Constantly i have been hitting with Resource pool allocation error after creating several tables in new schema.
After running the Database Designer in vertica for particular schema with all tables the queries are running fine.
Kindly help me to understand the concept.
The Database Designer is optional; you don't have to use it at all. Using it helps you optimize your physical layout, and if you're having trouble with resource-pool allocation it sounds like you might benefit from that.
From the documentation:
The HP Vertica Database Designer:
Analyzes your logical schema, sample data, and, optionally, your sample queries.
Creates a physical schema design (a set of projections) that can be deployed automatically or manually.
Can be used by anyone without specialized database knowledge.
Can be run and rerun any time for additional optimization without stopping the database.
Uses strategies to provide optimal query performance and data compression.
You can run DBD for just a particular query (optimizes whatever's needed to support that query) or for your entire database. It uses sample queries that you provide, so if your usage patterns change over time it can help to rerun it.
Related
I am assembling a Business Intelligence solution using the Pentaho software as a BI engine. Within this solution, I had to set up a requirements for a PostgreSQL database server.
The current situation is very easy, since no ETL process is being carried out for data extraction, so the PostgreSQL configuration has not changed it much, and it is practically as it is configured as "factory".
I would like to know what Postgres configuration parameters have to be touched and modified to optimize it as a Datawarehouse. I have seen a lot of documentation, but it is not clear to me at all, since one documentation says that such values have to be modified, and other documentation, other completely different values.
I would like to know just that, if there is a clearer and more precise documentation to optimize a postgres 9.6 to be used as a Pentaho DW.
Thank you very much
I have taken over an existing MVC website which uses entity framework and hangfire and is hosted on Azure and uses Azure database. Every so often the website times out.
I'm new to Azure portal, entity framework and hangfire.
If I increase the DTU's it clears the timeout issues?
I'm looking for ways of how to diagnose why the website times out. I have added error logging using elmah and checked hangfire but this doesn't give me any further information.
Is there anything in azure portal that can help?
If it "times out" and if "increasing DTU resolves timeouts" and these observations are true (I think it's on you to really convince yourself this is absolutely true, don't make this assumption lightly) then the usual and obvious candidate is "a slow sql query". Entity Framework is often used with linq to create sql queries without writing sql. These queries are often fine for very simple tasks, such as someData.Where(x=>x.Id == 1).First(), however, if linq is used to join tables, or create complex associations, the generated sql can become monstrously bad, from a performance perspective. You can add logging to write out the sql generated by linq, or you can try to trace the database to see what sql is running on it. If tracing is out of the question, there are still meta queries you can use to view things like cached query plans and SQL Server can give you estimated costs and cached execution counts.
You can still hang yourself without using linq. You can still use stored procedures with EF. Way too many developers are naive about SQL performance still; you need to comb over your back end and learn the schema, the stored procedures; inspect the sql contents of everything. Check for any database triggers (easy to miss). Red flags are subqueries, too many joining, too many results from a query, lots of string manipulation in a query, joining tables on strings, or XML/JSON-based SQL work.
Be aware that "slow sql queries" will become slower when load is high. And when slow sql queries build up, they only take more time to resolve. This can also cause debilitating table locking, depending on the nature of the query.
But queries can be performant and still cause locking. ie One table is being written to often and it's blocking other writes or reads from that table. This is a little harder to diagnose, but you can figure it out by carefully inspecting logs of database calls and how long they take to execute. There are also sql queries you can run on the database to diagnose long-running queries, or what tables are locked at a given point in time.
Finally, check for any back end webjobs for your application. If timeouts occur at reoccurring days or times, then somebody's batch SQL could be blocking your production database from being read.
But this is all speculation. I think you need to do more research to determine what is actually causing the site to become unresponsive. If you can log response times for common queries, you can rule out SQL-based latency as being the culprit or not and work from there. There's nothing inherently "amiss" about any of the technologies you specified.
If queries are perfomant but still causing issues, a long term solution is to add something like a message queue and batch your sql work intelligently, or just make the database work asynchronous and not block the UI.
You should correlate any logged timeouts with azure's monitoring. Azure can give you CPU/RAM/page visits and such on the dashboard.
SQL Azure is a bit of a different beast. It doesn't have the on-demand performance of a dedicated DB unless you're prepared to throw serious $$ at it. And even then ...
EF, when written for well can perform quite well. When written poorly it can be a dog, and those problems are compounded on a platform like SQL Azure.
The first thing is to check that your EF contexts are set up to use an execution strategy suited to Azure: https://learn.microsoft.com/en-us/ef/ef6/fundamentals/connection-resiliency/retry-logic
The next thing would be to see what kinds of SQL tracing you can run on Azure. Tracing is essential to see what EF is doing behind the scenes. I'm not familiar with tools available for Azure, in my case my Azure experience was running SQL Server on VMs because SQL Azure was too immature, not HIPAA compliant at the time, and expensive for the DTU estimates we were able to get. Worst case, can you restore an database backup into an SQL Server instance and point a copy of your application environment temporarily at that to run through common usage scenarios? Using an SQL Trace you can pick up on exactly when and how often EF is executing queries, and what queries it is executing.
Things to look at:
How many queries are running? If you are loading a set of records and expect one query, are there a whole heap of queries getting sent? This would indicate lazy-load calls being triggered.
