When creating Apache NiFi controller services, I'm interested in hearing about when it makes sense to create new ones and when to re-share existing ones.
Currently I have a CsvReader and CSVRecordSetWriter at the root process group and they are reused heavily in child process groups. I have tried to set them up to be as dynamic and flexible as possible to cover the widest number of use cases possible. I am setting the Schema Text property in each currently like this:
Reader Schema Text: ${avro.schema:notNull():ifElse(${avro.schema}, ${avro.schema.reader})}
Writer Schema Text: ${avro.schema:notNull():ifElse(${avro.schema}, ${avro.schema.writer})}
A very common pattern I have is to map files with different fields from different sources into a common format (common schema). So one thought is to use the ConvertRecord or UpdateRecord processors with avro.schema.reader and avro.schema.writerattributes set to the input and output schemas. Then I would have the writer always set the avro.schema attribute so any time I read records again further along in a flow it would default to using avro.schema. This feels dirty to leave the reader and writer schema attributes hanging around. Is there a better way from an architecture standpoint? Why have tons of controller services hanging around at different levels? Aside from some settings that may need to be different for some use cases, am I missing anything?
Also curious in hearing about how others organize their schemas? I don't have a need to reuse them at disparate locations across different processor blocks or reference different versions so it seems like a waste to centralize them or maintain a schema registry server that will also require upgrades and maintenance when I can just use AvroSchemaRegistry.
In the end, I decided it made more sense to split the controller into two controllers. One for conversions from Schema A to Schema B and another for using the same avro.schema property as normal/default readers and writers do when adding new ones. This allows for explicitly choosing the right pattern at processor block configuration time rather than relying on the implicit configuration of a single processor. Plus you get the added benefit of not stopping all flows (just a subset) when you only need to tweak settings on one of those two patterns.
Related
I have several SSIS Packages that we use to load in data from multiple different OLE DB data sources into our DB. Inside each package we have several Data Flow tasks that hold a large amount of OLE DB Sources and Destinations. What I'm looking to do is see if there is a way to get a text output the holds all of the Destinations flow configurations (Sources would be good to but not top of my list).
I'm trying to make sure that all my OLE DB Destination flows are pointed at the right table, as I've found a few hiccups, without having to double click on each Flow task and check that way, it just becomes tedious and still prone to missing things.
I'm viewing the packages in Visual Studio 2013. Any help is appreciated!
I am not aware of any programmatic ways to discover this data, other than building an application to read the XML within the *.dtsx package. Best advice, pack a lunch and have at it. I know for sure that there is nothing with respect to viewing and setting database tables (only server connections).
Though, a solution I may add once you have determined the list: create a variable(s) to store the unique connection strings and then set those connection strings inside the source/destination components. This will make it easier to manage going forward. In fact, you can take it one step further by setting the same values as parameter, as opposed to variables, which have the added benefit of being exposed on the server. This allows either you or the DBA to set the values as you promote through environments or change server nodes.
Also, I recommend rationalizing that solution into smaller solutions if possible. In my opinion, there is nothing worse than one giant solution that tries to do it all. I am not sure if any of this is helpful, but for what its worth, I do hope it helps.
You can use the SSIS Object Model for your needs..An example can be found here. Look in the method IterateAllDestinationComponentnsInPackage for the exact details. To start understanding the code, start in the Start method and follow the path.
Caveats: Make sure you use the appropriate Monikers and Class IDs for the Data Flow Tasks and your Destination Components. You can also use this for other Control Flow Tasks and Data Flow Components (for example, Source Components as your other need seems to be). Just keep in mind the appropriate Monikers and Class IDs.
Anyone who has experience with the Salesforce platform will know it can essentially be used as a backend for a lot of web applications. They let the end user define custom objects and the fields on those objects. So for instance, rather than having some entity as a strongly-typed class in the code, they have a generic "custom object", whose behaviour and data is defined by the fields you choose and the triggers and rules you apply to it. So they don't have to update the code, recompile and redeploy every time a user adds one (which, given they are a web service would be both impractical and cause serious downtime, a lot).
