I am not asking for opinions but more on documentations.
We have a lot of data files (XML, CSV, Plantext, etc...), and need to process them, data mine them.
The lead database person suggested using stored procedure to accomplish the task. Basically we have a staging table where the file get serialized, and saved into a clob, or XML column. Then from there he suggested to further use stored procedure to process the file.
I'm an application developer with db background, more so on application development, and I might be bias, but using this logic in the DB seems like a bad idea and I am unable to find any documentation to prove or disapprove what I refer to as putting a car on a train track to pull a load of freights.
So my questions are:
How well does the DB (Oracle, DB2, MySQL, SqlServer) perform when we talking about regular expression search, search and replace of data in a clob, dom traversal, recursion? In comparison to a programming language such as Java, PHP, or C# on the same issues.
Edit
So what I am looking for is documentation on comparison / runtime analysis of a particular programming language compare to a DBMS, in particular for string search and replace, regular expression search and replace. XML Dom traversal. Memory usage on recursive method calls. And in particular how well they scale when encountered with 10 - 100's of GB of data.
It sounds like you are going to throw business logic into the storage layer. For operations like you describe, you should not use the database. You may end up in trying to find workarounds for showstoppers or create quirky solutions because of inflexibility.
Also keep maintainability in mind. How many people will later be able to maintain the solution?
Speaking about speed, choosing the right programming language you will be able to process data in multiple threads. At the end, your feeling with the car n the train is right;)
It is better to pull the processing logic out of data layer.Profiling your implementation in Database will be difficult.
You get the freedom and option to choose between libraries and comparing their performance if the implementation is done with any language.
Moreover you can choose frameworks like (Spring-Batch for Java) to process bulk volume of data as batch process.
Related
I'm familiar with developing desktop apps in Clojure (written a multithreaded interactive visualization system). However, I'm fairly new to Web development using Clojure.
I plan to use Clojure on the server for handling logic; and ClojureScript for handing client side work. However, I don't know what to use for my database server. Should I use something like Monogodb? or Hadoop? Or .... ?
The app is something very simple; a basic forum. Total number of concurrent users will be < 100 at a given time. One thing that is important to me is the ability to easily backup / data consistency -- it's very very important to me that I can easily make daily backups (and not lose all the data.)
Thanks!
You can use many databases; if the database has an API for Java, you should be good to go. MySQL, MongoDB, Postgres, Hadoop… and more.
For a nice overview of the webstack in Clojure, check out brehaut's article on the matter.
For getting up and running quickly with Clojure and ClojureScript, try ClojureScriptOne.
There are many ways to write what you want to write; if you're already familiar with Clojure, it shouldn't be too hard to get going.
Haven't used it myself, but Datomic ( http://datomic.com/ ) looks great for anyone coming from Clojure.
Datomic is an amazing database, and I'd highly recommend it. It has many features which set it apart from other database systems:
Like Clojure's data structures, it's persistent, meaning that by default, adding new facts to the database doesn't delete old facts, allowing you to query the state of the database at a previous point in time, enhancing audit-ability and assistance in debugging.
The underlying Entity Attribute Value (EAV/triple) data model (at least partly inspired by RDF & the Semantic Web), is extremely flexible, allowing you to express arbitrary graph structures and effortlessly deal with polymorphism.
The query language is flavor of Datalog, a sort of pattern matching based query language strictly more expressive than SQL and the like in that it can do recursive queries, making it particularly well suited for dealing with graph data/queries.
In addition to Datalog queries, there's a pull api, which let's you pull data out of the database more simply using a GraphQL like expression which specifies the shape of a document-like structure you'd like to pull out of the database. These queries can even be used from within the :find clause of a Datalog query.
You can use Clojure functions from within your queries.
The indexing system is very smart and more or less automatic, in stark contrast with the work that typically goes into tuning SQL databases for performance.
Transactions go through a different API/function call than queries, meaning that the number one security risk identified by OWASP (SQL injection) is literally impossible in Datomic.
The transactor/read-replica design makes it super easy to scale reads/queries, while keeping pressure off the transactor.
It's fun as hell.
One of the things worth pointing out here is that by embracing the EAV data model and datalog/pull queries, Datomic ends up having structural flexibility closer to that of a NoSQL database, while still being fundamentally relational, and even more expressive in it's relational queries than SQL.
It's amazing and you should absolutely give it a shot. It will melt your brain a little. In the good way.
