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I was reading about DDD and CQRS (using Asp.Net Core ,MSSQL), and their different approaches, then I read a topic about separating Read and Write Database ,so I started to search web about how to do so and how to sync those databases, but sadly(maybe I was searching wrong) I didn't find any good source to find how to do so.
So here is my question :
How should I separate those databases, and then how should I sync the data between them, e.g. I have a table called "User" which is in read and write separated dbs,now if I add a new row to the write table in write db, I have to tell the read db to sync itself with write db so I can have the new data there to query and use later,but how? I also read something about Event Sourcing Pattern or Event-Driven Architecture,but they didn't help me find out how to sync.
so anyone know how to do so or have any good resources about this topic which can help a dummy :)
(consider you're explaining for a guy who is learning it for the first time!).
Thanks!
I have a related answer that may provide some background on how to approach CQRS.
The main point to keep in mind is that the "write" side is concerned with changes/transaction (OLTP) and the "read" side is concerned with queries (OLAP).
How you update your "read" side (read model) is going to depend on how you make the "write" side changes. When using an Event Store things may be easier in that each event has a global sequence number and each projection (read model) tracks where it is in terms of the global sequence number. So when new events arrive (projection polls) then they can be actioned if the event applies to the projection.
If you simply update the "write" side with, say, a SQL query then things are going to be a bit different, and possibly tricky, since you don't have any mechanism to replay those changes into the read model should you wish to make changes. In such a case you could use messaging, and possibly store those, or make the changes to the "read" side together with the "write" side... which isn't ideal; unless you need 100% consistency.
As mentioned by #Levi Ramsey, the read model is usually quite a bit different from the write model in that it is optimised for reading so it may include denormalized data or simply be in a data store that is more suited to read models.
The main benefit of CQRS is around being able to use different data models and/or different databases for queries vs. updates. If they are using the same data model, there's often not much benefit (at least not with a DB like SQL Server which is, at most scales, reasonable for both) to CQRS.
This in turn implies that it's generally not possible to just have the two databases automatically be in sync, because there's going to be some model translation involved (e.g. from a relational DB (with a normalized schema) like SQL Server to a denormalized document DB like Mongo).
One fairly common pattern is to have the software which writes to the DB also publish events describing what was updated to some event bus. Another piece of software subscribes to those events and performs the appropriate updates to the read DB. Note that this implies the existence of a period of time where queries against the read DB and the write DB will give different results.
We have many systems that talk to each other and its become a bit of a mess. e.g system B gets data from system A and System A gets data from System C which also gets data from System B etc etc. The data is passed around using a variety of methods. Some of the data is copied across using sql periodically thus duplicating the data. Some of the data is pulled using views locally and remotely in real-time. We want to come up with a better solution. My plan is to create a central repository that the systems dump and get data from. Does this sound like a good idea? Whats the best practice for handling data between remote systems?
Thanks in advance.
You mean like a data warehouse? This is pretty standard as long as you don't want to update the data, and just want to use it for reporting/driving other applications.
You have a variety of options for getting the data in there including linked servers, SSIS packages and replication (if between oracle servers or ms sql servers)
You can read Microsoft recomendation: http://technet.microsoft.com/en-us/library/dd459147(SQL.100).aspx
As Martin Booth and Dalex say, if the data is used only for reporting, a datawarehouse is the obvious solution.
If you use the data in transactional systems, there are some other options.
If your system is primarily about data, I'd consider using ETL tools (http://en.wikipedia.org/wiki/Extract,_transform,_load) to manage the copying around of data.
If your system is not just about data, you should look at a service-oriented architecture; this is a brilliantly vague term, and can result in many billable consulting hours, so it's worth doing your homework. In general, the idea is to decouple the underlying implementation (views, replication, dump/restore etc.) from the conceptual "services". This might be too big a jump from where you are now - but the principles are useful when design your solution.
I'd like to start a discussion about the implementation of a database system.
I'm working for a company having a database system grown over ca. the last 10 years.
Let me try to describe what it's doing and how it's implemented:
The system is divided into 3 main parts handled by 3 different teams.
Entry:
The Entry Team is responsible for creating GUIs for the system. In the background is a huge MS SQL database (ca. 100 tables) and the GUI is created using .NET. There are different GUI applications and each application has lots of different tabs to fill in the corresponding tables. If e.g. a new column is added to the database, this column is added manually to the GUI application.