I was thinking how this could be implemented, and I think Salesforce may do it in a very complex way but I'm specifically thinking how I can implement this. So far I've come up with this:
An "object defintion", which contains all the metadata for a specific record type. Equivalent to a hardcoded class definition.
A generic "record", probably with some sort of dictionary/map tying values to field identifiers that exist in the object definition.
When operating on user data, both the record and the object defintion need to be in memory so that the integrity of the data can be checked. Behaviour normally provided by methods can be applied using some kind of trigger system (again, I'm using a Salesforce example here because it's the best example I know of) with defined actions/events.
This whole system seems very clunky, slow (without serious optimisation), and like it would be prone to problems which wouldn't plague 99% of software projects, so I'd like to learn more about it, but I have no idea where to start looking.
Is the idea I've laid out above already an existing paradigm and if so what is it called?
You have encountered the custom-fields. The design is to enable tenant specific fields against a fixed entity. Since multi-tenancy at the highest level demand That a single codebase / database be used for all tenants with the options to full Customization. This design is the best approach. The below link points to a patent That was granted for managing the custom-fields per tenant.
https://www.google.com/patents/US7779039
I have a use case where I have millions of small files in S3 which needs to be processed by Spark. I have two options to reduce number of tasks:
1. Use Coalesce
2. Extend CombineFileInputFormat
But I'm not clear of performance implications with bot and when to use one over other.
Also, CombineFileInputFormat is an abstract class, that means I need to provide my implementation. But Spark API (newAPIHadoopRDD) takes the class name as param, I'm not sure how to pass configurable maxSplitSize
Another great option to consider for such scenarios is SparkContext.wholeTextFiles() which makes one record for each file with its name as the key and the content as the value -- see Documentation
I'm trying to introduce caching into an existing server application because the database is starting to become overloaded.
Like many server applications we have the concept of a data layer. This data layer has many different methods that return domain model objects. For example, we have an employee data access object with methods like:
findEmployeesForAccount(long accountId)
findEmployeesWorkingInDepartment(long accountId, long departmentId)
findEmployeesBySearch(long accountId, String search)
Each method queries the database and returns a list of Employee domain objects.
Obviously, we want to try and cache as much as possible to limit the number of queries hitting the database, but how would we go about doing that?
I see a couple possible solutions:
1) We create a cache for each method call. E.g. for findEmployeesForAccount we would add an entry with a key account-employees-accountId. For findEmployeesWorkingInDepartment we could add an entry with a key department-employees-accountId-departmentId and so on. The problem I see with this is when we add a new employee into the system, we need to ensure that we add it to every list where appropriate, which seems hard to maintain and bug-prone.
2) We create a more generic query for findEmployeesForAccount (with more joins and/or queries because more information will be required). For other methods, we use findEmployeesForAccount and remove entries from the list that don't fit the specified criteria.
I'm new to caching so I'm wondering what strategies people use to handle situations like this? Any advice and/or resources on this type of stuff would be greatly appreciated.
I've been struggling with the same question myself for a few weeks now... so consider this a half-answer at best. One bit of advice that has been working out well for me is to use the Decorator Pattern to implement the cache layer. For example, here is an article detailing this in C#:
http://stevesmithblog.com/blog/building-a-cachedrepository-via-strategy-pattern/
This allows you to literally "wrap" your existing data access methods without touching them. It also makes it very easy to swap out the cached version of your DAL for the direct access version at runtime quite easily (which can be useful for unit testing).
I'm still struggling to manage my cache keys, which seem to spiral out of control when there are numerous parameters involved. Inevitably, something ends up not being properly cleared from the cache and I have to resort to heavy-handed ClearAll() approaches that just wipe out everything. If you find a solution for cache key management, I would be interested, but I hope the decorator pattern layer approach is helpful.
So I was searching the web looking for best practices when implementing the repository pattern with multiple data stores when I found my entire way of looking at the problem turned upside down. Here's what I have...