It's also worth noting that it's popularity has inspired a number of successful open source projects, so the underlying approach is not going anywhere any time soon:
DataScript: In memory clj/cljs partial implementation
Datahike: Fork of DataScript which queries over on disk indices, meaning you don't have to keep everything in memory to query
Mentat: Mozilla project trying to make a Datomic-alike for a Mozilla project
I'm trying to design a database for a small project I'm working on. Eventually, I'd like to make it a web-app, but right now I don't mind just experimenting with data offline. However, I'm stuck in a crossroads.
The basic concept would be a user inputs values for 10 fields, to be compared against what is in the database, with each item having a weighted value. I know that if I was to code it, that I could use a look-up tables for each field, add up the values, and display the result to the end-user.
Another example would be having to get the distance between two points, each point stored in a row, with the X value getting its own column as well as the Y value.
Now, if I store data within a database, should I try to do everything within queries (which I think would involve temporary tables among other things), or just use simple queries, and manipulate the rows returned within the application code?
Right now, I'm thinking to go for the latter (manipulate data within the app) and just use queries to reduce the amount of data that I would have to go sort through. What would you guys suggest?
EDIT: Right now I'm using Microsoft Access to get the basics down pat and try to get a good design going. IIRC with my experience with Oracle and MySQL you can run commands together in a batch process and return just one result. But not sure if you can do that with Access.
If you're using a database I would strongly suggest using SQL to do all your manipulation. SQL is far more capable and powerful for this kind of job as compared to imperative programming languages.
Of course it does imply that you're comfortable in thinking about data as "sets" and programming in a declarative style. But spending time now to get really comfortable with SQL and manipulating data using SQL will pay off big time in the long run. Not only for this project but for projects in the future. I would also suggest using stored procedures over queries in code because stored procedure provide a beautiful abstraction layer allowing your table design to change over time without impacting the rest of the system.
A very big part of using and working with databases is understanding Data modeling, normalization and the like. Like everything else it will be a effort but in the long run it will pay off.
May I ask why you're using Access when you have a far better database available to you such as MSSQL Express? The migration path from MSSQL Express to MSSQL or SQL Azure even is quite seamless and everything you do and experience today (in this project) completely translates to MSSQL Server/SQL Azure for future projects as well as if this project grows beyond your expectations.
I don't understand your last statement about running a batch process and getting just one result, but if you can do it in Oracle and MySQL then you can do it in MSSQL Express as well.
What Shiv said, and also...
A good DBMS has quite a bit of solid engineering in it. There are two components that are especially carefully engineered, namely the query optimizer and the transaction controller. If you adopt the view of using the DBMS as just a stupid table retrieval tool, you will most likely end up inventing your own optimizer and transaction controller inside the application. You won't need the transaction controller until you move to an environment that supports multiple concurrent users.
Unless your engineering talents are extraordinary, you will probably end up with a home brew data management system that is not as good as the one in a good DBMS.
The learning curve for SQL is steep. You need to learn how to phrase queries that join, project, and restrict data from multiple tables. You need to learn how to handle updates in the context of a transaction.
You need to learn simple and sound table design and index design. This includes, but is not limited to, data normalization and data modeling. And you need a DBMS with a good optimizer and good transaction control.
The learning curve is steep. But the view from the top is worth the climb.
We have a database with ~100K business objects in it. Each object has about 40 properties which are stored amongst 15 tables. I have to get these objects, perform some transforms on them and then write them to a different database (with the same schema.)
This is ADO.Net 3.5, SQL Server 2005.
We have a library method to write a single property. It figures out which of the 15 tables the property goes into, creates and opens a connection, determines whether the property already exists and does an insert or update accordingly, and closes the connection.
My first pass at the program was to read an object from the source DB, perform the transform, and call the library routine on each of its 40 properties to write the object to the destination DB. Repeat 100,000 times. Obviously this is egregiously inefficent.
What are some good designs for handling this type of problem?
Thanks
This is exactly the sort of thing that SQL Server Integration Services (SSIS) is good for. It's documented in Books Online, same as SQL Server is.
Unfortunately, I would say that you need to forget your client-side library, and do it all in SQL.
How many times do you need to do this? If only once, and it can run unattended, I see no reason why you shouldn't reuse your existing client code. Automating the work of human beings is what computers are for. If it's inefficient, I know that sucks, but if you're going to do a week of work setting up a SSIS package, that's inefficient too. Plus, your client-side solution could contain business logic or validation code that you'd have to remember to carry over to SQL.
You might want to research Create_Assembly, moving your client code across the network to reside on your SQL box. This will avoid network latency, but could destabilize your SQL Server.