What queries are being run? Is it selecting a lot more fields than are being displayed? This would be potentially a case where entire entities are being loaded where a .Select() could be used to reduce the amount of data. Perhaps even the case where entire sets of entities are being loaded that aren't relevant to what is displayed/done, such as cases where someone is using .ToList() prior to just doing a .Count() or .Any() or doing a .FirstOrDefault() just to do a != null check.
Is the database properly indexed? Copy some of the heavier queries into SQL Manager and execute them with an execution plan. Are there indexing suggestions?
The common sins of developing with EF and other ORMs boil down to "pulling too much, too often." It's surprising how many clients I've worked with have development teams that have not used a profiler to inspect their ORM use efficiency. (and I'm talking 0% so far.)
There is a web application which is running for a years and during its life time the application has gathered a lot of user data. Data is stored in relational DB (postgres). Not all of this data is needed to run application (to do the business). However form time to time business people ask me to provide reports of this data data. And this causes some problems:
sometimes these SQL queries are long running
quires are executed against production DB (not cool)
not so easy to deliver reports on weekly or monthly base
some parts of data is stored in way which is not suitable for such
querying (queries are inefficient)
My idea (note that I am a developer not the data mining specialist) how to improve this whole process of delivering reports is:
create separate DB which regularly is update with production data
optimize how data is stored
create a dashboard to present reports
Question: But is there a better way? Is there another DB which better fits for such data analysis? Or should I look into modern data mining tools?
Thanks!
Do you really do data mining (as in: classification, clustering, anomaly detection), or is "data mining" for you any reporting on the data? In the latter case, all the "modern data mining tools" will disappoint you, because they serve a different purpose.
Have you used the indexing functionality of Postgres well? Your scenario sounds as if selection and aggregation are most of the work, and SQL databases are excellent for this - if well designed.
For example, materialized views and triggers can be used to process data into a scheme more usable for your reporting.
There are a thousand ways to approach this issue but I think that the path of least resistance for you would be postgres replication. Check out this Postgres replication tutorial for a quick, proof-of-concept. (There are many hits when you Google for postgres replication and that link is just one of them.) Here is a link documenting streaming replication from the PostgreSQL site's wiki.
I am suggesting this because it meets all of your criteria and also stays withing the bounds of the technology you're familiar with. The only learning curve would be the replication part.
Replication solves your issue because it would create a second database which would effectively become your "read-only" db which would be updated via the replication process. You would keep the schema the same but your indexing could be altered and reports/dashboards customized. This is the database you would query. Your main database would be your transactional database which serves the users and the replicated database would serve the stakeholders.
This is a wide topic, so please do your diligence and research it. But it's also something that can work for you and can be quickly turned around.
If you really want try Data Mining with PostgreSQL there are some tools which can be used.
The very simple way is KNIME. It is easy to install. It has full featured Data Mining tools. You can access your data directly from database, process and save it back to database.
Hardcore way is MADLib. It installs Data Mining functions in Python and C directly in Postgres so you can mine with SQL queries.
Both projects are stable enough to try it.
For reporting, we use non-transactional (read only) database. We don't care about normalization. If I were you, I would use another database for reporting. I will desing the tables following OLAP principals, (star schema, snow flake), and use an ETL tool to dump the data periodically (may be weekly) to the read only database to start creating reports.
Reports are used for decision support, so they don't have to be in realtime, and usually don't have to be current. In other words it is acceptable to create report up to last week or last month.
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
I am using Entity Framework to layer on my SQL Server 2008 database. The EF is present in my web service and the webservice is invoked by a Silverlight client.
I am seeing a serious performance issue in terms of the duration taken by a query to execute in the EF. This wouldn't happen in the consecutive calls.
A little bit of googling revealed that, it's caused per app domain to construct the in-memory model of the db objects. I found this Microsoft link explaining pre-generation of views for performance improvement. Even after implementing the steps, the performance actually degraded instead of improving. I am curious, if anyone has tried this approach successfully and if there are any other avenues for improving performance.
I am using .NET 3.5.
A couple areas to look at for EF performance
Do as much of the processing before calling things like tolist(). ToList will bring everything in the set into memory. By default, EF will keep building the expression tree and only actually process it when you need the data in memory. That first query will be against the database, but afterwards the processing will be in memory. When working with large data, you definitely want as much of the heavy lifting done by the database as possible.
EF 1 only has the option to pull the entire row back. Therefore if you have a column that is a large string or binary blob, it is going to be pulled down and into memory whether you need it or not. You can create a projection that doesn't include this column, but then you don't get the benefits of having it be an entity.
You can look at the sql generated by EF using the suggestion in this post
How do I view the SQL generated by the Entity Framework?
The same laws of physics apply for EF queries as they do for ordinary SQL. Check your database tables and make sure that you have indexes on primary and foreign keys, that your database is properly normalized, and so forth. If performance is degrading after Microsoft's suggestions, then that's my guess as to the problem area.
Are you hosting the webservice in IIS? Is it running on the same site as the Silverlight App? What about the database itself? Is it running on a dedicated machine? Are there other apps hitting it? The first call to a dormant database is painful (I've had situations where it would actually time out in my environment.)
There are a number of factors to take into consideration here. But it comes down to more than just EF's overhead.
edit I didn't fully qualify but the process of opening the first connection to SQL Server is slow regardless of your data access solution.
Use SQL Profiler to check how many queries executed to retrieve your data.If it's large number use Include() method of ObjectQuery to retrieve child objects with parent in one query.