what database to choose to store information about site visits, key characteristics: big amount of data, many page requests per second, different reports for data presentation, i think to use MySql, any suggestions?
Consider letting the server log the requests and parsing them asynchronously. You don't need ACID for analytics, and you don't need to process them while talking to a client.
Most mainstream databases are good for that (including mysql, postgres, oracle etc). MySql is fine though, especially if you've used it before.
Be sure look at licenses as well: MySql is GPL (the database and the connectors), Postgres is BSD, Oracle (and a few others) you need to pay for.
Most web analytics companies use some kind of distributed file system to store logs, such as HDFS, QFS... The reason is that the data is too big for the traditional database.
Analytics reports are generated via MapReduce job.
If you want to do an adhoc query, you normally use something like Hive/Pig/Sawzall.
Related
I am Asp.Net MVC/SQLSERVER developer and I am very new to all these and so I may be on compelete wrong path.
I came to know by googling that Snowwflake can put/get data from AWS-S3, Google Storage and Azure. And Snowflake has their database and tables as well.
I have following questions,
Why one should use Snowflake when you can compute your data with Cloud Storage(S3 etc) and Talend or any other ETL tool?
Can we use Snowflake as database for data driven web application? and if yes, could you provide link or something to start?
Once again I am very new to all these and expecting from you to get ideas and best way to work arround this.
Thak you in advance.
Why one should use Snowflake when you can compute your data with Cloud Storage(S3 etc) and Talend or any other ETL tool?
You're talking about three different classes of technology product there, which are not equivalent:
Snowflake is a database platform, similar to other database technologies it provides data storage and metadata and a SQL interface for data manipulation and management.
AWS S3 (and similar products) provides scalable cloud storage for files of any kind. You generally need to implement an additional technology such as Spark, Presto, or Amazon Athena to query data stored as files in cloud storage. Snowflake can also make use of data files in cloud storage, either querying the files directly as an "external table" or using a COPY statement to load the data into Snowflake itself.
Talend and other ETL or data integration tools are used to move data between source and target platforms. Usually this will be from a line of business application, such as an ERP system, to a data warehouse or data lake.
So you need to think about three things when considering Snowflake:
Where is your analytical data going to be stored? Is it going to be files in cloud storage, loaded into a database or a mix of both? There are advantages and disadvantages to each scenario.
How do you want to query the data? It's fairly likely you'll want something that supports the use of SQL queries, as mentioned above there are numerous technologies that support SQL on files in cloud storage. Query performance will generally be significantly better if the data is loaded into a dedicated analytical database though.
How will the data get from the data sources to the analytical data repository, whatever that may be? Typically this will involve either a third party ETL tool, or rolling your own solution (which can be a cheaper option initially but can become a significant management and support overhead).
Can we use Snowflake as database for data driven web application?
The answer to that is yes, in theory. It very much depends on what your web application does because Snowflake is a database designed for analytics, i.e. crunching through large amounts of data to find answers to questions. It's not designed as a transactional database for a system that involves lots of updates and inserts of small amounts of data. For example Snowflake doesn't support features like referential integrity.
However, if your web application is an analytical one (for example it has embedded reports that would query a large amount of data and users will typically be reading data and not adding it) then you could use Snowflake as a backend for the analytical part, although you would probably still want a traditional database to manage data for things like users and sessions.
You can connect your web application to Snowflake with one of the connectors, like https://docs.snowflake.com/en/user-guide/odbc.html
Snowflake excels for large analytic workloads that are difficult to scale and tune. If, for example, you have many (millions/billions) of events that you want to aggregate into dashboards, then Snowflake might be a good fit.
I agree with much of what Nathan said, to add to that, from my experience every time I've created a database for an application it's been with an OLTP database like PostgreSQL, Azure SQL Database, or SQL Server.
One big problem of using MPP/Distributed Databases is that they don't enforce referential integrity, so if that's important to you then you don't want to use MPP/Distributed Databases.
Snowflake and other MPP/Distributed Databases are NOT meant for OLTP workloads but instead for OLAP workloads. No matter what snake oil those companies like databricks and snowflake try to sell you MPP/Distributed databases are NOT meant for OLTP. The costs alone would be tremendous even with auto-scaling.
If you think about it, Databricks, Snowflake, etc. have a limit to how much they want to optimize their platforms because the longer a query runs the more money they make. For them to make money they have to optimize performance but not too much otherwise it will effect their income.
This can be an in-depth topic so I would recommend doing more research into OLTP Vs. OLAP.
Enforcing Referential integrity is a double edged sword, the downside being as the data volume grows the referential violation check significantly slows down the inserts and deletes. This results in the developer having to put the RI check in the program (with a dirty read) and turn off the RI enforcement by the database, finally ending up with a Snowflake like situation.
Bottom line is Snowflake not enforcing RI should not be a limitation for OLTP applications.
SQL Server Database Mail provides nice built-in features like logging. I would like to use it to send large amounts of email (millions+) really fast (several millions / hour).