Bad news: you have many options
use flatfile transformations: Extract all the data into flatfiles, manipulate them using grep, awk, sed, c, perl into the required insert/update statements and execute those against the target database
PRO: Fast; CON: extremly ugly ... nightmare for maintanance, don't do this if you need this for longer then a week. And a couple dozens of executions
use pure sql: I don't know much about sql server, but I assume it has away to access one database from within the other, so one of the fastes ways to do this is to write it as a collection of 'insert / update / merge statements fed with select statements.
PRO: Fast, one technology only; CON: Requires direct connection between databases You might reach the limit of SQL or the available SQL knowledge pretty fast, depending on the kind of transformation.
use t-sql, or whatever iterative language the database provides, everything else is similiar to pure sql aproach.
PRO: pretty fast since you don't leave the database CON: I don't know t-sql, but if it is anything like PL/SQL it is not the nicest language to do complex transformation.
use a high level language (Java, C#, VB ...): You would load your data into proper business objects manipulate those and store them in the database. Pretty much what you seem to be doing right now, although it sounds there are better ORMs available, e.g. nhibernate
use a ETL Tool: There are special tools for extracting, transforming and loading data. They often support various databases. And have many strategies readily available for deciding if an update or insert is in place.
PRO: Sorry, you'll have to ask somebody else for that, I so far have nothing but bad experience with those tools.
CON: A highly specialized tool, that you need to master. I my personal experience: slower in implementation and execution of the transformation then handwritten SQL. A nightmare for maintainability, since everything is hidden away in proprietary repositories, so for IDE, Version Control, CI, Testing you are stuck with whatever the tool provider gives you, if any.
PRO: Even complex manipulations can be implemented in a clean maintainable way, you can use all the fancy tools like good IDEs, Testing Frameworks, CI Systems to support you while developing the transformation.
CON: It adds a lot of overhead (retrieving the data, out of the database, instanciating the objects, and marshalling the objects back into the target database. I'd go this way if it is a process that is going to be around for a long time.
Building on the last option you could further glorify the architectur by using messaging and webservices, which could be relevant if you have more then one source database, or more then one target database. Or you could manually implement a multithreaded transformer, in order to gain through put. But I guess I am leaving the scope of your question.
I'm with John, SSIS is the way to go for any repeatable process to import large amounts of data. It should be much faster than the 30 hours you are currently getting. You could also write pure t-sql code to do this if the two database are on the same server or are linked servers. If you go the t-sql route, you may need to do a hybrid of set-based and looping code to run on batches (of say 2000 records at a time) rather than lock up the table for the whole time a large insert would take.
I'm writing a CAD (Computer-Aided Design) application. I'll need to ship a library of 3d objects with this product. These are simple objects made up of nothing more than 3d coordinates and there are going to be no more than about 300 of them.
I'm considering using a relational database for this purpose. But given my simple needs, I don't want any thing complicated. Till now, I'm leaning towards SQLite. It's small, runs within the client process and is claimed to be fast. Besides I'm a poor guy and it's free.
But before I commit myself to SQLite, I just wish to ask your opinion whether it is a good choice given my requirements. Also is there any equivalent alternative that I should try as well before making a decision?
Edit:
I failed to mention earlier that the above-said CAD objects that I'll ship are not going to be immutable. I expect the user to edit them (change dimensions, colors etc.) and save back to the library. I also expect users to add their own newly-created objects. Kindly consider this in your answers.
(Thanks for the answers so far.)
The real thing to consider is what your program does with the data. Relational databases are designed to handle complex relationships between sets of data. However, they're not designed to perform complex calculations.
Also, the amount of data and relative simplicity of it suggests to me that you could simply use a flat file to store the coordinates and read them into memory when needed. This way you can design your data structures to more closely reflect how you're going to be using this data, rather than how you're going to store it.
Many languages provide a mechanism to write data structures to a file and read them back in again called serialization. Python's pickle is one such library, and I'm sure you can find one for whatever language you use. Basically, just design your classes or data structures as dictated by how they're used by your program and use one of these serialization libraries to populate the instances of that class or data structure.
edit: The requirement that the structures be mutable doesn't really affect much with regard to my answer - I still think that serialization and deserialization is the best solution to this problem. The fact that users need to be able to modify and save the structures necessitates a bit of planning to ensure that the files are updated completely and correctly, but ultimately I think you'll end up spending less time and effort with this approach than trying to marshall SQLite or another embedded database into doing this job for you.
The only case in which a database would be better is if you have a system where multiple users are interacting with and updating a central data repository, and for a case like that you'd be looking at a database server like MySQL, PostgreSQL, or SQL Server for both speed and concurrency.
You also commented that you're going to be using C# as your language. .NET has support for serialization built in so you should be good to go.
