I do datamining and my work involves loading and unloading +1GB database dump files into MySQL. I am wondering is there any other free database engine that works better than MySQL on huge databases? is PostgreSQL better in terms of performance?
I only use basic SQL commands so speed is the only factor for me to choose a database
It is unlikely that substituting a different database engine will provide a huge increase in performance. The slow down you mention is more likely to be related to your schema design and data access patterns. Maybe you could provide some more information about that? For example, is the data stored as a time series? Are records written once sequentially or inserted / updated / deleted arbitrarily?
As long as you drop indexes before inserting huge data, should not be much difference between those two.
HDF is the storage choice of NASA's Earth Observing System, for instance. It's not exactly a database in the traditional sense, and it has its own quirks, but in terms of pure performance it's hard to beat.
If your datamining tool will support it, consider working from flat file sources. This should save most of your import/export operations. It does have some caveats, though:
You may need to get proficient with a scripting language like Perl or Python to do the data munging (assuming you're not already familiar with one).
You may need to expand the memory on your computer or go to a 64-bit platform if you need more memory.
Your data mining tool might not support working from flat data files in this manner, in which case you're buggered.
Modern disks - even SATA ones - will pull 100MB/sec or so off the disk in sequential reads. This means that something could inhale a 1GB file fairly quickly.
Alternatively, you could try getting SSDs on your machine and see if that improves the performance of your DBMS.
If you are doing datamining, perhaps you could use a document-oriented database.
These are faster than relational databases if you do not use my SQL.
MongoDB
and CouchDB are both good options. I prefer MongoDB because I don't know Java, and found CouchDB easier to get up and running.
Here are some articles on the topic:
Why we migrated from MySQL to MongoDB
MySQL vs. CouchDB vs. MongoDB
I am using PostgreSQL with my current project and also have to dump/restore databases pretty often. It takes less than 20 mins to restore 400Mb compressed dump.
You may give it a try, although some server configuration parameters need to be tweaked to comply with your hardware configuration. These parameters include, but not limited to:
shared_buffers
work_mem
temp_buffers
maintenance_work_mem
commit_delay
effective_cache_size
Your question is too ambiguous to answer usefully. "Performance" means many different things to different people. I can comment on how MySQL and PostgreSQL compare in a few areas that might be important, but without information it's hard to say which of these actually matter to you. I've written up a bunch more background information on this topic at Why PostgreSQL Instead of MySQL: Comparing Reliability and Speed. Which is faster certainly depends on what you're doing.
Is the problem that loading data into the database is too slow? That's one area that PostgreSQL doesn't do particularly well at, the COPY command in Postgres is not a particularly fastest bulk-loading mechanism.
Is the problem that queries run too slowly? Is so, how complicated are they? On complicated queries, the PostgreSQL optimizer can do a better job than the one in SQL, particularly if there are many table joins involved. Small, simple queries tend to run faster in MySQL because it isn't doing as much thinking about how to execute the query before beginning; smarter execution costs a bit of overhead.
How many clients are involved? MySQL can do a good job with a small number of clients, at higher client counts the locking mechanism in PostgreSQL might do a better job.
Do you care about transactional integrity? If not, it's easier to turn more of those features off in MySQL, which gives it a significant speed advantage compared to PostgreSQL.
Related
My Python application data structure is pure relational.
My estimation for the biggest table is around 10 billion rows each year (all the other tables are very small).
Each row size is about 20-30 bytes
What is the right database engine for me?
You might consider the following that I have used, but of course this will depend on what your data looks like and how your APP/Users need to interact with it. This is not an exhaustive list, it's only the stuff I have used.
Greenplum database is a open source distributed Postgres database. http://greenplum.org/
It scales nicely and supports pretty much all Postgres stuff except for full text indexing last I knew
Apache Phoenix: An open source sql layer on top of Hadoop/HBase. It scales nicely, but the ecosystem is a bit complex (as Per Hadoop). Cloudera's Impala is similar. https://phoenix.apache.org/
Oracle Partitioning (preferably on RAC). If you can afford the license, Oracle partitioning allows for sharding of your data in various ways. If you have it with RAC, that will also provide parallel query execution
Just partition your data (on any RDBMS) and put the partitions on good disk
Those are the 4 ideas I have actually used, and remember, on good hardware, with some table partitioning, 10B rows isn't really all that much, so you might just need to get a better box[s] and hook it to a SAN with SSD of some kind over 10G network or better. ALso think about putting indexes on a separate disk from where the db files are, and always use SSD if you can afford it.
Anyway, HTH
MG
At 30 bytes per row that's less than 300GB, which is a small database, well within the capabilities of Oracle or SQL Server Enterprise editions. You won't need Oracle RAC.
