighter database with much smaller queries.
In case of problems, the service doesn't go down for everyone and there will be limited to one or few tenants.
In general, for our purposes, this has been a great architectural decision and we are benefiting from it every day. One time we had one customer that didn't have their archiving active and their database size grew to over 3 GB. With offshore teams and slower internet as well as storage/bandwidth prices, one can see how things may become complicated very quickly.
Hope this helps someone.
--Rex