Dataflow:
The purpose of the Dataflow Team is to do do data calculations and prepare the data for the reporting team. This is done via multiple levels. Let me try to explain the process a little bit more in detail: The Dataflow Team uses the data from the Entry database copied to another server and another database via Transactional-Replication (this data contains information from all clients). Then once per hour a self-written application is checking for changed rows in the input tables (using a ChangedDate column) and then calling a stored procedure for each output table calculating new data using 1-N of the input tables. After that the data is copied to another database on another server using again Transaction-Replication. Here another stored procedure is called to calclulate additional new output tables. This stored procedure is started using a SQL job. From there the data is split to different databases, each database being client specific. This copying is done using another self-written application using the .NET bulkcopy command (filtering on the client). These client specific databases are copied to different client specific reporting databases on other servers via another self-written application which compares the reporting database with the client specific database to calculate the data difference. Just the data differences are copied (because the reporting database run in former times on the client servers).
This whole process is orchestrated by another self-written application to control e.g. if the Transactional-Replications are finished before starting the job to call the Stored procedure etc... Futhermore also the synchronisation between the different clients is orchestrated here. The process can be graphically displayed by a self-written monitoring tool which looks pretty complex as you can imagine...
The status of all this components is logged and can be viewed by another self-written application.
If new columns or tables are added all this components have to be manually changed.
For deployment installation instructions are written using MS Word. (ca. 10 people working in this team)
Reporting:
The Reporting Team created it's own platform written in .NET to allow the client to create custom reports via a GUI. The reports are accessible via the Web.
The biggest tables have around 1 million rows. So, I hope I didn't forget anything important.
Well, what I want to discuss is how other people realize this scenario, I can't imagine that every company writes it's own custom applications.
What are actually the possibilities to allow fast calculations on databases (next to using T-SQL). I'm somehow missing the link here to the object oriented programming I'm used to from my old company, but we never dealt with so much data and maybe for fast calculations this is the way to do it...Or is it possible using e.g. LINQ or BizTalk Server to create the algorithms and calculations, maybe even in a graphical way? The question is just how to convert the existing meter-long Stored procedures into the new format...
In future we want to use data warehousing, but that will take a while, so maybe it's possible to have a separate step to streamline the process.
Any comments are appreciated.
Thanks
Daniel
Why on earth would you want to convert existing working complex stored procs (which can be performance tuned) to LINQ (or am I misunderstanding you)? Because you personally don't like t-sql? Not a good enough reason. Are they too slow? Then they can be tuned (which is something you really don't want to try to do in LINQ). It is possible the process can be made better using SSIS, but as complex as SSIS is and the amount of time a rewrite of the process would take, I'm not sure you really would gain anything by doing so.
"I'm somehow missing the link here to the object oriented programming..." Relational databases are NOT Object-oriented and cannot perform well if you try to treat them like they are. Learn to think in terms of sets not objects when accessing databases. You are coming from the mindset of one user at a time inserting one record at a time, but this is not the mindset neeeded to deal with the transfer of large amounts of data. For these types of things, using the database to handle the problem is better than doing things in an object-oriented manner. Once you have a large amount of data and lots of reporting, people are far more interested in performance than you may have been used to in the past when you used some tools that might not be so good for performance. Whether you like T-SQL or not, it is SQL Server's native language and the database is optimized for it's use.
The best advice, having been here before, is to start by learning first how SQL works, and doing it in the context of the existing architecture sounds like a good way to start (since nothing you've described sounds irrational on the face of it.)
Whatever abstractions you try to lay on top (LINQ, Biztalk, whatever) all eventually resolve to pure SQL. And almost always they add overhead and complexity.
Your OO paradigms aren't transferable. Any suggestions about abstractions will need to be firmly defensible based on your firm grasp of the SQL consequences.
It will take a while, but it's all worth knowing, both professionally and personally.
I'm currently re-engineering a complex system which is moving from Focus (a database and language) to a data warehouse (separate team) and processing (my team) and reporting (separate team).
The current process is combined - data is loaded and managed in the Focus language and Focus database(s) and then reported (and historical data is retained)
In the new process, the DW is loaded and then our process begins. Our processes are completely coded in SQL, and a million row fact table (for one month) would be relatively small. We have some feeds where the monthly data is 25 million rows. There are some statistics tables produced which are over 200 million rows (a month). The processing can take several hours a month, end to end. We use tables to store intermediate results, and we ensure indexing strategies are suitable for the processing. Except for one piece implemented as an SSIS flow from the database back to itself because of extremely poor scalar UDF performance, the entire system is implemented as a series of T-SQl SPs.