You'll need to pay attention to application design and indexing/partitioning. Query and storage optimization will have a greater impact on performance than the choice of DBMS will.
I have a buddy who runs a web app for people listing cars for sale. There are a few thousand clients who use it, and each client has hundreds and sometimes thousands of rows in the database (some have been on for 5 years with hundreds of cars selling each month, and 10s of rows per sale (comments, messages, etc)). He has run this system in one SQL Server database in one physical server with like 20GB or RAM and a couple processors for the whole time, with no problems. Is this some sort of miracle?
Just like most programmers, I'm no DBA and just get by, thanks to ORMs, etc. Everywhere I look, people talk about having the need to shard or get a separate database server for big users of a web app. Why is this? Is it really that inefficient to have a large DB with lots or rows? Should I plan to use Cassandra or something, or can I rely on scaling up well with Postgres?
I personally don't think what you've described is that large of a database. The server (20 gigs of ram? ;)) sounds decent. It's more about usage and design. If the database is indexed and well designed, it can grow much, much larger on the current hardware.
Before doing any sort of switch, I'd simply look at archiving useless data and optimising queries if there's a fear of performance issues.
The reason for sharding and separate db servers is that at some point it's going to be cheaper to use multiple cheaper machines than one expensive one. Hardware price doesn't scale linearly with performance and once you reach a certain point it'll be much cheaper to get twice as many machines as to get a machine that's twice as fast.
You should have no problem in SQL server, Oracle, or any modern relational or non-relational database. I have administered databases with 100's of millions of records and Terabytes of data.
Typically you split components up across different servers so you can manage up time, resilience, and performance more easily.
It's certainly quite possible to have one monster machine which does it all, but then you may need another monster machine in case your motherboard dies, or your datacenter is unavailable.
By splitting a web site or application up, amongst different server's it's easier to get cheaper machines, and more of them.
Thus you can build in resilience, and not have components which have similiar demands on hardware clashing.
It's also important to think about restore times for servers, and recovery plans.
What happens when your machine dies, can you replace it in the agreed upon time? Can you restore from backups in that time?
SQL Server or other enterprise class databases shouldn't have any problems with 10's or 100GB databases, as long as they not designed too badly. (We have a few machines with that capacity/use which aren't struggling at all.).
In my mind that's nothing. Having tens of millions of rows on multiple tables with database size exceeding 10 GB has not caused problems for MS SQL Server. Of course it is not too fast with that much data, but otherwise it works just fine.
And to answer the question, too big is so big it does cause problems. And when it starts causing problems depends on the table structure and your performance demands.
Databases are extremely efficient at storing and retrieving relational data (i.e. data that is structured and has references to other data) - that's what they're designed to do. Honestly, 99% of the people spewing about key-value stores and Cassandra and whatnot have no clue what they're doing. A database server is just fine for storing large volumes of data, particularly if you're willing to put a bit of work into tuning it properly.
That said, there are use cases for Cassandra et. al. - if you have mostly unstructured key/value data or don't need consistency or want to shard for redundancy, it may be worth investigating.
Unless you're an extremely popular website, you probably can get by just fine with a decent database server - don't switch until you've determined why you need to switch. Switching is fine, just make sure you are switching because it serves your needs better, and not because it's the "cool web-scale thing to do"
I have to make a decision of which database server to use for my next project, but the simple decision to use MySQL like almost all the projects I did is harder now, because I expect very much records.
The database will store a user list, some other irrelevant tables, and the last one, some user-collected data. Let's say, if I have 6000 users responding to a quiz about each other. Simple math shows that from those users, if each one completes the quiz about everyone (and in my project that is 99% sure that will happen) I'll end up with 35.99million records(they will exclude themselves and in this particular situation the operation is 6000*5999). Unfortunately 6000 maybe is a small number, the real one growing day by day.
What to choose? MySQL and maybe if things go well and the project grows to expand it in a cluster? PostgreSQL, MSSQL? Oracle?
I've read about all of them, each one has it's pros and cons, but still don't know what to choose. The advantage of MySQL and PostgreSQL is of course, the starting price of $0 which is pretty nice in a usual self-funded startup.
Any opinions, pieces of advice? If you encountered this situation in your experience as developers, I'd love to hear from you.
These days, free isn't something that differenciates between databases any more. Both Oracle and SQL Server have free versions, but the limitations is resources - 4 GB database, RAM & single CPU utilization. Millions of records is not a concern - it's what datatypes you're using.
I saw the OPs comment about not liking MS software - that's your prerogative, but using the free versions of either Oracle or SQL Server do benefit from seamless transition to upscale versions of the respective database.