My application is a BI tool pulling data from (as of now) four different databases. Due to internal constraints, I am currently using LINQ-to-SQL for data access but require a design that will allow me to change to Entity Framework or NHibernate or the next data access du jour. I also hold steadfast to decoupled layers in my apps using an IoC framework (Castle Windsor in this case).
As such, I've used the Repository pattern to abstract the actual data access code from my business layer. As a result, my business object is coded against some I<Entity>Repository interface and the IoC Container is used to manage the actual implementation. In this case, I would expect to have a concrete Linq<Entity>Repository that implements the interface using LINQ-to-SQL to do the work. Later I could replace this with an EF<Entity>Repository with no changes required to my business layer.
Also, because I'm coding against the interface, I can easily mock the repository for unit testing purposes.
So the first question that I have as I begin coding the application is whether I should have one repository per DataContext or per entity (as I've typically done)? Let's say one database contains Customers and Sales with the expected relationship. Should I have a single OrderTrackingRepository with methods that work with both entities or have a separate CustomerRepository and a different SalesRepository?
Next, as a BI tool, the primary interface is for reporting, charting, etc and often will require a "mashup" of data across multiple sources. For instance, the reality is that one database contains customer information while another handles sales information and a third holds other financial information but one of my requirements is to display aggregated information that spans all three. Plus, I have to support dynamic filtering in the UI. Obviously working directly against the LINQ-to-SQL or EF DataContext objects (Table<Entity>, for instance) will allow me to pretty much do anything. What's the best approach to expose that same functionality to my business logic when abstracting the DAL with a repository interface?
This article: link text indicates that EF4 has turned this approach around and that the repository is nothing more than an IQueryable returned from the EF DataContext which brings up a whole other set of questions.
But, I think I've rambled on enough...
UPDATE (Thanks, Steven!)
Okay, let me put a more tangible (for me, at least) example on the table and clarify a few points that will hopefully lead to an approach I can better wrap my head around.
While I understand what Steven has proposed, I have a team of developers I have to consider when implementing such things and I'm afraid they will get lost in the complexity (yes, a real problem here!).
So, let's remove any direct tie-in with Linq-to-Sql because I don't want a solution that is dependant upon the way L2S works - or even EF, for that matter. My intent has been to abstract away the data access technology being used so that I can change it as needed without requiring collateral changes to the consuming code in my business layer. I've accomplished this in the past by presenting the business layer with IRepository interfaces to work against. Perhaps these should have been named IUnitOfWork or, more to my liking, IDataService, but the goal is the same. These interfaces typically exposed methods such as Add, Remove, Contains and GetByKey, for example.
Here's my situation. I have three databases to work with. One is DB2 and contains all of the business information for a customer (franchise) such as their info and their Products, Orders, etc. Another, SQL Server database contains their financial history while a third SQL Server database contains application-specific information. The first two databases are shared by multiple applications.
Through my application, the customer may enter/upload their financial information for a given time period. When entered, I have to perform the following steps:
1.Validate the entered data against a set of static rules. For example, the data must contain a legitimate customer ID value (in the case of an upload). This requires a lookup in the DB2 database to verify that the supplied customer ID exists and is current.
2.Next I have to validate the data against a set of dynamic rules which are contained in the third (SQL Server) database. An example may be that a given value cannot exceed a certain percentage of another value.
3.Once validated, I persist the data to the second SQL Server database containing the financial data.
All the while, my code must have loosely-coupled dependencies so I may mock them in my unit tests.
As part of the analysis, I know that I have three distinct data stores to work with and about a half-dozen or so entities (at this time) that I am working with. In generic terms, I presume that I would have three DataContexts in my application, one per data store, with the entities exposed by the appropriate data context.
I could then create a separate I{repository|unit of work|service} for each entity that would be consumed by my business logic with a concrete implementation that knows which data context to use. But this seems to be a risky proposition as the number of entities increases, so does the number of individual repository|UoW|service types.