I suggest you to consider using H2, it's really lightweight and fast.
When you say you'll have a library of 300 3D objects, I'll assume you mean objects for your code, not models that users will create.
I've read that object databases are well suited to help with CAD problems, because they're perfect for chasing down long reference chains that are characteristic of complex models. Perhaps something like db4o would be useful in your context.
How many objects are you shipping? Can you define each of these Objects and their coordinates in an xml file? So basically use a distinct xml file for each object? You can place these xml files in a directory. This can be a simple structure.
I would not use a SQL database. You can easy describe every 3D object with an XML file. Pack this files in a directory and pack (zip) all. If you need easy access to the meta data of the objects, you can generate an index file (only with name or description) so not all objects must be parsed and loaded to memory (nice if you have something like a library manager)
There are quick and easy SAX parsers available and you can easy write a XML writer (or found some free code you can use for this).
Many similar applications using XML today. Its easy to parse/write, human readable and needs not much space if zipped.
I have used Sqlite, its easy to use and easy to integrate with own objects. But I would prefer a SQL database like Sqlite more for applications where you need some good searching tools for a huge amount of data records.
For the specific requirement i.e. to provide a library of objects shipped with the application a database system is probably not the right answer.
First thing that springs to mind is that you probably want the file to be updatable i.e. you need to be able to drop and updated file into the application without changing the rest of the application.
Second thing is that the data you're shipping is immutable - for this purpose therefore you don't need the capabilities of a relational db, just to be able to access a particular model with adequate efficiency.
For simplicity (sort of) an XML file would do nicely as you've got good structure. Using that as a basis you can then choose to compress it, encrypt it, embed it as a resource in an assembly (if one were playing in .NET) etc, etc.
Obviously if SQLite stores its data in a single file per database and if you have other reasons to need the capabilities of a db in you storage system then yes, but I'd want to think about the utility of the db to the app as a whole first.
SQL Server CE is free, has a small footprint (no service running), and is SQL Server compatible
When dealing with small projects, what do you feel is the break even point for storing data in simple text files, hash tables, etc., versus using a real database? For small projects with simple data management requirements, a real database is unnecessary complexity and violates YAGNI. However, at some point the complexity of a database is obviously worth it. What are some signs that your problem is too complex for simple ad-hoc techniques and needs a real database?
Note: To people used to enterprise environments, this will probably sound like a weird question. However, my problem domain is bioinformatics. Most of my programming is prototypes, not production code. I'm primarily a domain expert and secondarily a programmer. Most of my code is algorithm-centric, not data management-centric. The purpose of this question is largely for me to figure out how much work I might save in the long run if I learn to use proper databases in my code instead of the more ad-hoc techniques I typically use.
1) Concurrency. Do you have multiple people accessing the same dataset? Then it's going to get pretty involved to broker all of the different readers and writers in a scalable fashion if you roll your own system.
2) Formatting and relationships: Is your data something that doesn't fit neatly into a table structure? Long nucleotide sequences and stuff like that? That's not really conveniently tabular data.
Another example: Nobody would consider implementing software like Photoshop to store PSDs in a relational format, because the data structures don't really lend themselves to that type of storage or query pattern.
3) ACID (sort of a corollary to #1): If Atomicity, Consistency, Integrity, and Durability are not challenges with a flat file, then go with a flat file.
For me, the line is crossed once I have to query my data in ways that involve more than a single relationship. Relating two flat data structures on disk is fairly simple, but once we get beyond that, a set-based language like SQL and formal database relationships actually reduce complexity.
I think at some point you'll miss the querying capabilities of a database, but you can consider some minimalistic database alternatives:
SQLite (Great, almost SQL-92 standard compliant)
shsql
SQL Server Compact
I would only write my own on-disk format under very special circumstances. Reusing someone else's code is nearly always faster.
For relational data, I would use SQLite. For key/value pairs, I would use BerkeleyDB (perhaps via KiokuDB). For simple objects, I would use JSON or YAML, but only if I only had a few.
With SQLite and BDB, "a real database" is literally two lines of code away. It is hard to beat that.
The problem with small projects is that they become bigger before we know it. And once they do , we start missing the sql capabilities.
Always design such that a db can be utilized later on if required without ripping apart half of the application.
It depends entirely on the domain-specific application needs. A lot of times direct text file/binary files access can be extremely fast, efficient, as well as providing you all the file access capabilities of your OS's file system.
Furthermore, your programming language most likely already has a built-in module (or is easy to make one) for specific parsing.
If what you need is many appends (INSERTS?) and sequential/few access little/no concurrency, files are the way to go.