Personally, my choice would be either Oracle or SQL Server because of IMHO, real feature considerations like hierarchical query support, subquery factoring/CTE, packages (long before I get concerned with functions/procedures), full text searching, xml support, etc.
MySQL will handle 35 million records no problem. Worry about scalability when you get there. You can easily add raid hard disks backing your database tables, and if you really start getting big you can get a compellant SAN that will scream... Don't worry about the DB engine as much as the underlying hardware.. MySQL rocks for us with millions of records.
I've had no problems handling tables as large as 36,000,000 rows on MySQL and Oracle.
Just be sure that you index the proper columns, run EXPLAINs for your queries, and maintain proper design principles.
Most of the truly large scale web properties use a distributed key-value store. That said, 35 million is large, but not that large. With most modern databases, your main two scaling worries should be throughput and what happens when no single box can contain your entire database anymore. And both of these problems can be solved to some degree for any database you choose to use. (Caching, replication, sharding, etc.)
Use MySQL until you can't anymore. At that point, you ought to be rolling in dough anyways and you now have a very desirable problem.
Use MySQL as it's free and you have experience with it.
Besides in my opinion it matters more on how you design the tables than which database you use.
35 million records can be easily handled by MS SQL Server (assuming proper database design, indices, etc.). You can start with the free SQL Server Express edition and later, if you need, you can upgrade to the full version which supports clustering, etc.
SQL Server Express does have some limitations - single CPU, 1 GB memory, max 4 GB database size and a few other things. I'm not sure how quickly these limitations will become a problem but you can always move to the full version when you run into them.
MySQL(i) & Postgre
0$ of costs
large community
many tutorials
well documentated
MSSQL
You can get "money" from MS if you promote that you are using MSSQL (secret information from some companies I worked for)
MS tools work very well
Complete tool set from C# IDE over .NET lib to Windows Server 2003
Oracle
Professional and commercial provider
Used by many large companies (I also heard about Blizzard (World of Warcraft) using Oracle)
- expensive
The final decision depends on the very special requirements of your project.
Make yourself a quick list of things , that ARE IMPORTANT for your project (e.g. quick performed queries) and look up which Database pros are matching the most to your requirements.
Everything is about design. SQL Database are some kind of cars, you just have to know which component has to be placed here and which there.
Make a clear design and you won't struggle with any of them.
May be you can test Firebird
Blog post about big Firebird database here
MySQL licence is here (not allways free).
Postgresql and Firebird are free.
First of all, don't think about performance. Premature optimization being the root of all evil and all that. You can always throw more hardware and/or tuning at it later.
All of the mentioned should perform nicely if tuned/maintained correctly. I'd focus on manageability and familiarity. IMHO open source databases excels on manageability (perhaps not the best GUIs, but the CLI has been my home for a long long time).
And if the database becomes the bottleneck, why limit yourself to those choices? How about a key-value distributed database? Or perhaps serialize data directly to disk? Storing data outside of a RDBMS, while often frowned upon, might be the correct path. Or simply use the common route of denormalization.
Always remember not to optimize prematurely.
As far as opinions go (since you specifically asked for it) I favor open source databases, specifically PostgreSQL. It's rock solid, fast and very well-featured. And even with (relatively) large datasets it has performed superbly on mediocre hardware (some tuning involved, of course, but you can't skip that step no matter which db you end up choosing).
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MS SQL Server and Oracle, which one is better in terms of scalability?
For example, if the data size reach 500 TB etc.
Both Oracle and SQL Server are shared-disk databases so they are constrained by disk bandwidth for queries that table scan over large volumes of data. Products such as Teradata, Netezza or DB/2 Parallel Edition are 'shared nothing' architectures where the database stores horizontal partitions on the individual nodes. This type of architecture gives the best parallel query performance as the local disks on each node are not constrained through a central bottleneck on a SAN.
Shared disk systems (such as Oracle Real Application Clusters or Clustered SQL Server installations still require a shared SAN, which has constrained bandwidth for streaming. On a VLDB this can seriously restrict the table-scanning performance that is possible to achieve. Most data warehouse queries run table or range scans across large blocks of data. If the query will hit more than a few percent of rows a single table scan is often the optimal query plan.
Multiple local direct-attach disk arrays on nodes gives more disk bandwidth.
Having said that I am aware of an Oracle DW shop (a major european telco) that has an oracle based data warehouse that loads 600 GB per day, so the shared disk architecture does not appear to impose unsurmountable limitations.
Between MS-SQL and Oracle there are some differences. IMHO Oracle has better VLDB support than SQL server for the following reasons:
Oracle has native support for bitmap indexes, which are an index structure suitable for high speed data warehouse queries. They essentially do a CPU for I/O tradeoff as they are run-length encoded and use relatively little space. On the other hand, Microsoft claim that Index Intersection is not appreciably slower.