We also have a process monitoring system similar to what you are discussing as well as having the dependencies in a table which ensures that each process runs only if all its prerequisites are satisfied. I've recently grafted on the MSAGL to graphically display and interact with the process (previously I was using graphviz to generate static images) from a .NET Windows application. The new system thus has much clearer dependency information as well as good information about process performance so effort can be concentrated on the slowest performing bottlenecks.
I would not plan on doing any re-engineering of any complex system without a clear strategy, a good inventory of the existing system and a large budget for time and money.
From the sounds of what you are saying, you have a three step process.
Input data
Analyze data
Report data
Steps one and three need to be completed by "users". Therefore, a GUI is needed for each respective team to do the task at hand, otherwise, they would be directly working on SQL Server, and would require extensive SQL knowledge. For these items, I do not see any issue with the approach your organization is taking, you are building a customized system to report on the data at hand. The only item that might be worth considering on these side, is standardization between the teams on common libraries and the technologies used.
Your middle step does seem to be a bit lengthy, with many moving parts. However, I've worked on a number of large reporting systems where that is truly the only way to get around it. WIthout knowing more of your organization and the exact nature of operations.
By "fast calculations" you must mean "fast retrieval" Data warehouses (both relational and otherwise) are fast with math because the answers are pre-calculated in advance. SQL, unless you are using CLR stored procedures, is usually a rather slow when it comes to math.
You'd be hard pressed to defeat the performance of BCP and SQL with anything else. If the update routines are long and bloated because they loop through the tables, then sure I can see why you'd want to go to .NET. But you'd probably increase performance by figuring out how to rewrite them all nice and SET based. BCP is not going to be able to be beaten. When I used SQL Server 2000 BCP was often faster than DTS. And SSIS in general (due to all the data type checking) seems to be way slower than DTS. If you kill performance no doubt people are going to be coming to you. Still if you are doing a ton of row by row complex calculations, optimizing that into a CLR stored procedure or even a .NET application that is called from SQL Server to do the processing will probably result in a speed up. Of course if you were row processing and you manage to rewrite the queries to do set processing you'd probably get a bigger speed up. But depending upon how complex the calculations are .NET may help.
Now if a front end change could immediately update and propagate the data, then you might want to change things to .NET so that as soon as a row is changed it can be recalculated and update all the clients. However if a lot of rows are changed or the database is just ginormous then you will kill performance. If the operation needs to be done in bulk then probably the way it is currently being done is the best.
The only thing I might as is that maybe there is a lot of duplicate SQL that looks exactly the same except for a table name and or the column names. If so, you can probably use .NET combined with SQL-SMO(or DMO if using SQL Server 2000) to code generate it.
Here's an example that I often see to load a datawarehouse
Assuming some row tables are loaded with the data from the source
select changed rows from source into temporary tables
see if any columns that matter were changed
if so terminate existing row (or clone it into some history table)
insert/update new row
I often see one of those queries per table and the only variations are the table/column names and maybe references to the key column. You can easily get the column definitions and key definitions out of SQL Server and then make a .NET program to create the INSERT/SELECT/ETC. In the worst case you may just have to store some type of table with TABLE_NAME, COLUMN_NAME for the columns that matter. Then instead of having to wrap your head around a complex ETL process and 20 or 200 update queries, you just need to wrap your head around UPDATE and one query. Any changes to the way things are done can be done once and applied to all the queries.
In particular my guess is that you can apply this technique to the individual client databases if you haven't already. Probably all the queries/bulk copy scripts are the same or almost the same with the exception of database/server name. So you can just autogenerate them based on a CLIENTs table or something.....
It may not be a pure programming question but I'm looking for information about enCapsa. Do you know what it is, have you ever used it? I'm reading some papers about it but I can't really see how it works and what it can be used for in an IT company (and this is what i am supposed to find out).
Basically enCapsa is a shared data storage system, focused on providing a way for storing any kind of data (even from etherogeneous data sources such as different designed db tables) and consequently obtain it through a sort of human friendly queries, just like on a search engine. They offer the possibility to upload data from everywhere (it's CSV based) and later download to use wherever you need it.
Usages are many, consider it's a centralized DB accessible through web and they say it meets high security standard.