On the other hand, when your requirements for concurrency, non-sequential reading/writing, atomicity, atomic permissions, your data is relational by the nature etc., you will be better off with a relational or OO database.
There is a lot that can be accomplished with SQLite3, which is extremely light (under 300kb), ACID compliant, written in C/C++, and highly ubiquitous (if it isn't already included in your programming language -for example Python-, there is surely one available). It can be useful even on db files as big as 1GB, possible more.
If your requirements where bigger, there wouldn't even be a discussion, go for a full-blown RDBMS.
For the kind of applications you are developing in bioinformatics, you are often doing one-shot applications (often scripts that define a workflow of calculations) that answer a specific questions, and you are not likely to be reusing these applications after you answered your question.
Often, you should therefore avoid creating databases to store the results, as after all you are not going to use their features very much.
You will probably be querying some webservices, files, or databases, run some local algorithms on the data gathered from different sources, and produce some tabular or structured output format (xml, json, etc).
For that, I would suggest you to use workflow tools like Knime (or a commercial solution like Inforsense KDE, Accelrys's Pipeline pilot, or Snaplogic, as they allow you to query data in a variety of formats and locations (rdbms, flat files, webservices), run algorithms, and build powerful web apps that allow you to easily publish your workflows to your users and let them interact at specific points).
If your prototype "grows" and you have to build more functionality on top of the data your workflows output, and if the output of your prototype is not likely to change everyday, then it's a wise decision to store a subset of the results in a database. This allows you to plug in powerful reporting tools like BusinessObjects, Crystal reports, jasper reports or whatever reporting solution available out there and show data to your users in a better shape than a spreadsheet or a csv file.
Finally, some development frameworks will make your choices more obvious : if you build a web application using an MVC framework, it is likely that your data will reside in an RDBMS (but please, don't put genomic sequences in a table column :-)).
All in all, it's a case by case choice, depending on your needs for each particular application.
In software I can usually get away with storing values in a XML configuration file or in the registry, e.g. software options. Once I need to persist objects I move to a database because the upfront cost is not that bad compared to the long term effects that relations and reporting can offer.
For bioinformatics you may be interested on that: Blast on DB. The guy who is working on that is a friend of mine and has a work on fast similarity sequence search, he found out to make his own binary storage better than using databases at this point.
I don't know specific details about his solution but you probably can exchange one or two ideias mailing the guy, even sharing code.
Do you need/want SQL queries?
Are multiple people going to want to access the data?
Is your data relational?
If you answered no to those questions, you (probably) don't need a full on database.
First, I'd consider:
How large will the database initially be: # of tables, # of rows
How quickly will it grow?
Is the data frequently queried?
If I were to create a personal recipe app, for example, I know I might add 50 favorite recipes to start and add no more than 5 recipes a year. With that being said, I could easily get by without a database since the size of the data store will have minimal impact on queries.
That said, I would probably use a database for any application where data entry and queries occur (even a small personal recipe app). I don't think it adds a lot of overhead especially when your framework (e.g. Rails) allows you to keep your database dumb (primarily tables, indexes, and constraints). It alleviates the chance that I'll have to eventually port to a database if I decide to scale up.
If you know the format of your data, flat files, if faster/easier to develop with, will be fine. If you expect your record formats to change frequently during development then I'd suggest that ALTER TABLE is your friend. Flat files will also tend to be faster (if you care about speed) unless you expect to implement the equivalent of joins across many combinations of files.
The real benefit of using a RDBMS during development is the flexibility with which you can modify your data schema and the ease with which you can access your data via queries.
Good design will ensure that you keep your data access layer relatively isolated (because of separation of concerns) so it should be a fairly straightforward (if tedious) matter to rework to a database later should it be worthwhile. Or, of course, if you use a database to develop your structures you may subsequently take the app back to flat/indexed files once those structures are crystallized in order to gain performance.
Use whatever persistence technology you're most comfortable with, and scales sufficiently.
YAGNI at least means "Don't add a new technology to your personal stack unless you can't be productive with whatever is already there."
For many (most?) of us, our comfort zone for data persistence is SQL. For some, it might be XML. Just don't write your own until (see paragraph 2).
As someone also doing research in Bioinformatics, I would suggest NOT using a database for these kinds of prototype projects unless you are sure it needs it. If you are on the fence, go with the databaseless solution and stick with flat files. It is also important to note that traditionally Bioinformatics researchers have go the flat file route, which means there are well defined file formats for most types of data in the feild. If you decide to go with a database solution, it may hurt your compatibility with existing research projects.