Oracle has better table partitioning facilities than SQL Server. IIRC The table partitioning in SQL Server 2005 can only be done on a single column.
Oracle can be run on somewhat larger hardware than SQL Server, although one can run SQL server on some quite respectably large systems.
Oracle has more mature support for Materialized views and Query rewrite to optimise relational queries. SQL2005 does have some query rewrite capability but it is poorly documented and I haven't seen it used in a production system. However, Microsoft will suggest that you use Analysis Services, which does actually support shared nothing configurations.
Unless you have truly biblical data volumes and are choosing between Oracle and a shared nothing architecture such as Teradata you will probably see little practical difference between Oracle and SQL Server. Particularly since the introduction of SQL2005 the partitioning facilities in SQL Server are viewed as good enough and there are plenty of examples of multi-terabyte systems that have been successfully implemented on it.
When you are talking 500TB, that is (a) big and (b) specialized.
I'd be going to a consultancy firm with appropriate specialists to look at the existing skill sets, integration with existing technology stacks, expected usage, backup/recovery/DR requirements....
In short, it's not the sort of project I'd be heading into based on opinions from stackoverflow. No offence intended, but there's simply too many factors to take into account, a lot of which would be business confidential.
Whether Oracle or MSSQL will scale / perform better is question #15. The data model is the first make-it or break-it item regardless of if you're running Oracle, MSSQL, Informix or anything else. Data model structure, what kind of applicaiton, how it accesses the db etc, which platform your developers know well enough to target for a large system etc are the first questions you should ask yourself.
I've worked as a DBA on Oracle (although some years back) and I use MSSQL extensively now, although not as a formal DBA. My advice would be that in the vast majority of cases both will meet everything you can throw at them and your performance issues will be much more dependent upon database design and deployment than the underlying characteristics of the products, which in both cases are absolutely and utterly solid (MSSQL is the best product that MS makes in many peoples opinion so don't let the usual perception of MS blind you on that).
Myself I would tend towards MSSQL unless your system is going to be very large and truly enterprise level (massive numbers of users, multiple 9's uptime etc.) simply because in my experience Oracle tends to require a higher level of DBA knowledge and maintenance than MSSQL to get the best out of it. Oracle also tends to be more expensive, both for initial deployment and in the cost to hire DBAs for it. OTOH if you are looking at an enterprise system then Oracle would have the edge, not least because if you can afford it their support is second to none.
I have to agree with those who said deisgn was more important.
I've worked with superfast and super slow databases of many different flavors (the absolute worst being an Oracle database, but it wasn't Oracle's fault). Design of the database and how you decide to index it and partition it and query it have far more to do with the scalability than whether the product is from MSSQL Server or Oracle.
I think you may more easily find more Oracle dbas with terrabyte database experience (running a large database is a specialty just like knowing a particular flavor of SQL) but that could depend on your local area.
oracle people will tell you oracle is better, sql server peopele will tell you sql server is better.
i say they scale pretty much the same. use what you know better. you have databases out there that are that size on oracle as well as sql server
When you get to OBSCENE database sizes (where over 1TB is really big enough, and 500TB is frigging massive), then operational support must come very high up on the list of requirements. With that much data, you don't mess about with penny pinching system specifications.
How are you going to backup that size of system? Upgrade the OS and patch the database? Scalability and reliability a concern?
I have experience of both Oracle and MS SQL, and for the really really big systems (users, data or importance) then Oracle is better designed for operational support and data management.
Every tried to backup and restore a 1TB+ SQL Server database split over multiple databases on multiple instances with transaction log files being spat out everywhere by each database and trying to keep it all in sync? Good luck with that.
With Oracle, you have ONE database (so I disagree with the "shared nothing" approach is better) with ONE set of REDO logs(1) and one set of archive logs(2) and you can just add extra hardware nodes without changing (i.e. repartitioning) you application and data.
(1) Redo logs are, of course, mirrored.
(2) Archive logs are, of course, stored in multiple locations.
It would also depend on what is your application meant for. If it uses only Inserts with very few updates, then I think MSSQL would be more scalable and better in terms of performance. However if one has lots of updates, then Oracle would scaleup better
I very much doubt that you are going to get an objective answer to that particular question, until you come across anyone that has implemented the same database (schema, data, etc.) on both platforms.
However given the fact that you can find millions of happy users of both databases, I dare say it's not too much of a stretch to say either will scale just fine (I've seen a snappy Sql 2005 implementation of 300 TB that seemed pretty responsive)
Oracle like a high-quality manual film camera, which needs the best photographer to take the best picture while MS SQL like an automatic digital camera. In old days, of course, all professional photographers will use film camera, now think about how many professional photographers use automatic digital camera.
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