A usefull way to employ this service is to have data stored in there without the need to keep them synchronized across company computers.
Can you please point to alternative data storage tools and give good reasons to use them instead of good-old relational databases? In my opinion, most applications rarely use the full power of SQL--it would be interesting to see how to build an SQL-free application.
Plain text files in a filesystem
Very simple to create and edit
Easy for users to manipulate with simple tools (i.e. text editors, grep etc)
Efficient storage of binary documents
XML or JSON files on disk
As above, but with a bit more ability to validate the structure.
Spreadsheet / CSV file
Very easy model for business users to understand
Subversion (or similar disk based version control system)
Very good support for versioning of data
Berkeley DB (Basically, a disk based hashtable)
Very simple conceptually (just un-typed key/value)
Quite fast
No administration overhead
Supports transactions I believe
Amazon's Simple DB
Much like Berkeley DB I believe, but hosted
Google's App Engine Datastore
Hosted and highly scalable
Per document key-value storage (i.e. flexible data model)
CouchDB
Document focus
Simple storage of semi-structured / document based data
Native language collections (stored in memory or serialised on disk)
Very tight language integration
Custom (hand-written) storage engine
Potentially very high performance in required uses cases
I can't claim to know anything much about them, but you might also like to look into object database systems.
Matt Sheppard's answer is great (mod up), but I would take account these factors when thinking about a spindle:
Structure : does it obviously break into pieces, or are you making tradeoffs?
Usage : how will the data be analyzed/retrieved/grokked?
Lifetime : how long is the data useful?
Size : how much data is there?
One particular advantage of CSV files over RDBMSes is that they can be easy to condense and move around to practically any other machine. We do large data transfers, and everything's simple enough we just use one big CSV file, and easy to script using tools like rsync. To reduce repetition on big CSV files, you could use something like YAML. I'm not sure I'd store anything like JSON or XML, unless you had significant relationship requirements.
As far as not-mentioned alternatives, don't discount Hadoop, which is an open source implementation of MapReduce. This should work well if you have a TON of loosely structured data that needs to be analyzed, and you want to be in a scenario where you can just add 10 more machines to handle data processing.
For example, I started trying to analyze performance that was essentially all timing numbers of different functions logged across around 20 machines. After trying to stick everything in a RDBMS, I realized that I really don't need to query the data again once I've aggregated it. And, it's only useful in it's aggregated format to me. So, I keep the log files around, compressed, and then leave the aggregated data in a DB.
Note I'm more used to thinking with "big" sizes.
The filesystem's prety handy for storing binary data, which never works amazingly well in relational databases.
Try Prevayler:
http://www.prevayler.org/wiki/
Prevayler is alternative to RDBMS. In the site have more info.
If you don't need ACID, you probably don't need the overhead of an RDBMS. So, determine whether you need that first. Most of the non-RDBMS answers provided here do not provide ACID.
Custom (hand-written) storage engine / Potentially very high performance in required uses cases
http://www.hdfgroup.org/
If you have enormous data sets, instead of rolling your own, you might use HDF, the Hierarchical Data Format.
http://en.wikipedia.org/wiki/Hierarchical_Data_Format:
HDF supports several different data models, including multidimensional arrays, raster images, and tables.
It's also hierarchical like a file system, but the data is stored in one magic binary file.
HDF5 is a suite that makes possible the management of extremely large and complex data collections.
Think petabytes of NASA/JPL remote sensing data.
G'day,
One case that I can think of is when the data you are modelling cannot be easily represented in a relational database.
Once such example is the database used by mobile phone operators to monitor and control base stations for mobile telephone networks.
I almost all of these cases, an OO DB is used, either a commercial product or a self-rolled system that allows heirarchies of objects.
I've worked on a 3G monitoring application for a large company who will remain nameless, but whose logo is a red wine stain (-: , and they used such an OO DB to keep track of all the various attributes for individual cells within the network.
Interrogation of such DBs is done using proprietary techniques that are, usually, completely free from SQL.
HTH.
cheers,
Rob
Object databases are not relational databases. They can be really handy if you just want to stuff some objects in a database. They also support versioning and modify classes for objects that already exist in the database. db4o is the first one that comes to mind.
In some cases (financial market data and process control for example) you might need to use a real-time database rather than a RDBMS. See wiki link
There was a RAD tool called JADE written a few years ago that has a built-in OODBMS. Earlier incarnations of the DB engine also supported Digitalk Smalltalk. If you want to sample application building using a non-RDBMS paradigm this might be a start.