Is Database Mail designed for this kind of usage and will I reach the performance I need in production with this solution?
No, it is not. Invest in some listserv software or go through a reputable service provider like Message Systems.
BTW what legitimate purpose requires millions of e-mails per hour?
As a .NET web developer, I've always used SQL Server as my database store because it's already in the MSFT ecosystem and easy to work with from the .NET platform.
Recently, however, I had a computer almost literally blow up, and consequently lost all my data in SQL Server on that machine.
Now that I've got a new computer, I want to start using an off-site database so that this doesn't happen again. A database hosted by a third-party (i.e. hosting company) or cloud service.
It doesn't have to be SQL Server or even RMDBS necessarily, but if it's not, it'd be be something cutting-edge (e.g. redis, Cassandra, MongoDB, CouchDB, etc.) and not just MySQL or Postgre or something.
Does anyone have an recommendations for those with little financial means?
I'd like to be able to use it during development of projects, and if they ever go live, not have to migrate the data anywhere to a new service--keep the data right there where it is and point my live domain requiring the data to the same service it pointed while in development.
It's not so much a question of available hosted services as of what setup you want for your standard development environment. If one of the cloud datastores doesn't work for you, you can always get a virtual server and install whatever you need.
However, you may want to rethink the idea of putting dev databases in the cloud. Performance will not be as good as something running locally (particularly if you are working with things like bulk import), and turning a dev database into a production database isn't a particularly good idea. I think what you are really looking for is a combination of easy backup, schema management and data setup.
Backup on a live server is easy enough - either you are backing up the entire server or have a script that uploads the backup file somewhere. For dev I don't bother as I prefer to set up disposable environments - have code that can set up the database if it doesn't already exist and add any necessary default data. Most apps don't need much data unless there is some sort of import process involved, and the same code works quite nicely when you first set up the live environment.
Schema management is one of the more painful aspects of working with SQL and where NoSQL systems can make life a lot easier as most have the schema defined entirely by the code that is using it - I mostly use redis myself, but whether or not it is appropriate for you will depend on the type of project you work on - if you need a lot of joins or transactions you probably need SQL, but if you just need basic data storage most NoSQL platforms would be better.
May I suggest looking into Windows Azure table storage? It is quiet different from pure relational play of SQL Server, is the "next big thing" from Microsoft and is in general a somewhat of a paradigm shift for folks used to relational databases.
If you're ever going to come face to face with Azure in the future (and I suspect many .NET people will), it maybe a beneficial of an experience to have.
With respect to costs, they're negligible for individual use. 10,000 transactions a month cost a penny. A gigabyte per month of storage costs 15 cents, and data transfers are 10-15cents per gigabyte.
If you have only "development" projects that store their data in the cloud, I'll be damned if you pay more than $2-3/month to MS... if that :)
Google Cloud Datastore is in beta now and could be a good option for you. It's free up to 1GB and 50K requests per day. The API is rather low level. However, I wrote a high level ORM for GCD called Pogo that serializes and deserializes plain old objects into GCD entities.
Take a look at the documentation and open source here - http://code.thecodeprose.com/pogo
It's also available on Nuget called "Pogo".
I would like to have a SQL database online, but don't want to deal with its care and feeding. There are some commercial offerings out there for hosted DBs, for example Amazon SimpleDB. Can anybody suggest others, and if they used any of these services what their impressions were? Anything that helps me make an informed decision would be appreciated.
Edit: Since there's no one true answer, I've made this a community wiki.
Did you take a look the Amazon Relational Database Service. It is a MySql instance, and it is priced in a similar fashion to the EC2 products.
Google's AppEngine also has a SQL Database: http://code.google.com/appengine that is free, but it doesn't scale very well.
Amazon's SimpleDB is lacking a large chunk of the MySQL API, so if you want to go this route try and stick to SQL92 as much as possible. Also, keep in mind that you are changed per query. This means you want to make every query count. One way of doing that is by using relative updates:
UPDATE persondata SET age=age+1;
To be honest SimpleDB is a waste of money unless you need a large SQL cluster. I'd start off with a local sql db, when your load starts to get out of hand, move the sql db to its own server. After that, you will be looking at clustering your SQL db, and then SimpleDB starts to become an attractive solution.
Is there a general rule of thumb to follow when storing web application data to know what database backend should be used? Is the number of hits per day, number of rows of data, or other metrics that I should consider when choosing?
My initial idea is that the order for this would look something like the following (but not necessarily, which is why I'm asking the question).
Flat Files
BDB
SQLite
MySQL
PostgreSQL
SQL Server
Oracle
It's not quite that easy. The only general rule of thumb is that you should look for another solution when the current one can't keep up anymore. That could include using different software (not necessarily in any globally fixed order), hardware or architecture.
You will probably get a lot more benefit out of caching data using something like memcached than switching to another random storage backend.