Then, take the case of my validation logic which works with multiple entities and, thereby, multiple data contexts. I'm not sure this is the most efficient way to do this.
The other requirement that I have yet to mention is on the reporting side where I will need to execute some complex queries on the data stores. As of right now, these queries will be limited to a single data store at a time, but the possibility is there that I might need to have the ability to mash data together from multiple sources.
Finally, I am considering the idea of pulling out all of the data access stuff for the first two (shared) databases into their own project and have been looking at WCF Data Services as a possible approach. This would give me the basis for a consistent approach for any application making use of this data.
How does this change your thinking?
In your case I would recommend returning IEnummerables's for your data queries for the repo. I usually aggregate calls from multiple repo's through a service class that represents the domain problem and encapsulates my business logic. To keep it clean I try keep my repros focused on the domain problem. I liken my Datacontext to a repo, and extract an interface using a T4 template to make life easier for mocking. But there is nothing stopping you using a traditional repo that encapsulates your calls. Doing it this way will allow you to switch ORM's at any stage.
EDIT: IQueryable IS NOT THE ANSWER! :-)
I have also done a lot of work in this area, and INITIALLY came to the same conclusion, however it is NOT a good solution. The point of the Repo is to abstract queries into discrete chunks of work. Exposing IQueryable is too adhoc and raises some issues later down the line. You loose your ability to scale. You loose your ability to optimize queries (Lets say I want to move to a highly optimized stored proc). You loose your ability to use IoC for the repo to switch out data access layers (switch the project from SQL to Mongo). You loose your ability to provide effective data caching in the Repo (Which is a major strength in the Repo pattern). I would recommend taking a CLOSE look as to WHY we have a Repo pattern. It isn't simply an "ORM" mapping layer. What made this really clear to me was the CQRS pattern.
Further to this allowing the ad-hoc nature of IQueryable opens you to misfitting reuse of queries. It is GENERALLY not a good idea to reuse queries, since query to query you see slight deviations, which ends up with 2 byproducts: Queries become too broad and inefficient. Queries become riddled with unmaintainable IF THEN statements to cater for the deviations.
IQueryable is easy, but opens you up to an unmaintainable mess.
Look at this SO answer. I think it shows a simplified model of what you want. IQueryable<T> is indeed our new Repository :-). DataContext and ObjectContext are our Unit of Work.
UPDATE 2:
Here is a blog post that describes the model you might be looking for.
UPDATE 3
It would be wise to hide the shared databases behind a service. This will solve several problems:
This will make the database private to the service, which makes it much easier to change the implementation when needed.
You can put the needed validation logic (for database 1) in that service and can create tests for that validation logic in that project.
Clients accessing that service can assume correctness of the service, and its validation logic.
The result of this is that your application will send data to the service to validate it. Call the service to fetch data. Query its own private database (database 3) and join the data of the three data source locally together. I've never been a fan of using cross-database or even cross-server (in your situation) database calls and letting the database join everything together. Transactions will be promoted to distributed-transactions and it's hard to predict how many data the servers will exchange.
When you abstract the shared databases behind the service, things get easier (at least from your application's point of view). Your application calls services it trusts which limits the amount of code in that application and the amount of tests. You still want to mock the calls to such a service, but that would be pretty easy. It should also solve the problem of validating over multiple data sources.
Validation is always a hard part. I'm very familiar with Validation Application block, and love it for it's flexibility. It isn't however an easy framework, but you might take a peek at what you can do with it. For instance, I've written several articles about integration with O/RM tools and how to 'embed' a context (context as in DataContext/Unit of Work) in Validation Application Block.
Please have a look at my IRepository pattern implementation using EF 4.0.
My solution has the following features:
supports connections to multiple dbs
One repository per entity
Support for execution of queries
Unit of work pattern implementation
Support for validating entities using VAB guidance
Common operations are kept at base class level. High use of OOPS techniques for code re-usability and ease of maintenance.