Other OODBMS products include Objectivity, GemStone (You will need to get VisualWorks Smalltalk to run the Smalltalk version but there is also a java version). There were also some open-source research projects in this space - EXODUS and its descendent SHORE come to mind.
Sadly, the concept seemed to die a death, probably due to the lack of a clearly visible standard and relatively poor ad-hoc query capability relative to SQL-based RDMBS systems.
An OODBMS is most suitable for applications with core data structures that are best represented as a graph of interconnected nodes. I used to say that the quintessential OODBMS application was a Multi-User Dungeon (MUD) where rooms would contain players' avatars and other objects.
You can go a long way just using files stored in the file system. RDBMSs are getting better at handling blobs, but this can be a natural way to handle image data and the like, particularly if the queries are simple (enumerating and selecting individual items.)
Other things that don't fit very well in a RDBMS are hierarchical data structures and I'm guessing geospatial data and 3D models aren't that easy to work with either.
Services like Amazon S3 provide simpler storage models (key->value) that don't support SQL. Scalability is the key there.
Excel files can be useful too, particularly if users need to be able to manipulate the data in a familiar environment and building a full application to do that isn't feasible.
There are a large number of ways to store data - even "relational databse" covers a range of alternatives from a simple library of code that manipulates a local file (or files) as if it were a relational database on a single user basis, through file based systems than can handle multiple-users to a generous selection of serious "server" based systems.
We use XML files a lot - you get well structured data, nice tools for querying same the ability to do edits if appropriate, something that's human readable and you don't then have to worry about the db engine working (or the workings of the db engine). This works well for stuff that's essentially read only (in our case more often than not generated from a db elsewhere) and also for single user systems where you can just load the data in and save it out as required - but you're creating opportunities for problems if you want multi-user editing - at least of a single file.
For us that's about it - we're either going to use something that will do SQL (MS offer a set of tools that run from a .DLL to do single user stuff all the way through to enterprise server and they all speak the same SQL (with limitations at the lower end)) or we're going to use XML as a format because (for us) the verbosity is seldom an issue.
We don't currently have to manipulate binary data in our apps so that question doesn't arise.
Murph
One might want to consider the use of an LDAP server in the place of a traditional SQL database if the application data is heavily key/value oriented and hierarchical in nature.
BTree files are often much faster than relational databases. SQLite contains within it a BTree library which is in the public domain (as in genuinely 'public domain', not using the term loosely).
Frankly though, if I wanted a multi-user system I would need a lot of persuading not to use a decent server relational database.
Full-text databases, which can be queried with proximity operators such as "within 10 words of," etc.
Relational databases are an ideal business tool for many purposes - easy enough to understand and design, fast enough, adequate even when they aren't designed and optimized by a genius who could "use the full power," etc.
But some business purposes require full-text indexing, which relational engines either don't provide or tack on as an afterthought. In particular, the legal and medical fields have large swaths of unstructured text to store and wade through.
Also:
* Embedded scenarios - Where usually it is required to use something smaller then a full fledged RDBMS. Db4o is an ODB that can be easily used in such case.
* Rapid or proof-of-concept development - where you wish to focus on the business and not worry about persistence layer
CAP theorem explains it succinctly. SQL mainly provides "Strong Consistency: all clients see the same view, even in presence of updates".
K.I.S.S: Keep It Small and Simple
I would offer RDBMS :)
If you do not wont to have troubles with set up/administration go for SQLite.
Built in RDBMS with full SQL support. It even allows you to store any type of data in any column.
Main advantage against for example log file: If you have huge one, how are you going to search in it? With SQL engine you just create index and speed up operation dramatically.
About full text search: SQLite has modules for full text search too..
Just enjoy nice standard interface to your data :)
One good reason not to use a relational database would be when you have a massive data set and want to do massively parallel and distributed processing on the data. The Google web index would be a perfect example of such a case.
Hadoop also has an implementation of the Google File System called the Hadoop Distributed File System.
I would strongly recommend Lua as an alternative to SQLite-kind of data storage.
Because:
The language was designed as a data description language to begin with
The syntax is human readable (XML is not)
One can compile Lua chunks to binary, for added performance
This is the "native language collection" option of the accepted answer. If you're using C/C++ as the application level, it is perfectly reasonable to throw in the Lua engine (100kB of binary) just for the sake of reading configs/data or writing them out.