If you think you are going to ever need one of the heavyweights (SqlServer, Oracle), you should start with one of those at the beginning. Data migrations are extremely difficult. In the long run it will cost you less to just start at the top and stay there.
I think you're being overly specific in your rankings. You can pretty much start with flat files and the like for very small data sets, go up to something like DBM for slightly bigger ones that don't require SQL-like syntax, and go to some kind of SQL database after that.
But who wants to do all that rewriting? If the application will benefit from access to joins, stored procedures, triggers, foreign key validation, and the like--just use a SQL database regardless of the dataset size.
Which one should depend more on the client's existing installations and what DBA skills are available than on the amount of data you're holding.
In other words, the size of your database is far from the only consideration, and maybe not the most important one.
There is no blanket answer to this, but ALMOST always, using flat files is not a good idea. You have to parse through them (i suppose) and they do not scale well. Starting with a proper database, like Oracle or SQL Server (or MySQL, Postgres if you are looking for free options) is a good idea. For very little overhead, you will save yourself a lot of effort and headache later on. They also allow you to structure your data in a non-stupid fashion, leaving you free to think of WHAT you will do with the data rather than HOW you will be getting it in/out.
It really depends on your data, and how you intend to use it. At one of my previous positions, we used Postgres due to the native geo-location and timezone extensions which existed because it allowed us to manage our data using polygonal datatypes. For us, we needed to do that, and we also wanted to use stored procedures, views and the like.
Now, another place I worked at used MySQL simply because the data was normalized, standard row by row data.
SQL Server, for a long time, had a 4gb database limit (see SQL Server 2000), but despite that limitation it remains a very stable platform for small to medium applications for which the old data is purged.
Now, from working with Oracle and SQL Server 05/08, all I can tell you is that if you want the creme of the crop for stability, scalability and flexibility, then these two are your best bet. For enterprise applications, I strongly recommend them (merely because that's what we use where I work now).
Other things to consider:
Language integration (ASP.NET session storage, role management, etc.)
Query types (Select, Update, Delete) [Although this is more of a schema design issue, not a DBMS issue)
Data storage requirements
Your application's utilization of the database is the most critical ones. Mainly what queries are used most often (SELECT, INSERT or UPDATE)?
Say if you use SQLite, it is gears for smaller application but for "web" application you might a bigger one like MySQL or SQL Server.
The way you write scripts and your web application platforms also matters. If you're developing on a Microsoft platform, then SQL Server is a better alternative.
Typically, I go with what is commonly accepted by whichever framework I am using. So, if I'm doing .NET => SQL Server, Python (via Django or Pylons) => MySQL or SQLite.
I almost never use flat files though.
There is more to choosing an RDBMS solution that just "back end horsepower". The ability to have commitment control, for example, so you can roll back a failed transaction is one. reason.
Unless you are in the megatransaction rate application, most database engines would be adequate - so it becomes a question of how much you want to pay for the software, whether it runs on the hardware and operating system environment you want, and what expertise you have in managing that software.
That progression sounds painful. If you're going to include MS products (especially the for-pay SQL Server) in there anywhere, you may as well use the whole stack, since you only have to pay for the last of these:
SQL Server Compact -> SQL Server Express -> SQL Server Enterprise (clustered).
If you target your app at SQL Server Compact initially, all your SQL code is guaranteed to scale up to the next version without modification. If you get bigger than SQL Server Enterprise, then congratulations. That's what they call a good problem to have.
Also: go back and check the SO podcasts. I believe they talked about this briefly.
This question depends on your situation really.
If you have control over the server you're deploying to and you can install whatever services you need, then the time to install a MySql or MSSQL Express server and code against an existing database framework VERSUS coding against flat file structure is not worth the effort of considering.
What about FireBird? Where would that fit into that list?
And lets not forget the requirements that the "customer" of your solution must also have in place. If your writing a commercial application for a small companies, then Oracle might not be a good choice... but if your writing a customized solution for a large enterprise which must share data among multiple campuses, and has a good sized IT department then the decision of Oracle vs Sql Server would come down to what does the customer most likely already have deployed.
Data migration nowdays isn't that bad since we have those great tools from Embarcadero, so I would instead let the customer needs drive the decision.
If you have the option SQL Server is a good choice from the word go, predominantly because you have access to solid procedures and functions and the database backup facilities are totally reliable. Wrapping up as much as your logic as you can inside the database itself (rather than in whatever language you are using) helps security and performance - indeed there's an good argument to be made for always using procedures for insert/update logic as these make you invulnerable to injection attacks.
If I have the choice the only time I'd consider MySQL in preference is with a large, fairly simple, database predominantly used for read access. This isn't to decry MySQL which has improved markedly of late and I happily use if I don't have the choice, but for more complex systems with update/insert activity MSSQL is generally the superior option.
I think your list is subjective but I will play your game.
Flat Files
BDB
SQLite
MySQL
PostgreSQL
SQL Server
Oracle
Teradata